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How cleaning your data can help your bottom line

20 February 2015

In fundraising it’s the cool stuff like viral campaigns and TV ads that get the headlines. However, it’s often the backroom activities that have the biggest impact on return on investment (ROI). 

At Children with Cancer we spend a significant proportion of our marketing budget on big direct mail campaigns to our own donor database as well as millions of bought-in prospect records. However, we were seeing an increase in the amount of mailing packs being returned to us as addresses were incorrect, duplicated or out-of-date.

For a charity, wasting budget is unacceptable but it’s not just about the money; being seen as wasteful and mailing deceased records can create seriously damaging PR. We knew we had to refine our data cleansing strategy, and fast, but we didn’t realise how great the impact of this action could be.

So what did we do? 

First, our data bureau, CCR, carried out an audit across our database and the bought-in mailing data. This identified areas for concern and informed the creation of a bespoke data cleansing process. For example, better data cleansing doesn’t necessarily mean suppressing more data – it’s also possible to ‘over suppress’ data which would mean us not mailing perfectly good addresses that we have paid for because matching levels have not been created properly, ie if you match at surname and initial – J.Smith, you may miss out on the fact that there is a Jack Smith and a John Smith who are both relevant for you to mail. A data audit is now carried out before each campaign.

Data was structured into a predefined format so that it could be easily imported into and exported from the data cleaning system, whilst also ensuring that addresses were formatted according to the Royal Mail’s Postcode Address File (PAF) to ensure optimum postage discounts. 

The process for each campaign is continually refined by testing elements such as deduplication hierarchies and using new suppression data, while incorporating other factors such as which data sources create most complaints. Every data record is flagged so that bad data sources can be tracked and handled accordingly. Data has also been segmented so that cleansing can be optimised for specific areas of concern within each segment. This ensures the right data is managed in the right way.

As a result of our data cleansing strategy, we have seen our return rates decrease. As an example, for our two most recent campaigns, we supplied over 1.7 million records for cleansing. 127,500 were identified as incorrect or not-for-mailing. In addition, 68,000 addresses were ‘rescued’ by ‘relocating’ people who had moved to their new address. This enabled us to save £48,000 that would have been wasted on incorrectly addressed mail – and that’s just across two campaigns! This is money we have ploughed back into our precious marketing budget.

As well as increasing our response rates and reducing costs, we have seen a distinct decrease in the number of complaints we receive for badly addressed mailings. This is important for protecting our brand, plus we have improved our environmental credentials too.

UK charities spend millions on creating beautiful direct marketing campaigns, but what’s the point if they don’t reach the intended recipient? Every year over six million people move home, there are over 600,000 bereavements and the Royal Mail amends over 500,000 addresses.  Data cleansing needs to be near the top of charity marketers’ agenda – it’s about getting the fundamentals right. Great creativity is then the icing on the cake.

Top tips for keeping data clean

1. Apply a unique ID

Often called a unique reference number (URN), applying an individual ID to each record is essential for managing data accurately including and tracking any changes.

2. Ensure accurate data capture and input

Create a guideline for data capture to improve basic data quality, ensuring that all required data fields are made mandatory, eg postcodes. Keep standards the same – Road or Rd, Limited or Ltd. This improves data cleaning accuracy and makes the processing time more efficient.

3. Check your supplier and give them time

You will need to find a data cleaning bureau as they will have the external data resources required (ie deceaseds, gone-aways etc) that will be matched to your data to identify what records need updating or suppressing. Take the time to investigate different data cleaning routines and choose the supplier who meets your needs – evidenced through case studies and testimonials. Then give them plenty of time to do the necessary checks etc to ensure it’s done properly. This is an area where it’s definitely a case of ‘more haste, less speed’.

4. Keep data secure

This isn’t just a consideration but an absolute requirement. Your supplier should provide you with a non disclosure agreement (NDA) and a secure method of transferring your data. You should also encrypt your data.

5. Little and often

Don’t leave yourself with a large data cleaning bill annually when you can run monthly or quarterly cleans. Cleaning data more frequently cuts mailing costs and reduces wastage.

Camelia Vasilcan

database manager, Children with Cancer

Camelia joined Children with Cancer in December 2004. She plays an important role in ensuring supporter databases are accurate and updated.