Data-driven CX is not what you think it is

From marketing and sales to customer service and e-commerce, companies have different departments for every major business function. And all of them collect massive amounts of information about each customer interaction.

To efficiently serve customers, however, departments must have access to one another’s data to know if, when, and how a person may have interacted with their brand. It requires all enterprise data to be collected and stored in a shared database that’s easily accessible by everyone. This is nothing new. Companies typically extract a customer’s identity from their CRM system and see if there’s a similar identity in, say, the e-commerce system and then merge the two to build a single view of the customer. There’s only one problem. It’s not complete. There’s a vital missing element: experience data.

Data-driven experience = (enterprise data + experience data)^AI

Experience data is related to what customers are doing when browsing a website or using an app; they may be clicking on several different things, showing interest in various product categories, or maybe abandoning their carts due to lengthy page loads or mandatory registration requirements – all of this is essential data to collect.

It can provide valuable insights into how to approach customers and how to customize the marketing message based on a user’s historical actions. For example, if enterprise data only shows that a customer has placed orders for furniture, they will be marketed to as just a random furniture buyer, despite them having shown specific interest in baby furniture or lighting. It can also be something as simple as determining who clicked on the latest ad and then targeting that customer with a more focused message. It’s important that the right message reaches the right audience because sending someone a buy one, get one free coupon for a product line they’ve never shown interest in will likely not incentivize them to buy. It won’t result in the trigger action the brand wants.

Joining enterprise data with experience data, then responding with the right message fast enough to engage customers is not easy. It certainly shouldn’t be done manually because algorithms can change on a whim due to constantly evolving customer behaviors. That’s why AI needs to be part of the equation, and until this is done, CX will not improve.

Three challenges to overcome

Privacy and consent. Most users will consent to their data being stored (including the use of cookies, but only first-party, and not third-party cookies where data is shared with others) as long as they know what information is being collected and they have full control over it. Brands must respect their relationship with users. This means sharing with them how their data will be used and having systems in place that guarantee data security.

Third-party cookies. Most companies rely on third-party cookies to follow a customer across their digital experiences, and when popular browsers like Google Chrome finally stop supporting them, they’ll have to turn to other means to understand their customers. Creating a first-party data strategy is key. Using tools like experience ID’s across multiple digital properties of a brand will help stich data across the different domains of an enterprise.

High customer expectations. Customers want Amazon-like or better experiences as well as more control of the information they share with brands. Better tools and technologies can help introduce transparency so brands can attract and retain customers and keep them happy.

What Capgemini can do and what clients can expect

Using our unique framework, we help clients start building their first-party data, understand customer intent, and stich customer identity across domains to provide a personalized experience. Our framework complements technology solutions provided by our customer data platform [SP2] (CDP) partners like Tealium, Adobe, Salesforce, Microsoft, Pega, SAP, Action IQ, and others. Our framework addresses challenges posed by the third-party cookie conundrum by creating cross-referencing experience IDs for customer interactions and stiches the customer journey to understand their behaviors and deliver personalization across marketing, sales, service, and commerce.

Next, we have CX-specific AI algorithms. Next best action (NBA) or next best offer (NBO) are terms commonly used in predictive analytics solutions. Knowing what those actions and offers should be at any moment is pivotal. Because if a customer is only looking for product information or wants to voice a complaint, but is instead targeted with an ad, frustration will grow and they may take their business elsewhere. CX algorithms are never perfect. But we have a structure that can calculate a customer’s intent and where they are on the customer engagement index to ensure our predictions are as accurate as possible.[NL3] 

Powered by AI algorithms, our data-driven CX solutions help save time and money and improve customer experience across three dimensions:

1. Acquisition. Thanks to optimized marketing spend, the cost to obtain new customers will decrease significantly, helping boost the return on investment from marketing campaigns.

• Customer engagement. More engaged customers will lead to higher sales per customer or a higher frequency of purchases across all customer segments.

• Customer retention. Since every department now has relevant, up-to-date data pertaining to each customer, they can respond accordingly. For example, when a customer is transferred to the call service center, an agent will already know about the order delay. It leads to higher retention and much lower churn rates.

Data-driven CX will result in things like not retargeting a logged-in website visitor with ads they’ve already seen to keep marketing costs down. The beauty of it is that once it’s implemented, it connects all parts of the business via data insights, allowing AI to automatically filter the data and recommend actions that deliver superior customer experience all around.

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