How to turn Customer Data into Customer Insight

Access to the right customer insight at the right time, is crucial for business success. Many businesses have demonstrated that by utilising their customer data in an effective manner they can serve their customers better while improving their operations to optimise customer satisfaction.

Despite the benefits being quite clear, a recent Forrester report found that most companies struggle to obtain value from their customer analytics efforts. The key inhibitors were found to be lack of data management, integration and quality.

Undoubtedly, having the right practices in place to gather customer data and transform it into insights requires a clear strategy and unreserved commitment.

For companies that are just starting out in their customer insights journey, or would like to improve on the steps they have already taken, we share below some advice by David Norton, EVP of Integrated Customer Engagements of Analytics at MDC Partners.  In an interview with Michael Koploy of the BI software and technology reviewer Software Advice, Norton highlights 4 key areas for developing excellent customer analytics practices.

Creating simple customer segments first by mining data

Companies should first start to build a basic view of their key customer segments. Even though focus groups and market research usually help to reach this aim, there is a significant difference between what people do and what they say they do. Hence it is suggested that as a first step organisations mine transactional data to understand what different segments do differently.

Prioritising data collection projects to build momentum

Forrester’s report also identified the velocity of data generation as another inhibitor to successful customer analytics. With copious amounts of rapidly-growing data, finding the right data and presenting it in an environment that marketing and operations teams use, becomes crucial. To determine priorities, Norton advocates a matrix system that assesses the expected value and relative complexity of getting data to a useable state. By starting with the high-value, low-complexity initiatives, the company has a greater chance of making significant impact across the business.


Abandoning intuition. Test offers to optimise marketing campaigns

Companies need to understand the importance of scientific testing as this will enable them to understand the impact of different offers on customer behaviour – resulting in a better ability to refine customer segments.

Ensuring marketing analytics is embedded with decision makers

Ensuring that marketing analysts are close to marketing decision makers is also key as this ensures that analysts are already familiar with key market and business issues before diving into the data. According to Norton, “being close to those they serve helps facilitate turning insights into action quickly.”