We do a lot of customer projects that focus on utilising customer data, whether it's marketing, improving the customer experience, measuring performance or developing the business as a whole.

And while leveraging customer data may sound obvious (Doesn't everyone do it? What else would you do with it?!), the topic is ultimately surprisingly complex.

The topic involves simple-sounding issues, such as storing customer data in a CRM system. But what about companies that don't have a CRM? Or those with multiple CRM systems? This brings us to the issue of data consistency and accuracy, which is often decentralised and rarely optimally managed.

In many companies, access to the customer data system is restricted in order to maintain data integrity. But at the same time access to data becomes unnecessarily cumbersome.

We frequently address the challenges of this type of siloed thinking or approach and the resulting fragmented data ecosystem. That is because it is impossible to make use of customer data if it is not stored properly and easily accessible.

Let's go back to basics for a moment.

What are we talking about when we talk about customer data?

Customer data is any data that relates to a company's existing customers. And, specifically data that is stored in electronic systems. Identifiable customer data, such as personal and contact information, makes up the surface layer. Demographic data, such as customer's age, gender, place of residence and so on is also important.

What is even more valueable than demographic data, is customer history.  I.e. what each customer has bought, when and where, and what services they have used. This information is essential for additional sales and customer services and should therefore always be easily accessible.

Identifiable customer data can also include customer feedback, product reviews, chat conversations and customer service contacts, as well as social media messages and comments. Often this data however is only considered at a more general level in terms of customer satisfaction and feedback.

The collection and analysis of individualised customer data can be further deepened by including each individual's use of online services and content consumption, as well as their interaction with the company's social media content and online advertising. These too are often erroneously considered only as anonymised mass data on customer behaviour, rather than individual-level customer data.

Enriched customer data, deeper customer insight

The activity I have described above, i.e. combining data collected from different data sources such as web analytics and social media with identifiable customer data stored in a CRM or similar customer information system, is customer data enrichment. Enriching data requires a little more effort than simply storing a customer's contact information in a file. The amount of work required depends on the data system that is used and the integrations that it has as well as the cohesiveness of the whole customer data ecosystem.

Customer data can also be enriched with third party data. In a B2B context in particular, it is typical to retrieve and store background information on the customer company, i.e. in addition to known contact persons, the same customer data card will include the company's industry, size and turnover, perhaps also the names of the key decision makers. And while this information is often retrieved and processed during the prospecting stage, whether the salesperson stores it in the customer information system or perhaps just on his or her own computer makes a difference.

The reasonable extra effort required to enrich customer data can be easily justified. The more information you have your on customers, the more complete a picture you can build by analysing the enriched data. At the same time, the possibilities for profiling customers will be extended from demographic criteria to their purchasing behaviour, for example. This allows you to model more precise customer segments and target groups for sales and marketing purposes.

Of course enriched data is only useful if all interested parties have access to it.

Enriched data helps predict customer needs and behaviour

All employees within a company should be able to use enriched customer data in their own work and in their own customer encounters. While annual analytics compilations and monthly reports on the customer base may certainly increase customer insight within an organisation, access to real-time customer data whenever needed is where the real value is measured.

Comprehensive and up-to-date individual-level customer data is extremely valuable in sales, in customer relationship management and in customer service situations. If a sales rep or a customer service person can see a specific customer's whole situation at a glance, at the same time as talking to the subject, this can be crucial for ensuring a smooth service experience. Problems are resolved faster and potential customer frustrations can be turned into delightful experiences and additional sales.

However, enriched customer data is not just for human-to-human interactions. AI can learn to predict customer needs and behaviour based on enriched data. This opens the door to predictive analytics, more targeted communications and personalised product recommendations.

Need help to make more effective use of customer data?

If this article has left you wondering that perhaps your organisation isn't using its customer data in the most value-added way, we have a suggestion.

Call us for help. We'll map your organisation's current data systems and integrations as well as data collection and data utilisation practices to see where there's room for improvement.

Contact us to talk in more detail!