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:
- Mobile devices
- IoT devices
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.
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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.”
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Data Science Facilitates a Better Customer Journey
Michael Bamberger, Founder and CEO of Tetra Insights, a qualitative research software solution provider, told CMSWire that brands use data science to build beginning-to-end customer journey maps.
“The first motion companies are taking is implementing the right ‘sensor’ data collection — that is, capturing the interactions and associated metadata from each individual customer,” Bamberger said. “From there, they can build comprehensive customer journey models to understand how individuals move from awareness of their company all the way through transacting, returning and evangelizing.”
Once a brand has created the entire customer journey, it’s in a much better position to enhance every touchpoint the customer has with it. “Basically, by collecting the right data, finding models of positive customer journeys and then optimizing to compel people along those tracks, businesses are creating a customer experience that achieves their business goals by understanding what is most valuable and compelling to their prospects,” said Bamberger.
Avinob Roy, senior director of product management at IQVIA, a healthcare data science company, spoke with CMSWire about the ways that data science is used to improve the customer experience. Roy said that due to the massive increase in data pushed to potential customers across multiple channels, brands are finding it challenging to stand out from competitors. The increase in data, however, can be the very tool that enables these brands to enhance and improve the customer experience.
By leveraging customer data, brands are in a better position to be able to understand their customer’s preferences — what they like, dislike and are potentially interested in. “Data is essential to understand customer preferences to drive better engagement with highly personalized and relevant content,” said Roy.
“Modern data platforms and AI-driven technology use multi-source datasets to learn over time what the customer is looking for, much like Netflix recommends new content based on past activity. These learnings translate to actionable recommendations for engagements surrounding what action, communication channel, content and piece of information will best resonate with customers. Demographic information can be used to optimize engagements.”
A brand needs to ensure it has a well-defined data strategy, along with a unified data management solution, to effectively improve the relationship between the customer and brand.
“When considering an approach to leverage data for optimized customer interactions, a unified data management solution with AI-supported embedded intelligence and analytic processes enables companies to better design and deploy marketing strategies based on data,” explained Roy. “It’s critical for IT and data managers to have a clear strategy and roadmap for data acquisition, data integration flows, data governance and stewardship, as having clean refreshed data is key to impactful insights that lead to better adoption with end-users.”
Explorium’s Khanna suggested that brands are using data science across all customer-facing functions, that it should be at the core of the entire customer journey and that it depends on having clean, relevant data.
“In marketing to deliver personalized offers at the right time, in sales for precise lead scoring and prioritizing targets, in support to predict and respond to any customer queries or complaints and to determine ongoing purchase or churn propensity,” said Khanna. “So, data science is the backbone of end-to-end customer experience from marketing to post-sales.
“However, the effectiveness of data science or the performance of machine learning models depends on the quality and relevance of underlying data. Having access to the right internal and external data, quickly bringing them together, and using them to determine the next-best action in the customer journey is vital to delivering the desired and connected customer experience.”
Data science combines a wide range of skills to analyze data from various channels to obtain actionable insights. These insights allow brands to enhance personalization, deliver more relevant advertising and recommendations and improve the overall customer journey.