How To Improve Your CX Strategy With Data Science

Are you getting the most out of your data? Learn how to leverage this information to better understand your customers and strengthen CX.

Data science enables brands to gain a deeper understanding of each touchpoint in the customer journey by analyzing the information from previous customer purchases and interactions, providing a more personalized and positive customer experience. But that’s not the only way that data science contributes to CX. It’s also a key part of improving the customer experience.

Data Science Consolidates, Cleans and Manipulates Data

Data science is comprised of multiple domains and includes statistics, scientific methods, artificial intelligence (AI) and data analysis, all of which extract value from data.

Data scientists combine a wide range of skills so that they are better able to analyze information collected from various sources, including:

  • Websites
  • Mobile devices
  • Sensors
  • IoT devices
  • Customers

In turn, each of these data points leads to actionable insights.

Data preparation typically includes aggregating, cleaning and manipulating data for specific types of processing. Data scientists apply machine learning algorithms to data – which include images, numbers, text, video, audio and more – that, when combined with AI applications, can suggest the “next best action” based on actionable insights gained from that data.

Lisa Loftis, principal product marketing manager on the SAS Global Customer Intelligence Team, told CMSWire that without data science, brands would be unable to deliver the types of experiences that today’s customers demand.

“There is simply too much data, too many interaction points and too much fragmentation across both data and channels for a human to possibly know enough about any single individual to really personalize the interaction. Many of the marketing and CX predictions for 2022 and beyond illustrate how data science will contribute to great experiences, ”said Loftis.

Related Article: The 3 Key Components of a First-Party Data Strategy

Data Science Facilitates Hyper-Personalization

A report from Epsilon indicated that 80% of customers are more likely to purchase from a brand if that brand provides them with a personalized experience. Likewise, a report from Accenture revealed that 91% of those polled are more likely to do business with a brand that knows them and presents them with relevant offers and recommendations.

Contrast that information with findings from a Forrester study (registration required for download), which revealed that 90% of brands see personalization as critically important to their business strategies, while only 39% of consumers said they received relevant brand communications, and 41% said they received valuable offers. Clearly, there is work to be done when it comes to providing a personalized customer experience.

Even personalization may not be enough for today’s customers, who now expect hyper-personalization, which takes personalization to a much higher level. “Hyper-personalization is getting lots of buzz,” said Loftis. “This involves using data science and AI to create contextual communications and experiences for every single customer – to fit their specific and individual needs – along every step of their unique journey.

“This is a drastic shift from mass marketing, from customizing communications to customer segments and even from contextualizing to certain high-value customers or in certain channels. This is truly marketing to a segment of one – for every interaction.”

“Deloitte predicts that 75% of companies will invest in hyper-personalization with the express intent of increasing personalization, helping people to feel more connected and offering more inclusive experiences,” added Loftis. Hyper-personalization pays dividends, as Deloitte also predicts that it can result in an 8X higher marketing ROI and a 10% sales lift.

A great example of how brands use data science to improve customer experience is Boots UK, a British health and beauty retailer. With insights using the IBM SPSS Modeler, it lifted incremental spend through personalized promotions for their loyalty card customers. It then used those insights to offer relevant promotions to customers.

By leveraging data from its 15 million Boots Advantage Card customers, the company built predictive models matching transactions to individual loyalty card customers, allowing it to determine the next best action for individuals based on preferences and purchase history. The result was a 70% increase in personalized messages, along with a visible increase in incremental spend from loyalty card customers.

Ajay Khanna, CMO at Explorium, an external data enrichment and integration tool provider, spoke with CMSWire about the ways data science enables brands to provide hyper-personalized experiences to their customers. “Data science is critical to delivering hyper-personalized experiences. Delivering to customers what they want, when they want and via the channel of their choice needs a deep understanding of their behavior and preferences,” said Khanna.

“Getting that understanding starts with data. Data-driven organizations use their internal data and enrich it with many external data signals from various sources outside their four walls to build rich customer profiles.”

Related Article: The Perfect Storm Propels Personalization Into Must-Have Status

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