When it comes to marketing data, data cleansing costs pay for themselves, in almost all cases.
(By the way, by data cleansing, I’m considering all activities involved in improving data from address cleansing to contact validation through to telemarketing.)
So why should this be an obvious statement?
If you know the cost of a marketing campaign, the conversions involved and the ultimate revenue return then calculating what to spend on data cleansing is easy.
Before I run through an example, it’s not the cost that is significant, it’s the lost sales opportunities that makes the difference.
So let’s start with an example with numbers:
You have an email marketing campaign that either generates leads or makes sales online. Let’s look at the lead generation scenario for a database of 10,000 contacts.
Here are the key assumptions:
- 20% of emails are missing and 1% of emails are incorrect
- The leads generated from this email campaign is 2%
- Of these leads one in four close to an order (25%)
- The average order value is £1,000.00
Here’s the calculation:
This campaign generated £40,000 in sales.
Now let’s see what happens if we have all the missing emails and we have fixed those incorrect ones:
Because campaigns are statistically predictable, by having the missing emails and fixing the incorrect ones, a further £10,000 could be generated (an increase of 25% - that’s a big percentage!)
This calculation can be applied to direct marketing, telemarketing, etc. for both B2B and B2C campaigns.
Just add in your numbers and see what comes out.
Finding and fixing 2,080 emails will not cost a great deal compared to the sales opportunities available.
Once you have an email, then it can be used on multiple campaigns, hence the initial cleansing exercise costs become smaller as the email is used more often.
When you look at the total Customer Life-Time Value (the total revenues from a customer over the average time a customer is with you), then it really becomes obvious that placing a great deal of effort into the right data is a must.
In the majority of cases, ensuring you have high quality data is an expenditure with a positive ROI.