What is it?
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It’s all about finding out who are your best prospects.
Here’s a simple profiling example:
A client has 1,000 customers who have made a transaction over the last two years. How do we find more customers?
Responsiva Data require just three pieces of information on each customer:
- Telephone number
- Postcode
- Total spend (over the last two years)
STEP1: Split customers by spend value
Responsiva Data don’t just look for more customers; we look for the high spending ones too! This way we can provide you with a perfect data list for marketing.
The process typically involves banding your customers into quartiles, but for the purpose of a simple example, the data list would be split into:
- Highest spending 500 customers
- Lowest spending 500 customers
And typically we find that 80% – 90% of the client’s revenue comes from the highest spending 50% of customers.
STEP2: Match to the business universe
There are two files to match: a & b from step 1.
Where a match occurs on telephone number and postcode, Responsiva Data pull through the profiling variables for the subsequent analysis. Lets say 400 records from each file matched the universe, giving the following:
| File | Non-Matched Customer Records | Matched Customer Records |
|---|---|---|
| A: High-spending customers | 100 | 400 |
| B:Low-spending customers | 100 | 400 |
STEP 3: Profiling
All of the variables are considered during the data list profiling:
- Geography (postcode)
- Company size (employees and/or turnover)
- Job title at site (manager or director?)
- Industry classifications
- Premise types
- Date established
For this example we will just select one variable (employee size) and perform a very simple profile, based on the percentage difference between the customer file and the universe of businesses across the UK.
File A: High-Spending Customers
| Employees | % of customers | % of Universe | Profile Score |
|---|---|---|---|
| 0 – 19 | 50% | 87% | 0.6 |
| 20 – 99 | 40% | 11% | 3.6 |
| 100+ | 10% | 2% | 5.0 |
Mean employee size: 38
File B: Low-Spending Customers
| Employees | % of customers | % of Universe | Profile Score |
|---|---|---|---|
| 0 – 19 | 50% | 87% | 0.6 |
| 20 – 99 | 40% | 11% | 3.6 |
| 100+ | 10% | 2% | 5.0 |
Mean employee size: 18
What this tells us is that the larger companies spend more. We would also recommend that the client targets not just any prospect customers but the high-spending ones. In this case, a reasonable cut-off point would be to only select prospects with 20+ employees.
Step 4: B2B Data Counts / Data List Selection
The first thing Responsiva Data do is to exclude any customers from the universe before presenting the data list count.
Then the data is sorted by the screening criteria applied in the profile. For example; 20+ employees, certain postcodes and industry types.
The numbers are presented as the available marketing pot to select the data list from. The client then chooses how many prospects are required for purchase.
If you would like any further information, contact Responsiva Data on 0800 118 5000 or send an email to info@Responsiva.biz
With 20 years data experience behind Responsiva Data, you can rest assured that your data list requirements will be fully satisfied to ensure the best return on your B2B data marketing budget.