Recently our VP of Analytic Services, Simon Poole, was featured on Discussing how third-party data can be used to help companies find and target their customers better.

Third-Party Data

It is generally accepted that third-party data provides foundational knowledge for any marketer. But, it can also be effectively used to enable more precise target marketing that can dramatically improve marketing KPIs.

Third-party data comes in lots of flavors. At a basic level, there are examples like age, income, gender, and presence of kids; but there are potentially thousands of available variables. Some are collected from various public records, magazine subscriptions, sourced from social media and some are even modeled from survey data. Data is also available on behavioral and attitudinal characteristics; for example, likelihood to subscribe to financial newsletters, or interest in gourmet cooking or gardening, as well as likely political affiliation.

A valid question to ask is whether apparently interesting information on an individual or household can be correlated with a client’s very specific customer behaviors. It might seem surprising, but if correlations (data relationships) exist, which is typical, one can often find proxies for specific customer behaviors. This enables us to describe who are our best customers at a fundamental level but can also help us create predictive models to use in target marketing.

Over the past few years, we have built models at Lityx which really illustrate the power of third-party data particularly when it is combined with advanced targeting methods. For example, for a Hearing Aid device provider it was generally accepted that targeting those aged 65 and over would generate a higher response than just targeting say those aged 50 and over. Without advanced analytic-driven targeting, this is true, and it is clearly an obvious way to target the most qualified prospects at a reasonable Cost-Per-Sale. However, while Age and Income select work they leave customers and dollars on the table. This is because a targeting model enables you to further segment or “cherry-pick” from different age groups based on other key variables that help predict response.

How does it work?

Age is powerful in predicting response but combining it with other variables one can create a complex algorithm that assigns a probability score to every possible prospect. A model is essentially a scorecard based on a set of variables (8-12 is common). Although Age is the most predictive variable for this product, segmenting on multiple variables allows you to find sub-segments that are not as obvious. Once the score has been assigned, prospects are ranked from highest score to lowest score. Note that it only takes a few incremental sales to generate a significant ROI given the low cost of applying the model.

So why shouldn’t the client continue what they have been doing?

Invariably potential customers and dollars will be left on the table. If you have competitors that are not using third-party data and advanced analytics, you will have a competitive advantage since you can tap into segments they are not. If your competitors are using advanced analytics and you are not, you will get left behind.

By deploying advanced techniques with third-party data you will invariably see Cost-Per-Sale decrease which will free up dollars to acquire even more customers and/or to spend on other channels.

Original article


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