The post What Is Website Analysis? Your Complete Guide to Success appeared first on Northpeak.

The landscape of customer feedback is changing. The old standbys, like surveys and traditional review sites, are losing their effectiveness. Businesses must adapt their analysis strategies to keep up with these evolving dynamics. Think about it: how often do you consult online reviews versus asking a friend or checking social media before buying something? This illustrates the shift in how we gather information.

Where Did All the Reviews Go?

Traditional feedback methods are losing their punch. This is due to the rise of new communication channels and shifting consumer behavior. Simply monitoring review sites and sending yearly surveys is no longer sufficient. Understanding why this shift is happening is key to effectively analyzing customer feedback.

One reason is the declining trust in traditional review platforms. Customers are more skeptical, suspecting manipulation or bias. The sheer volume of reviews can also be overwhelming, making it hard to find genuine feedback. This leads to fewer reviews being submitted and less reliance on these platforms.

Customer feedback has changed dramatically over the past ten years. Capturing authentic feedback has become increasingly difficult. In 2021, around 22.9% of consumers used third-party review sites to share their opinions. By 2024, this dropped to 22% (a 4.2-point decrease). Social media use for voicing complaints also fell, from about 23% in 2021 to just 16% in 2024 (a 6.9-point drop). These statistics, available in the Global Consumer Trends report, highlight the need for businesses to explore other ways to gather customer insights. For further reading on utilizing both qualitative and quantitative research, check out this article: How to master using qualitative and quantitative research to understand your customer.

The Rise of New Feedback Channels

So, if traditional methods are fading, where are customers sharing their opinions? The answer is in the rise of newer, more direct channels. These include private messaging platforms, online communities, and even in-product feedback features. These options offer more personalized and interactive experiences, building a sense of community and direct engagement. This presents both challenges and opportunities for businesses.

Adapting to the New Landscape

Analyzing feedback effectively in this new environment requires a multi-pronged strategy. Businesses must actively monitor a broader range of channels, embrace new technologies like sentiment analysis, and prioritize direct customer engagement. By diversifying their listening points and adopting new analytical tools, businesses can gain a deeper understanding of customer needs, even as the methods for obtaining feedback continue to change.

Building Your Multi-Channel Feedback Collection System

Stop relying on single-source feedback. To truly understand your customers, you need a complete picture of their experiences. This means building a multi-channel feedback collection system. This section explores how successful companies create these systems, gathering feedback from various touchpoints to gain a holistic view of the customer journey.

Diversifying Your Feedback Channels

Think of customer feedback as puzzle pieces. Each channel—surveys, social media, email, in-app feedback, and even customer service interactions—provides a unique piece. Combining these pieces reveals the full picture. Surveys deliver structured data on customer satisfaction, while social media listening reveals real-time reactions and sentiment. Email offers insights into individual customer issues. This multi-faceted approach ensures you capture vital information that might be missed with just one channel.

To help visualize the key differences between feedback collection methods, let’s look at a comparison table:

Multi-Channel Feedback Collection Comparison

This table compares different feedback collection channels, highlighting their effectiveness, response rates, and implementation complexity.

This table shows how each channel offers unique strengths and weaknesses. While surveys provide structured data, social media offers immediate feedback. In-app feedback is highly contextual, while email and customer service interactions offer personalized insights.

Strategies for Monitoring Across Channels

• Website Feedback Tools: Implement tools to capture rage clicks, abandoned carts, and user behavior. This identifies friction points and areas for user experience improvement.
• Social Media Monitoring: Track brand mentions and sentiment on platforms like Twitter and Facebook. Social listening software automates this, surfacing critical insights.
• Email Sentiment Analysis: Analyze the tone of customer emails to understand underlying emotions. This helps identify potential issues and proactively address them.
• In-App Feedback: Integrate feedback forms within your product to capture contextual feedback, leading to specific, actionable insights.

Organizing and Maintaining Data Quality

A multi-channel system needs organized, maintainable data. Use a centralized system to store and categorize feedback. This allows easy access and comparison across channels. Establishing clear quality standards ensures reliable insights. Balance automated collection with human analysis to filter noise and uncover deeper meaning, capturing both the breadth and depth of customer experiences.

The Real ROI of Analyzing Customer Feedback

Analyzing customer feedback is more than just a feel-good exercise; it has a direct impact on your bottom line. Businesses that prioritize feedback analysis often see significant returns. These returns can manifest in various ways, from improved customer retention to accelerated revenue growth and a stronger competitive edge. This section explores the tangible benefits of feedback analysis and highlights why it’s a worthwhile investment. Providing easy and accessible communication channels for your customers is crucial for collecting valuable feedback. A clearly visible Contact Us page is a key component of this strategy.

The Cost of Ignoring Customer Signals

Ignoring customer feedback can be detrimental to your business. Unhappy customers are more likely to churn and seek alternative solutions from competitors. This not only results in lost revenue but also increases the cost of acquiring new customers. Furthermore, negative word-of-mouth can severely damage your brand reputation, creating obstacles in attracting new business. This can quickly snowball into a difficult situation to recover from.

The impact of customer feedback, or the lack thereof, has a profound and measurable effect on business outcomes. For instance, a staggering 73% of consumers are willing to switch brands after just a handful of negative experiences. This underscores the importance of customer satisfaction and prompt issue resolution. Additionally, feedback analysis is now directly linked to consumer spending. 86% of buyers worldwide are willing to pay a premium for products or services that consistently deliver excellent customer experiences. More detailed statistics on customer service and its impact can be found here.

Translating Feedback Into Revenue

Analyzing customer feedback reveals areas for improvement across your entire operation. This feedback can pinpoint product flaws, service gaps, and ineffective marketing messages. By addressing these issues directly, businesses can improve customer satisfaction, which, in turn, fuels revenue growth.

Feedback and Customer Lifetime Value

Proactive feedback analysis is a key driver of customer lifetime value (CLTV). By understanding customer needs and preferences, businesses can create personalized experiences, offer relevant products and services, and cultivate stronger relationships. These actions promote customer loyalty, encourage repeat purchases, and ultimately increase the value of each customer.

Measuring Your Success With Feedback Analysis

So, how do you measure the ROI of your feedback analysis efforts? Focus on these key performance indicators:

• Customer Retention Rate: A rising retention rate indicates growing customer satisfaction with your products and services.
• Customer Churn Rate: A declining churn rate signifies fewer customers are leaving your business.
• Customer Lifetime Value (CLTV): A higher CLTV demonstrates increased customer spending over time.
• Net Promoter Score (NPS): An improved NPS suggests a greater likelihood of customers recommending your brand.

By monitoring these metrics, you can quantify the impact of your feedback analysis initiatives and demonstrate its value to stakeholders. This data-driven approach keeps your feedback program focused on delivering real, tangible business results.

Harnessing AI For Smarter Feedback Analysis

Artificial intelligence is changing how businesses analyze customer feedback. AI offers practical solutions for understanding what customers need and want. This, in turn, helps improve products and drives business growth. This section explores how AI is used in the real world, from natural language processing to predictive analytics.

AI-Powered Tools for Analyzing Customer Feedback

Several AI-driven tools are making feedback analysis easier and more effective:

• Sentiment Analysis: This technology digs deeper than just positive or negative words. It interprets the subtleties of language, detecting things like sarcasm and frustration. This detailed understanding helps businesses accurately measure customer sentiment.
• Automated Categorization: AI can automatically tag and organize feedback based on topics and keywords. This saves time and provides a structured overview of customer input. Businesses can more easily spot recurring themes and issues.
• Predictive Analytics: Using past feedback, AI can anticipate future trends. It helps identify potential problems before they become big issues. This lets businesses address customer concerns proactively, improving satisfaction and reducing customer churn.

Real-World Applications of AI in Feedback Analysis

Companies are now using AI to analyze customer feedback in numerous ways:

• 24/7 Analysis and Reduced Response Times: AI can constantly analyze feedback, offering continuous insights. It also allows businesses to respond to customer feedback faster. This is especially helpful for companies with a global customer base.
• Improved Accuracy and Efficiency: AI removes human error and bias from feedback analysis, providing objective insights. It also speeds up the analysis process, helping companies make better decisions, faster.
• Escalation Systems for Critical Issues: AI can quickly spot serious issues in customer feedback. It can automatically send these urgent matters to the right people, helping prevent negative impacts on the business.

This increasing use of AI reflects how businesses are adapting to the changing market. For example, in 2024, 50% of businesses reported that AI made 24/7 support possible. Meeting customer expectations for quick service is crucial in today’s market. Furthermore, 45% of companies found AI significantly reduced time spent on analysis, while 44% experienced quicker problem resolution. You can find more statistics here.

The Future of AI-Powered Feedback Analysis

AI offers many advantages, but it’s important to understand its limitations. It’s a powerful tool, not a substitute for human judgment. The best approach involves combining AI-driven analysis with human oversight. This ensures accurate interpretation and appropriate responses. The combination of AI and human expertise will define the future of feedback analysis. This will allow businesses to form better customer relationships and achieve lasting growth.

Converting Feedback Data Into Business Growth

Customer feedback is a goldmine of information, but it’s useless if it just sits there. To truly benefit from it, you need to transform raw data into actionable strategies. This section explores proven methods for turning feedback into tangible improvements. We’ll also examine how advanced analytics can reveal hidden patterns and unlock growth opportunities.

Uncovering Hidden Opportunities Through Analysis

Simply reading customer comments isn’t enough. Effective feedback analysis requires a structured approach. We need to identify recurring themes, pinpoint key issues, and extract actionable insights. It’s like searching for gold – you have to sift through a lot of material to find the valuable nuggets.

Advanced analytical techniques are crucial in this process. Sentiment analysis, for example, goes beyond basic positive/negative categorization. It digs deeper into the nuances of language, identifying underlying emotions like frustration or excitement. This provides a richer understanding of customer sentiment. Automated categorization, powered by AI, tags and organizes feedback by topic, streamlining analysis and revealing recurring themes quickly.

From Complaints to Concrete Improvements

Successful businesses use feedback to fuel improvements across all operations. Customer complaints are transformed into product enhancements. Processes are optimized to eliminate customer pain points. Service offerings are refined to better meet expectations.

For example, negative feedback about a complicated checkout process can lead to a streamlined, more user-friendly design. Complaints about long wait times for customer support might inspire the creation of new self-service resources. These are just a few examples of how listening to customers leads to positive change.

Communicating Insights and Driving Action

Sharing insights is essential for turning feedback into real growth. Clear and concise feedback dashboards empower stakeholders across departments to understand key findings and prioritize action. This fosters collaboration and keeps everyone aligned. For more on aligning teams for growth, check out How to master customer acquisition and retention.

Imagine a product team using feedback data to uncover a recurring issue with a particular feature. They use a dashboard to communicate this issue to the engineering team, including data showing its impact on customer satisfaction and churn. This clear presentation of the problem and its consequences facilitates buy-in and ensures the issue receives the attention it deserves.

To further illustrate different analytical methods and their potential business impact, consider the following table:

Feedback Analysis Techniques And Outcomes

Overview of different analytical methods showing their strengths, use cases, and expected business outcomes

This table summarizes the key strengths and applications of various feedback analysis techniques. Choosing the right method allows businesses to gain valuable insights and drive meaningful improvements.

Measuring the Impact of Feedback-Driven Changes

After implementing changes based on feedback, it’s crucial to measure their effectiveness. Track key metrics like customer satisfaction scores, retention rates, and customer lifetime value. Monitoring these metrics lets you quantify your efforts and demonstrate the value of feedback analysis to stakeholders. By linking feedback to tangible business results, you build a strong case for continued investment in feedback analysis.

Creating A Sustainable Feedback Analysis Process

Building a system for analyzing customer feedback that consistently delivers actionable insights requires more than just initial enthusiasm. It demands a structured, sustainable process that can adapt and scale with your business. This section outlines the essential components of a feedback analysis process that truly delivers long-term value. We’ll explore how to establish clear ownership, build efficient workflows, and implement quality control measures that ensure reliable insights over time.

Establishing Clear Ownership and Workflows

Imagine a basketball team with no designated point guard. The ball might get passed around, but there’s no clear direction or strategy. Analyzing customer feedback is similar. Without clear ownership, valuable insights can get lost.

Assigning specific roles and responsibilities is key. Who collects the feedback? Who analyzes it? Who acts on it? Answering these questions is the first step in creating an effective process. This ensures everyone understands their role and prevents important tasks from being overlooked.

Once roles are defined, establish efficient workflows. These workflows should outline how feedback is collected, categorized, analyzed, and ultimately translated into action. Think of it as creating a clear path for the feedback to travel, from the customer to the teams responsible for implementing changes. For further reading on optimizing conversions, check out this article: How to master conversion optimization.

Implementing Quality Control and Training

Just like any valuable data, customer feedback requires quality control. Implementing checks and balances ensures the insights you’re working with are accurate and reliable. This might involve regular reviews of the data collection process.

It’s also important to be vigilant in checking for bias or inconsistencies, and validating findings with multiple data sources. Maintaining data integrity is crucial for informed decision-making.

Additionally, team training is essential. Equip your team with the skills they need to effectively collect, analyze, and interpret customer feedback. This could include training on specific analysis techniques, software tools like Tableau, or best practices for identifying actionable insights. A well-trained team extracts maximum value from customer feedback.

Integrating Technology and Measuring Performance

Technology plays a vital role in streamlining and scaling your feedback analysis process. Consider using tools that automate data collection, sentiment analysis, and reporting. For more efficient data handling, explore ways to automate data entry.

Automating tasks frees up your team to focus on strategic opportunities and action plans.

Finally, measuring the performance of your feedback analysis process is essential. Tracking key metrics, like customer satisfaction scores and retention rates, demonstrates the value of your process. Also track the number of implemented changes based on feedback. This data-driven approach ensures your feedback analysis process stays focused on delivering tangible results.

Maintaining Momentum and Overcoming Roadblocks

Sustaining a feedback analysis process requires ongoing effort. It’s normal for initial enthusiasm to decrease, so it’s important to build mechanisms that maintain momentum.

Regularly communicate the value and impact of the program. Celebrate successes and address any challenges proactively. This reinforces the importance of feedback analysis and ensures its long-term sustainability.

Key Takeaways

Analyzing customer feedback is essential for business growth. It’s no longer enough to simply collect feedback; you must analyze it effectively to understand what your customers truly want and need. This section offers actionable takeaways to transform feedback into a competitive advantage. These takeaways create a practical roadmap, complete with timelines and measurable milestones.

The Evolving Landscape of Feedback

• Diversify Your Listening: Don’t rely solely on traditional methods like surveys. Explore new channels like in-app feedback, online communities, and social media listening to gather diverse perspectives. For example, consider integrating feedback forms directly within your product to capture real-time insights as customers interact with your features.
• Embrace New Technologies: Sentiment analysis tools can help dissect the emotional nuances within customer feedback. Understanding how customers feel, not just what they say, can reveal hidden motivations and unmet needs.

The Real Value of Analyzing Customer Feedback

• Reduced Customer Churn: Proactively addressing customer pain points identified through feedback can significantly increase customer retention. It’s like preventative maintenance for your customer relationships, keeping them strong and healthy.
• Increased Revenue: Translating feedback into product improvements and service enhancements leads to higher customer satisfaction, ultimately boosting sales. Optimizing your checkout process based on feedback, for example, can directly impact conversion rates.
• Data-Driven Decision Making: Feedback analysis provides the concrete data needed to make strategic, informed decisions, not decisions based on intuition. Imagine using feedback data to justify a new product feature, backed by solid evidence of customer demand.

Building a Sustainable Process

• Clear Ownership: Assign clear roles and responsibilities for feedback collection, analysis, and the subsequent actions. This ensures accountability and prevents valuable insights from being overlooked.
• Continuous Improvement: Regularly review your feedback analysis process, identify areas for improvement, and adapt your strategies accordingly. This fosters a culture of continuous learning and growth within your organization.
• Technology Integration: Utilize software and tools to streamline feedback collection, automate analysis, and generate insightful reports. This frees up your team to focus on implementing the necessary changes and improvements.

To get started with data-driven experimentation and optimize your customer journey for increased revenue, contact Northpeak today. Our team of growth experts can help you build a sustainable feedback analysis process tailored to your specific business needs.

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The internet is increasingly accessed through smartphones and tablets. People use these devices to browse, shop, and connect with others. This makes mobile website optimization essential for online business success. It’s all about providing a smooth, engaging experience, no matter the device.

This shift to mobile has a big impact on how businesses operate online. One key area is search engine rankings. With mobile-first indexing, Google prioritizes the mobile version of your website for ranking. A poorly optimized mobile site can hurt your search visibility, even if your desktop site is excellent.

User experience is another critical aspect of mobile optimization. A slow or difficult-to-navigate mobile site frustrates users and leads to higher bounce rates. Imagine a crowded store with narrow aisles. You’re more likely to leave without buying anything. Mobile optimization makes those digital “aisles” easier to navigate, creating a better user journey.

This focus on users builds engagement, brand loyalty, and drives conversions. By 2025, mobile phones are projected to account for 61% of all website traffic. This highlights the need for a mobile-friendly site to improve user experience and boost search engine rankings. For more on mobile marketing trends, check out these mobile marketing statistics.

The Business Impact of Mobile Optimization

Mobile website optimization offers more than just technical enhancements. It directly impacts your bottom line. A well-optimized mobile site can boost conversions, increase customer lifetime value, and improve how people view your brand. Businesses now see mobile optimization as a way to increase revenue.

Prioritizing Mobile for a Competitive Edge

In today’s market, prioritizing mobile optimization gives you an edge over competitors. By offering a top-notch mobile experience, you can stand out, attract more mobile users, and strengthen your industry position. This involves adopting a mobile-first strategy that truly prioritizes mobile users’ needs. Investing in mobile website optimization is an investment in your business’s future.

Mobile SEO Strategies That Actually Drive Results

The table above highlights some key industry example benchmarks. On mobile, the average page load time across industries is 5–7 seconds, though best practice is under 3 seconds, as each additional second can reduce conversions by up to 20%. Bounce rates tend to be higher on mobile, typically between 51–70%, with anything under 50% considered strong. Common causes of high bounce rates include slow load times, poor mobile UX, and confusing navigation. Mobile conversion rates average around 1.5–2.0%, compared to 2.5–3.5% on desktop, with top performers reaching 3–5% or more. These numbers show how important speed and user engagement are for conversions on mobile. If your site is slow, your bounce rate goes up, and your conversions suffer. This underscores the need for effective mobile website optimization.

Optimizing for mobile is essential for online success. Your mobile website needs to perform as well as, or even better than, your desktop version. Here are some effective strategies to turn your website into a mobile powerhouse.

Technical Tune-Up: Laying the Foundation

A strong mobile presence starts with technical improvements. First, your site needs to be responsive. This means it adapts to different screen sizes without any issues. Page speed is also critically important. You can improve page speed by compressing images, minimizing HTTP requests, and using browser caching.

Using next-gen image formats like WebP can significantly reduce image file size, leading to much faster load times. A fast-loading website provides a better user experience, which can lead to higher rankings in search results.

Content Optimization: Speaking the Mobile Language

Once the technical aspects are handled, we can focus on the content. Mobile users often search differently than desktop users. They often use shorter phrases, location-based searches, and voice search. Your keyword strategy needs to reflect this.

Consider how people use their phones to search. Integrate mobile-specific keywords into your content to reach the right people. Understanding voice search is crucial. Optimize for conversational queries, reflecting how people speak to their devices.

Content structure matters on mobile. Use short paragraphs, clear headings, and bullet points. This makes your content easy to read on smaller screens, creating a more enjoyable user experience and boosting visitor engagement. Using schema markup also helps search engines understand your content better, improving your mobile search visibility.

The numbers surrounding mobile usage are striking. Over 63% of website traffic is expected to come from mobile devices by 2025. With 96.3% of internet users now accessing the internet via mobile phones, mobile SEO is more important than ever. 94% of local searches are done on mobile devices. 58% of users rely on voice search for local business information. Find more statistics here.

Let’s take a look at different mobile SEO strategies:

To help illustrate the various approaches, the following table summarizes the key differences and provides a comparison of their effectiveness:

Mobile SEO Strategy Comparison

The table above shows that responsive design offers the best balance of implementation difficulty and expected impact for most businesses. While dynamic serving can be highly effective, its complexity makes it more suitable for larger organizations. Separate mobile sites (m-dot) are becoming less common as responsive design becomes the standard. AMP is a powerful tool for specific types of content but may not be necessary for all websites.

Local SEO: Capturing Nearby Customers

Local SEO is incredibly important for mobile. Many mobile searches are location-based, with users searching for businesses “near me.” Make sure your Google My Business profile is up-to-date with accurate information like your address, phone number, and hours of operation. This boosts your ranking in local searches and brings in nearby customers.

By combining these strategies, you can create a powerful mobile experience that not only attracts visitors but also turns them into loyal customers. This comprehensive approach ensures that your mobile website optimization efforts deliver real, tangible business results.

Page Speed Secrets That Transform Mobile Performance

Speed is critical for a positive mobile experience. This means prioritizing performance during mobile website optimization. It’s more than just a technical requirement; it’s a fundamental element that separates successful websites from those struggling to stay afloat. Slow loading times directly affect user engagement and conversion rates.

Identifying and Eliminating Speed Bottlenecks

Knowing where your site is slow is the first step to improvement. Tools like Google PageSpeed Insights and GTmetrix are invaluable for finding these bottlenecks. GTmetrix offers detailed reports, showing exactly what’s slowing down your website, allowing you to prioritize impactful changes. Think of them as diagnostic tools for your site.

Running performance audits is also a good idea to understand your site’s speed. This information helps you make smart choices about optimization strategies. For tips on landing page optimization, check out this helpful guide: How to master landing page optimization.

Image Optimization and Lazy Loading: A Powerful Duo

Images often add a lot of weight to a webpage. Advanced image optimization, including compression and the right file format (like WebP), is crucial. This reduces image size without affecting quality. It’s like packing a suitcase efficiently.

Lazy loading is another useful technique, delaying the loading of off-screen images until needed. Users only download what they currently see, which improves initial load times. This is especially important for mobile users with limited data and slower connections.

The cost of ignoring mobile optimization is significant. 53% of users abandon a page if it takes more than three seconds to load, and 70% abandon their shopping carts if checkout is slow. Businesses risk lost revenue with unoptimized mobile sites. More statistics on mobile website traffic can be found here.

Server Optimization and CDN: Speeding Up Delivery

Server optimization, including caching and minimizing HTTP requests, makes your server respond faster. This is similar to streamlining a restaurant kitchen for quick order fulfillment.

Combined with a Content Delivery Network (CDN), website assets are served from servers located closer to the user. This decreases latency and makes your site globally accessible faster. For more on mobile SEO, this guide provides useful insights: SEO for Education Websites. These optimizations can directly improve your bottom line by enhancing user experience and boosting conversion rates.

Responsive Design That Actually Works For Users

Creating a truly responsive design involves more than just a flexible layout. It’s about building a mobile experience users will actually enjoy. This means adopting a mobile-first design methodology, prioritizing the mobile user experience from the very beginning, not as an afterthought. This ensures core content and functionality are optimized for smaller screens right from the start.

Touch-Friendly Interfaces: Designing For Fingers, Not Mice

Mobile users interact with their devices using their fingers, not a mouse. This fundamental difference necessitates a touch-friendly design. Buttons and interactive elements should be large enough for comfortable tapping, with sufficient spacing to prevent accidental clicks. Think about the frustration of trying to hit a tiny “close” button on a pop-up ad—that’s precisely the experience you want to avoid.

Common Responsive Design Mistakes and Their Solutions

Many websites miss the mark with responsive design, making critical errors that negatively impact conversions. One common oversight is mobile navigation. Trying to squeeze a full desktop menu onto a small screen can easily overwhelm users. A simplified menu, like a “hamburger” menu, or a prioritized list of essential pages is often a better solution.

Another common mistake is failing to optimize images for mobile. Large image files not only increase page load times but also consume significant mobile data. This can be incredibly frustrating, especially for users with limited data plans. Using adaptive images, serving different image sizes based on the device, significantly enhances performance. For websites built with Webflow, consider optimizing assets to increase speed: speeding up Webflow sites.

Adaptive Content Strategies: Tailoring Content For Context

Not all content translates well to mobile. Adaptive content takes responsiveness a step further, adjusting the content itself to the user’s specific context. This could involve displaying shorter versions of articles, hiding less important elements, or even presenting entirely different content based on the device.

Mobile Menu Systems: Streamlining Navigation

Mobile menus can make or break the user experience. A poorly designed menu can quickly drive users away. Keep these best practices in mind when optimizing your mobile menu:

• Prioritize key pages: Focus on the pages most relevant to mobile users.
• Use clear labels: Ensure users understand where each link leads.
• Keep it concise: Avoid overwhelming users with too many choices.
• Test different menu styles: Experiment to find what works best for your audience, whether it’s a hamburger menu, tab bar, or another navigation pattern.

By avoiding these common pitfalls and implementing these solutions, you can create a responsive design that not only looks good but also performs seamlessly across devices, ultimately enhancing the user experience and improving conversions. This attention to detail ensures your mobile optimization creates a truly user-focused experience.

Mobile UX Optimization That Converts Visitors Into Customers

Mobile website optimization is more than just aesthetics; it’s about crafting an engaging experience that turns casual browsers into loyal customers. This involves understanding mobile user behavior and designing a user experience (UX) that drives tangible business results. This shifts the focus from technical tweaks to revenue generation.

Designing For Mobile User Behavior

Mobile users are often on the go, seeking quick information and even faster transactions. They value efficiency and ease of use. This requires a shift in how we approach navigation, forms, and the overall checkout process.

Simplified navigation is paramount. Think of it as a clear, concise roadmap guiding users effortlessly to their destination. The easier it is to find what they need, the higher the chance of conversion.

Form design also plays a crucial role. Lengthy, complicated forms can deter users and lead to abandoned carts. Streamlining forms, minimizing required fields, and incorporating auto-fill can drastically improve completion rates.

Streamlining The Mobile Checkout

A frictionless checkout experience is essential for mobile conversions. One-click purchasing for returning customers can significantly reduce barriers and simplify the buying process. This allows for effortless purchases on the go.

Optimizing mobile payments is equally important. Offering various payment methods like Apple Pay and Google Pay caters to individual preferences and expands conversion opportunities. For more tips on improving conversion rates, check out this helpful resource: How to master website conversion rates.

Usability Testing and User Journey Analysis

To ensure your mobile site is truly optimized, usability testing is essential. Testing with real users on actual devices uncovers pain points and areas for improvement. It’s like a test drive before committing to a purchase – ensuring a smooth ride in real-world conditions.

Analyzing mobile user journeys provides valuable insights into how users interact with your site, revealing opportunities to optimize the path to conversion. This data-driven approach helps refine your mobile strategy for maximum impact.

Leveraging Progressive Web App (PWA) Features

Progressive Web Apps (PWAs) offer a compelling blend of web and native app functionalities. Features like offline access, push notifications, and home screen installation enhance engagement and retention.

PWAs can be particularly beneficial for businesses with repeat customers. They offer an app-like experience without requiring a full app download. This seamless, integrated experience fosters repeat visits and drives conversions.

To further illustrate effective mobile conversion tactics, consider the following table:

Mobile Conversion Optimization Tactics

This table presents data showcasing the impact of various mobile UX improvements on conversion rates.

These statistics highlight the significant impact seemingly small UX changes can have on overall conversion rates. By focusing on these key areas, businesses can unlock substantial growth potential.

Technical Mobile Optimization And Performance Monitoring

A truly optimized mobile website isn’t a one-time project. It requires ongoing monitoring and technical adjustments to maintain peak performance and adapt to the constantly evolving mobile landscape. This means consistently tracking key metrics and making data-driven decisions.

Mobile-Specific Structured Data and Viewport Configuration

Structured data, specifically schema markup designed for mobile, helps search engines like Google understand your content. This improved comprehension leads to better search visibility and richer search results. It’s like providing search engines a clear, concise summary of what your site is all about.

Proper viewport configuration is crucial for correct website display on various screen sizes. It instructs the browser on how to scale the page, preventing awkward zooming or horizontal scrolling. This ensures a smooth, user-friendly browsing experience.

Comprehensive Mobile Testing Protocols

Thorough testing is essential for uncovering and fixing mobile-specific issues that might not be apparent on desktop browsers. Testing across diverse devices, operating systems, and network conditions is key to delivering a consistent user experience. Think of it as quality control for your mobile site.

Tools like Google’s Lighthouse and PageSpeed Insights offer actionable data on your website’s mobile performance. These tools provide detailed reports that help pinpoint areas for improvement and optimization.

You might be interested in: How to master conversion optimization.

Establishing Robust Mobile Performance Monitoring

Beyond initial testing, continuous monitoring provides valuable long-term data. Use tools like Google Search Console and Google Analytics to track vital metrics:

• Page load times
• Bounce rates
• Conversion rates

Focus on data specifically for mobile users to gain a clear picture of your site’s performance on these devices.

Specialized mobile testing platforms offer real user experience data, providing deeper insights into how actual visitors interact with your site. This information can highlight specific areas for improvement and guide future optimization decisions.

Implementing Continuous Improvements Based on Real User Metrics

Regular mobile audits, using data gathered from monitoring tools, reveal areas needing attention. Tracking KPIs such as mobile conversion rates and average session duration provides insights into user behavior.

This data-driven approach helps prioritize improvements and use resources wisely. Focus your optimization efforts on changes that will have the biggest positive effect. A continuous cycle of audits, analysis, and improvement ensures your mobile site stays competitive and delivers a consistently excellent experience.

Key Takeaways

Mobile website optimization is essential for success in today’s mobile-centric online world. With most internet traffic originating from mobile devices, a smooth, user-friendly mobile experience is no longer optional, but a core requirement for any business hoping to thrive. This section summarizes the essential strategies we’ve covered, providing a practical guide for you to start implementing these changes today.

Prioritized Action Items For Mobile Optimization

Identifying the most impactful areas for optimization is your first step. Focusing on these key elements will deliver the most significant improvements:

• Page Speed: A website that loads quickly is crucial. Aim for a load time under three seconds. A slow-loading site leads to frustrated users and a higher bounce rate.
• Responsive Design: Your website should adapt flawlessly to any screen size. This provides a consistent user experience whether someone is browsing on their phone, tablet, or desktop computer.
• Mobile-First Content: Create content specifically with mobile users in mind. Use shorter paragraphs, clear headings, and concise language for easier readability on smaller screens.
• User Experience (UX): Make navigation simple and intuitive. Optimize forms for mobile entry and streamline the checkout process for a frictionless experience.

Realistic Implementation Strategies

While a complete website overhaul might seem daunting, starting with smaller, manageable changes will allow you to make consistent progress toward your mobile optimization goals.

• Image Optimization: Compress images to reduce file size without sacrificing quality. Consider using next-gen formats like WebP for even better performance.
• Lazy Loading: Implement lazy loading for images and videos. This technique only loads media when it’s visible on the user’s screen, improving initial page load times. This is especially beneficial for mobile users.
• Caching: Leverage browser caching to store frequently used files locally on the user’s device. This reduces server load and speeds up subsequent page visits.
• Mobile-Specific SEO: Optimize your website for mobile-specific keywords and voice search queries to capture valuable mobile search traffic.

Measuring Mobile Optimization ROI

Tracking your progress is essential for demonstrating the positive impact of your optimization work. Pay close attention to these key metrics:

• Conversion Rate: Monitor how mobile optimization efforts affect your website’s conversion rate, whether that’s measured in purchases, sign-ups, or lead generation.
• Bounce Rate: A lower bounce rate generally indicates an improved user experience and better engagement with your mobile site.
• Page Load Time: Track page load time to see how effectively your speed optimization techniques are working.
• Mobile Traffic: Monitor your mobile traffic to understand how your optimization efforts are affecting your visibility and reach on mobile devices.

Emerging Mobile Trends

To stay competitive, it’s important to keep up with emerging trends. Progressive Web Apps (PWAs) offer a compelling alternative to traditional native apps, providing improved functionality and user engagement. Exploring AI integration can create a more personalized mobile experience for your users, potentially leading to higher conversion rates and increased customer satisfaction.

Each of these takeaways offers valuable insights and actionable strategies for continuous improvement. This focus on mobile excellence can drive tangible business growth and ensure you stay ahead in the dynamic world of mobile technology.

Ready to enhance your mobile performance and unlock significant growth potential? Partner with Northpeak, a growth agency specializing in data-driven experimentation and optimization. We help SaaS, eCommerce, B2B, and venture capital-backed startups accelerate revenue through tailored strategies.

The post Mobile Website Optimization Mastery: Transform Performance appeared first on Northpeak.

Conversion rate optimization tools can help your business generate more leads. They can be the key to persuading your users to take more actions that lead to sales. Learn which you should use here.

CRO Tools to Track Conversion Issues on Your Website

It’s one thing to get customers flooding to your website and another thing to turn them into repeat buyers. If users experience issues on your website, your lead generation efforts may be in vain.

It’s like entering a restaurant and being greeted by unenthusiastic hosts and a dirty environment set-up. You might even leave without ordering─ and this is what happens to your website visitors. You’ll have low engagement rates and poor sales if visitors don’t enjoy using your website.

Your CRO strategy should begin by looking at what your website lacks. You have to understand what’s going wrong to know where to make improvements. In short, get the best CRO tracking tools at the beginning of the CRO process.

CRO tracking tools gather data to give insights about your websites and visitors. You can learn about the exact issues pulling your conversions down with the data. You get to understand metrics like bounce rates, user sessions, and the length of sessions.

What Are the Most Popular Conversion Tracking Tools?

If you want to start taking advantage of these great benefits then you’ll need to use the best tools. Keep reading for the top conversion rate optimization tools.

Google Analytics

This is one of the most popular CRO tools today. 56.7% of the websites use Google analytics and at Northpeak.io, it’s top of the list of our best tracking tools.

One of the best parts about Google Analytics is that it’s free. It’s used for analyzing traffic, bounce rates, sessions duration, and pages per session. It helps you to know your current performance and revamp your marketing strategy.

You can establish user pain points and use data to inform your improvements.

Adobe Analytics

Adobe Analytics is an analytics tool used to track marketing on different channels. It helps break down marketing results from web, email, social media, and paid advertising channels.

You can receive performance feedback on traffic, engagement, and conversions as a marketer. In short, it gives you an overview of the user experience across your sales funnel. Your team enjoys actionable data insights that they can leverage to improve sales.

Mixpanel

Mixpanel is different from other analytics tools because it focuses on product interactions. It’s a behavior measuring tool intended to help users understand how people use products.

You would want to get Mixpanel to boost user experience, test new products, and increase sales. With the reports showing you features people like, you can quickly figure out the right improvements.

CRO Tools to Measure User Behavior

Seth Godin once said, “Don’t find customers for your products. Find products for your customers”. If you want to create a top product, it’s crucial to understand what your customers want.

We need CRO tools to measure users’ behavior if we ever get to the root of their problem. By understanding their behavior, you can produce a product that solves their problem.

You need to know how your visitors interact with your site for conversion rate optimization. For example, how much time they spend on your website and what they click the most.

You’ll need to invest in behavioral analytics tools to know why your users behave the way they do on your site. Behavioral analytic tools can help you understand why issues like cart abandonment rates.

What Are the Best CRO Tools for Measuring User Behavior?

Now that you know the most popular tools, you’ll want to know which are the best tools for specific features. Read on for the best CRO tools for measuring user behavior.

Hot Jar

Hot Jar is an insights tool that gives you your user’s behavioral data. It shows how users interact with your website through heatmaps, session recordings, and surveys.

Above 900,000 websites use Hot Jar to track user experience and customers’ behavior. Visual feedback like Heatmaps and session recordings makes it easy to establish users’ challenges.

You can quickly see where users are getting stuck on your website and what content or products they like. To gain in-depth insights about UX, all it takes is to peep into your tool. Visual analytics can be handy for busy marketers who may not have enough time to read data.

CrazyEgg

CrazyEgg, like Hot Jar, is a user experience and behavioral analytics tool. It also comes with some easy-to-understand visual reports that help you understand how users interact with your website.

Both CrazyEgg and Hot Jar provide you with visual reports to learn how users use your content. Whichever tool you use, you can know how your customers are faring at a glance.

FullStory

If you want a CRO tool to track user experience issues on different channels, Fullstory is a great choice. FullStory captures everything about a user’s journey regardless of their channel.

FullStory is the best for visual reports such as heat maps that help you diagnose bugs and UX design issues. You can also test and experiment with optimization strategies to boost conversions.

In the end, you access data showing the impact of applied improvements. This is a must-have funnel analysis tool in your arsenal of CRO tools.

CRO Tools and Personalized Marketing Campaigns

Building personalized experiences is one single strategy that can increase conversion rates. 69% of customers want a customized experience. 41% will go elsewhere if you haven’t personalized your dealings.

It’s a no-brainer─ you need CRO tools that can help tailor your marketing campaigns. Here are the most popular CRO personalization tools.

Amplitude

Amplitude is one of the best CRO tools when it comes to personalization. Amplitude leverages user insights to recommend optimal solutions for user experience improvements. You can segment and customize campaigns to reach users based on their behaviors.

Woopra

This is a customer analytics tool tracking a user’s journey that arms you with their entire data. It comes with a personalization feature that allows for highly effective targeting messages. Marketers can dispense tailored messages founded on users’ data to increase conversion rates.

Heap

Heap is easy to use and offers powerful conversion rate optimization features. An installed code on your website tracks every single user interaction.

You can capture data, visualize events, check user interactions and analyze your sales funnel. It’s easy to get an in-depth understanding of a customer and customize campaigns.

Thanks to its filtering options, you can segment users based on their interests. Heap customization options allow you to group or segment audiences and improve targeting.

Intellimize

Intellimize is backed by machine learning to help marketers produce personalized pages based on data. Their main focus is to help create high conversions by finding the most optimized page variation.

CRO Testing Tools to Test Changes

You need to launch high-potential marketing campaigns to increase conversion rates. Optimizing your sales funnel is one practice that can make a world of difference in your marketing campaigns.

Despite access to loads of data, you may still be in the dark about the potential of your campaigns. To remove the guesswork in your campaigns, you need CRO testing tools.

CRO or A/B testing is a practice that helps establish the changes that might charm your users beforehand. If you’re not testing before launching, you’re likely to lose your money. 60% of companies believe that A/B testing is valuable in conversion rate optimization.

What Are the Best CRO Testing Tools?

Testing is an important feature to take into account when it comes to CRO tools. Keep reading for the best CRO testing tools.

Google Optimize 

Google optimize is a free CRO testing tool that integrates with Google analytics. It’s best for A/B testing variations of your web pages. You can measure different versions of a web page to choose the likely one to perform well.

Optimizely

This SaaS platform is designed for personalized marketing to boost user experience. Its key feature is that it allows you to tailor your interface to your audience’s preferences.

Over 9,000 businesses─ including eBay and Dolby─ use Optimizely for conversion rate optimization. Optimizely is famous for its feature that tracks customer behavior throughout the sales funnel. Other features of Optimizely include:

• A/B testing
• Multivariate testing
• Predictive analytics
• Audience segments
• Behavioral audience targeting

Optimizely is also a CRO testing tool. It helps you meet your conversion goals by informing your strategies with campaign comparisons. It’s more of a sales funnel analysis tool that you need to use in all the stages of CRO.

VWO

VWO is another great testing tool. You can test the impact of your experimenting by using features that allow you to import metrics. One of the most engaging features is the visual editor that allows you to conduct website A/B testing.

CRO Tools to Analyze Performance of Changes

Conversion rate optimization is an ongoing process or strategy. It takes you around through a set of strategies and back to the beginning.

In the end, you have to analyze the performance of made changes and check conversion issues. You’ll use conversion tracking tools such as Google Analytics to track progress.

Use the Top Conversion Rate Optimization Tools

Conversion rate optimization tools are some of the essential marketing tools. The best CRO tools can help you improve user experience and conversion rates.

CRO tools can help you create effective marketing strategies that strike home and influence growth in your business. Many of our clients who invest in CRO tools see a marked increase in conversions and repeat business. If you’re looking to optimize conversion rates, get a free proposal today.

The post Conversion Rate Optimization Tools Your Business Needs appeared first on Northpeak.

76% of Google and Facebook budgets are wasted.

A/B testing can help you reduce that waste. No amount of research and statistics can point you to the creation of the most effective campaign right from its inception. Not to mention it takes time for any algorithm to “warm up”. Optimizing your marketing and sales funnel as you look to achieve the best conversion rate possible is a best practice that you should be incorporating in every aspect of your business.

What is A/B Testing?

If you’re not A/B testing, it’s likely that you’re wasting your money. In 2018, Invesp reported that 71 percent of companies conduct two or more A/B tests every month. These tests are conducted on their corporate websites, landing pages, e-mails and paid search campaigns.

A great way to understand A/B testing is to think of it like organizing a tournament, kind of like what goes on during March Madness, to figure out which version of an ad, landing page, sales copy or other promotional efforts best converts your audience into clients. Let’s look at the example of an online ad.

You create two different versions of the ad while targeting the same audience. You allow the two ads to run on an advertising platform, keeping an eye out for which one performs better. Once you have enough data to arrive at a conclusion, you eliminate the least effective ad and replace it with another challenger that runs in its place. Eventually, your testing should give you a near-ironclad advertisement that has the most effective sales copy and visual media attached to it.

How Does A/B Testing Work?

This method of testing works by giving you an actionable and measurable way of figuring out what your audience really wants as well as what the various algorithms that govern online advertising are looking for. If you think of creating an effective ad as a process akin to being placed in a pitch-dark warehouse and asked to find a small object, A/B testing is like being able to box off the parts of the warehouse that you’ve already searched so that you know which space you’ve covered.

Even though you may know your product or service very well, attracting potential customers is not as easy as sharing a feature-rich sales pitch with them. You need to figure out how your prospective customers think. A/B testing gives you the ability to systematically understand how users are best pitched to and to what they respond to.

What Are the Benefits of A/B Testing?

There are two main benefits to running an A/B test and they are both equally significant in contributing to a sustainable business.

Get More Out of Your Marketing Budget

When you A/B test, you are creating more and more efficient marketing. Let’s use the example of a paid ad again. Let’s assume you are running an ad in an attempt to sell a 100 dollar product. If you have to spend 100 dollars on ads in order to make a sale, you are ending up with a loss. When you subtract your ad budget and operational costs, not to mention the value of your time spent, from the money you made, you are putting in more than you are getting out.

Now, without changing any of the variables except for the effectiveness of your ad, let’s assume that with that same 100 dollars you are spending on your advertising, you improve your ads so that you make 2 sales. Automatically you have made your advertising campaign viable. You now know that every 100 dollars you put in will get you 2 sales on average. How about selling 4 units of your product with the same ad budget?

Dominate Your Niche

The second benefit of A/B testing is the fact that it helps you gain on your competition. In the example mentioned above, the lower you can get your marketing costs, the more aggressively you will be able to compete against the other market players in the online space. If you are paying less per conversion, you are automatically putting your competitors at a disadvantage.

Landing Page Testing

Taking this concept a little further, it is important to understand how a strong online presence is built. Search engines such as Google will rank your content based on how relevant and appealing it is for your chosen audience. Search engines have algorithms that rank websites based on how internet users respond to the content they interact with.

For example, you have a website for your product and users tend to leave your page only a few seconds after arriving. This means that a whole host of metrics that Google tracks, such as bounce rate, average on-page time and pages viewed per visit, will be abysmal.

Bounce rate is the percentage that denotes how often users click through to your page but leave your website without venturing to a second page to see what your business is all about. Average on-page time shows how long visitors stay on your website and is important in Google’s logic because it means that the more time people spend on your internet property, the more they enjoy what you have to offer. The average number of pages viewed further reinforces this point by showing how many pages people are willing to navigate on your website.

If your competitors are keeping their visitors on their landing pages longer and users are flipping through their websites in the search for more information, they will be valued higher by Google and other search engines.

 

This is where landing page testing comes in. By testing two different versions of your content, you can track metrics to see which one is more effective. You can expedite your results by running ads that send visitors to your two landing page versions.

 

Also, landing page effectiveness should be viewed in conjunction with the ads that you run. If you create a terrific ad that leads to a horrible landing page, your ad effectiveness will suffer. The opposite is also true. A weak ad that doesn’t manage to send users to a great landing page will be detrimental to your results. You need to consider each part of your marketing as an important gear in your promotional engine.

Should You Be A/B Testing?

In short, yes. Most companies should be using this method of optimizing their marketing. If your website is lacking significant traffic, A/B testing may not be your primary concern and may not yield helpful results. In this case, try communicating with current customers to gain valuable insight into how you can improve your numbers.

Setting Up an A/B Test

Now that you understand the benefits you can obtain from A/B testing, it’s time to go through the process of setting up an A/B test. You should be keeping detailed notes throughout the process so that you can gain maximum insight and start to understand some of the nuances of what works and what doesn’t. This will prove to be a time-saving technique that will pay dividends in the future. Setting up an A/B test for the first time will remind you of the scientific process and rightfully so.

 

1. Start With Research

The time you devote to research will move your starting point further ahead and make your campaigns more efficient from the get-go. Gather any data you have on your landing page from analytics so that you have a baseline to work with. If you’ve run any similar actions in the past, you’ll want to dig that data up and have it available as a control.

Research should include looking into tools and methods that will make your campaign more effective. Investing in a heat map tool can be a game-changer in your quest to understand how your visitors interact with your landing pages. A heat map will give you a view of how your visitors navigate your website and what elements they deem significant enough to spend time on and which ones they ignore.

Places to start:

• most visited pages on your website
• pages with the highest bounce rate
• key step in the funnel where customers are falling off

 

2. State Your Hypothesis

You should have a clear goal right from the start that you state as precisely as possible. For example, your hypothesis is that you can run an ad campaign that will achieve a 5 percent click-through-rate with 10 percent of the users that click-through becoming paying customers or signing up for your mailing list.

Your initial hypotheses may be slightly arbitrary but try as much as you can to use data to support the values you are shooting for. Obviously, achieving metrics that are profitable will be a good starting point. Calculate how much you are willing to pay for each click. Then, figure out the number of clicks that are acceptable in order to achieve a conversion; sale, newsletter signup or otherwise.

Understand key metrics:

• What is your current conversion rate?
• Which channels are driving the bulk of your traffic?
• Survey customers to understand what they like and what they would like to see

 

3. Create the First Variations

Depending on the platform on which you will be advertising, you will have specifications as to what your ad can resemble. Facebook, Google and other networks that offer ad placement have limits on the number of characters of which your copy can be composed. Image sizes have to meet dimension guidelines and be compliant to terms of service as to what is shown.

Once you understand the specifications, create two different versions of your ad. Make sure that the differences concern pivotal variants, meaning that the difference between the two versions will allow you to draw an important conclusion about the future of your campaign. To better illustrate this point, consider your call to action. “Buy Now,” and “Don’t Miss this Offer,” will invoke different responses in relation to your product offering and give you a measurable answer as to which is best for you.

 

4. Run the Test

It is now time to place your ads to the test and find out which version is better.

Predetermine a sample size and run the test for full weeks, usually at least two weeks. Be sure to factor in any seasonal or periodical factors that may affect your conclusions. Running an A/B test during the Christmas holiday season may yield different results than during mid-August. A typical A/B test can take anywhere between three to four weeks.

How do you predetermine sample size? You can calculate your sample size with tool like Evan Miller’s A/B Testing tool:

In this example, we told the tool that we have a 20% conversion rate and want to detect at least 5% uplift. The tool tells us that we need 1,030 visitors per variation before we can look at statistical significance levels. For practical purposes, know that 80% power is the standard for A/B testing tools.

In addition to significance level, there’s something called statistical power. Statistical power attempts to avoid Type II errors (false negatives). In other words, it makes it more likely that you’ll detect an effect if there actually was one.

5. Evaluate

The evaluation phase is where the magic happens. If you’ve done extensive research and planning, you will get data sets that lead you to actionable measures. Which ad had a better click-through-rate? Which ad had the lower cost-per-click? How many conversions resulted from each ad?

6. Optimize

Since you now have two ads of which one was better at achieving your goal, you can spend some time determining what makes an ad more successful. Apply what you have learned and create a third ad that will harness the power of your newly-attained insights. Run this ad alongside your initial winner and see which one prevails.

7. Record Your Conclusions

Recording the conclusions you derive from this process will not only help you create better ads, but it may just help you improve your brand overall. You will find that users respond better to certain words and phrases and that some images attract their attention more than others. Maybe they find clicking-through to a page with more information about your product to be more helpful than being taken directly to a sales page.

Furthermore, an A/B test can be a valuable product research and development tool. A/B testing can tell you what your prospective customers like and dislike about your product or service so that you can improve your design and production.

Examples of Split Testing You Can Run

Here are 3 actionable examples of tests you can run to improve your results.

1. Testing Your Call-To-Action Button

Colors, shapes and fonts can play an important role in whether or not your website’s visitors click on your call-to-action button. Test a few different options before arriving at the one that maximizes conversions.

The below A/B test showed that a red CTA button outperformed a green one by 21 percent based on 2,000 page visits.

2. Test Your Product Launch Offering

 

The well-known game company Electronic Arts (EA) ran a split test for the release of its SimCity 5 game. The company created a control version of their pre-order page where they offered a 20 percent discount on a future purchase for those that bought the game and tested it against a page where the game was sold at regular price with the option of upgrading to a deluxe version. The surprising conclusion was that their fans were not interested in the discount. The variation without the offer brought 43.4 percent more purchases. This would suggest customers just really wanted to buy the game, no extra incentive necessary. Customers were anxious to get their hands on the game as soon as possible.

Slit-testing a product launch can help put your product on the right track to maximizing sales. Don’t hesitate to test your current products and services against sales offerings that could perform better than your current marketing.

 

 

3. Test Personalization

 

Imagine I’m interested in buying a necklace for my new girlfriend. I’m not ready to purchase something on my first visit, but, on my second visit the homepage features a new necklace collection while removing any rings, earrings, and bracelets. This will keep me engaged.

Now let’s take it one step further. Imagine that I spend 3 minutes on the site (assume the average page view time is 2 minutes) – this means I’m probably seriously interested in purchasing something, which triggers a coupon code for 20% off and an email with a unique coupon code.

 

Then I return back to work.

While checking my emails, I see the coupon code and click on the button in the email – that’s when the coupon code is automatically applied. That might seem subtle but if I had to go find the coupon code or feel like I needed to remember my coupon code, I might lose interest and just close the session.

This example shows how you can make the experience simple and effective at the same time.

 

Additional actionable A/B tests

• test your headlines
• start with your layout
• experiment with copy
• try tweaking your checkout page
• test your forms

 

What you can expect

A successful test can lead to a 50% improvement in conversion rate.

 

Mistakes to Avoid

 

There are some common mistakes you should avoid so that your conclusions and budget are not thrown off. Mistakes can also produce incorrect conclusions that you may not uncover until you’ve already gone through the process.

 

Mistake #1: Testing Too Many Variables

 

If your car doesn’t start, a mechanic will attempt to find the cause of breakdown by using the process of elimination. There are plenty of components to examine, from ignition to exhaust pipe. If the mechanic were to start concurrently replacing different parts that determine your car’s operation, you may never know what the cause of breakdown truly is. You won’t know if your car is now less likely to stop working or if changing a part means prolonging the vehicle’s operation by a few weeks.

 

Similarly, when running an A/B test, your goal is to be as surgical as possible in creating an effective campaign. Choose variables to test for that are easy to isolate and track. If your two variations differ wildly and you suspect that concrete conclusions may not be extracted from the process, simplify the ads. Focus on aspects that are important for users and tweak as necessary.

 

 

Mistake #2: Not Balancing Traffic

 

Driving adequate amounts of traffic necessary to draw conclusions is key to a successful A/B test. A small amount of clicks may not be statistically significant for deciding whether the action is successful or not. Also, sending similar amounts of traffic to both versions is important for making your choice between the two.

 

 

Mistake #3: Incorrect Campaign Duration

 

Not only will you have to run your campaign for a duration that is appropriate for attracting enough visitors to drawing your conclusions, but you will also have to account for the context of your campaign. We mentioned seasonality earlier and it is quite relevant here. Your data needs to reflect a uniform set of circumstances and running a campaign for too long can cause factors to change, rendering your results void and inconsequential.

A/B Test Result Analysis

When analyzing your results, look to draw conclusions on the most actionable aspects of your marketing. Conversion metrics are the most important to keep in mind. Consider the user’s journey through the process: your ad appeared on their screen, they clicked on it and were taken to your landing page where they either completed an action or they didn’t.

 

Advanced A/B Testing

When you have gone through a few campaigns and start to understand how A/B testing works, you may be looking for more advanced techniques with which to elevate your campaign effectiveness. Here are some tactics that can boost the results you derive from an A/B test.

 

Testing Multiple Variables, Also Known As A/B/n Testing

The difference between testing too many variables and being able to successfully examine multiple unknowns has to do with the specificity of the experiment you design. A/B/n testing involves testing more than a single variant against your control, in this case, your initial landing page or ad. You can break you’re A/B test down to more than two versions, thus focusing on different aspects you want to test.

Instead of creating two ads that go to the same landing page, you can create four different ads that each have a specific, important variation. Conversely, you can send one ad to four different landing pages, each with a different conversion strategy.

 

Leveraging Remarketing

Remarketing is the ability to sell to a user that has visited your website but left without converting. Internet platforms like Google and Facebook provide you the opportunity to approach these users with your ads at a later time while they are on another internet location.

Here’s a simple graphic by Southern Web explaining remarketing:

Being able to include remarketing into your A/B test requires that you plan your campaign in a way that will allow you to cross-reference data from users that converted on their first visit and those that had to be marketed to on a remote destination.

 

 

Final Thoughts

 

Be thorough in your planning and conduct your A/B testing in a methodical fashion. You will enjoy significant results visible in your bottom line while simultaneously improving your visitors’ user experience and your brand’s online presence. Don’t be surprised if your newfound A/B testing prowess transfers to other areas of your business, such as research and development, human resources and logistics.

The post A Complete Guide to A/B Testing appeared first on Northpeak.

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This is an introductory article for people who are curious about machine learning.

Artificial Intelligence may shape our future more than anything else this century.

When we think of the Hollywood version of artificial intelligence — a super intelligent machine (that is much smarter than the best human minds in every subject) which can make jokes, trade stocks, manipulate people, and reprogram itself — we are not there…yet.

This version of artificial intelligence is called Artificial Superintelligence or ASI. When we’re at this point, we may or may not survive.

However, we’re currently at what experts call Artificial Narrow Intelligence (ANI) or weak AI. Basically, this is AI that focuses only on one area. For example, when AlphaGo defeated one of the best human players at Go (an ancient Chinese war game) — this was a major accomplishment because the game had been dominated by humans for two decades after machines first conquered chess in 1997.

Machine Learning is a subfield of artificial intelligence. Machine Learning is a method of data analysis where the goal is to enable computers to learn on their own.

How is machine learning being used today?

Here are a few examples:

• Email spam filtering.
• Recommendation systems such as Netflix and Amazon.
• Predicting customer churn.
• Customer segmentation.
• Image recognition.

Let’s say you’re an innovative eCommerce company that sells AI inspired sunglasses.

Let’s say you’re an innovative eCommerce company that sells AI inspired sunglasses.

Your sunglasses are unique because the technology in the sunglasses is able to recognize popular brands and products people are wearing. Customer love wearing them out at concerts because the sunglasses allow a person to pick up on trends and what’s in style. For instance, a customer might notice someone wearing a pair of fresh sneakers. With your AI inspired sunglasses, a customer could find out they’re limited edition Nike Air Max.

In the past year, your sunglasses have become the go-to sunglasses brand for outdoor events and concerts.

You also have a store in the Short North (Columbus, Ohio) where you provide demos and styling consultations. Customers are able to go back home and order their choice of sunglasses from your website or mobile app.

As an owner of a growing business, you’re trying to decide whether to focus more on the mobile app experience or website. Most of your sales come from your website but mobile sales are growing faster.

1

Develop Business Case

Your proposition (aka hypothesis) is that your mobile app will overtake your website sales and therefore that’s where you think you should invest. However, you’re not certain and you wish there was a way to make a more informed data-driven decision.

You realize you have some useful data on your customers that could help with this. You’ve also read of how companies are using machine learning for problems related to pricing models, sentiment analysis, and customer segmentation.

You decide to reach out to your friends at Northpeak, a digital innovation studio that helps founders with solutions in data analytics, design, and marketing.

You come to learn that machine learning is a method of data analysis. This method uses algorithms that iteratively learn from data.

You also learn that most of a data scientists time is actually spent on acquiring, cleaning, and exploring data. Pradeep Menon of Alibaba Cloud estimates 80% of your time is spent there, while only 20% is on modeling, deploying, and evaluating.

At the highest level, there are 3 main types of Machine Learning algorithms:

1. Supervised Learning.
2. Unsupervised Learning.
3. Reinforcement Learning.

Supervised learning algorithms are trained using labeled examples, where the desired output is known. If you are producing some sort of product or equipment as an example, you might label data points either “F” (failed) or “R” (runs).

Unsupervised learning is used against data that has no historical labels. In this case, you don’t have the known answer, rather you’re looking for structure within the data. Think about a mobile company that needs to segment its customers for marketing and sales campaigns.

With reinforcement learning, the algorithm learns through trial and error which action produces the greatest rewards. It’s often used in robotics, gaming, and navigation.

In our case, we can use a supervised learning algorithm, specifically a linear regression model in order to predict whether investing in your mobile app or website will lead to additional revenue in the future.

2

Acquire Data

The second step for you is to gather all your data into a place where you can work with it. As a growing but relatively early stage startup, your best bet is to use third-party tools like Redshift to set up your data infrastructure.

With the improvements in technology and a growing number of tools, this is what most early-stage startups do. This idea is to create pipelines from disparate data sources and funnel the data into a single data warehouse.

For this analysis, we’ll be using Python in Jupyter, along with Pandas, Numpy, and other data science libraries. Again this is meant to be a simple example for those looking to better understand how you can use machine learning for practical business applications. So if you don’t understand the code or some of the tools mentioned, that is totally fine — just skip it!

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

The data we’ll use includes customer info, such as Email, Address, and the Avatar color. It also has numerical value columns:

• Avg. Wear Time: Average time spent wearing glasses in minutes
• Time on App: Average time spent on App in minutes
• Time on Website: Average time spent on Website in minutes
• Length of Membership: How many years the customer has been a member

Note: This data is artificial.

3

Clean and Explore Data

Because data is usually messy, most of the time you will need to clean your data. We’re going to skip this step for the purposes of this hypothetical situation (and because it’s really not a fun process:)).

Explore your data

Once your data has been cleaned and standardized, you can quickly start to get a bird’s eye view from summary statistics. We can see from the output below that customers spend more time on your website and that the mean amount spent is roughly $494/year.

As mentioned earlier, before we train and test the model, we should do some data exploration to get a better feel of what’s going on.

You might start by comparing the correlation of time on website and yearly amount spent.

sns.jointplot(x=’Time on App’,y=’Yearly Amount Spent’,data=customers)
sns.jointplot(x=’Time on Website’,y=’Yearly Amount Spent’,data=customers)

From above, it seems like there’s a more linear relationship with time on app and yearly amount spent versus time on website and yearly amount spent. Interesting.

Another question you might ask is, what’s the relationship between time on app and length of membership?

Linear plot using seaborn:

sns.jointplot (x = ’Time on App’, y = ’Length of Membership’, kind = ’hex’, data = customers)

There doesn’t seem to be a strong linear relationship here.

You can continue exploring your data with tools such as pairplots to get a better understanding of the correlation between different variables.

4

Train Model

The next step is for to split your data into a training and testing set. The idea here is to take a portion of our data and use it to train our model. We will use the test dataset to see how accurate our model predicts by comparing it to the actual output.

Again that’s why it’s called supervised learning, we already know the answer or desired output. We continue this process of iteration (see first chart) until we feel comfortable with the accuracy and evaluation of our model.

Below we split into our test dataset by .3 and use the rest for training our model.

Note: The first step would be to start by setting your X and Y variables to the appropriate columns. Next use model_selection.train_test_split from sklearn to split the data into training and testing sets.

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
lm = LinearRegression()
lm.fit(X_train,y_train)

Now we print out the coefficients of the model. We interpret these coefficients further down below.

print(‘Coefficients: \n’, lm.coef_)

Coefficients:
[0.17825135 43.86015875 0.34813890 63.04039211]

5

Predict Test Data

Now that we have fit our model, let’s evaluate its performance by predicting off the test values.

We can use lm.predict() to predict off the X_test set of the data.

Looking at the scatterplot of the real test values versus the predicted values we can see that our model is actually doing pretty good!

6

Evaluate Model

You can calculate the Mean Absolute Error, Mean Squared Error, and the Root Mean Squared Error as well as use a histogram to make sure the residuals are normally distributed. If you’re interested in learning more about these metrics, check out this link:

https://towardsdatascience.com/how-to-select-the-right-evaluation-metric-for-machine-learning-models-part-1-regrression-metrics-3606e25beae0

MAE: 6.918249653330853
MSE: 78.97115165397457
RMSE: 8.71812066078243

Our R-squared value actually came out to 99%. For those not familiar with R-squared, it’s a measurement of how much variance your model explains. So we have a very good fit model for our test data.

Looking at the histogram below, we can confirm that the discrepancy between our data and estimated model was fairly minimal and normally distributed.

Conclusion

So, do we focus our efforts on mobile app or website development?

How can you interpret these coefficients?

• Holding all other features fixed, a 1 unit increase in Time on App is associated with an increase of 43.86 total dollars spent. Similarly, this is how you would interpret the rest of the coefficients.

Well, it seems like we should explore the relationship between length of membership and the app and the length of membership and website before coming to a conclusion.

It actually seems like maybe length of membership might be more important than whether to invest in the mobile experience or website!

In fact, the answer is a bit more complex.

You could invest more on the website so it catches up to the performance of the app or develop the app more since it’s already working better. Here is where someone that can understand both the data science side and business side is extremely useful.

You really would want to understand all the factors of the company and costs before making a decision. However, what’s important here is the approach and using all available information to make a strategic business decision.

Hopefully, as a business owner, you now have a better idea of how you can utilize machine learning to make more data-driven decisions.

Thank you!

The post Machine Learning Intro: A Simple Model For A Growing Ecommerce Startup appeared first on Northpeak.

“Without data, you’re simply another person with an opinion.”

— W. Edwards Deming

The data science and analytic ecosystems are changing rapidly.

The options you have at your disposal have changed significantly in the past 24–36 months.

If you don’t adopt a proper process, you expose yourself to increased risk of misunderstood metrics or plain guesswork.

So, what can you do right now to move towards a data-driven strategy?

One thing I’ve learned in my years of trying, failing, and winning (albeit more failures than wins) — culture plays a pivotal role in overall and long-term success. This could be culture inside the locker room, the office, or at home. As a startup, if you’re able to create the right culture, you can then transform your way of doing business into dominant advantages.

But what defines culture?

I played in the U.S. Soccer Development Academy growing up. I was part of a team that had some of the best soccer players in the state at my age. We traveled across the country playing against some of the best players I’d ever seen. We had a great team. What separated us from good versus great was not talent, but culture. The culture created in our ecosystem put us all on the same wavelength. This was obvious through our fluid ball movement and disciplined organization on defense. It’s also how we beat teams that were athletically superior to us. So how did we beat teams that were stronger and faster than us?

Well, we had a player named Michael Green. Arguably our best player and the best player I’ve ever played with (voted Ohio Player of the Year 2007). Michael was no doubt a very talented soccer player but what differentiated him as our team leader was his unselfishness and ability to raise the game of those around him. He loved to share the wealth with everyone and we tried to reciprocate it. I will never forget the intense focus we had before games. It was as if the air we were breathing was different…there was a certain energy you could feel in the guy next to you. Nothing had to be said. Not only did we all work on our craft each day, but we also traveled long distances together, we ate together, and we even shared hotel rooms together. Because of this culture, we were on the same page — always. We had a competitive advantage and it’s no surprise we won our fair share of games. But what I enjoyed most was the process we went through and the magic we felt during gameday. It felt as if we were always going to win.

Link: U.S. Soccer Development Academy

The reason I bring this up is because culture is something that is thoughtfully created with the people you spend time with. Companies with great culture focus on values such as sharing, transparency, and accountability.

By now, most startups and established companies realize the importance of a data-driven culture.

“Analytics pays back $13.01 for every dollar spent.”

— Nucleus Research

However, what you do with data is what defines your culture.

Based on my experience, including my time on a data science team at JPMorgan Chase and while at Northpeak helping other startups find insights from their data — in this article I share my thoughts on how to build a data-driven culture and what it takes to turn it into a competitive advantage.

“In God we trust, all others bring data.”

— Framed plaque from the ’60s at NASA’s Johnson Space Center

The first step is:

1

Get buy-in from company leaders

Before you decide on a clear strategy for how to use data, the highest level leaders in your company need to buy-in. Everybody needs analytics — marketing, sales, finance, product, etc. Data powers every decision, from the strategic to the tactical, from the board room to your line level employees.

The co-founders of Airbnb from the very beginning bought into data and its role as an important piece in overall company aspirations. Riley Newman, Airbnb’s first data scientist, talks about how bringing him on was a forward-looking move on the part of its founders and their 7 person team. At the time the company was still working out of an apartment when Riley was brought on.

“Back then we knew so little about the business that any insight was groundbreaking; data infrastructure was fast, stable, and real-time (I was querying our production MySQL database); the company was so small that everyone was in the loop about every decision; and the data team (me) was aligned around a singular set of metrics and methodologies.”

— Riley Newman

Riley also mentions how important it is that startups build a strong data culture early. In many cases, those insights are the difference between shutdown and success.

For a data-driven culture to truly take hold, it must be accepted and fostered by the entire organization. Every group, from product to marketing, sales, engineering, and others, must recognize the value in data and use it to improve on an iterative basis.

Actionable steps:

• Start to craft a high-level strategy in line with company goals
• Use Google Analytics and Google Tag Manager if you aren’t already (free)
• Prioritize data strategy in company meetings
• Start to think about time and resource commitments
• Assign/hire someone to be Head of Data — this should be someone who can speak to the business and technical side + influence culture. If these concepts are new to you as a business owner, it’s best to partner with/hire individuals who understand data warehousing, analysis, and presentation. Putting an actionable data process in place requires the right skill set and likely will need to be customized for your organization.
• Don’t get bogged down in the details yet

“I’m bought-in, how do I build a business that produces actionable data?”

The next three steps are critical steps that create the underlying support for a strong data analytics culture.

2

Create a data strategy

Avoid the common trap of asking what data can do for you. Rather, let your curiosity be driven by business goals.

First, identify a hypothesis as to why a change is required.

As a team, try to understand how having data-driven processes could improve your decision making. What are you trying to achieve?

This process should look to understand the role data plays in different parts of your company and what metrics are most important during different life cycles of a customer journey.

“Data Integrators, who thrive on evidenced-based way of operating, need to establish a data-driven culture, whereas Business Optimizers and Market Innovators, need to adopt a “fail-fast” agile software development culture to increase speed-to-market and innovation.”

— Dwayne Gefferie

Additionally, identify the long-term monetary benefits of the shift towards a data-driven culture. You may not have the exact answer but this will help you visualize how and where a data and test-driven process could impact your bottom line.

Two questions to get you started:

1. How will data improve our product?
2. What decisions could we make if we had all the information we need?

Actionable steps:

• Conduct meetings to gain agreement among stakeholders
• List out key business goals and how this investment will solve customer needs
• Define and validate the scope of work
• Build a requirements document
• Partner with digital and IT resources

3

Choose the right data

Planning your destination determines the route.

Look at the goals you’ve identified and back your way into what metrics are most important to track. This may seem like a daunting task but think about the 80/20 rule here. You need to start with what’s most important and will quickly make an impact.

In marketing, the change into a data-driven culture might focus on goals of identifying the target audience and minimizing ad costs through customer segmentation. At JP Morgan Chase, some of the data we collected, measured, and analyzed was to help our sales team. A typical goal might’ve been to identify team opportunities and strengths, understand customer needs, and maximize sales by connecting the right team member in the cycle (with the right information) with the most likely purchaser.

Look at what data you currently have and see what evidence-based decision making is possible. Then create a future state analysis and see what gaps there are. Think about the complete customer journey and what motivates a customer.

For example, if customer experience or operations can log requests for new features or products, a trend may emerge in which your tech team can conduct additional research and your growth team can incorporate new hypothesis into the next cycle of A/B tests.

Actionable steps:

• Conduct hands-on meetings with key partners across the company, including your Analytics Lead
• Create a spreadsheet of what data and metrics are most important
• Leverage process and customer journey maps
• Create a taxonomy that is standardized

4

Use the right tools to collect data

This is where the fun happens and it’s usually the most critical step. If you have less than 150 employees, you probably should use off the shelf tools. Building your own tool at this point may lead to countless hours of wasted engineering time. Analytics technology is getting better and cheaper at a fast rate. This will help you obtain more reliable data with more flexible and scalable platforms.

Here’s what a typical process might look like with a data warehouse such as Amazon Redshift:

You can also get creative about where you get your data from, specifically external and new sources of data.

“Social media generates terabytes of nontraditional, unstructured data in the form of conversations, photos, and video. Add to that the streams of data flowing in from sensors, monitored processes, and external sources ranging from local demographics to weather forecasts.”

-McKinsey & Company

Even though the exact tools you use will vary based on company size and needs, all companies should have a single source of truth and a data dictionary.

Carl Anderson, a product researcher at WeWork provides more context in a TechCrunch article –

“At WeWork, a global provider of co-working spaces, we provide our analytics users with a core table called the “activity stream,” a single narrow table that provides web page views, office reservations, tour bookings, payments, Zendesk tickets, key card swipes and more. The table is easy for users to work with, such as slicing and dicing different segments of our members or locations, even though the underlying data comes from many heterogeneous systems. Moreover, having this centralized, relatively holistic view of the business means that we also can build more automated tools on top of those data to look for patterns in large numbers of different segments.”

Larger companies must be able to identify, combine, and manage multiple sources of data.

He continues with, “There are often historical reasons why data are siloed. For example, large organizations are more likely to acquire data systems through company acquisitions, thereby resulting in additional independent systems. Thus, a single source of truth can represent a large and complex investment. However, in the interim, the central data team or office can still make a big difference by providing official guideposts: listing what’s available, where it is and where there are multiple sources, the best place to get it. Everyone needs to know: “if you need customer orders, use system X or database table Y” and nowhere else.”

Actionable steps:

• Identify potential data infrastructure tools (Data Warehouse, ETL, and BI tools)
• Consider hiring a consultant. While it’s great that you’ve found your analytics lead, that person likely isn’t going to have the expertise required to put together all of the components of your tech stack or the experience to solve all of the different analytics problems you’ll face across your business. Mistakes made at this critical stage have serious costs in both time and money as you grow, so it’s important to lay a solid foundation.
• Setup your data infrastructure
• Build a data dictionary

The remaining 3 steps will help build a high functioning data-driven company that produces ROI.

5

Using the right processes to measure, predict, and optimize

Okay, maybe this is where the real fun begins. Think about a company like Spotify. 40% of its employees are dedicated to engineering and software. Many factors have led to Spotify’s success, but a key element has been the company’s commitment to data and technology.

If you’re a regular user, you’ve definitely noticed their personalized playlists based on your music preferences. How does Spotify categorize and recommend that music? You can thank their data science teams for that.

Once you decide on what data to track and you start to collect this data, then you can start to perform analytics, create dashboards, and optimize results. This is also when you put your hypotheses to test. Startups should use at least a weekly cadence for A/B testing.

If you want to learn more about the underlying mindset of this new environment, you can read more here:

Link: The Mindset Of A Growth Practitioner — A New Growth Formula

“You can’t manage what you don’t measure,”
-W. Edwards Deming and Peter Drucker.

Think about Netflix’s ability to recommend the perfect movie during date night on Thursdays. It’s actually Valentine’s Day this Thursday (at the time this was written) and Netflix is going to recommend a certain set of romantic movies to a female living in New York City who loves adventure versus another set of recommendations to her male counterpart living in Columbus, Ohio who loves comedy. An important reason why that’s possible is because of the company’s ability to not only collect and track data, but their ability to accurately measure, predict, and optimize outcomes.

When you’re building a model, don’t focus on the data, rather focus on what the business opportunity is and how you can use the model to improve results.

Modeling that is test driven and hypothesis-led will not only generate faster outcomes, but the relationship of data and its business impact will also be better understood by the broader team.

I also think it’s important to recognize any modeling has its inherent risks. Therefore, companies should look for the least complex model that would improve performance without unnecessarily wasting company resources.

Actionable steps:

• Build dashboards for specific goals using a BI tool
• Implement a solid SQL process
• Be thoughtful about your team structure
• Implement data testing
• Document accurately
• Exploratory analysis
• Predictive modeling
• Experimentation!

6

Package the data so it can be easily digested, analyzed and reacted to

Here is where you get everyone on board by sharing the map. The entire company should be at ease with a data-driven approach. Make data easily accessible and don’t fall into the trap of using it to further a personal hunch. Don’t just use this new capability for one-off reports either.

This doesn’t mean you need to hand the keys out but you should think about the specific needs of your organization.

For instance, at Warby Parker, a retailer of prescription glasses and sunglasses, associates on the retail shop floor have access to a dashboard that provides details on their performance, as well as that of the store as a whole.

It’s also important to realize not everyone on your team will be comfortable with this approach and skills will vary. That’s why I believe coaching and training are vital to transforming culture (this also leads to the last step in this article). Dedicating office hours, email communication, and distributing a data dictionary are simple ways to support your team.

Actionable steps:

• Make decisions based on data and challenge others to produce similar logic
• Have time set weekly to discuss key metrics and their significance
• Share results of weekly A/B test and model performance
• Distribute data dictionary
• Reinforce data literacy across the company

Think about Netflix’s ability to recommend the perfect movie during date night on Thursdays. It’s actually Valentine’s Day this Thursday (at the time this was written) and Netflix is going to recommend a certain set of romantic movies to a female living in New York City who loves adventure versus another set of recommendations to her male counterpart living in Columbus, Ohio who loves comedy. An important reason why that’s possible is because of the company’s ability to not only collect and track data, but their ability to accurately measure, predict, and optimize outcomes.When you’re building a model, don’t focus on the data, rather focus on what the business opportunity is and how you can use the model to improve results.

Modeling that is test driven and hypothesis-led will not only generate faster outcomes, but the relationship of data and its business impact will also be better understood by the broader team.

I also think it’s important to recognize any modeling has its inherent risks. Therefore, companies should look for the least complex model that would improve performance without unnecessarily wasting company resources.

Actionable steps:

• Build dashboards for specific goals using a BI tool
• Implement a solid SQL process
• Be thoughtful about your team structure
• Implement data testing
• Document accurately
• Exploratory analysis
• Predictive modeling
• Experimentation!

7

Transform your company’s capabilities into a competitive advantage

Maybe most critical for long-term sustainability, founders and senior leaders must possess the strength to transform the organization so that the data and models actually yield better decisions.

Make analytics part of the fabric of daily operations. This will inspire team members to view it as central to solving problems and identifying opportunities.

And remember, adjusting culture and mind-sets typically requires a multifaceted approach that includes training, role modeling by leaders, and incentives and metrics to reinforce behavior.

Actionable steps:

• Build company dashboards using a BI tool
• Use data when communicating during meetings and through email
• Ask teammates for numbers to back up recommendations
• Measure individual progress using key performance indicators
• Get prescriptive (not just predictive)

Better late than never

Simply put — if you don’t have a data-driven culture, you’re missing opportunities to make incremental improvements in your business.

My years of experience have taught me that the collection and cleaning of data are usually the most difficult and time-consuming. The most rewarding part, generating insights for your team, might only take 20% of your time. Using this experience and the expertise of my team, at Northpeak we work from strategy and data warehouse setup to predictive analytics and machine learning. Having the opportunity to work with a diverse range of clients has shown me how incredibly difficult it is for companies to do well in this space.

Also, I want to acknowledge that some of this advice may vary and/or not pertain to you, as this requires a customized approach for each business. Some teams may be ahead of others in building a data-driven culture, but understand that this is a continuous process that is rapidly growing based on the evolution of technology — so it’s always good to revisit prior steps! I’ve seen if you can take this approach, your team can turn into a high functioning data-driven company and build serious long-term competitive advantages.

Lastly, but not least, remember that — data provides a picture into your company, but it does not drive your company, people do.

Summary

• Get buy-in from company leaders
• Create a data strategy
• Choose the right data
• Use the right tools to collect that data
• Build processes to measure, predict, and optimize
• Package the data so it can be easily digested, analyzed and reacted to
• Transform your company’s capabilities into a competitive advantage

If you enjoyed this article or thought it was helpful, give it some love and share it with other community members.
With respect,
Navi

The post Your 7 Step Guide To Building A Data-Driven Culture (And Another Competitive Advantage) appeared first on Northpeak.