11 simple data principles to maximise customer profitability

Here at R-cubed we’ve decided to jot down what, in terms of data analysis would most benefit clients. Having stuffed R-Bot with everything we know, a pattern emerged, a set of 11 basic data principles that all modern marketers should stick on their wall…

1. Define the ends exactly – only then talk data.
2. Find the 20 per cent of effort that delivers 80 per cent of results.
3. Never talk about the average customer.
4. Deselect your worst customers.
5. Contact your best customers more often.
6. Spend more on new customers and new prospects.
7. Ask your best enquirers and lapsers to come back.
8. Sell when your customer is ready to buy.
9. Keep and use your contact history with individuals.
10. Use silent controls to prove real incremental impact.
11. Ruthlessly keep demanding ‘why did they do that?’

Our Data Scientists thought you might want a little clarification on those. Here’s what they had to say.

1. Define the ends exactly – only then talk data

Quantify ‘success’ exactly, or you won’t know when you achieve it. Most marketers aim to do ‘better’, but too few define how much better
Nail your colours to the mast! Have the guts to tell everyone how to define success.
Will you be able to see that you have achieved success when and if it occurs?
Supposing it doesn’t, will you be able to learn from failure – and if so, what?
If you say no to any of the above, R-cubed thinks you should start all over again. Most of the money spent on creating, analysing and exploiting data is lost at this first stage.

2. Find the 20 per cent of effort that delivers 80 per cent of results

The Pareto principle to which I’ve already referred applies here.
It has strong financial implications which you ignore at your peril as this is not an
academic exercise.
It’s not about response but about money.

Only invest in data if you can see it’s likely to produce more money than it costs.
If you think it makes sense as a loss leader, can you point to and measure where
else the profit will be made?

Watch out for weasel words about strategy or brand building. This must pay off
sometime, somewhere.

R-cubed’s resident Data Scientists consider investment as ‘data-money’, and asks: how will you and your data-money make more than it would at the bank?

Image of 11 data principles for customer segmentation

3. Never talk about the average customer

There is no such beast. We are talking about individuals. Don’t treat them all the same.
While drafting this R-cubed were working on a client’s problems. We found that 10 per cent of one file of customers provided over 90 percent of the results. The same principle will apply to you.
You must vary your investment by individual, not list or segment. In every cell there are better and worse customers.
Don’t be average: it only leads to average results. R-cubed’s Data Scientists, like all good marketers, are enraged by the second rate. ‘Why do so many people still base their targeting on segmentation, the land of the average consumer?’

4. Deselect your worst customers

Direct marketing is about spending your money where it does most good.
What’s better, £80 sales for £20 cost, or £100 for £50? The answer should
obvious – but it isn’t to many marketers.
Finding the worst is far easier than finding the best.
It’s far easier to predict the many least likely than the few most likely to respond –
and you need far less data.

You lose very few sales by dropping the worst but you save lots of money.
You can reinvest that money in talking to the best or testing,
You must find the 20 per cent that delivers the 80 per cent,

Then you must quickly apply the principle everywhere: data, systems, data
preparation, selection, analysis.
Speed and flexibility make money, not completeness.
Data, targeting, analysis and so on can never be perfect! Trying to make them so is very, very expensive. As Voltaire noted, ‘the best is the enemy of the good’.

5. Contact your best customers more often

First you must define what you mean by ‘best’. It is what achieves your objectives.
Usually that is the greatest return for the least cost – the most possible ROI.

Nearly always, your most recent, most frequent and highest-spending customers
deliver that, and it’s not hard to see.

This is actually the oldest, simplest rule: RFM- Recency, Frequency,
Monetary value.