![]() ![]() The data that’s ultimately needed can be derived from any moments that exist between a brand and a customer, a prospect, or a user, regardless of where that moment occurs. For both, brands need data and lots of it. Think of competing on experience in the same way you would think about competing on analytics. In turn, these insights can be used to create better customer experiences based on revolving optimization. That was in 2007, and it is shifting once again with what the industry is calling “experience data.”Įxperience data gives insights to finding and resolving customer problems. In the book, aptly titled “Competing on Analytics: The Science of Winning,” Davenport argued that the frontier of analytics-driven business strategy had shifted. Tackling experience data in the “Experience Era”Ī decade ago Thomas Davenport wrote a book that revolutionized the way businesses competed by using analytics. Like our retail customer, let’s look at the steps experience era brands can take to collect experience data, analyze it, then turn it into actionable insights to continually improve the customer experience. That, in short, is what experience data is all about - fixing broken customer experiences and looking for ways to proactively reach out to consumers to continually improve the customer experience. The solution solved the problem that affected each individual customer while implementing a solution scaled to the entire online market. With contribution analysis data insights in hand, the retailer immediately pushed out a patch to fix the bug, thereby resolving the issue for their customers. Because the demand for these items was high, the company was losing an estimated $1.2 million per day leading up to Valentine’s Day. Contribution analysis auto-analyzed tens of millions of queries and applied machine learning to reveal why the rise occurred.Īs it turned out, their third-party tag management solution had a bug in it, which automatically booted trending ball gowns and boyfriend jeans from customer carts before they could purchase them. In turn, using contribution analysis, the retailer identified the “why” behind the increase within a matter of seconds. With analytics tools, this retailer caught the metric immediately, finding it statistically significant against the benchmark of how that metric historically performed and its predictable expectations. You can imagine their surprise when, on February 12, 2015, they discovered a 300 percent increase in a metric called “cart-remove revenue,” a metric that tracks potential revenue lost when a customer removes an item from his cart and doesn’t follow through on purchasing it. ![]() This company primarily targets shoppers of department stores, an audience that values quality at reasonable prices. New Sessions (i.e.Data, Analytics, and Benchmarks - Making Sense of Experience Business DataĪ large international retailer in the discount clothing and houseware niche came across an interesting piece of data while looking through their analytics.You can compare your data against benchmarks for the following metrics: Social, Direct, Referral, Organic Search, Paid Search, Display, and Email channels) Available dataīenchmarking data is available for each value of the following dimensions: For example, you can compare your property with all properties in the “All Hotels and Accommodations” industry in the United Kingdom that receive 500 to 1000 average daily sessions. ![]() You can further refine the data by geographic location and select from seven traffic size classifications, allowing you to compare your property against properties with similar traffic levels in your industry. You can choose from over 1600 industry categories, using a menu in the Benchmarking reports. ![]()
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