Donât fall at the first hurdle: the importance of data quality | DMA

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Donât fall at the first hurdle: the importance of data quality

Successful marketing campaigns depend on accurate, high quality data. It’s the cornerstone of delivering the right message to the right person in the right way. If data isn’t captured well, or isn’t maintained, communications may not reach their destination or they may be targeted badly. Customer relationships can be damaged and business opportunities missed. Not surprisingly, studies have shown that up to 25 percent of data held by small businesses is inaccurate or wrong.

The impact of this is felt on the bottom line. A variety of statistics tell a horror story of the cost to businesses of bad data; Ovum Research has previously calculated the damage as at least 30 per cent of revenues.

What turns data ‘bad’?

Simply put, change turns data bad. That is, assuming it was captured accurately in the first place. And of course, change happens all the time. Product information that feeds customer communications changes, as do business terms and conditions.

Customers cease services, move house or premises, change their name or email address, and alter their payment methods. Customer data is never static, it is dynamic and the methodology for managing it needs to take this into account.

Successful customer relationships depend on having the right information and on being able to personalise communications so that each marketing message stands a chance of appealing to each target. Customer mailings – digital or physical – that don’t reach the right destination or that contain inaccurate or out-of-date information at best represent cost for no return, and at worst can damage a business’ reputation.

The three stages of data management

Effective data management depends upon:

Accurate data capture – Bad data entry is the most common cause of data issues and the inaccuracies can be difficult to root out and correct once they’re in the system. Bad data records include both those that contain errors in the entry and those that are incomplete. Auto-checking, auto-completion and auto-correction of data minimises the chances of human error creating bad entries. This is most often seen with auto-address completion. As well as saving time and improving accuracy, this can also ensure that field entries are standardised. Standardisation helps duplicate records be automatically spotted and ensures consistency of records across databases and systems.

Ongoing data maintenance – data that is not maintained will quickly go out-of-date. It de-generates and ultimately becomes obsolete. Within each organisation responsibilities around data need to be clear, including how data is shared, for what purpose and who maintains records. Data should be validated at each point of use, its accuracy checked and any remedial action needed taken. Duplicate record entries should be weeded out to prevent communications being issued twice to the same contact. This not only avoids unnecessary cost but also prevents an unprofessional impression being made on the recipient.

Effective data use – standardised data entry streamlines processes so that direct marketing materials can be automatically prepared and data can be auto-populated across multiple systems, for example those that deal with invoicing. Co-ordination over the use of data within the business is key. Without this, different campaign teams could contact the same customer at the same time, causing customer confusion and negating any possible positive impact of either campaign.

Data underpins business and marketing operations. Its accuracy cannot be left to chance. All companies, large and small, need to ensure data quality through procedures that govern the accurate capture of data, its ongoing maintenance and effective use.

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