Analyzing Customer Behavior Trends

D2C brands in India are spending upwards of ₹2000 to acquire a single customer, yet most struggle to generate a second purchase. Despite heavy investments in acquisition channels, these brands face a fundamental challenge: They don't have effective systems to drive repeat purchases. The data shows that over 70% of customers make just one purchase and never return. The problem isn't just about acquisition costs—it's about not leveraging customer data and engagement tools effectively. Most D2C brands collect valuable customer information but fail to use it for personalized communication, product recommendations, or targeted offers. Modern customer engagement platforms offer solutions through features like behavior tracking, journey mapping, and automated campaigns. These tools can identify purchase patterns, predict customer churn, and trigger relevant communications at the right moment. The key is shifting focus from pure acquisition to building systematic engagement workflows. Brands need to implement tools that can segment customers based on their behavior, automate personalized communications, and measure interaction impact. Those who adapt quickly will see significantly better unit economics.

As founders, we're bombarded with advice: "Know your customer!" "Listen to your audience!" But amidst the buzzwords, a crucial question lingers: how do we truly understand what matters to our customers, beyond the surface-level preferences and fleeting opinions? My journey as a founder has been a constant dance between chasing "customer feedback" and uncovering the deeper desires fueling that feedback. I've learned that listening isn't enough; we need to actively decode and prioritize what truly resonates with our users. Enter the Customer Value Compass: Step 1: Chart the Terrain: 1. Gather diverse data: Collect feedback through surveys, interviews, user observations, social media sentiment analysis, and support tickets. 2. Identify recurring themes: Analyze the data for common threads, challenges, and desires expressed by your customers. Don't get bogged down in individual details; look for patterns. 3. Categorize by impact: Segment your identified themes into two categories: "surface-level preferences" and "core value drivers." Surface-level preferences: These are fleeting opinions, often influenced by trends or personal experiences. They can provide valuable insights for specific features or campaigns, but shouldn't define your core offering. Core value drivers: These are deeply held needs, desires, and motivations that underpin customer behavior. These are the true north stars you need to align with. Step 2: Calibrate the Compass: 1. Dig deeper into core value drivers: Conduct in-depth interviews, focus groups, or user testing to truly understand the "why" behind these themes. 2. Prioritize based on impact: Not all core value drivers hold equal weight. Assess their prevalence, intensity, and alignment with your business goals to determine which ones deserve the most attention. 3. Validate with data: Look for quantitative evidence to support your qualitative findings. Analyze usage data, conversion rates, and customer satisfaction metrics to ensure your understanding aligns with actual behavior. Step 3: Navigate with Confidence: 1. Align your product and strategy: Use your Customer Value Compass to inform product development, marketing messages, and customer support initiatives. 2. Communicate with clarity: When making changes or introducing new features, explain how they address the core value drivers you've identified. 3. Continuously iterate: The Customer Value Compass is a living document. Gather new data, conduct regular reviews, and be prepared to adjust your understanding as your customer base and market evolve. Remember, the Customer Value Compass is not a destination, but a journey. By prioritizing what truly matters to your users, you build a foundation for sustainable growth, loyalty, and success. So, silence the buzzwords, listen deeply, and let your customers guide your voyage. #FoundersJourney #CustomerInsights #DecodingValue #ValueCompass #CustomerCentricity #BuildingForUsers

One thing I've noticed when working with clients and doing discovery calls is that a lot of companies are not using customer signals to be proactive instead of reactive. Being proactive rather than reactive is the key to ensuring customer satisfaction and retention. One effective strategy to stay ahead of potential issues is by documenting and understanding "customer signals" – subtle behaviors and indicators that can serve as red flags. Recognizing these signals across the organization allows businesses to engage with customers at the right moment, preventing issues from escalating and ultimately fostering a more positive customer experience. Teams should not just try to save the account once there is a request to cancel or an escalation. You need to pay attention to the signs before you hit this point. Ensuring the entire team knows what to look for means that everyone is empowered to care and improve the customer experience. Here's a list of customer behaviors that could be potential red flags, gradually increasing as they check out or consider leaving: 🔷 Reduced Engagement: Decreased interactions with your product or service. Limited participation in surveys, webinars, or other engagement opportunities. 🔷 Decreased Usage Patterns: A decline in frequency or duration of product usage. Reduced utilization of features or services. 🔷 Unresolved Support Tickets: Multiple open support tickets that remain unresolved. Frequent escalations or dissatisfaction with support responses. 🔷 Negative Feedback or Reviews: Public expression of dissatisfaction on review platforms or social media. Consistently low scores in customer feedback surveys. 🔷 Inactive Account Behavior: Extended periods of inactivity in their account. No logins or interactions over an extended timeframe. 🔷 Communication Breakdown: Ignoring or not responding to communication attempts. Lack of response to personalized outreach or engagement efforts. 🔷 Changes in Buying Patterns: Drastic reduction in purchase frequency or order size. Shifting to lower-tier plans or downgrading services. 🔷 Exploration of Alternatives: Visiting competitor websites or exploring alternative solutions. Engaging in product comparisons and evaluations. 🔷 Billing and Payment Issues: Frequent delays or issues with payments. Unusual changes in billing patterns.

I’ve been playing around with Customer Journey Analytics, and here’s what I realized: If you look at it in isolation, it doesn’t tell you much. But once you start comparing different time periods, and especially once you define your own rates, like the add-to-cart drop-off rate or whatever makes sense for your brand , that’s where it gets really interesting. When you start tracking those over time, it becomes insanely insightful. Every time we make a change — running Brand Tailored Promotions, coupons, new ad strategies, or AMC audiences— I go back to this tool. I use it to see if those experiments actually changed how people move through the funnel. Here’s one example: let’s say we target people who added to cart with a Brand Tailored Promotion. Some people might say, “You’re just cannibalizing — they were gonna buy anyway.” Maybe. But I don’t like guessing — I want proof. So I look at how many people added to cart but didn’t buy. Then I track that drop-off rate over time. If the drop-off goes down after our promo, great — it worked. If not, maybe we’re just handing out discounts for no reason. That’s what I love about this tool — it’s not just a funnel snapshot. It’s a way to see how your experiments actually impact behavior over time.

Data analysts and aspirants differentiate their work from other 90% of data analysts by developing compelling proposals based on their analyses. Here is the breakdown: 𝐄𝐱𝐚𝐦𝐩𝐥𝐞: 𝐏𝐫𝐨𝐩𝐨𝐬𝐚𝐥 𝐟𝐨𝐫 𝐄𝐧𝐡𝐚𝐧𝐜𝐢𝐧𝐠 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐑𝐞𝐭𝐞𝐧𝐭𝐢𝐨𝐧 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐚𝐧𝐝 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐈𝐦𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬: In response to declining customer retention rates over the past quarter, our company seeks to investigate the root causes and implement strategies to improve customer loyalty. Retaining customers is crucial for sustaining long-term profitability and maintaining market competitiveness. 𝐎𝐛𝐣𝐞𝐜𝐭𝐢𝐯𝐞: The objective of this analysis is to identify factors contributing to customer churn and develop effective retention strategies to mitigate churn rates by at least 15% within the next six months. 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞: Our analysis will focus on customer behavior patterns, product engagement metrics, and customer feedback to identify potential areas for improvement in our retention strategies. 𝐌𝐞𝐭𝐡𝐨𝐝𝐨𝐥𝐨𝐠𝐲: We will employ a combination of descriptive and predictive analytics techniques using historical customer data, including cohort analysis, survival analysis, and machine learning algorithms to predict customer churn. 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 The analysis will include examining customer demographics, purchase history, engagement with marketing campaigns, customer support interactions, and product usage patterns. 𝐓𝐫𝐚𝐜𝐤𝐞𝐫𝐬 : We will utilize key performance indicators (KPIs) such as customer churn rate, customer lifetime value (CLV), customer satisfaction scores, and Net Promoter Score (NPS) to monitor the effectiveness of our retention strategies. 𝐏𝐫𝐨𝐩𝐨𝐬𝐞𝐝 𝐍𝐞𝐱𝐭 𝐒𝐭𝐞𝐩𝐬: Conduct exploratory data analysis to identify correlations and trends. Develop predictive models to forecast customer churn. Segment customers based on their likelihood to churn and tailor retention strategies accordingly. Implement A/B testing for new retention initiatives. Monitor and evaluate the impact of implemented strategies through ongoing analysis. 𝐒𝐮𝐜𝐜𝐞𝐬𝐬 𝐂𝐫𝐢𝐭𝐞𝐫𝐢𝐚: Reduction in customer churn rate by at least 15%. Increase in customer satisfaction scores by 10%. Improvement in CLV by 5%. 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐓𝐢𝐦𝐞𝐥𝐢𝐧𝐞𝐬: Week 1-2: Data collection and preprocessing. Week 3-4: Exploratory data analysis and initial insights. Week 5-8: Model development and validation. Week 9-12: Implementation of retention strategies and monitoring. 𝐑𝐞𝐯𝐞𝐧𝐮𝐞 𝐈𝐦𝐩𝐚𝐜𝐭: The implementation of effective customer retention strategies is expected to result in increased revenue through higher customer lifetime value, reduced acquisition costs for new customers, and enhanced customer advocacy leading to improved sales conversions. A detailed revenue impact analysis will be provided upon approval of the proposal.

Most brands segment by demographics. Top performing brands segment by behavior. Demographics tell you who someone is. Behavior tells you what they're about to do. 𝗧𝗵𝗲 𝘀𝗲𝗴𝗺𝗲𝗻𝘁𝘀 𝘁𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗱𝗿𝗶𝘃𝗲 𝗿𝗲𝘃𝗲𝗻𝘂𝗲: → Engaged non-buyers (opened 3+ emails, no purchase) → One-time buyers who haven't returned in 60 days → High AOV repeat customers → Cart abandoners by product category → Browse abandoners by price tier 𝗧𝗵𝗲 𝘀𝗲𝗴𝗺𝗲𝗻𝘁𝘀 𝗺𝗼𝘀𝘁 𝗯𝗿𝗮𝗻𝗱𝘀 𝗼𝘃𝗲𝗿𝗶𝗻𝘃𝗲𝘀𝘁 𝗶𝗻: → Age ranges → Location → Gender → "VIP" based on spend alone These aren't useless. But they don't predict action. 𝗧𝗵𝗲 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸: Start with purchase behavior. Recency, frequency, monetary value. Layer in engagement. Opens, clicks, site visits. Add intent signals. Browse history, cart activity, wishlist adds. Build flows around each segment. Not one welcome series for everyone. 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹𝗶𝘁𝘆: A 35-year-old in Texas and a 35-year-old in New York might have nothing in common. But two people who both browsed the same $80 product three times this week? They're the same segment. Segment by what people do. Not just who they are.

For the last 6 years, I've taught a class at LBS on applied data science, AI and customer value management. For the next few weeks, I plan to share the main ideas that have resonated with students. 𝗜𝗱𝗲𝗮 #𝟭: 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀𝗲𝘀 𝗹𝗶𝘃𝗲 𝗼𝗿 𝗱𝗶𝗲 𝗯𝘆 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝘁𝗵𝗲 𝗵𝗲𝗮𝗹𝘁𝗵 𝗮𝗻𝗱 𝗱𝘆𝗻𝗮𝗺𝗶𝗰𝘀 𝗼𝗳 𝘁𝗵𝗲𝗶𝗿 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗯𝗮𝘀𝗲. A challenge for all businesses is setting an achievable plan, and the key to this is to understand how much of this year’s revenue and profit will come from the existing customer base and how many new customers are required to deliver the plan.  Unfortunately, for many businesses, this often ends up being an exercise in multiplication rather than based on a fundamental understanding of customer dynamics.  Customer cohorts are the key to make sense of customer behaviour. And here I am defining cohorts as a group of customers acquired in a particular period. A segment can represent any group of customers but cohorts have a specific meaning and a special role. Why?  • Isolates Behavioural Changes: Unlike demographic analysis, cohorts prevent confusing changes in your customer base composition with actual shifts in individual customer behaviour. Seeing your "age 25-34" spending increase might just mean you acquired more high-spending new customers in that group, not that existing customers are spending more. Cohorts show the true story of a specific group acquired at the same time.  • Tracks Customer Lifecycle: Demographics are static, but customers evolve. Cohorts follow the same group over time, revealing their lifecycle patterns and how their behaviour changes as they stay with you.  • Measures Impact of Interventions: Launched a new campaign? Cohort analysis lets you compare the behaviour of customers acquired before and after, helping you isolate the true impact of your efforts.  • Simple and Powerful: Cohorts are MECE (mutually exclusive and collectively exhaustive), persistent (membership doesn't change), and interpretable (easy to understand), making them a robust starting point for analysis.  • Diagnoses Performance Issues: Cohorts immediately help distinguish between acquisition and retention challenges. If it's a retention problem, are all cohorts affected (market issue) or just specific ones (acquisition quality issue)?  • Fundamental Truth: In customer-centric businesses, new customers are acquired over time, and cohorts naturally decline. Overall growth is the balance of these dynamics, making time-based cohorts the basic ground truth. #CustomerBaseAudit Bruce Hardie Peter Fader Daniel McCarthy https://lnkd.in/e_vUzSfD https://lnkd.in/edK_aDky https://lnkd.in/e9d3uX-U

Traditional approaches to explaining customer behavior are no longer enough to ensure your company remains on the cutting edge. 🫢   Innovative solutions require fresh approaches to penetrating layers of conventional wisdom.   And that begins by acknowledging that the motivations your customers act on are seldom logical, predictable, or even conscious.   Instead, their strongest responses stem from one source: emotion.   Numerous studies, including two conducted by Gallup, have documented that over 70% of all B2B and B2C customer decisions are driven by emotion.   It’s a deceptively simple reality yet one that many companies often resist preferring instead to concentrate exclusively on seemingly quantifiable metrics because they seem safe and reliable.   But whether customers are consumers or other businesses, all customers are people. And people are emotional beings.   That’s why the best data in the world isn’t necessarily indicative of how they’ll respond.   Traditional research techniques are often unproductive because they generate predictable confirmation of a pre-conceived hypothesis.   Old methods are designed to measure rather than inform and therefore may fail to uncover genuine insights.   Frequently they focus on mapping reactions against existing services, products or internally based assumptions.   Emotional Trigger Research is a methodology that exposes the core, unfiltered and spontaneous triggers that drive behavior.   These triggers provide actionable intelligence that will enable your business to convert emotional considerations into winning marketing strategies.   Based on an indirect approach that features provocative open-ended questions paired with in-depth one-on-one conversations, the results are uniquely spontaneous and enlightening.   When asked the unexpected, most customers have no ready answers. Consequently their unplanned responses are more revealing, providing the most authentic window into the emotional triggers that explain their actions.   Emotional Trigger Research transcends the superficiality of what customers say to the far deeper level of what they really mean.   As the Pioneer of Emotional Trigger Research and Author of “Why Customers Really Buy: Uncovering the Emotional Triggers That Drive Sales”, I’ve spent my career helping CEOs and Owners of U.S. based companies leverage customer emotions to generate over $100 million in additional sales.   If you’d like to innovate a competitive marketing strategy that resonates with your customers emotionally, DM me and let’s chat.   Illustration: Your Marketing Rules    Ring the 🔔 on my profile to follow Linda Goodman for marketing strategy and business development content.   #MarketingStrategy #Sales #BusinessDevelopment #EmotionalTriggerResearch #Leadership #CEO #Entrepreneurship

Track customer UX metrics during design to improve business results. Relying only on analytics to guide your design decisions is a missed opportunity to truly understand your customers. Analytics only show what customers did, not why they did it. Tracking customer interactions throughout the product lifecycle helps businesses measure and understand how customers engage with their products before and after launch. The goal is to ensure the design meets customer needs and achieves desired outcomes before building. By dividing the process into three key stages—customer understanding (attitudinal metrics), customer behavior (behavioral metrics), and customer activity (performance metrics)—you get a clearer picture of customer needs and how your design addresses them. → Customer Understanding In the pre-market phase, gathering insights about how well customers get your product’s value guides your design decisions. Attitudinal metrics collected through surveys or interviews help gauge preferences, needs, and expectations. The goal is to understand how potential customers feel about the product concept. → Customer Behavior Tracking how customers interact with prototype screens or products shows whether the design is effective. Behavioral metrics like click-through rates and session times provide insights into how users engage with the design. This phase bridges the pre-market and post-market stages and helps identify any friction points in the design. →  Customer Activity After launch, post-market performance metrics like task completion and error rates measure how customers use the product in real-world scenarios. These insights help determine if the product meets its goals and how well it supports user needs. Designers should take a data-informed approach by collecting and analyzing data at each stage to make sure the product continues evolving to meet customer needs and business goals. #productdesign #productdiscovery #userresearch #uxresearch

“Personas are pointless.” I used to disagree. Then I agreed. Now? "It depends." Once, I spent six weeks building a set of personas (you can see one below). Blood, sweat, and not-so-fun tears. I put everything I knew into them which, to be fair, wasn’t much back then. I couldn’t sleep the night before the big reveal. And then... ↳ "Oh yeah, we already knew that." ↳ "This isn't our exact focus anymore" ↳ Nods but no action A big old flop. So, can personas be pointless? Absolutely. - If they’re made in isolation - If they aren’t tied to real decisions - If they don’t change how people work But when they do work, it’s because they’re built for decision-making, not lamination. Here are 5 ways to make personas actually useful, based on years of trial, error, and one too many sad personas gathering dust in Google Drive: 1. Run an “Information Needs” workshop before you start Ask your PMs, designers, and devs: “What do you wish you knew about our users to make better decisions?” Document their needs → design your research to answer them → bake those answers into your persona. 2. Build proto-personas collaboratively to surface assumptions early Before you do any research, map out what people think they know. Use sticky notes color-coded by: - Assumption - Analytics - Existing research This reveals gaps, misalignment, and gives you a jumpstart on where to dig deeper during interviews and information to include in your personas. 3. Anchor personas in journey stages, not personality traits Forget personality sliders or random hobbies. Instead, map: - What users are trying to accomplish - What frustrates them at each stage - Which tools they use and why If your persona doesn’t help answer: “What would break their flow here?," rewrite it. 4. Activate personas through workshops, not PDFs Don’t “present” personas, use them. Host an ideation workshop where teams solve for a key need or pain point. Or run a mini-hackathon based on persona insights. 5. Embed personas into rituals and review them quarterly Add a persona lens to roadmap planning: “Which persona does this initiative support?” Post them in your workspace, tag bugs/features with persona names, and revisit them every quarter to update insights. So no, personas aren’t inherently pointless. But pointless personas are everywhere. Always ask yourself: “Will this persona change what we do next?” // If you're struggling to put personas together and don't know what "bad" or "good" really look like, watch this video where I share and diagnose all the problems (and good parts) of the personas I created through the years: https://lnkd.in/etMeeSS9