Analytics, Cognitive Make It Personal

By leveraging data and machine learning, companies may be able to deliver more relevant, personalised experiences to consumers.

In today’s customercentric environment, delivering superior experiences can create competitive advantage for companies. However, many companies face challenges providing highly personalised, real-time experiences to consumers across websites, mobile apps, call centres, retail stores, and other venues.

Consider this example: Jane receives a coupon in the mail, addressed to “current resident,” for a 10 percent discount on an instore or online purchase. She takes the coupon to the store on her next visit and pays for a blouse in cash. Jane is a loyal customer at this store; just last week she spent over €500 on clothing. However, because the coupon is not tagged to Jane, the cashier does not identify her as a valuable customer, and fails to ask her about her previous purchases or product preferences. When Jane gets home and logs on to the retailer’s website to write a review, she is presented with an online ad for the blouse she just bought, based on her browsing history in recent weeks.

This example illustrates some of the problems with the way companies currently engage with customers across the lifecycle. The most effective customer experience (CX) often involves personalisation and optimisation—providing the right message at the right time, in the right channel, and in the right sequence. This can be challenging for companies trying to reach consumers at different stages of the customer journey, on different devices and media channels, in the “moments that matter.”

Leveraging Data to Provide Personalised Experiences

To address some of these challenges and provide consistent, relevant, and highly personalised experiences to consumers, companies can jointly consider an always-on experience management approach, which includes the following steps:

Define the end-to-end CX. First, map every potential interaction consumers may have with the company along their journey, from initial awareness through post-purchase. What does the company want consumers to experience when they interact with its brand at every touchpoint—from the company’s website and social media platforms to its call centre and even the music customers hear when on hold? All the interactions—across potentially hundreds of touchpoints—create the overall experience. When considered thoughtfully and in a wellorchestrated manner, the end-to-end customer journey can result in a consistent, engaging, and positive experience for consumers.

Create a single view of the customer.

Many companies have an abundance of customer data, including personal information, transaction history, online activity, call centre interactions, and other information. Often, this data resides in different systems and is not integrated. To provide consistent, relevant, personalised experiences to customers, CIOs can develop a technology platform that integrates customer data across channels and touchpoints and presents a complete picture of every consumer interaction. This complete picture may include actions taken on a website, tweets about a brand, products returned and reasons why, white papers downloaded, or chatbot conversations. Companies can leverage customer data to manage interactions with consumers on an ongoing, real-time basis, moving away from marketing campaigns and toward experiences customers have with brands every day across multiple channels.

Use machine learning microsegmentation. to create

Once companies have consolidated their customer data, they can begin to identify variables that have the biggest impact on purchase behavior. Some companies may consider thousands of variables when making decisions about how to reach customers, including age, household income, hobbies, shirt size, and preferred brands. With advances in cognitive computing, companies can now use machine learning to reduce the number of variables and perform microsegmentation — providing the most relevant information to audience segments based on which factors most affect their decision-making. For example, in retail, three important variables may be how recently consumers last made a purchase, how often they purchase, and how much they spend on average. Using microsegmentation, companies can begin to provide more personalised experiences based on where consumers are in their journey, what matters most to them, and which customers are most valuable and loyal. Deliver real-time experiences. Technology can help enable content delivery as well as improve human interactions with customers. In some cases, the interactions may be automated—for example, when Jane logs on to the retail website to write a review, a live chatbot might pop up, asking her to rate her in-store experience. In other cases, companies can use data and analytics to help improve employee engagement with consumers. If Jane’s coupon is delivered to her mobile phone, the clerk could scan it, see her transaction history, and perhaps receive a prompt suggesting she ask Jane about her previous purchases.

Monitor and optimise. To continually improve CX, companies can track and measure their performance through solicited customer feedback (e.g., surveys) or unsolicited feedback (e.g., social media posts). Companies can use this data to optimise messages and campaigns and determine whether they are delivering on their brand promise. Employees may also be able to provide feedback on how data and analytics help them interact with customers, as well as what can be improved.

*** To stand out in today’s competitive landscape, companies can leverage data and machine learning to deliver personal, engaging experiences to consumers. Doing so may help them acquire new customers, build loyalty with existing customers, increase revenue, and improve profitability.