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How to Use Data in Every Industry to Predict Optimized Next Steps

Blog: The Tibco Blog

Building stronger, longer, and more profitable relationships with customers isn’t a goal limited to a certain type of business, which is why we bring together experts from across industries to discuss how they execute effective engagement strategies at TIBCO NOW. One such panel at TIBCO NOW London last month was Reimagining Engagement: Delivering Best Next Actions, where TIBCO Head of FSI UKI Richard Price sat down with Dan Akrigg, Head of Systems Development of Skipton Building Society, and Gerhard Kreß, Vice President Data Services of Siemens Mobility, to discuss their strategies for utilizing data to deliver services. 

While the two businesses represented had very little in common from a business perspective—Skipton Building Society is a financial services company, and Siemens Mobility is a transportation provider—they share a lot of similarities in the ways they approach data in order to take the ‘best next step’ to achieve customer satisfaction. In the session, they discussed how they both focus on personalized customer journeys, machine learning, and use automation to help provide the best customer experiences. But these do come with precautions, both say. 

Providing A Personalized Customer Experience

At Skipton, personalization comes in many forms, because financial service firms often have many different channels through which they engage with customers. For example, if a customer asks about a product while in a branch, or via the telephone, Skipton wants to be able to tailor their mobile app interface to present related products and services the next time that customer logs on. It also pays close attention to demographics, because different ages and lifestyles require different financial services. For example, Skipton makes sure to only target older demographics with information on will and estate planning versus Lifetime ISA (a form of IRA) offers that it aims at 18-39 year olds.

Personalized customer experience for Siemens looks a bit different, but is still key to their strategy. “Passengers don’t get on a train because they love trains, they go because they need to travel somewhere,” Kreß explained, “and predicting best next steps for our customers to take is crucial to providing the best possible experience.” Today, Siemens is more focused on ensuring their services are delivered as expected by focusing on providing transportation that is comfortable, safe, and guarantees delivery. “It is not just about manufacturing a train for our buyers, it is about making sure you get an efficient, well-running public transport system.” 

Machine Learning in Data Models

Using data modeling is a great way for a digital business to determine its best next action, but it can be difficult to know when to allow automation to make the decisions and when human involvement is necessary. Siemens understands that while machine learning technology is a very useful tool, it can’t be the sole decision-maker for a business. It helps train engineers get labor times down from months to a few weeks or days, but it can’t always account for outside factors, including geographical conditions. Sometimes, informed recommendations by a human can be of great benefit—for example, health and safety-related decisions cannot be made without human involvement—but driving significant operational efficiencies, and therefore an improved customer experience, can be gained by shortening decision time via machine learning.

Skipton is not quite as advanced with its machine learning models, but is hoping to delve deep into automation in the near future. As Akrigg explained, Skipton has been running analytical models for a long time in order to assess the propensity for mortgage default. “We have to test and validate models over time. We are crunching the numbers, and hopefully, stitching that data back together is next on the horizon,” Akrigg said. 

Regardless of the industry, utilizing data in order to predict the best next actions is imperative for digital business today. Whether B2B or B2C, if you’re customer-facing or behind-the-scenes, the potential to improve the customer experience by using data to predict the best next actions is an opportunity not to be missed. 

To learn more about how TIBCO can help your organization enhance Customer Experience, please visit 

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