Making Customer Data Fit For Marketing Purposes

23 Oct 2017 | 07.57 am

Making Customer Data Fit For Marketing Purposes

Top tips from Dara Keogh of GeoDirectory

23 Oct 2017 | 07.57 am

Big Data and Data Analytics loom large in the lexicon of most organisations these days. The impression sometimes given is that there is a golden nugget of information that can be derived from all of this big data stuff that will make a business successful and deliver an unbeatable USP.

At the same time, Dara Keogh, chief executive of GeoDirectory, often hears about campaigns or initiatives being delayed or under-delivering as a result of data issues. “This occurs because many organisations are simply not ready to take the dive and use their data. This is most probably because they don’t trust it,” he says.

GeoDirectory services enable businesses to identify their core customer groupings. The system classifies every business address with one of the 495 NACE codes, and the company also encodes consumer addresses with one of 16 demographic codes that are based on life stage and income. “We can help customers identify new areas or sectors to target, and the type of organisation and people that have previously done business with a company,” says Keogh (pictured).

Keogh believes that when it comes to gathering data for marketing purposes, large organisations and SMEs need to concentrate on the base unit of the whole process. “The starting point for anybody setting out on the road of using customer data is to have standards around that data,” Keogh advises. “By following the seven simple rules of data quality, you will get through a long leg of that journey.”

Quality customer data results from producing robust data across an organisation that is trusted, relied upon and used. For this to happen, certain data collation and processing procedures should be followed. In Keogh’s opinion, these are the Golden Rules of data quality:

Completeness. There needs to be a specific and detailed description of what the data requirements are to meet the needs of the organisation.

Accuracy. The data that is being captured in correspondence should be captured once at the first point of activity, and ideally be usable across multiple systems as required.

Validity. The data should be captured and held in accordance specified syntax, format and range parameters that are used across the organisation.

Reliability. There needs to be clearly understood and consistent data collection process.

Cleanliness. This means that the data is free of any duplicates, is organised, standardised, structured and labelled. Most data, such as is found in emails, social media, videos and reports, does not fit into neat data tables. It has to be documented and structured to be useful.

Timeliness. Data should be captured as quickly as possible after an event and must be available for use as soon as possible thereafter. It helps to apply ‘time stamping’ when the data is captured.

Relevance. Data should be relevant to the purpose for which it is being used. “What this means is that you have to keep the data that is being collected under review to make sure it is in line with ongoing and changing organisational needs,” Keogh explains.

To achieve some of these aims, Keogh advises businesses to check out AddressFix, an online GeoDirectory service. Users upload an Excel sheet of customer addresses and the system tells the client how many addresses can be fixed. “If you are happy, you can proceed and then add additional value by choosing to add social demographic profiling, business categories, Eircodes and location codes to your customer data. There are no set-up costs or fixed fees, and you only pay for what we match.”

Why should businesses bother? According to Keogh: “Well, the old IT adage keeps coming back to haunt you: ‘Rubbish In, Rubbish Out’. So stop, check, and think — do you have the data management process in place to use your data wisely?”

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