The first consideration is that data management is a process and not a one-off project. The data held in any CRM system is volatile and needs reviewing and managing on a day-to-day basis to maintain acceptable standards.
If you have any doubts consider these statistics; each year:
Â£18 million is spent mailing the deceased;
1.5 million addresses are affected by postal address changes;
150,000 people complain about telemarketing calls;
One in three employees will change their job role ;
240,000 people join the Mailing Preference Service;
480,000 people join the Telephone Preference Service;
70 per cent of consumers state that receiving duplicate mail annoys them intensely;
More than Â£80 million is spent on out-of-date data;
More than 70% of consumers are annoyed by direct mail.
The implications of these statistics are that inaccurate data will reduce the success of your marketing campaigns. Conversely, accurate, clean and targeted data can immediately and dramatically increase your return on investment in marketing.
This begs the question of how to improve and maintain the quality of your data. Leaving aside the obvious issue of removing duplicates from the database, one of the most effective approaches is to have effective and simple mechanisms for classifying your contacts.
To illustrate this, if from a marketing perspective your objective is to acquire new clients, you typically require only a few classifications to determine if an organisation is a potential customer or not.
For example, a company marketing fuel credit cards in a business-to-business environment will need to know just a few things to determine potential.
Do they operate a fleet of vehicles? How large is the fleet? Do they operate in the UK or Europe? Three simple questions will determine whether they should be the focus of marketing activities.
When looking at these type of classifications, there are a couple of ground rules:
Only maintain data that is relevant.
The fewer elements of data you have to record and maintain, the easier and cheaper the process. Before you consider recording information, ask yourself â€˜How and where am I going to use this element of data? What sort of analysis is it going to support?â€™
If you are not going to use the information, donâ€™t record it. All you will achieve is an increase in overheads and complexity.
Keep classifications simple.
If you attempt to classify too precisely, it will be difficult to enter and maintain the data accurately. For example, many companies define their potential market by the number of staff employed at their target companies.
In this circumstance, a simple three-level banding will often suffice: â€˜Under 100â€™, â€˜100-500â€™ and â€˜500-plusâ€™. This level of data is easier to maintain and analyse.
In the business-to-business environment, one of the most common problems is working out who in the target company should be the focus of your marketing activities. Job titles are often used but the sheer number of permutations makes this at best a very difficult task.
A simple and effective alternative is to define and record your interpretation of their job role in terms of function and seniority, so each contact on the database has a job classification based on their function and seniority: for example, HR Senior, HR Other, IT Senior, IT Other.
Using this technique, all contacts in an organisation can be placed into one of 20 or so categories. Segmentation by job role then becomes easy. If your marketing campaign is targeted at senior finance staff, you merely select the job role Finance Senior rather than wading through numerous variations of job title.
For further information contact
Pam Mannell or Richard Dodds, RD Associates (MK) Ltd, Cranfield Innovation Centre, University Way, Cranfield MK43 0BT. Tel: 01234 756011
For more information, visit www.rdassociates.co.uk“