3 Simple steps for immediate results and cleaner data

Q1. Why should regular data cleansing be such a priority for organisations and what clear benefits will this bring?

There are several reasons and benefits in establishing regular and robust data cleansing practices within a business. Clearly these need to be tailored to the type of business, i.e. size, complexity of data, and frequency of contacts and changes. But no matter how small the business is data cleansing will drive the following types of benefits:

1. Marketing budget cost savings – inaccurate data will lead to wasted marketing communications (e.g. print and postage costs – the DMA estimate that c.£95m is wasted per annum on this alone). Accurate mailing addresses will also allow a business to achieve savings in postage using mailsort (subject to volumes).
2. Operational cost savings – retain the wrong address and deliveries will fail; incorrect personal details (email address, mobile number, name, address details) will cause inbound customer calls or messages that will have to be dealt with; some could turn into complaints (e.g. mailing sent to those on the bereavement register).
3. Improved cash flow and reduced debt – accurate billing details will facilitate improved revenue collection and follow up.
4. Improved sales revenue – through better response and conversion rates to correctly addressed outbound communications; a person is less likely to engage with a brand if their details are wrong.
5. Higher customer satisfaction – customer experiences with a brand are better when they are based on accurate details (contacts are right, brand lives up to its promises).
6. Reduced churn – if poor data leads to poor customer experiences, customer churn is likely to be higher.
7. Better brand reputation – fewer complaints, better customer experiences, improved financial performance will all register positives for a brand across a range of stakeholders.
8. Avoid breaking the law – there are requirements under the Data Protection Act of 1998 to maintain accurate and up to date personal data (Principle 4). Maintaining an accurate address is quoted as an example. Fines could be incurred for failure to do this, let alone bad brand PR.
9. Inaccuracies and poor decision making – duplicate records could give you the wrong information about your customer base and not only lead to wasted costs (in communications), but in business decisions being made using inaccurate views of the business. As the old adage goes ‘rubbish in, rubbish out’.

Q2. What, in your opinion, do some organisations see as a barrier to data cleansing and what is the impact of them not undertaking these regular cleanses?

In my experiences there are four types of barriers; lack of understanding and awareness, not seen as a high priority, lack of resources and lack of ownership.

• Lack of understanding and awareness – in these cases the business isn’t aware of neither the scale nor the impact of inaccurate data. There is a perception that if it is collected okay then that’s the end of the task. They simply do not appreciate the scale of change and the impact on their business.
• Not a high priority – some in sales and marketing delegate the management of data to the IT or Operations teams as they are more focused on creativity or sales campaigns. Data cleansing probably isn’t the most sexy job within a business. They are not bought into the urgency or impact of the concept of data cleansing and see it as a chore.
• Lack of resources – at first glance it may seem like an onerous task to maintain data and some businesses, particularly smaller ones, perhaps feel that they don’t have the time or resource to commit to it. However, there are simple steps and on demand/pay as you go services available that can at least address the basics of data cleansing.
• Lack of ownership – even if there is awareness and some resource to address data cleansing, effective data management is impeded by weak processes and lack of ownership and responsibility.

The consequences and impact of not managing regular data cleansing are the opposites of the benefits listed above, i.e. higher costs, lower sales, wastage of resources in operations, weaker cash management, poor customer experiences and brand reputation leading to higher churn and the risk of non-compliance.

Q3. Can you suggest some simple steps that organisations can put in place to improve the quality of their data?

Here are some simple steps to take to improve the quality of data:

1. Agree on priorities and responsibilities for data cleansing – i.e. which data is business critical and needs to be maintained. Then agree the end-to-end process for cleansing from capture to regular updates and archiving. Assign someone to manage this process and give them responsibility and KPIs to manage this process and demonstrate value to the business.
2. Review data capture processes – get it right first time. Ensure that all touch points where data is collected are covered. Agree which data is to be collected at each stage (avoid collecting too much at once as customers will be put off), look at form design and how it will be validated. Seek out the relevant PAF validation services to suit your business and consider email verification as well. Build quick dedupe checks to make sure this is a new customer and if an existing one that the records are merged and updated as required.
3. Review ongoing contact points – as well as initial contact, assess ongoing contact points and use these (where relevant and practical) to verify details, e.g. in store or in call centres. Customer service emails may also be used to verify details.
4. Cleanse for home movers – approx 18,000 people typically move a day, so data will decay every day. Not everyone will tell you that they have moved (depends on how critical your brand/service is to the customer). Seek out external data providers who can offer updates on change of address (e.g. NCOA, GAS) and put in steps to have regular updates and feeds.
5. Remove the deceased from your records – a sad and inevitable situation and it is important for brand reputation and sensitivity that records are updated for bereavement. Again, not everyone will tell you this, particularly in prospecting databases. Seek out data providers who have the bereavement register data and keep records updated.
6. Update customer preferences – customers are more aware about their rights to opt-out, so it is important to ensure that customer preferences are managed. There is national mail and telephone preference services (MPS and TPS) which you need to adhere to, particularly for prospecting. Your own preference centre is also important to manage. Ensure that you are compliant and seek and acquire customer permissions and consent for contacts (by channel) and that these are stored with source and date (i.e. where and when they opted in or out). Retain a history of these records and update as soon as a customer preference changes, e.g. unsubscribing from emails. This can become complex so the person responsible for data quality will play a key role in managing all the aspects and ensure that the business is compliant.
7. Avoid duplication – this is also a key part of data cleansing and data quality. This can involve simple merge/purge routines or simple rules to check for duplicates on an ongoing basis in a database. In larger and more complex businesses and systems, the use of Single Customer View (SCV) keys becomes best practice. These keys allow an individual to be tagged and allow records from disparate sources to be merged together into a single view of an individual. This is core to an effective CRM or customer database.

If you would like a FREE analysis of your database and a return file of your clients addresses that do not meet clean mail standards, you can then work on these to improve your Direct Mail success rates. please call 121 Direct Mail on 0845 4000 121