How to improve your CX strategy with data science
Data science enables brands to gain a deeper understanding of every touchpoint in the customer journey by analyzing information from previous customer purchases and interactions, delivering a more personalized and positive customer experience. But that’s not the only way data science contributes to customer experience. It is also a key element in improving the customer experience.
Data science consolidates, cleans and manipulates data
Data science spans several fields and includes statistics, scientific methods, artificial intelligence (AI), and data analytics, all of which derive value from data.
Data scientists combine a wide range of skills in order to be better able to analyze information collected from a variety of sources, including:
- Mobile devices
- IoT devices
In turn, each of these data points leads to actionable insights.
Data preparation typically includes data aggregation, cleansing, and manipulation for specific types of processing. Data scientists apply machine learning algorithms to data – which includes images, numbers, text, video, audio and more – which, when combined with AI applications , can suggest the “next best action” based on actionable insights from that data.
Lisa Loftis, senior product marketing manager with the SAS Global Customer Intelligence team, told CMSWire that without data science, brands would be unable to deliver the kinds of experiences that customers today require.
“There is simply too much data, too many interaction points, and too much fragmentation between data and channels for a human to know enough about a single individual to truly personalize the interaction. Many marketing forecasts and CX for 2022 and beyond illustrate how data science will contribute to great experiences,” said Loftis.
Related Article: The 3 Key Elements of a First-Party Data Strategy
Data science facilitates hyper-personalization
A report by Epsilon indicates that 80% of customers are more likely to buy from a brand if that brand provides them with a personalized experience. Similarly, a report from Accenture found that 91% of respondents are more likely to do business with a brand that knows them and presents them with relevant offers and recommendations.
Compare this information with the results of a Forrester study (registration required to download), which found that 90% of brands consider personalization essential to their business strategies, while only 39% of consumers said they had received communications relevant branding, and 41% said they received interesting offers. Clearly, there’s work to be done when it comes to delivering 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 buzzing,” Loftis said. “It involves using data science and AI to create contextual communications and experiences for each customer, to meet their specific and individual needs, at every stage of their unique journey.
“It’s a radical shift from mass marketing, from personalizing communications to customer segments and even contextualizing to certain high-value customers or in certain channels. It’s really marketing for a customer segment. ‘one – for each interaction.
“Deloitte predicts that 75% of companies will invest in hyper-personalization with the express intention of increasing personalization, helping people feel more connected, and delivering more inclusive experiences,” Loftis added. Hyper-personalization is paying dividends, as Deloitte also predicts it can lead to an 8x higher marketing ROI and a 10% increase in sales.
A great example of how brands are using data science to improve customer experience is Boots UK, a British health and beauty retailer. With insights using IBM SPSS Modeler, he increased incremental spending through personalized promotions for his loyalty card customers. He then used this information to offer relevant promotions to customers.
Leveraging data from its 15 million Boots Advantage Card customers, the company has created predictive models matching transactions to individual loyalty card customers, allowing it to determine the best next action for individuals based on preferences and purchase history. The result was a 70% increase in personalized messages, as well as a visible increase in additional spending by loyalty card customers.
Ajay Khanna, CMO at Explorium, an external provider of data enrichment and integration tools, spoke to CMSWire about how data science enables brands to deliver hyper-personalized experiences to their customers. “Data science is key to delivering hyper-personalized experiences. Delivering customers what they want, when they want it, through their channel of choice, requires a deep understanding of their behavior and preferences,” Khanna said.
“Achieving this understanding starts with data. Data-driven organizations take their internal data and enrich it with many external data signals from various sources outside their four walls to create rich customer profiles.”
Related article: The Perfect Storm propels personalization into must-have status
Data science facilitates a better customer journey
Michael Bamberger, founder and CEO of Tetra Insights, a provider of qualitative research software solutions, told CMSWire that brands use data science to create end-to-end customer journey maps.
“The first move businesses take is to implement the right ‘sensor’ data collection, that is, capturing the interactions and associated metadata from each individual customer,” Bamberger said. “From there, they can create comprehensive customer journey models to understand how individuals move from awareness of their business to transaction, feedback, and evangelism.”
Once a brand has created the entire customer journey, they are in a much better position to improve every touchpoint the customer has with them. “Fundamentally, by collecting the right data, finding positive customer journey patterns, and then optimizing to compel people to follow those paths, companies create a customer experience that achieves their business goals by understanding what is most valuable and compelling to their prospects,” Bamberger said.
Avinob Roy, Senior Director of Product Management at IQVIA, a healthcare data science company, spoke with CMSWire about how data science is being used to improve customer experience. Roy said that due to the massive increase in data being pushed to potential customers across multiple channels, brands are struggling to stand out from their competitors. Augmenting data, however, may be the very tool that allows these brands to improve and improve the customer experience.
By leveraging customer data, brands are in a better position to understand their customers’ preferences – what they like, dislike and what potentially interests them. “Data is key to understanding customer preferences to drive better engagement with highly personalized and relevant content,” Roy says.
“Modern data platforms and AI-based 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 into actionable recommendations for engagements around which action, communication channel, content and piece of information will resonate best with customers. Demographic insights can be used to optimize engagements.”
A brand should ensure that it has a well-defined data strategy, as well as a unified data management solution, to effectively improve the relationship between the customer and the brand.
“When considering an approach to leverage data to optimize customer interactions, a unified data management solution with built-in intelligence and AI-supported analytics processes enables businesses to better design and deploy strategies. data-driven marketing,” explained Roy. “It is critical that IT and data managers have a clear strategy and roadmap for data acquisition, data integration flows, data governance and data management because having clean data up-to-date information is key to getting impactful insights that lead to better end-user adoption.”
Explorium’s Khanna suggested that brands use data science in all customer-facing functions, that it should be at the heart of the entire customer journey and that depends on the availability of clean and relevant data. .
“In marketing to deliver personalized offers at the right time, in sales for accurate lead scoring and prioritization goals, in support to anticipate and respond to any customer questions or complaints, and to determine customer readiness. purchase or unsubscribe in progress,” Khanna said. “Thus, data science is the backbone of the end-to-end customer experience, from marketing to aftersales.
“However, the effectiveness of data science or the performance of machine learning models depends on the quality and relevance of the underlying data. Having access to the right internal and external data, gathering it quickly and using it to Determining the next best action in the customer journey is key to delivering the desired, connected customer experience.”
Data science combines a wide range of skills to analyze data from different channels to gain actionable insights. This information allows brands to improve personalization, deliver more relevant ads and recommendations, and improve the overall customer journey.
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