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Three Ways Decision Science Facilitates Better Customer Experience

By   /  July 25, 2018  /  No Comments

Click to learn more about author Pieter van Ispelen.

Within the growing field of Data Science, two high level functions complement each other to derive value from data: Information Management and Decision Science. One can’t optimally function without the other and often Data Scientists are expected to perform both functions. In the most effective data-driven organizations, specialists are at the core of these two functions while their skill sets allow them to effectively bridge across.

Information Management is tasked with all major data sourcing, cleaning, and organizing. While crucial, an Information Management team needs a Decision Science division to take their groundwork and turn it into something strategic. Decision Scientists analyze and report on their carefully collected data and use it to facilitate better decision making across the company. It also directly enhances decision making of customers, as CX is more and more tailored to people’s needs.

Decision Science not only includes Machine Learning and deep statistical methods–it also mixes in UX components, so we can more precisely evaluate and improve our customers’ experiences. We effectively determine the “what” through deep Analytics and couple this with the “why” of understanding the underlying customer psychology. As such, Decision Science facilitates strategic decision-making across our business that truly serves our customers.

The following cases are a few examples of improvements that Decision Science has inspired. Use them to guide your own Data Science division, large or small, as you strive toward truly intelligent customer experiences.

  1. Mapping the Customer Journey and Calculating Its Success Rate

Before instituting any changes, you should map out your customers’ entire journey. For our sales process, that meant taking an in-depth look from the start of their journey on our web properties, following them from research to making initial contact – through a variety of methods including chat, online buy flows, or our machine learning-infused IVR system – to ultimately fulfilling their needs. We have mapped each step to understand what the customer journey looks like from start to finish, and for each step on this journey we have calculated a probability of success based on historical data. For example, for every step in the IVR we know the likelihood this person will be successful in getting their needs met as opposed to abandoning the path.

Eventually, we found that some customers were abandoning the journey at higher rates for some steps compared to others. We took those abandonment rates and analyzed the correlation with the paths leading up to them to determine customers’ intent and back up recommendations to either eliminate or change individual steps or paths. This approach leads to a much improved CX, and subsequently to increased conversion rates.

  1. There is Power in Your Own Data. Use It!

When examining the customer journey, we found that at certain points customers were routed based on geographic lists and data that we obtained from partners and third parties. While the routing was conceptually correct, when we analyzed our own data based on our own customer success rates, we detected areas for potential improvements.

A key learning here is that third party data can assist you in areas where there may be an absence of proprietary data, but once you have enough historical data based on your own processes and customers—nothing can beat those insights. Use that data and you are on your way to develop proprietary methods that will quickly shape into a competitive advantage. This is truly where Data Science has the opportunity to start shaping your business model. Once we started using our own data in directing customer traffic where appropriate, the experience for affected customers was tremendously better, conversion rates jumped—and ample additional value was created for both us and our partners. A triple win across the board was achieved purely from looking at our data through a Decision Science lens.

  1. Right and Left Brain: Joining UX and Analytics Perspectives

One of the unique approaches that exists is having UX specialists and Data Scientists work closely together. By integrating these two disciplines within the Decision Science team, it’s possible to build a relationship that sets a company apart from companies that keep Data Science and UX siloed away from one another. Broadly speaking, Data Scientists study what customers are doing, and UX brings value to our insights by telling a company why customers make certain choices. Having a UX team trained to understand the customer journey from a psychological perspective, helps identify a range of opportunities and have truly enriched our Decision Science.

One of those opportunities was with a landing page we were using across our search traffic that led with popular internet bundles. While generally successful, analyzing the UX through heatmaps showed a scattered set of clicks across the page indicating either a level of confusion or a more disparate set of needs than earlier assumed. Instead of assuming that all customers were looking for these bundles, we presented a small set of clear, yet broader, options that led each customer to what they were truly looking to go with one click. These options were further customized based on keywords people used to create a much more frictionless experience. Using the tools and perspective that UX provides, coupled with data analytics, we restructured that page into a menu style, which appealed to the needs of more customers and generated another win-win situation.

Making Decision Science Work for Your Company

If you’re looking for new ways to use Decision Science or feel like your data isn’t being used to its fullest potential, start by shifting your attention to focus on the customer. Before you do anything else, map your customers’ entire journey. Then, think about what a customer goes through during that journey and explore the reasoning behind their choices with your UX team.

Understanding each step of the journey will allow you to influence every touch point that the customer has with your business—offering a more intelligent customer experience. In nearly all cases, doing right by the customer means doing right by the business. Keep that in mind, and never stop searching for opportunities to improve.

About the author

Pieter van Ispelen is the Head of Decision Science at Clearlink. As the head of the analytics function within Clearlink's data science division, Pieter is passionate about making smarter decisions through deep learning and driving a better end-to-end customer journey through carefully designed AI components.

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