{"id":112020,"date":"2022-03-24T20:28:36","date_gmt":"2022-03-24T20:28:36","guid":{"rendered":"https:\/\/staging.wingify.com\/blog\/maximizing-the-value-of-customer-data-through-experimentation\/"},"modified":"2026-09-04T17:27:10","modified_gmt":"2026-09-04T11:57:10","slug":"maximizing-the-value-of-customer-data-through-experimentation","status":"publish","type":"post","link":"https:\/\/wingify.com\/blog\/maximizing-the-value-of-customer-data-through-experimentation\/","title":{"rendered":"Maximizing the Value of Customer Data Through Experimentation"},"content":{"rendered":"

Check out the <\/span>introduction to the Customer-Centric Data Series here<\/span><\/a>.<\/span><\/p>\n

For the first blog in our series on the different ways you can utilize data to help brands build a more customer-centric vision<\/a>, our partner Aimee Bos, VP of Analytics and Data Strategy<\/strong> at<\/strong>\u00a0Zion & Zion,<\/strong> and Wingify’s Chief Data Scientist, Hubert Wassner<\/strong>, delve into how experimentation data can help you better understand your customers. They explore the who, what, and when of testing, discuss key customer insight metrics, the importance of audience sizes, where your best ideas for testing are lurking, and more.\u00a0<\/span><\/i><\/p>\n

 <\/p>\n

Why is experimentation important for understanding customers?\u00a0<\/b><\/p>\n

Put simply, experimentation enables brands to \u201cperfect\u201d their products. By improving upon the value that\u2019s already been developed, the customer experience is improved. And each time a new feature or option is added to a product, consistent A\/B testing ensures consistent customer reactions. Experimentation operates in a feedback loop with customers, moving beyond conversions or acquisition, improving adoption and retention, eventually making your product indispensable to your customers.<\/span><\/p>\n

 <\/p>\n

Which key metrics deliver the best insights about customers?\u00a0<\/b><\/p>\n

Hubert says, \u201cBasically, the metrics that deliver reliable customer insights are conversion rate and average cart value, segmented on meaningful criteria such as geolocation, or CRM data. But there are others that are interesting, such as revenue per visitor (RPV). It\u2019s a low-value metric but important to monitor.\u00a0<\/span><\/p>\n

\u201cAnd average order value (AOV) is another. This metric will vary enormously over time so it shouldn\u2019t be taken as fact. Seasonality (think Christmas or Black Friday, for example), or even one huge buyer can skew the statistics. It needs to be viewed in multiple contexts to get a better understanding of progress – not just Year over Year but Month over Month and even Week over Week to be effectively computed.\u00a0<\/span><\/p>\n

\u201cAOV and RPV are important because their omission can lead to data bias. People often forget to analyze metrics about non-converting visitors. Of course, AOV only gives you data about those who actually make it fully through the purchase cycle.\u201d\u00a0<\/span><\/p>\n

And Aimee agrees, \u201cWell, win rate, of course. For e-commerce it\u2019s conversions, value, RPV, how they\u2019re moving the needle, are they increasing the value of the average order? We want as much data as possible at the most granular level possible for lead generation, gated content, and micro-conversions\u2026 These smaller tests can be tied to more customer-centric metrics, as opposed to larger business-level metrics such as revenue, growth, number of customers, ROI, etc.”<\/span><\/p>\n

 <\/p>\n

Where are the best sources for experimentation ideas?<\/b><\/p>\n

Aimee has her own process. \u201cI start by asking myself what my business objectives are (micro\/macro). Then I check Google Analytics and ask myself \u2018Where are conversions not happening?\u2019 For experimentation ideas, I check tools like HotJar, voice-of-customer data (<\/span>VoC<\/span><\/a>), Qualtrics data, see actual customer feedback, user panelists: give them choices, ask what they prefer. Always hypothesize friction points, these will give you your best ideas for testing!\u201d<\/span><\/p>\n

Hubert likes to get his ideas from NPS scores. \u201cNet promoter score (<\/span>NPS<\/span><\/a>) has useful information and comments and can be a good starting point for fact-based rather than random hypotheses, which are a waste of time. NPS can add some real focus to well-designed tests. It\u2019s based on a single question: On a scale of 0 to 10, how likely are you to recommend this company\u2019s product or service to a friend or a colleague? NPS is a good way to identify areas that need improvement, but as a signifier of a company\u2019s CX score, it needs to be paired with qualitative insights to understand the context behind the score.\u201d<\/span><\/p>\n

\u00a0<\/b><\/p>\n

How do I pull everything together? What do I need to carry out my tests?<\/b><\/p>\n

Obviously, you need a tool to run your AB tests and collect the data necessary to make good hypotheses, b<\/span>ut a good way to add a big boost to your testing program \u2013 and help drive more ROI – is with tools like<\/span> Contentsquare<\/span><\/a> or<\/span> Fullstory<\/span><\/a> which offer more data on customer behavior and experience to focus your testing data. Designed to bridge the gap between the digital experiences companies think they\u2019re offering their customers and what customers are actually getting, analytics platforms can provide real opportunities for useful testing hypotheses by offering more educated guesses about variables for testing to improve CX.\u00a0<\/span><\/p>\n

Aimee has an important note about initial data collection, too.\u00a0 \u201cYou also need three months of data before you begin testing if you want reliable results, and you need to be sure it\u2019s accurate. Most people rely on Google Analytics (GA). That\u2019s a lot of data to handle and organize. A Customer Data Platform (CDP) represents a significant investment, but centralizing your data in one is extremely useful for customer segmentation and detailed analysis. The sooner you can invest in a tool like a CDP, the better for a sustainable data architecture strategy.<\/span><\/p>\n

 <\/p>\n

I\u2019m ready to test, but I have several hypotheses. How to begin?<\/b><\/p>\n

According to Aimee, \u201cwhen that happens, we break large problems into smaller ones. We have a customer that wants to triple their business and also wants a CDP<\/a> this year among other goals. It\u2019s a lot! To help them, we build out a customer journey roadmap to see what influences the client\u2019s goals. We select five or six high-level goals (landing page, navigation measured against click-through rate, for example), then test various aspects of each of these goals.\u201d<\/span><\/p>\n

Hubert notes, \u201cit\u2019s possible to test more than one hypothesis at once if your sample size is big enough. But first, you need to know what the statistical power of your experiment is. Small sample sizes can only detect big effects: it\u2019s important to know the order of magnitude in order to carry out meaningful experiments. It\u2019s always best to test your variables on a large audience, with varied behaviors and needs, in order to get the most reliable and informed results.\u201d<\/span><\/p>\n

 <\/p>\n

Is there value in running data-gathering experiments (as opposed to improving conversion \/ driving a specific metric)?<\/b><\/p>\n

Hubert is a full believer in testing no matter what you think may happen. \u201cTesting is always useful because a good test teaches you something, you learn something, win or lose. As long as you have a hypothesis. For instance, measuring the effect of a (supposed) selling feature (like an ad or sale) is useful. You know how much an ad or a sale costs, but without experimenting\u00a0you don’t know how much it pays.\u00a0<\/span><\/p>\n

\u201cOr say you have a 100% win rate. That means you\u2019re not learning anymore. So you test to gain new information in other areas, you don\u2019t just stand still. You minimize losses to maximize wins.\u201d<\/span><\/p>\n

 <\/p>\n

\n

 <\/p>\n

Enjoy what you read? Be sure to read part 2 of the Customer-Centric Data Series here.<\/a><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Check out the introduction to the Customer-Centric Data Series here. For the first blog in our series on the different ways you can utilize data to help brands build a more customer-centric vision, our partner Aimee Bos, VP of Analytics and Data Strategy at\u00a0Zion & Zion, and Wingify’s Chief Data Scientist, Hubert Wassner, delve into…<\/p>\n","protected":false},"author":1435,"featured_media":115268,"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-112020","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-science"],"acf":[],"yoast_head":"\nMaximizing the Value of Customer Data Through Experimentation - Wingify Blog<\/title>\n<meta name=\"description\" content=\"Experts from Wingify and Zion & Zion explore how experimentation data can help you better understand customers to build better experiences.\" \/>\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\/maximizing-the-value-of-customer-data-through-experimentation\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Maximizing the Value of Customer Data Through Experimentation - 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