Cleansed, de-duplicated customer information is our starting point. Then to get the maximum value out of your data, we add information that helps you develop rich profiles. Geography, job title, sector industry classifications (SIC), and employee size are the most common list selections and the start of basic company profiling. For more profiling power, we add corporate family linkage and other relationship indicators to help understand related companies and factor that insight into profiles.

We can create even more powerful customer profiles by adding marketing analytics that help to predict future behaviour. Every customer or prospect is ranked from most to least likely to respond/ purchase/ defect, etc., enabling us to gain valuable insights on your customer and prospect base. We’ll then help you to be able to recognise prospects that look and act like your highest value customers; the ones who have a high demand for your product or service. Once we have created a profile or a look-alike model of your highest value customer, we can help you determine who among your prospects most resembles the profile model.

For example, we could compare your current customer base to DataBud’s complete database. You might find that your target company is 50% younger on average than the rest of the companies contained in DataBud’s database, allowing you to focus future prospecting on newer companies.

Data profiling
In a nutshell

Establish a look-alike model

This enables a clear picture of the prospects most likely to become your best customers.

Segment your prospect

Not all customers are the same, we’ll break your prospects down into different behavioural lists based on likeness to your “best customer” models.

Place a value on your prospect segments

Determine the prospect segments that will yield highest value as customers based on a predictive model.

Estimate demand

How much revenue potential does a prospect have for your products or services? Who is most likely to be receptive to direct marketing or a sales offer?

Screen out ‘risky’ customers

Who is most likely to be approved by your credit department?

What's your challenge? We love solving data problems

Let's create a clear path to new customers

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