By analyzing previous customer purchases and interactions, data science helps brands provide a more personalized and positive customer experience. But data science contributes to CX in other ways. It also helps improve customer service.

• Data Science Consolidates, Cleans and Manipulates Data
• Data Science Enables Hyper-Personalizing
• Data Science Improves Customer Experience

Data Science Consolidates, Cleans and Manipulates Data

Data science is a broad field that includes statistics, scientific methods, artificial intelligence (AI), and data analysis.

Data scientists combine skills to better analyze data from various sources, including:

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

These data points yield actionable insights.

Data preparation typically involves aggregating, cleaning, and manipulating data. These algorithms can suggest the “next best action” based on actionable insights gained from the data.

According to Lisa Loftis, principal product marketing manager on SAS Global Customer Intelligence Team, brands cannot deliver the experiences today’s customers expect without data science.

“There is simply too much data, too many points of interaction, and too much fragmentation across data and channels for a human to truly personalize an interaction. “Many of the marketing and CX predictions for 2022 and beyond include data science,” said Loftis.

Data Science Enables Hyper-Personalizing

According to an Epsilon study, 80 percent of customers are more likely to buy from a brand that offers them a personalized experience. Similarly, Accenture found that 91% of those polled prefer to do business with a brand that knows them and makes relevant offers and recommendations.

A Forrester study (registration required) found that while 90% of brands see personalization as critical to their business strategies, only 39% of consumers said they received relevant brand communications, and 41% said they received valuable offers. Clearly, more can be done to provide a personalized customer experience.

Customers today expect hyper-personalization, which takes personalization to a whole new level. “Hyper-personalization is hot,” Loftis said. For each customer, this means tailoring communications and experiences to their specific needs at each stage of their journey.

“This is a huge shift from mass marketing, segmentation, and even contextualization to high-value customers or channels.” This is truly one-to-one marketing — every time.”

Loftis added that Deloitte expects 75 percent of companies to invest in hyper-personalization to increase personalization, connect people, and offer more inclusive experiences. According to Deloitte, hyper-personalization can increase marketing ROI by 8X and sales by 10%.

Boots UK, a British health and beauty retailer, uses data science to improve customer experience. Personalized promotions for their loyalty card customers increased incremental spend using IBM SPSS Modeler insights. After that, it used the data to target customers’ promotions.

In order to determine the next best action for individuals based on preferences and purchase history, the company used data from its 15 million Boots Advantage Card customers to build predictive models. Customer loyalty card holders spent more money as a result of the increased personalized messages.

Data science enables brands to provide hyper-personalized experiences to their customers, says Ajay Khanna, CMO of Explorium, a provider of external data enrichment and integration tools. “Data science is essential for hyper-personalization. Understanding customer behavior and preferences is critical to providing what they want when they want it,” said Khanna.

“Data helps us understand.” “Data-driven companies use internal data and external data signals from various sources to build rich customer profiles.”

Data Science Improves Customer Experience

According to Michael Bamberger, CEO of Tetra Insights, brands use data science to build end-to-end customer journey maps.

According to Bamberger, the first step companies take is to implement the right “sensor” data collection. “They can then build comprehensive customer journey models to understand how people move from awareness to buying, returning, and evangelizing.”

A brand can better enhance every touchpoint with a customer once it has created the entire customer journey. “By understanding what is most valuable and compelling to their prospects, businesses can create a customer experience that achieves their business goals,” said Bamberger.

Avinob Roy, senior director of product management at IQVIA, spoke with CMSWire about how data science can improve customer experience. Roy claims that with so much data being pushed to potential customers via multiple channels, brands are struggling to stand out. Increasing data can help these brands improve customer experience.

Brands can better understand their customers’ preferences by leveraging customer data. “Data is essential to understand customer preferences,” Roy said.

“Much like Netflix recommends new content based on past activity, modern data platforms and AI-driven technology use multi-source datasets to learn what customers want. These findings inform recommendations for actions, communication channels, content, and information that best resonate with customers. Demographic data can optimize engagements.”

To effectively improve the customer-brand relationship, a brand must have a clearly defined data strategy and a unified data management solution.

“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,” Roy said. Clean data is key to impactful insights that lead to better adoption with end-users, so having a clear strategy and roadmap for data acquisition, integration flows, governance, and stewardship is critical.

According to Explorium’s Khanna, brands should be using data science across all customer-facing functions, and it should be at the core of the entire customer journey.

“In marketing, to deliver personalized offers at the optimal time, in sales, to accurately score and prioritize leads, and in support, to anticipate and respond to customer queries or complaints, as well as to determine ongoing purchase or churn propensity,” Khanna explained. “As a result, data science serves as the foundation for the end-to-end customer experience, from marketing to post-sales.”

” The effectiveness of data science and the performance of machine learning models, on the other hand, are contingent upon the quality and relevance of the underlying data. Access to the appropriate internal and external data, their rapid integration, and use to determine the next best action in the customer journey are critical components of delivering the desired and connected customer experience.”