In part one of my ‘back to basics’ series we looked at why data quality is more important, yet more challenging, than ever, as organisations look to leverage the data opportunities available in the digital age. With this in mind, the second part will take a closer look at data cleansing and how to manage it.
Data degrades over time. Our latest global research indicates that organisations suspect that an average of 30% of their customer/prospect data is inaccurate. Unless individuals (customers, citizens, staff, supporters) actively update their data on a regular basis, there will clearly be a degradation in usefulness and value of that data. In some cases, this fall in quality may lead to an increase in potential risk, which could lead to brand damage or regulatory action.
So, what can organisations do if they can’t be certain that everyone in their databases will regularly update their information if, for example, they move home, get a new mobile number or experience a family bereavement? There are a number of simple steps you can take to remove this risk. Here’s a roundup…
Your primary contact data (address, email, phone number) should be regularly validated to flag potential errors and where possible, make updates.
For postal addresses, postcode recodes (e.g. a change from CO10 6 to CO10 2 for a street in Suffolk) can occur. In fact, looking at the Royal Mail PAF database in December 2017, there were just under 5,000 new postcodes and around 2,500 postcodes deleted.
To ensure that these changes are incorporated, cleansing your data against the latest vintage of address reference information is a wise move. This can help you keep delivery success rates high and save on the cost of returns.
Email addresses should also be regularly tested to ensure that they are still ‘operational inboxes’ that can accept mail. This is an important activity because sending email to invalid addresses negatively affects your sender reputation and causes ISPs to filter and block your email. The good news is that regular testing allows you to avoid this by suppressing any invalid addresses you find.
Mobile phone numbers should also be regularly tested to ensure the number is still live – when used for service updates (such as delivery alerts), the mobile number can be critical to success. Where numbers are no longer live, other channels can be used to keep in touch with an individual or an error flagged for update the next time you interact with that customer.
Suppression is a basic data management technique to avoid risk and ensure an ethical approach to the use of personal data. There are a number of forms of suppression which all can be a great way to maintain data accuracy. Our latest research with Data IQ indicates that suppression is not as widely used as perhaps it should be, with only 38% of organisations surveyed regularly suppressing their data. To dispel any confusion, let’s run through the data available and some of the key benefits.
This form of suppression uses names linked to an address to flag that someone of that name no longer lives there. This can then help organisations to avoid waste when marketing and can be an important part of an anti-fraud toolkit by ensuring that sensitive post is not sent to an address that may now be incorrect.
What Goneaway suppressions cannot do though is update your data with the correct address. For that, you need to consider Movers data.
Often linked to a Goneaway file, Movers data identifies that, for example, someone by the name of Sue Smith no longer lives at No. 1 Church Road but now lives at No. 38 The Crescent. This additional insight can clearly enable a brand or charity to stay in touch with important customers or supporters.
One of the most commonly used products is NCOA (National Change of Address) Update from the Royal Mail. This highly accurate dataset contains the information of those who register their move with the Royal Mail via the redirections service. These records also move onto an additional Goneaway file (NCOA Suppress) after the redirection period ends.
The passing of a loved one is a difficult time for families. Mortalities suppression data can be used to help organisations avoid further distress for those individuals.
Whether it is used to permanently suppress a deceased individual or temporarily remove an entire household from marketing, mortalities suppression should form part of any data quality strategy.
Mortalities data is either generated directly from forms made available from funeral homes or is populated by sourcing data from financial organisations who are normally the first to be made aware of a passing, so accounts can move to the next of kin. Whichever method is used to collect the data, mortalities suppression is a valuable tool to prevent further distress, protect brands and reduce cost.
The Direct Marketing Association (DMA) have offered their Mailing (MPS) and Telephone Preference Services (TPS) for many years. If you haven’t heard of them already, these are central databases where individuals can register their wish to be removed from mailing lists or not to receive unsolicited sales and marketing calls. Whilst the TPS is a legal requirement for any organisation taking part in telemarketing, the MPS should also not be ignored.
Brands can use the MPS to take an ethical approach to marketing. Where no existing relationship with a household exists, the MPS can be used to suppress them from marketing campaigns. This can prevent brand damage and reduce costs as those who are registered on the MPS are perhaps not as receptive to direct marketing as others.
Data is a critical asset to your organisation. Just like your other critical assets (buildings, people, intellectual property, IT) it needs to be cared for to prevent it failing you.
Basic hygiene and cleansing can not only save money and time, but it can prevent damage to your brand, protect the identity of individuals who trust you with their data and could even be a part of your on-going management of regulations such as the GDPR.
Experian can support you with a range of software and bureau-based services to cleanse your data as a one-off job or regular occurrence. Data cleansing can even be automated and integrated into your key database tools.
If you take one thing away from this blog, I’d encourage you to speak to your team about what happens when your data lets you down? How much is returned mail costing you? How many complaints do you get from bereaved families? If the costs and brand damage are significant, then there’s a case for cleaning your data.