{"id":112021,"date":"2022-04-07T16:23:28","date_gmt":"2022-04-07T16:23:28","guid":{"rendered":"https:\/\/staging.wingify.com\/blog\/using-experimentation-data-to-uncover-customer-needs\/"},"modified":"2026-09-04T16:08:53","modified_gmt":"2026-09-04T10:38:53","slug":"using-experimentation-data-to-uncover-customer-needs","status":"publish","type":"post","link":"https:\/\/wingify.com\/blog\/using-experimentation-data-to-uncover-customer-needs\/","title":{"rendered":"Using Experimentation Data to Uncover Customer Needs"},"content":{"rendered":"

For the second blog in our series on<\/span><\/i> a <\/span>data-driven approach to customer-centric marketing<\/a>, we talked with our partner <\/span><\/i>Ryan Lucht, Director of Strategy at Cro Metrics<\/i><\/b>, and <\/span><\/i>Wingify data expert Hubert Wassner<\/i><\/b>, who explore the evolution of customer needs, why understanding them is so important for brands today, and the role experimentation plays in meeting those needs. Be sure to check out the <\/span><\/i>series introduction<\/span><\/i><\/a> and <\/span><\/i>part 1<\/span><\/i><\/a> if you missed those.<\/span><\/i><\/p>\n


\n<\/b>What do we mean when we say \u201ccustomer needs\u201d?<\/b><\/p>\n

Today\u2019s savvy customers expect a lot from brands: connected journeys, personalization, innovation, data protection. They\u2019re used to seamless online interactions. They want to find products easily. They want a frictionless experience and flexible payment methods. And if a brand doesn\u2019t deliver, they\u2019ll switch to one that does.\u201cIt\u2019s a good thing, it\u2019s forced us to become a lot more customer-centric and make our websites easier to use,\u201d says Ryan.<\/span><\/p>\n

And it pays to meet customer needs and deliver great customer experiences. In a<\/span> 2020 CX poll<\/span><\/a>, 91% of respondents said they were more likely to make a repeat purchase after a positive experience, and 71% said they\u2019d made a purchase decision based on experience quality.\u00a0<\/span><\/p>\n

Why is it important for businesses and brands to understand customer needs?<\/b>\u00a0<\/span><\/p>\n

Ryan believes it\u2019s vital to offer customers the right information at the right time. \u201cCustomer needs look different at different stages in the customer journey. When you\u2019re running an experimentation program, the order in which information is presented matters. If you dive into the details too soon, you risk overloading or boring your visitors, but if you wait too long to introduce core information like a refund policy, or a piece of your value proposition, you\u2019ll lose business.<\/span><\/p>\n

\u201cOne of our clients, a leading chain of gyms in the United States, is a good example. A crucial part of their strategy is reassuring customers they\u2019ll feel comfortable in their gyms \u2013 that their gyms are inclusive, body-positive, and everyone fits in. It\u2019s an important customer need. But on their homepage, the first thing people wanted to find was \u2018Where\u2019s the closest gym to me?\u2019 So they shifted to putting location-centric information first, and establishing their brand afterward: It increased subscriptions immediately. It\u2019s bottom-line revenue impact: if you\u2019re not solving for the right needs at the right time, you\u2019re leaving money on the table.\u201d\u00a0<\/span><\/p>\n

Hubert adds, \u201cIn online business, your competitors are just a click away, so if users get bored on your site, or it takes them too long to find what they need, they\u2019ll simply go elsewhere. The experience you offer needs to be frustration-free. There are growth results to be found at every stage of the purchase journey, so always think competitively, someone else is surely doing it better somewhere.\u201d<\/span>\u00a0<\/b><\/p>\n

Ryan agrees: \u201cWhen it comes to understanding your customers\u2019 needs, the more you leverage experimentation and advanced forms of data science, the better your competitive advantage. The reason is that customer needs are unique from one brand to another. An established brand can sell itself differently than a niche brand or a newcomer brand.\u201d\u00a0<\/span><\/p>\n

\u00a0<\/span><\/p>\n

Where can the most valuable data about customer needs be accessed?<\/b> \u00a0<\/span><\/p>\n

There are tools that automate data access. \u201cAt Wingify,\u201d says Hubert, \u201cwe\u2019d begin by installing a tag to gather transaction information for developers to see, for example, when users visit sites and when they leave, to measure bounce rate. Agencies can also gather information about products and purchases, as well as metrics like conversion rate, access to the cart page, dates of sale, and how much conversion rates change within <\/span>engagement levels<\/span><\/a> (see chart). Once you have enough data, you can begin to test different metrics to see which ones increase the engagement levels of different audience segments.\u201d\u00a0<\/span><\/p>\n

\"\"<\/p>\n

Does your site adapt to frequent users or does it treat them as new each time?\u00a0<\/b><\/p>\n

Ryan likes to ask executives, \u201cDo you think someone coming to your website for the very first time has the same needs as someone coming for the fifth time? And of course, the answer is \u2018no.\u2019 But think \u2018new\u2019 vs \u2018returning\u2019, or traffic sources \u2013 people come to the website from different places: email, advertisements, Facebook, TikTok, Google. All these people have very different contexts, and this gives us a hundred different test ideas. When he asks that same group whether or not they treat a new visitor differently than a returning visitor, unfortunately, the answer is still usually \u2018no.\u2019 This is just one of the countless opportunities for brands to use even the most basic data points to start to differentiate experiences.<\/span><\/p>\n

\u201cIt can get very complex very quickly. If you\u2019re going to do an AI or an ML model, it\u2019s important to understand which of the variables we\u2019re asking the model to look at are actually correlative and which are just noise.\u201d<\/span><\/p>\n

How can data from disparate sources be organized\/prioritized for testing?<\/b><\/p>\n

To combine data in order to act on it in a meaningful way, Hubert explains that the most common tools are CRM plug-ins. \u201cIf your user is identified, you can automatically gather more first-party information to save along with the customer journey, which can help you to segment for experimentation a posteriori.\u201d<\/span><\/p>\n

For Ryan, using disparate data sources isn\u2019t just about prioritizing test ideas, it\u2019s about better understanding test outcomes. \u201cI\u2019ve never met anybody who can use data to predict which test ideas will win. If they could, everyone would have staggering win rates, but even when you look at Microsoft, Google and Netflix, only one in ten ideas pans out. The more impactful side of using disparate data sources occurs after you run an experiment, <\/span>because experiment results are really only one specific learning; it’<\/span>s <\/span>how you tie those results back to other sources of customer data or analytics to piece together a narrative you think might be true and how you use those results to inform your next test idea.\u201d<\/span><\/p>\n

How can experimentation help you gain insight into customer needs?<\/b>\u00a0<\/span><\/p>\n

For Ryan, insight can be gained on two levels. \u201cAn individual experiment can tell you whether you\u2019re getting closer to or further away from fulfilling customer needs, based on if they\u2019re doing the thing you hoped they\u2019d do, but there\u2019s also a meta-analysis level, where once you\u2019ve run a lot of experiments, you start to recognize the patterns. For example, every time we emphasize pricing and make it clearer, we get better outcomes, so perhaps one customer need is better price understanding. Individual experiments help us, but we really learn the broader themes once we\u2019ve run a lot of experiments.\u201d\u00a0<\/span><\/p>\n

Hubert adds, \u201cIn my mind, when you build the test, you have to answer a question, not focus on making an improvement, because maybe you won\u2019t improve anything, but you will learn something, you\u2019ll increase your knowledge, and that\u2019s the real goal.\u201d\u00a0<\/span><\/p>\n

\u00a0<\/span><\/p>\n

How can insights about customer needs contribute to a better customer experience?<\/b>\u00a0<\/span><\/p>\n

\u201c<\/b>The better and more granularly you understand your customers and their needs, the more it will seem to them that you\u2019re able to read their minds; you\u2019ll please them and win their loyalty and win in the marketplace. The businesses that are doing this \u2013 doing a lot of experiments and doing them well \u2013 are the businesses that win. It\u2019s as simple as that\u201d, states Ryan.<\/span>\u00a0<\/b><\/p>\n

\u201c<\/b>When testing, we shouldn\u2019t be asking business questions, but user questions and only then adding business KPIs,” says Hubert. \u201cIt\u2019s vital to ask customer-centric questions first, not business-centric ones. This way, you can boost your business while better satisfying your customers.\u201d\u00a0<\/span><\/p>\n

Ryan agrees: \u201cIt\u2019s a very positive way to frame it. This is how we win <\/span>with<\/span><\/i> our customers because if we\u2019re better at meeting their needs, everyone wins.\u201d<\/span><\/p>\n

\n

Want more coverage of data topics? Be sure to come back in two weeks for the third installment in our Customer-Centric Data Series!<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

For the second blog in our series on a data-driven approach to customer-centric marketing, we talked with our partner Ryan Lucht, Director of Strategy at Cro Metrics, and Wingify data expert Hubert Wassner, who explore the evolution of customer needs, why understanding them is so important for brands today, and the role experimentation plays in…<\/p>\n","protected":false},"author":1435,"featured_media":115273,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"post_read_time":0,"footnotes":""},"categories":[10834],"tags":[],"feature":[],"industry-type":[],"product":[],"role":[],"region":[],"class_list":["post-112021","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-science"],"acf":[],"yoast_head":"\nUsing Experimentation Data to Uncover Customer Needs - Wingify Blog<\/title>\n<meta name=\"description\" content=\"Today\u2019s savvy customers expect a lot from brands: connected journeys, personalization, innovation, data protection.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/wingify.com\/blog\/using-experimentation-data-to-uncover-customer-needs\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Using Experimentation Data to Uncover Customer Needs - 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