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Customer Intelligence: Solutions For Better Customer Service We're each generating huge amounts of data for product and service providers, amounting to little short of a goldmine that can help brands predict a consumer's likes, dislikes and preferences

By Projjol Banerjea

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Our screens have become our gateway into a digitally transforming world. Whether it's shopping, social media, work, or just completing daily tasks, smartphones and the Internet drive a significant part of our behavior.

This means that we're each generating huge amounts of data for product and service providers, amounting to little short of a goldmine that can help brands predict a consumer's likes, dislikes and preferences. From a customer service perspective, this can provide an invaluable foundation for personalizing service to the individual customer, creating helpful and efficient experiences that foster loyalty.

For example, a customer service representative can provide a better experience if they have access to the customer's full purchase history, or will know to escalate problems quicker if they see that a customer's record of complaints is extensive.

However, this is easier said than done: having data is not the same as being able to effectively use it. This is where customer intelligence comes in.

While there is no concrete definition for this term, customer intelligence broadly refers to the practice of not only unifying customer data, but leveraging it effectively across a multitude of channels in order to deliver scalable personalized experiences. Here, we break down how to apply it to customer service in a few steps.

Unifying customer data

Today's customers connect with brands through dozens (sometimes hundreds) of touchpoints, from websites, apps, support calls to chatbots. This means that customer data is stored in a multitude of different places, such as data warehouses and data lakes, as well as in individual parts of the martech stack.

From a customer experience standpoint, this means that the data is not only hard to access, but impossible to rationalize around the individual customer. For example, an individual customer's purchase history may be stored in one place while their email interaction history is in another: the two records are never tied together.

The first step to better customer service starts with unifying customer data - using tools like customer data platforms (CDPs) to bring together disparate sources of data and forming a single customer view or "golden record'. This system of record is the foundation that creates a holistic view of the customer, not only helping a customer service agent make better decisions, but also avoiding the customer-facing pain point of having to continually re-supply information (for example, having to reiterate name, account numbers and purchase details at every point of interaction).

Getting signal from the noise

With customer data now unified into coherent views of the individual customer, the next challenge is to be able to get insight from it, and to do so at scale. This is where machine learning has an important role to play.

One of the best use cases for applying machine learning to customer data is to create segments of customer based on their propensity to take a certain action. For customer service, one of the most valuable segments will be customers who are likely to churn, flagging an individual customer as such will allow a customer service representative to proactively engage the customer in question to prevent churn before it takes place.

At the opposite end of the spectrum, machine learning models can also scalably identify those customers who are - or likely to be - high-value. For example, they may identify customers whose spending patterns are increasing in value or frequency, allowing customer service agents to ensure these individuals receive special treatment.

Applying it in real life

With customer data unified and grouped into scalable segments, the final step in being able to apply customer intelligence lies in being able to integrate this data with the tools and platforms that customer service representatives are using day-to-day.

In practice, this means using a Customer Data Platform as a system of record to store and manage data, but having it fully integrated with the rest of the marketing and customer experience stack.

The potential gains

Effectively applied to customer service, the benefits of customer intelligence are wide-ranging. Most notably, the impact lies in retention and loyalty metrics. According to a Forbes study in 2018, 58% of enterprise businesses see a significant rise in customer loyalty through enhanced customer analysis or intelligence.

By implementing a customer intelligence strategy, businesses can reap the benefits of having complete insight into their customers, setting them apart from the competition.

Projjol Banerjea

Founder and Chief Product Officer, Zeotap

Projjol Banerjea is Founder and Chief Product Officer at Zeotap, a premium mobile data platform with $20+M in funding and seven offices across India, Europe and the US. Previously, he was CPO at Moboqo (acquired by Applovin) and VP Marketing & Business Development at SponsorPay/Fyber(acquired by RNTS Media for $190M).

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