Jed Mooney describes why raw data is far more important than any subsequent analytical profiling or modelling. Get both right, however, and the results can be exceptional.
Our company holds more data rental lists than any other data bureaux in the UK, yet one of the biggest tasks we face is convincing direct marketers that the actual capture of raw data is far more important than any subsequent modelling or profiling of the data sets they hold.
Why? If the raw data is poor, any subsequent activity using that data will also be poor. 34 per cent of companies don’t even validate any of the information they collect* (research by QAS). Yet these very same companies will change marketing strategies based on such information. It’s madness! If a database is already poor by qualitative standards, detailed analytics will not make a huge difference to responses or ROI. It will be a waste of time.
Marketers must therefore start paying huge attention to the procurement and capture of their lists. It is only once the quality of the data can be verified that any subsequent deduplication, profiling or mail file purging can be deemed accurate and worthwhile.
Once attention has been paid to capturing and refining a database, you can then look at segmenting the data into viable categories. The three criteria of recency, frequency and value should be the benchmark parameters that form the foundation of the segmentation because they divide the data according to the profitability of a customer. Yes, categories such as age, gender, geography, socio-economic status are important but they are dwarfed by data that places value and recency on a customer.
For that reason, when segmenting and analysing your mail file, it is crucial that you know the profitability of each customer. You’ll find that if the quality of the raw data is good, your basic assumptions are more likely to be correct too. That is why having good quality data is so utterly important.
By using common sense and basic elementary mathematics, you can segment out of your mail file any customer whose frequency, recency and value precludes him or her from the probability of responding to your offer affirmatively. Set a benchmark criteria, for example, recency (nine months or less); frequency (more than once) and value (£above average) and watch your data bring in a far enhanced response and ROI.
On that note, please be aware that with a well managed mail file, good segmentation and modelling will bring in a good uplift of a few percentage points. A poorer managed database might see a dramatic uplift simply because nobody had done any cleaning and analysis previously. It’s a bit like a house clean – if it’s already clean then another clean will simply maintain it. If it’s dirty, even a mild clean will give spectacular results. So don’t be disappointed if you hear tales of a badly managed mail file subsequently seeing far superior uplift whilst your mail file sees only a mild improvement.
Database marketing is becoming more important, not least because the volume of data and the accompanying computing power is ever increasing. To that end, I’ll leave you with two key points: (1) Make sure your original data is of a very high quality and (2) keep your segmentation and analysis clear and simple. Stick to those two points and you can’t go wrong.
Jed Mooney
Managing Director
Datahold
Source: DM Weekly
Publication Date: October 06 2008