Reading Time: 9 minutes In marketing, data isn’t a buzzword. It’s the lifeblood of all successful campaigns. But are you truly harnessing its power, or are you drowning in a sea of information? To answer this question (and many others), we sat down with Ankur Kothari, a seasoned Martech expert, to dive deep into this crucial topic. This interview, originally conducted for Chapter 6 of “The Customer Engagement Book: Adapt or Die” explores how businesses can translate raw data into actionable insights that drive real results. Ankur shares his wealth of knowledge on identifying valuable customer engagement data, distinguishing between signal and noise, and ultimately, shaping real-time strategies that keep companies ahead of the curve.   Ankur Kothari Q&A Interview 1. What types of customer engagement data are most valuable for making strategic business decisions? Primarily, there are four different buckets of customer engagement data. I would begin with behavioral data, encompassing website interaction, purchase history, and other app usage patterns. Second would be demographic information: age, location, income, and other relevant personal characteristics. Third would be sentiment analysis, where we derive information from social media interaction, customer feedback, or other customer reviews. Fourth would be the customer journey data. We track touchpoints across various channels of the customers to understand the customer journey path and conversion. Combining these four primary sources helps us understand the engagement data. 2. How do you distinguish between data that is actionable versus data that is just noise? First is keeping relevant to your business objectives, making actionable data that directly relates to your specific goals or KPIs, and then taking help from statistical significance. Actionable data shows clear patterns or trends that are statistically valid, whereas other data consists of random fluctuations or outliers, which may not be what you are interested in. You also want to make sure that there is consistency across sources. Actionable insights are typically corroborated by multiple data points or channels, while other data or noise can be more isolated and contradictory. Actionable data suggests clear opportunities for improvement or decision making, whereas noise does not lead to meaningful actions or changes in strategy. By applying these criteria, I can effectively filter out the noise and focus on data that delivers or drives valuable business decisions. 3. How can customer engagement data be used to identify and prioritize new business opportunities? First, it helps us to uncover unmet needs. By analyzing the customer feedback, touch points, support interactions, or usage patterns, we can identify the gaps in our current offerings or areas where customers are experiencing pain points. Second would be identifying emerging needs. Monitoring changes in customer behavior or preferences over time can reveal new market trends or shifts in demand, allowing my company to adapt their products or services accordingly. Third would be segmentation analysis. Detailed customer data analysis enables us to identify unserved or underserved segments or niche markets that may represent untapped opportunities for growth or expansion into newer areas and new geographies. Last is to build competitive differentiation. Engagement data can highlight where our companies outperform competitors, helping us to prioritize opportunities that leverage existing strengths and unique selling propositions. 4. Can you share an example of where data insights directly influenced a critical decision? I will share an example from my previous organization at one of the financial services where we were very data-driven, which made a major impact on our critical decision regarding our credit card offerings. We analyzed the customer engagement data, and we discovered that a large segment of our millennial customers were underutilizing our traditional credit cards but showed high engagement with mobile payment platforms. That insight led us to develop and launch our first digital credit card product with enhanced mobile features and rewards tailored to the millennial spending habits. Since we had access to a lot of transactional data as well, we were able to build a financial product which met that specific segment’s needs. That data-driven decision resulted in a 40% increase in our new credit card applications from this demographic within the first quarter of the launch. Subsequently, our market share improved in that specific segment, which was very crucial. 5. Are there any other examples of ways that you see customer engagement data being able to shape marketing strategy in real time? When it comes to using the engagement data in real-time, we do quite a few things. In the recent past two, three years, we are using that for dynamic content personalization, adjusting the website content, email messaging, or ad creative based on real-time user behavior and preferences. We automate campaign optimization using specific AI-driven tools to continuously analyze performance metrics and automatically reallocate the budget to top-performing channels or ad segments. Then we also build responsive social media engagement platforms like monitoring social media sentiments and trending topics to quickly adapt the messaging and create timely and relevant content. With one-on-one personalization, we do a lot of A/B testing as part of the overall rapid testing and market elements like subject lines, CTAs, and building various successful variants of the campaigns. 6. How are you doing the 1:1 personalization? We have advanced CDP systems, and we are tracking each customer’s behavior in real-time. So the moment they move to different channels, we know what the context is, what the relevance is, and the recent interaction points, so we can cater the right offer. So for example, if you looked at a certain offer on the website and you came from Google, and then the next day you walk into an in-person interaction, our agent will already know that you were looking at that offer. That gives our customer or potential customer more one-to-one personalization instead of just segment-based or bulk interaction kind of experience. We have a huge team of data scientists, data analysts, and AI model creators who help us to analyze big volumes of data and bring the right insights to our marketing and sales team so that they can provide the right experience to our customers. 7. What role does customer engagement data play in influencing cross-functional decisions, such as with product development, sales, and customer service? Primarily with product development — we have different products, not just the financial products or products whichever organizations sell, but also various products like mobile apps or websites they use for transactions. So that kind of product development gets improved. The engagement data helps our sales and marketing teams create more targeted campaigns, optimize channel selection, and refine messaging to resonate with specific customer segments. Customer service also gets helped by anticipating common issues, personalizing support interactions over the phone or email or chat, and proactively addressing potential problems, leading to improved customer satisfaction and retention. So in general, cross-functional application of engagement improves the customer-centric approach throughout the organization. 8. What do you think some of the main challenges marketers face when trying to translate customer engagement data into actionable business insights? I think the huge amount of data we are dealing with. As we are getting more digitally savvy and most of the customers are moving to digital channels, we are getting a lot of data, and that sheer volume of data can be overwhelming, making it very difficult to identify truly meaningful patterns and insights. Because of the huge data overload, we create data silos in this process, so information often exists in separate systems across different departments. We are not able to build a holistic view of customer engagement. Because of data silos and overload of data, data quality issues appear. There is inconsistency, and inaccurate data can lead to incorrect insights or poor decision-making. Quality issues could also be due to the wrong format of the data, or the data is stale and no longer relevant. As we are growing and adding more people to help us understand customer engagement, I’ve also noticed that technical folks, especially data scientists and data analysts, lack skills to properly interpret the data or apply data insights effectively. So there’s a lack of understanding of marketing and sales as domains. It’s a huge effort and can take a lot of investment. Not being able to calculate the ROI of your overall investment is a big challenge that many organizations are facing. 9. Why do you think the analysts don’t have the business acumen to properly do more than analyze the data? If people do not have the right idea of why we are collecting this data, we collect a lot of noise, and that brings in huge volumes of data. If you cannot stop that from step one—not bringing noise into the data system—that cannot be done by just technical folks or people who do not have business knowledge. Business people do not know everything about what data is being collected from which source and what data they need. It’s a gap between business domain knowledge, specifically marketing and sales needs, and technical folks who don’t have a lot of exposure to that side. Similarly, marketing business people do not have much exposure to the technical side — what’s possible to do with data, how much effort it takes, what’s relevant versus not relevant, and how to prioritize which data sources will be most important. 10. Do you have any suggestions for how this can be overcome, or have you seen it in action where it has been solved before? First, cross-functional training: training different roles to help them understand why we’re doing this and what the business goals are, giving technical people exposure to what marketing and sales teams do. And giving business folks exposure to the technology side through training on different tools, strategies, and the roadmap of data integrations. The second is helping teams work more collaboratively. So it’s not like the technology team works in a silo and comes back when their work is done, and then marketing and sales teams act upon it. Now we’re making it more like one team. You work together so that you can complement each other, and we have a better strategy from day one. 11. How do you address skepticism or resistance from stakeholders when presenting data-driven recommendations? We present clear business cases where we demonstrate how data-driven recommendations can directly align with business objectives and potential ROI. We build compelling visualizations, easy-to-understand charts and graphs that clearly illustrate the insights and the implications for business goals. We also do a lot of POCs and pilot projects with small-scale implementations to showcase tangible results and build confidence in the data-driven approach throughout the organization. 12. What technologies or tools have you found most effective for gathering and analyzing customer engagement data? I’ve found that Customer Data Platforms help us unify customer data from various sources, providing a comprehensive view of customer interactions across touch points. Having advanced analytics platforms — tools with AI and machine learning capabilities that can process large volumes of data and uncover complex patterns and insights — is a great value to us. We always use, or many organizations use, marketing automation systems to improve marketing team productivity, helping us track and analyze customer interactions across multiple channels. Another thing is social media listening tools, wherever your brand is mentioned or you want to measure customer sentiment over social media, or track the engagement of your campaigns across social media platforms. Last is web analytical tools, which provide detailed insights into your website visitors’ behaviors and engagement metrics, for browser apps, small browser apps, various devices, and mobile apps. 13. How do you ensure data quality and consistency across multiple channels to make these informed decisions? We established clear guidelines for data collection, storage, and usage across all channels to maintain consistency. Then we use data integration platforms — tools that consolidate data from various sources into a single unified view, reducing discrepancies and inconsistencies. While we collect data from different sources, we clean the data so it becomes cleaner with every stage of processing. We also conduct regular data audits — performing periodic checks to identify and rectify data quality issues, ensuring accuracy and reliability of information. We also deploy standardized data formats. On top of that, we have various automated data cleansing tools, specific software to detect and correct data errors, redundancies, duplicates, and inconsistencies in data sets automatically. 14. How do you see the role of customer engagement data evolving in shaping business strategies over the next five years? The first thing that’s been the biggest trend from the past two years is AI-driven decision making, which I think will become more prevalent, with advanced algorithms processing vast amounts of engagement data in real-time to inform strategic choices. Somewhat related to this is predictive analytics, which will play an even larger role, enabling businesses to anticipate customer needs and market trends with more accuracy and better predictive capabilities. We also touched upon hyper-personalization. We are all trying to strive toward more hyper-personalization at scale, which is more one-on-one personalization, as we are increasingly capturing more engagement data and have bigger systems and infrastructure to support processing those large volumes of data so we can achieve those hyper-personalization use cases. As the world is collecting more data, privacy concerns and regulations come into play. I believe in the next few years there will be more innovation toward how businesses can collect data ethically and what the usage practices are, leading to more transparent and consent-based engagement data strategies. And lastly, I think about the integration of engagement data, which is always a big challenge. I believe as we’re solving those integration challenges, we are adding more and more complex data sources to the picture. So I think there will need to be more innovation or sophistication brought into data integration strategies, which will help us take a truly customer-centric approach to strategy formulation.   This interview Q&A was hosted with Ankur Kothari, a previous Martech Executive, for Chapter 6 of The Customer Engagement Book: Adapt or Die. Download the PDF or request a physical copy of the book here. The post Ankur Kothari Q&A: Customer Engagement Book Interview appeared first on MoEngage.

Insights How to Create a Successful Leadership Development Program At Harvard Business Impact, we partner with organizations to craft tailored learning experiences for leaders across all levels. Though each collaboration is unique, there is a proven process for designing and developing impactful learning initiatives. Leverage our checklist to help your organization develop a leadership development program that delivers results. View the infographic Leadership DevelopmentStrategic Alignment Share this resource Share on LinkedIn Share on Facebook Share on X Share on WhatsApp Email this Page Connect with us Change isn’t easy, but we can help. Together we’ll create informed and inspired leaders ready to shape the future of your business. Contact us Latest Insights Strategic Alignment Harvard Business Publishing Unveils Harvard Business Impact as New Brand for Corporate Learning and Education Units Harvard Business Publishing announced the launch of Harvard Business Impact, a new brand identity for… Read more: Harvard Business Publishing Unveils Harvard Business Impact as New Brand for Corporate Learning and Education Units News Digital Intelligence Succeeding in the Digital Age: Why AI-First Leadership Is Essential While AI makes powerful operational efficiencies possible, it cannot yet replace the creativity, adaptability, and… Read more: Succeeding in the Digital Age: Why AI-First Leadership Is Essential Perspectives Digital Intelligence 4 Keys to AI-First Leadership: The New Imperative for Digital Transformation AI has become a defining force in reshaping industries and determining competitive advantage. To support… Read more: 4 Keys to AI-First Leadership: The New Imperative for Digital Transformation Infographic Talent Management Leadership Fitness Behavioral Assessment In our study, “Leadership Fitness: Developing the Capacity to See and Lead Differently Amid Complexity,”… Read more: Leadership Fitness Behavioral Assessment Job Aid The post How to Create a Successful Leadership Development Program appeared first on Harvard Business Impact.

Reading Time: 6 minutes What is marketing without data? Assumptions. Guesses. Fluff. For Chapter 6 of our book, “The Customer Engagement Book: Adapt or Die,” we spoke with Alec Haase, Product GTM Lead, Commerce and AI at Hightouch, to explore how engagement data can truly inform critical business decisions.  Alec discusses the different types of customer behaviors that matter most, how to separate meaningful information from the rest, and the role of systems that learn over time to create tailored customer experiences. This interview provides insights into using data for real-time actions and shaping the future of marketing. Prepare to learn about AI decision-making and how a focus on data is changing how we engage with customers.   Alec Haase Q&A Interview 1. What types of customer engagement data are most valuable for making strategic business decisions? It’s a culmination of everything. Behavioral signals — the actual conversions and micro-conversions that users take within your product or website. Obviously, that’s things like purchases. But there are also other behavioral signals marketers should be using and thinking about. Things like micro-conversions — maybe that’s shopping for a product, clicking to learn more about a product, or visiting a certain page on your website. Behind that, you also need to have all your user data to tie that to. So I know someone took said action; I can follow up with them in email or out on paid social. I need the user identifiers to do that. 2. How do you distinguish between data that is actionable versus data that is just noise? Data that’s actionable includes the conversions and micro-conversions — very clear instances of “someone did this.” I can react to or measure those. What’s becoming a bit of a challenge for marketers is understanding that there’s other data that is valuable for machine learning or reinforcement learning models, things like tags on the types of products customers are interacting with. Maybe there’s category information about that product, or color information. That would otherwise look like noise to the average marketer. But behind the scenes, it can be used for reinforcement learning. There is definitely the “clear-cut” actionable data, but marketers shouldn’t be quick to classify things as noise because the rise in machine learning and reinforcement learning will make that data more valuable. 3. How can customer engagement data be used to identify and prioritize new business opportunities? At Hightouch, we don’t necessarily think about retroactive analysis. We have a system where we have customer engagement data firing in that we then have real-time scores reacting to. An interesting example is when you have machine learning and reinforcement learning models running. In the pet retailer example I gave you, the system is able to figure out what to prioritize. The concept of reinforcement learning is not a marketer making rules to say, “I know this type of thing works well on this type of audience.” It’s the machine itself using the data to determine what attribute responds well to which offer, recommendation, or marketing campaign. 4. How can marketers ensure their use of customer engagement data aligns with the broader business objectives? It starts with the objectives. It’s starting with the desired outcome and working your way back. That whole flip of the paradigm is starting with outcomes and letting the system optimize. What are you trying to drive, and then back into the types of experiences that can make that happen? There’s personalization. When we talk about data-driven experiences and personalization, Spotify Wrapped is the North Star. For Spotify Wrapped, you want to drive customer stickiness and create a brand. To make that happen, you want to send a personalized email. What components do you want in that email? Maybe it’s top five songs, top five artists, and then you can back into the actual event data you need to make that happen. 5. What role does engagement data play in influencing cross-functional decisions such as those in product development, sales, or customer service? For product development, it’s product analytics — knowing what features users are using, or seeing in heat maps where users are clicking. Sales is similar. We’re using behavioral signals like what types of content they’re reading on the site to help inform what they would be interested in — the types of products or the types of use cases. For customer service, you can look at errors they’ve run into in the past or specific purchases they’ve made, so that when you’re helping them the next time they engage with you, you know exactly what their past behaviors were and what products they could be calling about. 6. What are some challenges marketers face when trying to translate customer engagement data into actionable insights? Access to data is one challenge. You might not know what data you have because marketers historically may not have been used to the systems where data is stored. Historically, that’s been pretty siloed away from them. Rich behavioral data and other data across the business was stored somewhere else. Now, as more companies embrace the data warehouse at the center of their business, it gives everyone a true single place where data can be stored. Marketers are working more with data teams, understanding more about the data they have, and using that data to power downstream use cases, personalization, reinforcement learning, or general business insights. 7. How do you address skepticism or resistance from stakeholders when presenting data-driven recommendations? As a marketer, I think proof is key. The best thing is if you’ve actually run a test. “I think we should do this. I ran a small test, and it’s showing that this is actually proving out.” Being able to clearly explain and justify your reasoning with data is super important. 8. What technology or tools have you found most effective for gathering and analyzing customer engagement data? Any type of behavioral event collection, specifically ones that write to the cloud data warehouse, is the critical component. Your data team is operating off the data warehouse. Having an event collection product that stores data in that central spot is really important if you want to use the other data when making recommendations. You want to get everything into the data warehouse where it can be used both for insights and for putting into action. For Spotify Wrapped, you want to collect behavioral event signals like songs listened to or concerts attended, writing to the warehouse so that you can get insights back — how many songs were played this year, projections for next month — but then you can also use those behavioral events in downstream platforms to fire off personalized emails with product recommendations or Spotify Wrapped-style experiences. 9. How do you see the role of customer engagement data evolving in shaping business strategies over the next five years? What we’re excited about is the concept of AI Decisioning — having AI agents actually using customer data to train their own models and decision-making to create personalized experiences. We’re sitting on top of all this behavioral data, engagement data, and user attributes, and our system is learning from all of that to make the best decisions across downstream systems. Whether that’s as simple as driving a loyalty program and figuring out what emails to send or what on-site experiences to show, or exposing insights that might lead you to completely change your business strategy, we see engagement data as the fuel to the engine of reinforcement learning, machine learning, AI agents, this whole next wave of Martech that’s just now coming. But it all starts with having the data to train those systems. I think that behavioral data is the fuel of modern Martech, and that only holds more true as Martech platforms adopt these decisioning and AI capabilities, because they’re only as good as the data that’s training the models.     This interview Q&A was hosted with Alec Haase, Product GTM Lead, Commerce and AI at Hightouch, for Chapter 6 of The Customer Engagement Book: Adapt or Die. Download the PDF or request a physical copy of the book here. The post Alec Haase Q&A: Customer Engagement Book Interview appeared first on MoEngage.

We are pleased to announce the release of Matali Physics 6.9, the next significant step on the way to the seventh major version of the environment. Matali Physics 6.9 introduces a number of improvements and fixes to Matali Physics Core, Matali Render and Matali Games modules, presents physics-driven, completely dynamic light sources, real-time object scaling with destruction, lighting model simulating global illumination (GI) in some aspects, comprehensive support for Wayland on Linux, and more. Posted by komires on Jun 3rd, 2025 What is Matali Physics? Matali Physics is an advanced, modern, multi-platform, high-performance 3d physics environment intended for games, VR, AR, physics-based simulations and robotics. Matali Physics consists of the advanced 3d physics engine Matali Physics Core and other physics-driven modules that all together provide comprehensive simulation of physical phenomena and physics-based modeling of both real and imaginary objects. What's new in version 6.9? Physics-driven, completely dynamic light sources. The introduced solution allows for processing hundreds of movable, long-range and shadow-casting light sources, where with each source can be assigned logic that controls its behavior, changes light parameters, volumetric effects parameters and others; Real-time object scaling with destruction. All groups of physics objects and groups of physics objects with constraints may be subject to destruction process during real-time scaling, allowing group members to break off at different sizes; Lighting model simulating global illumination (GI) in some aspects. Based on own research and development work, processed in real time, ready for dynamic scenes, fast on mobile devices, not based on lightmaps, light probes, baked lights, etc.; Comprehensive support for Wayland on Linux. The latest version allows Matali Physics SDK users to create advanced, high-performance, physics-based, Vulkan-based games for modern Linux distributions where Wayland is the main display server protocol; Other improvements and fixes which complete list is available on the History webpage. What platforms does Matali Physics support? Android Android TV *BSD iOS iPadOS Linux (distributions) macOS Steam Deck tvOS UWP (Desktop, Xbox Series X/S) Windows (Classic, GDK, Handheld consoles) What are the benefits of using Matali Physics? Physics simulation, graphics, sound and music integrated into one total multimedia solution where creating complex interactions and behaviors is common and relatively easy Composed of dedicated modules that do not require additional licences and fees Supports fully dynamic and destructible scenes Supports physics-based behavioral animations Supports physical AI, object motion and state change control Supports physics-based GUI Supports physics-based particle effects Supports multi-scene physics simulation and scene combining Supports physics-based photo mode Supports physics-driven sound Supports physics-driven music Supports debug visualization Fully serializable and deserializable Available for all major mobile, desktop and TV platforms New features on request Dedicated technical support Regular updates and fixes If you have questions related to the latest version and the use of Matali Physics environment as a game creation solution, please do not hesitate to contact us.

Insights Learning to Lead in the Digital Age: The AI Readiness Reflection As the race to integrate generative AI accelerates, organizations face a dual challenge: fostering tech-savviness across teams while developing next-generation leadership competencies. These are critical to ensuring that “everyone” in the organization is prepared for continuous adaptation and change. This AI Readiness Reflection is designed to help you assess where your leaders stand today and identify the optimal path to build the digital knowledge, mindset, skills, and leadership capabilities required to thrive in the future. Take the assessment now to discover how your current practices align with AI maturity—and gain actionable insights tailored to your organization’s readiness level. To download the full report, tell us a bit about yourself. 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To opt-out, please visit our Privacy Policy. Digital Intelligence Share this resource Share on LinkedIn Share on Facebook Share on X Share on WhatsApp Email this Page Connect with us Change isn’t easy, but we can help. Together we’ll create informed and inspired leaders ready to shape the future of your business. Contact us Latest Insights Strategic Alignment Harvard Business Publishing Unveils Harvard Business Impact as New Brand for Corporate Learning and Education Units Harvard Business Publishing announced the launch of Harvard Business Impact, a new brand identity for… Read more: Harvard Business Publishing Unveils Harvard Business Impact as New Brand for Corporate Learning and Education Units News Digital Intelligence Succeeding in the Digital Age: Why AI-First Leadership Is Essential While AI makes powerful operational efficiencies possible, it cannot yet replace the creativity, adaptability, and… Read more: Succeeding in the Digital Age: Why AI-First Leadership Is Essential Perspectives Digital Intelligence 4 Keys to AI-First Leadership: The New Imperative for Digital Transformation AI has become a defining force in reshaping industries and determining competitive advantage. To support… Read more: 4 Keys to AI-First Leadership: The New Imperative for Digital Transformation Infographic Talent Management Leadership Fitness Behavioral Assessment In our study, “Leadership Fitness: Developing the Capacity to See and Lead Differently Amid Complexity,”… Read more: Leadership Fitness Behavioral Assessment Job Aid The post Learning to Lead in the Digital Age: The AI Readiness Reflection appeared first on Harvard Business Impact.

Insights 4 Key Objectives for Leadership Development that Support Transformation Organizational and industry-wide transformation initiatives pose difficulties for Learning and Development in readying leaders to ensure the success of these changes. Our 2024 global survey, which included 1,134 L&D and HR professionals and functional heads across 15 countries, pinpointed four main goals in leadership development that aid in facilitating transformation. To find out more, download the infographic. View the infographic Leadership DevelopmentTransformation Share this resource Share on LinkedIn Share on Facebook Share on X Share on WhatsApp Email this Page Connect with us Change isn’t easy, but we can help. Together we’ll create informed and inspired leaders ready to shape the future of your business. Contact us Latest Insights Strategic Alignment Harvard Business Publishing Unveils Harvard Business Impact as New Brand for Corporate Learning and Education Units Harvard Business Publishing announced the launch of Harvard Business Impact, a new brand identity for… Read more: Harvard Business Publishing Unveils Harvard Business Impact as New Brand for Corporate Learning and Education Units News Digital Intelligence Succeeding in the Digital Age: Why AI-First Leadership Is Essential While AI makes powerful operational efficiencies possible, it cannot yet replace the creativity, adaptability, and… Read more: Succeeding in the Digital Age: Why AI-First Leadership Is Essential Perspectives Digital Intelligence 4 Keys to AI-First Leadership: The New Imperative for Digital Transformation AI has become a defining force in reshaping industries and determining competitive advantage. To support… Read more: 4 Keys to AI-First Leadership: The New Imperative for Digital Transformation Infographic Talent Management Leadership Fitness Behavioral Assessment In our study, “Leadership Fitness: Developing the Capacity to See and Lead Differently Amid Complexity,”… Read more: Leadership Fitness Behavioral Assessment Job Aid The post 4 Key Objectives for Leadership Development that Support Transformation appeared first on Harvard Business Impact.

We are told to customize, expand, and provide more options, but that might be a silent killer for our conversion rate. Using behavioral psychology and modern product design, this piece explains why brands like Apple use fewer, smarter choices to convert better.Image generated using ChatgptJam-packed decisionsImagine standing in a supermarket aisle in front of the jam section. How do you decide which jam to buy? You could go for your usual jam, or maybe this is your first time buying jam. Either way, a choice has to be made. Or does it?You may have seen the vast number of choices, gotten overwhelmed, and walked away. The same scenario was reflected in the findings of a 2000 study by Iyengar and Lepper that explored how the number of choice options can affect decision-making.Iyengar and Lepper set up two scenarios; the first customers in a random supermarket being offered 24 jams for a free tasting. In another, they were offered only 6. One would expect that the first scenario would see more sales. After all, more variety means a happier customer. However:Image created using CanvaWhile 60% of customers stopped by for a tasting, only 3% ended up making a purchase.On the other hand, when faced with 6 options, 40% of customers stopped by, but 30% of this number ended up making a purchase.The implications of the study were evident. While one may think that more choices are better when faced with the same, decision-makers prefer fewer.This phenomenon is known as the Paradox of Choice. More choice leads to less satisfaction because one gets overwhelmed.This analysis paralysis results from humans being cognitive misers that is decisions that require deeper thinking feel exhausting and like they come at a cognitive cost. In such scenarios, we tend not to make a choice or choose a default option. Even after a decision has been made, in many cases, regret or the thought of whether you have made the ‘right’ choice can linger.A sticky situationHowever, a 2010 meta-analysis by Benjamin Scheibehenne was unable to replicate the findings. Scheibehenne questioned whether it was choice overload or information overload that was the issue. Other researchers have argued that it is the lack of meaningful choice that affects satisfaction. Additionally, Barry Schwartz, a renowned psychologist and the author of the book ‘The Paradox of Choice: Why Less Is More,’ also later suggested that the paradox of choice diminishes in the presence of a person’s knowledge of the options and if the choices have been presented well.Does that mean the paradox of choice was an overhyped notion? I conducted a mini-study to test this hypothesis.From shelves to spreadsheets: testing the jam jar theoryI created a simple scatterplot in R using a publicly available dataset from the Brazilian e-commerce site Olist. Olist is Brazil’s largest department store on marketplaces. After delivery, customers are asked to fill out a satisfaction survey with a rating or comment option. I analysed the relationship between the number of distinct products in a category (choices) and the average customer review (satisfaction).Scatterplot generated in R using the Olist datasetBased on the almost horizontal regression line on the plot above, it is evident that more choice does not lead to more satisfaction. Furthermore, categories with fewer than 200 products tend to have average review scores between 4.0 and 4.3. Whereas, categories with more than 1,000 products do not have a higher average satisfaction score, with some even falling below 4.0. This suggests that more choices do not equal more satisfaction and could also reduce satisfaction levels.These findings support the Paradox of Choice, and the dataset helps bring theory into real-world commerce. A curation of lesser, well-presented, and differentiated options could lead to more customer satisfaction.Image created using CanvaFurthermore, the plot could help suggest a more nuanced perspective; people want more choices, as this gives them autonomy. However, beyond a certain point, excessive choice overwhelms rather than empowers, leaving people dissatisfied. Many product strategies reflect this insight: the goal is to inspire confident decision-making rather than limiting freedom. A powerful example of this shift in thinking comes from Apple’s history.Simple tastes, sweeter decisionsImage source: Apple InsiderIt was 1997, and Steve Jobs had just made his return to Apple. The company at the time offered 40 different products; however, its sales were declining. Jobs made one question the company’s mantra,“What are the four products we should be building?”The following year, Apple saw itself return to profitability after introducing the iMac G3. While its success can be attributed to the introduction of a new product line and increased efficiency, one cannot deny that the reduction in the product line simplified the decision-making process for its consumers.To this day, Apple continues to implement this strategy by having a few SKUs and confident defaults.Apple does not just sell premium products; it sells a premium decision-making experience by reducing friction in decision-making for the consumer.Furthermore, a 2015 study based on analyzing scenarios where fewer choice options led to increased sales found the following mitigating factors in buying choices:Time Pressure: Easier and quicker choices led to more sales.Complexity of options: The easier it was to understand what a product was, the better the outcome.Clarity of Preference: How easy it was to compare alternatives and the clarity of one’s preferences.Motivation to Optimize: Whether the consumer wanted to put in the effort to find the ‘best’ option.Picking the right spreadWhile the extent of the validity of the Paradox of Choice is up for debate, its impact cannot be denied. It is still a helpful model that can be used to drive sales and boost customer satisfaction. So, how can one use it as a part of your business’s strategy?Remember, what people want isn’t 50 good choices. They want one confident, easy-to-understand decision that they think they will not regret.Here are some common mistakes that confuse consumers and how you can apply the Jam Jar strategy to curate choices instead:Image is created using CanvaToo many choices lead to decision fatigue.Offering many SKU options usually causes customers to get overwhelmed. Instead, try curating 2–3 strong options that will cover the majority of their needs.2. Being dependent on the users to use filters and specificationsWhen users have to compare specifications themselves, they usually end up doing nothing. Instead, it is better to replace filters with clear labels like “Best for beginners” or “Best for oily skin.”3. Leaving users to make comparisons by themselvesToo many options can make users overwhelmed. Instead, offer default options to show what you recommend. This instills within them a sense of confidence when making the final decision.4. More transparency does not always mean more trustInformation overload never leads to conversions. Instead, create a thoughtful flow that guides the users to the right choices.5. Users do not aim for optimizationAssuming that users will weigh every detail before making a decision is not rooted in reality. In most cases, they will go with their gut. Instead, highlight emotional outcomes, benefits, and uses instead of numbers.6. Not onboarding users is a critical mistakeHoping that users will easily navigate a sea of products without guidance is unrealistic. Instead, use onboarding tools like starter kits, quizzes, or bundles that act as starting points.7. Variety for the sake of varietyUsers crave clarity more than they crave variety. Instead, focus on simplicity when it comes to differentiation.And lastly, remember that while the paradox of choice is a helpful tool in your business strategy arsenal, more choice is not inherently bad. It is the lack of structure in the decision-making process that is the problem. Clear framing will always make decision-making a seamless experience for both your consumers and your business.How jam jars explain Apple’s success was originally published in UX Collective on Medium, where people are continuing the conversation by highlighting and responding to this story.