What is the role of data enhancement?

How does data enhancement extend the value of your customer data?

Data Enhancement Role

So what is data enhancement (often called data enrichment, or data appending)? It is the process of adding data variables to a contact (or business) record from your database. These may be consumer-based demographics such as gender, age, property type, income, credit score, interests, lifestyle, etc. Or they can be business-based, such as industry type, number of employees, turnover, business age, job titles, etc. 

Understand Your Customers

Targeting customers

Understanding who your customers are is an absolute necessity to delivering the best customer satisfaction possible. By knowing the profiles of your key segments, you can provide more relevant and targeted marketing and sales campaigns, offer suitable products and services and you will see your ROI improve. Using this kind of customer insight to shape your products, solutions and the buying experience is becoming increasingly common and is proven to uplift customer engagement and ultimately, your profits! 

Target Lookalike Prospects

Data enhancement is also a great way to improve your customer acquisition. If you understand the profile of your customers (especially your “best”customers), you can then target “lookalike” prospects who share the same characteristics. This continuous move away from scattergun marketing to a more personalised, targeted, one-to-one approach has been happening for a number of years now, but technology continues to simplify and improve the process. 

Drive Insight

When combined with your internal transactional data, data enhancements can really start to provide powerful insight. What is the profile of your most profitable customer segments, compared to that of the lowest profit segment? Now compare that information to the profiles of your newly acquired customers. This can help you to predict Lifetime Value and enables you to determine the suitable investment levels for each new customer – spend more on those with the “high profit” profile and spend less on the “low profit” profile customers. 

Data Enhancement Uplifts Sales

A Mckinsey report entitle “Capturing Value From Your Customer Data” states: “Customer data should be enriched to incorporate digital profiles, life events, community information, transaction-based insights, customer preferences, sentiment scoring, and so forth in order to get a full picture of the customer. Organizations can capture digital profiles and digital activity by linking web, mobile, and social-presence data. Marketing or customer teams can start by attaching activities to customer profiles. Those activities might include customer-sentiment-behavior scores, insights derived from purchasing transactions, call-center queries, and online behaviour” The report goes on to say “By pulling together rich customer profiles and rigorously tracking response rates, marketers can know precisely what types of content over what channel and format are likely to have the greatest impact on key segments and microsegments. A decade ago, the tools weren’t available to do this. Now they are. And nearly all companies can benefit. An automotive insurer, for instance, learned that the customer journey to buy car-insurance policies typically starts 60 days before customers receive their first quote and usually involves an average of 15 signals. They can use that information to tailor the tone and timing of their outreach. Such personalization can deliver five to eight times the return on investment on marketing expenditure, and can lift sales by 10 percent or more”. 

Using Statistics With Data Enhancements

Having enhanced your records, the next step should be to create a profile report which compares the profile of the target audience to that of the base (typically, this may be the rest of your database, or the population of your country). This will highlight where you have a strong or a weak penetration. In the example below, Manual Workers account for 1.3% of the target audience, but 6.4% of the base – therefore Manual Workers are under-penetrated. In comparison, Managers account for 15.5% of the target and 5.9% of the base – so they are over-penetrated. Therefore Managers should be targeted more than Manual Workers. 

Propensity Modelling & Segmentation

The example above is uni-variate (looking at one variable at a time). The next step is to perform multi-variate analysis, whereby the variables are combined. As previously mentioned, incorporating your internal transaction information such as web-browsing history, digital profiles, life events, community information, transaction-based insights, customer preferences, sentiment scoring, etc gives a full picture of the customer.Combining all the data you have available means you can create powerful customer segmentation's. Customers (or prospects) can be scored and ranked based on their propensity (likelihood) to undertake a given action (such as buying from you). In the example below, the database was segmented and post-campaign analysis shows that the top segment from this predictive model generated a 29% response rate, compared to just 1% from the lowest model segment. The model clearly worked! It identified the records that would be the best responders. All because the customer profile was analysed and interpreted, following the data enhancement stage.

Contact Us!

For more information on the Kudos data enhancement services, please contact 0330 808 9390