Customer communication

What is the challenge?

Communication with new and existing customers is an essential component for companies to build and maintain profitable and stable customer relationships in the long term. This is often based on the concept of the customer lifecycle: the individual phases of acquiring new customers, maintaining existing customers or winning back customers via suitable communication strategies must be designed accordingly. This can be challenging for companies, depending on how many customers there are, how complex the individual customer history is, and how dynamically customer preferences change. 

What data can help?

• Individual and aggregated customer data
• Definition and assignment to a customer group (aggregated buying habits, similarities in sociodemographic data such as income and lifestyle)
• Personal customer data (contact person, location or place of residence, contact and purchase history, buying habits, preferences, motives)
• Information along the lifecycle (frequently asked questions before purchase decisions, semantic information from chat messages)
• Social media data

How can companies use their data?

Data analytics can support and optimize customer communication processes along the customer lifecycle. The focus of data analytics is on creating customer groups and scoring individual and aggregated customer potential. Companies thus gain deeper insights into the customer journey or lifecycle. Web analytics or social media analytics can be integrated as additional data. The benefit of data-based customer communication lies in the insights generated on the basis of a large amount of data to make communication strategies for customers and customer groups much more targeted and efficient. The following questions can be answered in this way: which customer wants to receive which information at which time via which channel or in which way? 

Where is this use of data already being applied?

The online fashion retailer YOOX has automated customer communication on Instagram. Based on customer data and historical interactions, the avatar “Daisy” enters into contact with customers.

The CRM team at car manufacturer Jaguar uses data analytics of the customer journey to create more targeted offers. For example, the marketing department can use the system information to target customers more specifically and offer deals such as test drives of new models. In the sales department, suitable contract options can already be noted and contract management can be designed more effectively.

The automotive company Lexus uses website visits to analyze the behavior of its customers. If, for example, they frequently refer to reviews, communication about reviews is increased. This strengthens trust and increases the time spent on the website.

Telecommunication company Vodafone is using a chatbot (“TOBi”)  to support customer service and make communication with customers more efficient. TOBi is trained to also recognize image data such as bills and screenshots. Once TOBi’s capabilities are exhausted, human staff will take over customer communications.

How does this use of data contribute to value creation?

Insights from data analytics enable more targeted customer engagement and increased customer loyalty. Other areas of value creation also benefit from insights into customer needs, such as product development and service.

Sources: Économie numérique (2019), Niessing, J. & Henry, B., Harvard Business Review (2018), BuildFire (2019), CE-Markt (2020)