Insights from Customer Data for Product Development

As an analyst, I was intrigued to read an article about Instacart's innovative "Ask Instacart" feature integrating chatbots and chatgpt, allowing customers to create and refine shopping lists by asking questions like, 'What is a healthy lunch option for my kids?' Ask Instacart then provides potential options based on user's past buying habits and provides recipes and a shopping list once users have selected the option they want to try! This tool not only provides a personalized shopping experience but also offers a gold mine of customer insights that can inform various aspects of a business strategy. Here's what I inferred as an analyst : 1️⃣ Customer Preferences Uncovered: By analyzing the questions and options selected, we can understand what products, recipes, and meal ideas resonate with different customer segments, enabling better product assortment and personalized marketing. 2️⃣ Personalization Opportunities: The tool leverages past buying habits to make recommendations, presenting opportunities to tailor the shopping experience based on individual preferences. 3️⃣ Trend Identification: Tracking the types of questions and preferences expressed through the tool can help identify emerging trends in areas like healthy eating, dietary restrictions, or cuisine preferences, allowing businesses to stay ahead of the curve. 4️⃣ Shopping List Insights: Analyzing the generated shopping lists can reveal common item combinations, complementary products, and opportunities for bundle deals or cross-selling recommendations. 5️⃣ Recipe and Meal Planning: The tool's integration with recipes and meal planning provides valuable insights into customers' cooking habits, preferred ingredients, and meal types, informing content creation and potential partnerships. The "Ask Instacart" tool is a prime example of how innovative technologies can not only enhance the customer experience but also generate valuable data-driven insights that can drive strategic business decisions. A great way to extract meaningful insights from such data sources and translate them into actionable strategies that create value for customers and businesses alike. Article to refer : https://lnkd.in/gAW4A2db #DataAnalytics #CustomerInsights #Innovation #ECommerce #GroceryRetail

Customer Success Leaders—If you're not actively shaping the Product Roadmap, you're missing a critical opportunity. The most effective organizations don’t treat CS as a participant—they rely on it as a strategic partner. Product teams should be co-designing the future with their customers. That means: ✅ Understanding emerging use cases and evolving needs ✅ Enhancing the product based on real customer insights ✅ Prioritizing with business impact and revenue in mind In today’s market—where consolidation, cost-cutting, and efficiency are top priorities—building a product that truly solves business challenges is the difference between success and irrelevance. So, how do you drive better alignment between CS and Product? Here’s what I've seen work: 1️⃣ Lead with Data & Insights -Identify the most adopted and least adopted product features -Pinpoint where customers are dropping off and why -Find personas and use cases that drive the most value -Look for patterns and trends across your customer base 2️⃣ Support Data with Customer Stories -Conduct interviews and surveys to capture direct feedback -Dive into workflows and edge cases to understand nuances -Align product evolution with customer goals and business objectives 3️⃣ Prioritize Product Feedback Strategically -Leverage customer data to rank impact and urgency -Tie feedback to revenue—renewals, expansions, and upsells -Ensure recommendations align with the broader product vision 4️⃣ Maintain an Open Dialogue -Establish a structured collaboration rhythm (bi-weekly syncs, Slack channels, shared roadmaps) -Keep all teams informed on designs, timelines, and priorities -Be clear, concise, and adaptable—Product is balancing competing priorities across the org 5️⃣ Close the Loop—Every Time -Set clear expectations with customers early and often -Enable Product teams to engage directly with customers for firsthand learning -Continue gathering feedback even after launch (beta programs, customer advisory boards) At the end of the day, great products are built by teams who stay close to the customer. CS should not be a passive observer in product development—it should be a driving force. When you get this right, you influence retention, expansion, and advocacy. And that’s a business win. __________________ 📣 If you liked my post, you’ll love my newsletter. Every week I share learnings, advice and strategies from my experience going from CSM to CCO. Join 12k+ subscribers of The Journey and turn insights into action. Sign up on my profile.

Survey data often ends up as static reports, but it doesn’t have to stop there. With the right tools, those responses can help us predict what users will do next and what changes will matter most. In recent years, predictive modeling has become one of the most exciting ways to extend the value of UX surveys. Whether you’re forecasting churn, identifying what actually drives your NPS score, or segmenting users into meaningful groups, these methods offer new levels of clarity. One technique I keep coming back to is key driver analysis using machine learning. Traditional regression models often struggle when survey variables are correlated. But newer approaches like Shapley value analysis are much better at estimating how each factor contributes to an outcome. It works by simulating all possible combinations of inputs, helping surface drivers that might be masked in a linear model. For example, instead of wondering whether UI clarity or response time matters more, you can get a clear ranked breakdown - and that turns into a sharper product roadmap. Another area that’s taken off is modeling behavior from survey feedback. You might train a model to predict churn based on dissatisfaction scores, or forecast which feature requests are likely to lead to higher engagement. Even a simple decision tree or logistic regression can identify risk signals early. This kind of modeling lets us treat feedback as a live input to product strategy rather than just a postmortem. Segmentation is another win. Using clustering algorithms like k-means or hierarchical clustering, we can go beyond generic personas and find real behavioral patterns - like users who rate the product moderately but are deeply engaged, or those who are new and struggling. These insights help teams build more tailored experiences. And the most exciting part for me is combining surveys with product analytics. When you pair someone’s satisfaction score with their actual usage behavior, the insights become much more powerful. It tells us when a complaint is just noise and when it’s a warning sign. And it can guide which users to reach out to before they walk away.

When it comes to innovation, the key isn’t just the idea, it’s in the data. Take Kraft Heinz with their Crystal Light Vodka Refreshers. The company saw that nearly 20% of their existing Crystal Light consumers were already using it as a mixer in alcoholic drinks. That’s a consumer signal they could act on. With this insight, Kraft launched a product that not only aligns with customer behavior but taps into an established demand. Then there’s PepsiCo with Cheetos Mac n’ Cheese. PepsiCo Mexico identified a growing demand for household cooking staples and a lack of innovation in the mac and cheese category. Once again, this wasn’t a shot in the dark; it was informed by consumer research. The result? 35M+ pesos in sales in the first two years. Understanding consumer preferences gave Pepsi the edge to succeed. Consumer insights data helps you understand your audience to make confident, data-backed decisions to launch innovations that expand your brand’s reach and drive volume growth. Consumer insights data helps you understand your audience to make confident, data-backed decisions to launch innovations that expand your brand’s reach and drive volume growth. How are you using consumer insights for your next innovation? #RetailMedia #ConsumerInsights #MarketingStrategy #ExperientialMarketing #Innovation #CPG