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article imageQ&A: Leveraging predictive analytics to improve the CX Special

By Tim Sandle     May 10, 2019 in Business
Retailers need to focus more on leveraging predictive analytics as so to customize a customer’s journey path and improve their experience. Doing so will increase sales and revenue, according to expert Sean Kendall.
Many retailers are turning to automation to cut down on the number of physical employees for tasks like taking inventory of stock. A notable example comes from Walmart. While automation delivers benefits, according to Sean Kendall, director of customer experience at TetraVX, this will not drive customer engagement and loyalty.
To achieve customer loyalty, Kendall argues, retailers need to be making better use of predictive analytics to customize a customer’s journey path and to improve their experience. To understand the benefits further, Digital Journal spoke with Kendall about when retailers should begin a predictive analytics approach and the benefits that can be realized.
Digital Journal: What are the key digital transformation trends with customer-facing businesses?
Sean Kendall: I think we are going to see the majority of enterprise businesses adopt cloud platforms in bulk, with a shift in the marketplace back to a more custom micro service-based model as opposed to an off-the-shelf SaaS offering. Plug and play works for home users, but not for a business that has personnel positions driving multiple use cases. That is why the customer experience that is tailored to their journey is bleeding into more than just conversations and data, it’s seeping into end-to-end solutions.
DJ: How are businesses utilizing automation?
Kendall: Automation is really picking up the pace thanks to the rapidly maturing AI models we are seeing and nowhere is that more prevalent than in business today. With customer service options and allowing the end user to take control of their experience instead of waiting for a response, it eliminates the need for putting customers on hold – a common source of frustration. Utilizing automation such as chatbots and virtual assistants allows the customer to be serviced in an immediate and self-driven way that’s on their schedule, their terms, and their device – all of which improve the customer experience.
DJ: How important is it to focus on the customer experience? Is this being neglected in the current technology wave?
Kendall: We are entering the golden age of customer experience. Everyone wants a tailored moment. Netflix, Spotify and YouTube were the first to successfully understand this with AI-driven algorithms and tracking users’ paths with just a few clicks. Despite algorithms doing the leg work and populating suggestions based on users’ previous behaviors and search history, the ‘suggestions’ that populate on their screens don’t feel automated, they feel personalized. Resonating with customers, this level of personalization is what every marketer across every industry is trying to achieve.
Amazon and Nordstrom have perfected the ‘whatever-it-takes’ model to make everything customers do with them simple, and this type of consumer spoils are now bleeding into the workplace. There is not one service provider out there today in the marketplace that isn’t trying to harness the power of CX to not only make an enjoyable and easy experience for their clients, but to create a-ha moments that keep them loyal to the same product.
DJ: How can predictive analytics help to improve the customer experience?
Kendall:By tracking end-to-end journeys, any company has an embarrassing amount of rich data on how to predict an outcome. Sure, to be human is to error. But it’s those moments that allow us to see the wrong paths and predict a better outcome based on alternatives. Take any repeatable project journey; by looking at canceled events, or changes to the original plan, you can begin to measure the chances for failure elsewhere based on historical data. A simple ‘if this then that’ approach with data is the entry point for all business to harness what they already have and not have analyst paralysis.
DJ: How can such processes help with the customer journey?
Kendall:In the same example of a project journey, we cannot tell the customer - with crisp data to back it up - if you do this here, here, and here, you will fail there. But X% of the time, if you at least complete A, B, and E, you will have a higher ROI.
6. Can predictive analytics help to drive up customer loyalty?
Predictive analytics doesn’t help - it owns - customer loyalty. Take drive assistant Google Maps. Five years ago, it made printing maps with MapQuest obsolete (unless you are my Dad). But then competitors started embracing data that was available to them via the IoT with traffic cameras, sensors, and layering that over historical references with traffic patterns. And presto - Waze was born. Their predictive analytics combines historical information, end-user input, and live traffic monitoring to predict an optimized journey for the driver to get from point A to B the fastest. I doubt anyone who has used Waze can go back to Google Maps if they shave off even just five minutes from their drive since convenience is one of, if not the greatest driving factor of customer loyalty.
DJ: How should a firm set out adopting a predictive analytics approach?
Kendall:Know where your data is, define what is good data vs. bad data, and then actually execute based on the data you’re seeing to form a predictive model. Far too often, company’s get to the point where they have so much data they can’t figure out how to pull the trigger. Don’t let that happen. Focus on key areas in which you have a rich amount of data points and create the change.
DJ: What types of things can go wrong when establishing predictive analytics? What do some companies miss?
Kendall:Predictive analytics is not a silver bullet. Any black swan event - something that is unforeseen like natural disasters - can throw a wrench in what otherwise seemed to be clear cut. Furthermore, if you do not understand the domain you will fail. Domain expertise is not just using AI, machine learning and algorithms, it’s actually understanding them. You need to understand the outputs of these devices enough to smell the B.S. from the gold and act accordingly.
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