Companies are aspiring to out-compete each other by upping their customer experience game, but the reality is that many are falling short of their aspiration to gather and collate valuable customer data that can create customer experiences that lift satisfaction, loyalty and advocacy.
That’s the premise behind the presentation by Augie Ray, Research Director for Gartner, at the recent Gartner Digital Marketing Conference in San Diego, where Ray introduced CUPID, a five-step framework for multi-channel customer love based on Collecting, Unifying, Predicting, Identifying and Delivering exceptional customer experiences.
Step 1: Collect
According to Ray, it all starts with collecting and listening to big data on customers. Specifically, tracking what customers do on their websites versus other channels, including competitors’ sites and even non-competitive social channels and search engines. The goal is to understand customer intent—what are they looking for, when are they looking for it, and what do they do when they find it?
Step 2: Unify
While there are plenty of marketing tools and platforms available to track and capture customer data, much of that data is siloed. Unifying that data is essential if brands are to speak back to target audiences. This requires a platform or back-end database that can collect it all and help form strategies to normalize captured data to better understand customers. For example, if data reveals that a customer asked about a Widgetco product on a social channel, then performed a search for the Widgetco product before checking it out on Widgetco’s website, unifying these insights can lead to understanding that the customer is 40% interested in Widgetco’s product—enabling the marketer to develop strategies to raise that interest level to 90%.
Step 3: Predict
Collecting and unifying disparate customer data provides a firm foundation for segmenting customers into personas. This expanded view of a brand’s audiences delivers key insights into their behavior and intents, which can inform personalization strategies designed to predict, intercept and move customers into sales funnels.
Step 4: Timing
Using machine learning and artificial intelligence to predict known customer behaviors provides opportunities for marketers to touch base with customers at key opportunities. Remember that customer who was 40% interested in a product? We know this persona looked at Widgetco’s website. We know they’ve looked at competitor WidgetAid’s website. We also know that this customer persona reads the New York Times every morning. Now we can leverage those insights to reach these customers by placing an advertisement in the New York Times with a 20% discount coupon for the Widgetco product we know the customer has been researching.
Step 5: Tone
With a near infinite number of traits within each customer persona, attribution models can be used to simplify the data to determine how best to talk to each persona. One persona may be inclined to click on a 10% coupon. Another may be looking for 20% off. One may prefer a hard sell tone while another may be open to a more transactional tone. Communicating to each at the right time in the right voice can be the difference between losing and closing a sale.
Sagepath is leading the way in positioning our customers to harness the full power of marketing stacks to reach their customers with the right message at the right time in the right channel. If your enterprise is ready to make the leap to the next level of AI-enhanced personalization, drop us a line.