Real-World Ways to Use Data to Improve Customer Experience

Whether you call it a funnel, a flywheel, a tower, a loop, a corridor, or any of the many terms we use to describe the customer journey, there’s one common challenge among brand marketers: How do we make the “customer journey” more productive? The easy answer, supported by mountains of research, is to improve the customer experience. But as you would expect, it’s a bit complex in practice. Most customer experience experts can agree on one practical solution: Use data to improve the customer experience. If Gartner’s prediction that 85% of customer interactions will be void of human interaction is correct, then data will be a major way to affect the customer journey. Consulting firm McKinsey & Company argues that leading companies understand that how an organization delivers for customers is starting to be as important as what it delivers. In a McKinsey Quarterly executive brief analyzing customer experience, the firm posits that if armed with advanced analytics, leaders in customer experience will see revenue gains of 5% to 10% and cost reductions of 15% to 25% within two to three years. Clearly, the stakes are high and the opportunity is great. But how, exactly, can brands employ data to manage the customer experience? Practical examples and case studies for employing data to hone the customer experience are hard to find, while potential technology solutions and their claims are abundant. In an effort to provide some useful insight, let’s go down two different but complementary paths — customer journey analytics and personalization-at-scale — with some real-life scenarios of how these tools combined with action solve problems and drive revenue. Customer Journey Analytics There are many vendors who provide different customer journey analytics methodologies. But generally, there are several categories of analysis that, when combined together, provide the traditional analytics that describe what is happening with insights on why customers behave as they do, including event tracking, heat maps, and session replays. The following examples to consider come from an e-commerce clothing giant (let’s call it Retailer X) that analyzed day-to-day customer journeys to diagnose issues and implement solutions that had a significant positive impact on revenue. Spotting Page Load Issues: Google’s Think with Google insights tool cites the metric that 53% of visits are abandoned when a mobile site takes longer than three seconds to load. Further, they state that every second in load time drives conversions down by 12%. Retailer X found the average page load speed on its mobile site was 13 seconds, which translates to a 31% estimated visitor loss. In coordination with the tech team, a number of fixes were enacted to improve “time to load” and save a significant traffic loss. API Call Latency: Retailer X has numerous API calls working behind the scenes to provide content from affiliate programs. One affiliate API was not functioning optimally and was creating a significant time delay in the page load for key products on some very specific pages. That experience was a death sentence to all sessions affected. Users left in droves, but a simple outreach to the affiliate corrected the issue. Channel-Differentiated Journeys: Through customer journey analytics, detailed funnels can be created and evaluated discretely. For example, an online education provider had very little insight into why so many of the paid social visitors he drives convert so poorly. By analyzing this specific channel and using session replay features of the customer journey analytics tool, he pinpointed creative issues with their landing page. The landing page was simply too long and social visitors were losing steam during many page swipes, particularly in mobile. A new, shorter landing page and some testing solidified the new approach and turned a non-performing channel into a winner. Personalization-at-Scale Another approach to maximizing the customer journey is to leverage personalization. In this method, marketers are aligning messaging, channel delivery, and timing to the individual, rather than a mass marketing methodology. To do this at-scale requires a combination of data, technology, and coordination. The customer data platform (CDP) technology category has emerged as a key tool for personalization by providing the ability to assemble customer data from disparate data sources, resolve identity, and connect to the marketing channel. These are key features of CDP technologies. Many platforms also provide a data science-driven approach to selecting the right time, right message, and right channel for targeting individuals. This personalization drives real bottom-line results. Here are some examples of how marketers have harnessed the power of data and personalization-at-scale to drive real results. Identifying New Consumers: A leading CPG pet brand (Pet Brand Z) is using a CDP to create deep, trusting relationships very early in the customer journey. By creating an adoptable pet database from 14,000 shelters and rescues in North America, this brand is not only helping create the love connection between needy pets and new homes, it was gathering tremendous data on new potential customers. Using the CDP to gather the first-party data given under consent by users of the adoption database created a dataset of individuals interested in new adoptable friends and were connected to the brand. Leveraging this insight, Pet Brand Z was able to create highly customized Facebook campaigns to both retarget the individuals and build high-performing lookalike audiences that drove conversion rates at three times other non-personalized efforts. Those conversions at scale cost 1/10th a normal conversion and 1,000 times the volume of conversions. Improved Attribution: Next, let’s look at an online retailer who utilized a CDP to connect the new brick-and-mortar locations and sales with online consumers. Using online promotions to drive appointments at the brick-and-mortar locations meant that the user journey was disconnected at the point of purchase. With the CDP, the retailer was able to assemble customer profiles from both online and offline (POS system) data and build a comprehensive picture of the online-to-offline journey. With data coming together in the CDP, they could see the specific purchase behaviors, both online and offline, for the individuals responding to the online-only campaign. Analyzing not only where the purchase happened, but what was bought, helped them fine-tune the ongoing campaign elements creating a three-times increase in offline purchases, two-times improvement in ROAS, and three-times increase on website purchases. Whatever the shape of your customer path — a funnel, tower, corridor, flywheel, or loop — customer journey management is deeply meaningful, in terms of outcomes and value. Mastering the delight of your customers is challenging, but data and the right technologies have the power to drive that progression to profitable outcomes.

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Real-World Ways to Use Data to Improve Customer Experience

AnnMarie Wills

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AnnMarie Wills