overview

uncommon knowledge, uncommon sensesophic uncommon knowledge, uncommon sense

OVERVIEW

Sophic’s client traditionally used selections based on recency, frequency and value of transactions, to identify those customers who would receive a mailing pack. Whilst this method delivered positive returns on direct marketing activity, on Sophic’s recommendation, the client commissioned the development of a more sophisticated approach on the basis that Sophic were confident these returns could be significantly improved.

A bespoke statistical model (using CHAID) was built based on previous results from campaign activity. The model was then used to score each customer for propensity to respond to direct mail activity. The top scoring customers could then be identified and used for mailing.

A test mailing was carried out where the performance of those customers’ selected by the model could be compared directly to the performance of those chosen by the traditional RFV technique.

The results over the page are from the actual mailing results although some figures have been indexed for confidentiality reasons.


3.75%

1.66%

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

4.0%

Model Traditional

Comparison of Response Rates

Model delivered a 125% increase in response rates……..

Response defined as being anyone who was mailed and then subsequently purchased in the three weeks after receiving mailing


214

100

0

50

100

150

200

250

Model Traditional

Revenue per customer mailed (Indexed)

…and a 114% increase in average revenues.


The graph shows how the model can be used to effectively differentiate customer responsiveness..

Analysis of Response Rate By Model Score

0%

1%

2%

3%

4%

5%

6%

7%

6 9 13 13 14 14 16 20 21 28 29 29 29 36 45 48 51 64 96

Model Score

Res

po

nse

Rat

e %

Interpretation: those customers scoring 96 or more delivered response rate of around 6.5% whilst of those scoring between 6 and 9 only 1% responded.


…by applying this to generated revenues, break even points can be established for future campaigns…..

Analysis of Average Margins By Model Score

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6 9 13 13 14 14 16 20 21 28 29 29 29 36 45 48 51 64 96

Model Score

Av

era

ge

Ma

rgin

(In

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d)

Break Even Line

…so don’t mail any customers with a score of below 21!!


…the model can therefore be used to ‘score’ customers on a regular basis so that the response rates are maximised and the mailing volumes optimised!

And in this instance the client replaced their traditional RFV approach and used the model to dictate mailing volumes and predict response rates!

overview

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