Tame the Daunting Data Overload with Cognitive Personalization

Editor’s Note: This is a contributed guest blog from Accenture, a Redpoint partner

The National Retail Federation (NRF) predicts a rosy 2021. In March, it forecast a year-over-year retail sales growth between 6.5 and 8.2 percent – likely to top 2020’s record growth of 6.7 percent. Online shopping, which increased nearly 22 percent in 2020, is projected to rise between 18 and 23 percent.

This forecast was made prior to the newest $1.9 trillion economic stimulus package. With about half of all U.S. adults now vaccinated against COVID-19, businesses hiring, and Americans eager to put the pandemic in the rear view, it seems as if whatever will pass as the “new normal” is on our doorstep. “There will be extraordinary spending. I don’t know how else to put it,” says NRF chief economist Jack Kleinhenz.

While pent-up consumer demand is a great, retailers must be aware that behaviors have significantly changed, ecommerce adoption has accelerated, and consumers show preference for digital-first, contact-less journeys. These changes in consumer behavior set the expectation that winning share of that pent-up demand is going to take more than just re-opening a shuttered store. Rather, it will take connecting with consistently relevant messages and empathy with how an individual consumer will engage going forward. Retailers must offer more dynamic and unpredictable customer journeys.

A daunting data overload in this new environment makes it more and more difficult for marketers to duplicate the traditional shopkeeper experience, where a local proprietor of a mom-and-pop store knew every customer by name and used the familiarity to create a perfect experience every time.

A Shopkeeper View Starts with Data

Getting data right (ideally getting to perfect data as Redpoint Global describes it) helps brands recreate the traditional mom-and-pop experience, but there are hurdles to clear before achieving the deep, personal understanding needed to create relevant interactions at scale for tens or hundreds of millions of customers.

To truly understand an individual customer journey, data must be tracked, it must be current and it must be mapped to actual purpose. And to deliver the right customer experience at the right moment, marketers need to be confident that they have the right data, which means that they must first have a sense for what it is they’re actually trying to create.

Once the source of the data becomes abstracted, it becomes an enormous challenge for marketers to determine whether the data is fit for purpose – that it truly means what marketers may think it means and that it is representative of the moment they’re trying to create. Marketers also must contend with data governance, and the need to align with brand and corporate objectives.

Data overload issues make it hard for marketers to plan a campaign, decide what they need and what will matter in terms of creating the perfect moment for a customer. To discern meaning to the customer data that’s available, marketers often request access to all the data, which adds to the complexity. The better course of action is to decipher the data that is already on hand, figuring out key indicators of purpose and what’s relevant for an individual customer.

This is where machine learning comes into play, as the key requirement to tame data overload complexities, filling both reporting and transactional needs.

Making Sense of Data with Machine Learning

Machine learning detects patterns that humans can’t detect, and it enables the personalization of experiences at scale. By continually learning and improving, it optimizes existing touchpoints and the touchpoints marketers are trying to create.

From a reporting standpoint, by providing insight into which experiences provide value and where there are gaps in experience, machine learning can then predict what content aspects will resonate the most and make predictions for what additional communications are needed – creating continual understanding and optimization of the customer journey.

To advance maturity level with machine learning and other advanced technologies, brands must start thinking about content in terms of data. To better predict customer intent, for example, analysis should include all customer behaviors; details such as which images or words on a page a customer engaged may be important indicators. Message analysis must move beyond basic segmentation analytics to actually understand how a specific message resonates with a customer – not just on a channel basis, but throughout the entire customer journey.

Process, data and channel siloes are the main barriers to reaching this state of maturity, but once brands start to think about this maturity model they will quickly recognize the need to form a single view of the customer. A single customer view is the foundation for cognitive personalization, which bases content on individual behavior; it is messaging not tied to a moment or to a channel, but rather derived from intelligent automation that is fully aligned with and possessed of a deep understanding of a customer’s purpose. Perfect customer data replicates the old-school shopkeeper view, except with machine learning we’re able to do it at scale for the always-on, omnichannel consumer.

A CX Partnership that Delivers

To reach a level of sophistication that aligns with customer expectations for consistent and dynamic customer journeys, the right technology platform critical.

Accenture Interactive has partnered with Redpoint because the company’s rgOne platform uniquely delivers the level of perfect data integration needed to support the different types of communications and the marketing maturity model I alluded to.

Working with Redpoint, we’ve started to help clients layer existing, static experiences with innovative cognitive capabilities that optimize the experience for the customer – and ultimately strengthen loyalty for the brand.

The capabilities of a platform like Redpoint allow us to build the types of experiences that are generally too complex and costly to do via traditional approaches. Traditional approaches with heavy data integration needs, even including integrating offline machine learning and analytics, can provide some of the same results but are more costly, rigid, and most importantly not available in the moment of customer interaction. Timing is mission critical in CX. The integrated platform’s inline, automated machine learning and real-time decisioning enables us to do things in the cadence of the customer and help bring up the overall marketing maturity level faster.

Delivering a personalized customer experience through an understanding of a customer and optimizing interactions used to be a differentiator that drove loyalty and lift. Now, it’s survival, particularly with the dramatic online shift and other pandemic-related repercussions.

The new normal is here, and it demands a new approach. Cognitive personalization through machine learning gives marketers the confidence that every experience delivered is as close to perfect as possible. The confidence stems from perfect data.

Related Content

What is Automated Machine Learning?

Algorithmic Optimization and the Magic of AML

Humanizing a Customer Experience without Humans: Let Machine Learning Take Control

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