Download Customer relationship management: organise for maximum ROI (Return on Information)

CRM for ROI : CUSTOMER RELATIONSHIP MANAGEMENT – ORGANISE
FOR MAXIMUM ROI, i.e. RETURN ON INFORMATION!
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Allow me to start at the end: by saying thank you to some very special people –
namely all our clients. So - to all the customers out there… thank you! If it were
not for you, we would have had no relationships to manage and hence no
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business reasons for exploiting data.
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And to all of you here…welcome! My name is Lynette Stassen and I am going to
show you how to organise for maximum return on your customer information. But
we have an extra benefit - I think. Dr Nigel Wigram, a previous speaker at
SEUGI, who has been attending the executive session for the past few days,
kindly agreed to assist with my presentation today by focusing attention on those
really crucial areas of data mining. Our functions at Old Mutual are to manage
the activities to generate additional business through our existing client base, so
we are business users of the results of these datamining techniques – and today
I want to talk about business uses rather than the underlying statistics.
First, a little bit about Old Mutual and its client base. Old Mutual is South Africa’s
largest financial institution and is in the process of listing on the London Stock
Exchange. Our marketing data is held in a special datastore using SAS data
warehouse programs. It contains information on some 10 million people
associated with some 5 million present or recent policies or unit trust accounts.
About 3.5 million are current contract owners. We have information on all the
contracts which they are associated with, as well as the tens of thousands of
sales intermediaries who have sold or serviced these contracts.
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This marketing datastore is used to source a large variety of marketing
campaigns, and also feeds our datamining and OLAP tools … which are also
SAS based.
The data in our store has been swept from our operational files. Date-stamped
history has been maintained over the past 18 months. The combination of all this
data gives rise to hundreds of potential sorting fields, either as direct attributes or
derived from the combination of other fields. Clients can also be studied either as
individuals or as households.
As you can see, we at Old Mutual have considerable advantages. Yet we are not
getting all our clients’ business. Our brand makes us very acceptable, but it is in
the way we exploit our data (information) that we will get to the client. This
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creates the need for understanding and mining of the data.
To get real return on information (data), it is absolutely vital to understand what
the data means in business terms and to focus on areas where there is potential
to make a significant difference. I can think of no better way to illustrate this than
by walking you through an example of data mining being used to tackle a real
business problem at Old Mutual.
Last year I described our initial attempt at data mining when, after a few false
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starts, we used decision trees to identify that the event of an intermediary’s
resignation
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discontinuances, and how we had instituted defensive actions to help to
conserve the business.
…..but simply using a decision tree is a fairly mickey mouse form of data mining,
and it wasn’t really a great breakthrough to link the two events. You did not dig
deeply to discover whether the real cause was that intermediaries with bad
selling habits resigned and thus left a trail of poorly sales products, or whether it
was well sold products that were endangered by the intermediary, possibly
moving to be a competitor.
…Yes, I agree that better information would have led to better knowledge of the
causes, but the reality is that the intervention has made a substantial impact on
the problem…..and the analysis did initiate the intervention….and knowing the
real causes would probably not have changed the intervention significantly.
A deeper analysis did help us to identify some cases which are unlikely to
respond to conservation interventions, and others where no intervention was
necessary, thus improving our profitability ….but a model to avoid lapse prone
business holds out a different set of reasons why it should be beneficial – as well
as rather than instead of. Our so-called Mickey Mouse operation has been
profitable enough to allow you to go ahead with more esoteric research…
perhaps that is why Mickey Mouse is one of the most profitable marketing
concepts ever – ask Disney.
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Always remember that research and data mining must be appropriate to the
complexity of the problem and the possible solutions. We are not looking for the
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holy grail.
So… to start any data mining exercise right, it seems that we have to first answer
the simple question of ‘why?’. Why do we believe that the data we own can be
mined to the benefit of the business?
The answer lies in the nature of a successful business, which always comes
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back to the bottom line results.
The key to profit is to find the right person to contact at the right time through the
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right medium with the right message.
All this tells us to focus our efforts on reaching the individual customer. We can
only do that effectively through the intelligent mining of data.
And if companies want to do all these “right” things, they need to move towards
knowledge management with much more speed and urgency!
It’s quite concerning that a recent global financial services study by Deloitte
Consulting showed that, despite citing the critical role that technology can play in
segmenting the customer data base, data mining was rated as very important by
only 13% of those surveyed. Obviously a lot of companies have no future.
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I find Al Dunlap said it quite aptly: ‘These days, there are two types of
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companies; the quick and the dead’.
However, truth is that the business world is increasing in complexity and
competitiveness, so even if you think you understand your clients and the role
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that technology can play, don’t just jump in! To be fast and effective, you must be
structured. The SEMMA technology developed by the SAS Institute, not only
gives structure to the process, but also adds discipline and allows a
measurement for the whole process.
I’d now like to illustrate how to use this SEMMA methodology to uncover the
hidden relationships in client data. My time is very limited so I am going to
exclude the technical details and highligh only the business thinking process.
Let’s look at Old Mutual’s latest data mining model exercise – the Repeat
Purchaser Model.
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Define business problem
To define a business problem seems easy, but it is not. Business managers
must make time to discuss the business objectives with the business analysts
and to adapt the expectations according to the limitations of the available data.
And please – while you are making time for these discussions, get value out of it
through knowledge transfer amongst the team members!
A tip for the experts - from a business perspective, it is important to present the
project in a straightforward manner. Your objective is to solve a business problem
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- not to show that you can be very clever using these new tools. Don’t frighten
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business managers by such terms as ‘pattern recognition’ and ‘neural networks’
when describing the exercise. The whole process of data mining appears
complex to the average manager… and then there’s the further danger that these
business people will rather shy away from mathematical and
statistical tools if
they are not presented in a user-friendly way.
My business objective is to grow the profitable clients. Growing profitable clients
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means that we must maximise repeat purchases, whilst limiting defections
amongst those we want to keep. To achieve this growth, we needed to gain
insight into non-repeat purchasers as well, so that we could understand where
we are losing share of wallet even where we are nominally keeping the customer.
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A key part of the analysis was to determine which clients are potentially
profitable and to grow them.
After some deliberation, the overall business rationale for the repeat purchase
model was defined as follows:-
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‘Generating repeat business is the key to profitable clients’.
Evaluate the environment
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Never underestimate the impact of the environment in which you do business.
Looking at your own past activities whilst industry figures can tell you where you
have been gaining or losing. These can help to ensure that the objectives you set
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are realistic. But recognise that environmental changes will impact on your
business results – positively or negatively.
Datamining helps you to understand the dynamics of the market and can help
you to improve your position by moving from less profitable opportunities to more
profitable opportunities … but less and more are only relative terms.
It is however necessary to have business standards and hurdle rates which must
be achievable for any intervention to be economically viable.
The hurdle rate for a sales campaign is determined by the cost of the campaign
and the likely profit and can be expressed as the formula
Hurdle rate = S x P x R
C
Where S is the probability of a sale, P the likely unit profit or commission content
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if seen from the point of view of a salesman and R the likely retention rate. C is
the unit cost. The actual target rate will depend on whether the campaign is a
mail campaign (where C will be fairly low) or where a salesman’s direct
involvement will be required where he will put a cost on his time and involvement.
When dealing with salesmen it is also necessary to recognise that they have a
psychological hurdle rate where, if they don’t taste success frequently, they
become discouraged. So we say that the probability of a sale must be greater
than 20% (one in five). Equally public opinion - or the press - is very critical of an
industry with a high discontinuance rate so we say that retention must be better
than 80%…
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Where we are able to generate leads to a household where we have several
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potential purchasers we can consider the hurdle rate as the sum of the values
(S1 x P1 x R1) + (S2 x P2 x R2) + …..
C
And the sales probability S1 + S2 + S3 …
Can you see what we did? Always put the objective in such a way that it
becomes a business opportunity and is measurable. Remember the saying that
you cannot manage what you cannot measure.
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Make data available
By understanding the business objective through the business opportunity as
presented by the environment, the business analyst can now make the data
available. For our model, client and intermediary data were analysed with a view
to developing an understanding of what factors cause clients to purchase new
contracts (predict change triggers), and through whom (the role of the
intermediary)
But how do you know what data to make available? We learnt through
experience that we need to build some hypotheses as to the relevant data up
front so that we can test and evaluate it. This doesn’t mean that you must limit
yourself to the data to verify your hypothese – include other data that might be
relevant as well – just make sure the critical data is there.
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And that brings us back to the golden rule. Understand the data – and of course
its limitations…those non-existing fields and missing values needed for the
hypotheses problem. This data availability is the main reason for a number of
iterative processes that you will not be able to escape within the data modeling
process.
The target population was our 1st test in understanding the data. Remember Dr
Wigram’s comment about having to build some kind of hypothesis to test and
evaluate which data should be made available? Well, we decided to test our data
by looking at all clients who were members with 1 policy in April 1998 and still
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had only 1 policy (the same) in Oct/Nov 1998. These were the non-repeaters. All
clients who were members with 1 policy in April 1998 and bought 1 policy in
Oct/Nov 1998, thus having more than 1 policy in month 2, were the repeat
purchasers, and we hypothesised that age, duration since purchase, size of
purchase etc. would be likely to be determinants.
Develop the model (within a feedback loop)
Data exploration issues surrounding the interpretation of the data must be
discussed with the business managers and if he/she is not available, the data
analyst must simply stop working.
Be prepared to modify the data set a number of times in a number of different
ways.
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I am not going into the technical detail of the development of training and
validating data or the use of matched samples here but they were obviously part
of the process.
To derive the best and most useful repeat purchase model, you can use four
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different approaches:1. Decision tree
2. Stepwise logistic regression
3. Neural networks
4. User-defined variable selection
Results showed that we should go with the regression model as it gave the best
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predictions with both the validation as well as the test data set.
Implement in production
Building models can be great fun and very interesting … but they only become
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profitable when you use them to do something different as a result. So you have
to take the tekkie’s toy away from him (he will keep on perfecting it) and put it into
production.
Now, of all Murphy’s sayings, there is a really, really old one that I always
remember…
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‘Nothing is ever as simple as it seems’
…..and to implement a data mining model into production is not as simple as it
seems.
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In this case we have been looking for a batch of clients who will provide our
salesmen with good prospects of a repeat sale… above the hurdle rate we
described earlier. These became the targets for two overlapping initiatives. Firstly
the scoring model was used to generate a number of qualified “leads” for the
salesmen.
Clients with inforce policies aged 36 – 55 male or female
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Premiums R150 – R750pm
Latest purchase 12 – 36 months ago
……product types ………
….. residential areas ……
We then reinforced the lead with a magazine which was sent to a subset of these
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prospects with the intention of improving the probability of the sale.
Review
This brings us to the most important final part of any marketing activity.
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Measure the results and learn from them.
This is where we have found an OLAP tool – we use the SAS based FUTRIX
system to be really critical in enabling us to dig through the results.
We started with a simple top-line analysis of the results of the two target groups.
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We can see that the model more or less identified clients with the appropriate
repurchasing characteristics…. And we can see that the magazine intervention
also worked to the extent that it boosted the take up figures.
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But when we dug down further we found some additional pointers which were not
apparent from the original model.
Firstly we found that the campaign recorded no sales in Namibia … but a bit of
checking reminded us that the environment has changed and that we are not
longer allowed to sell South African based products in that territory… Remember
to check your model for environmental changes.
As we explored the database further we found some other pockets where there
was no possibility of a repeat purchase, and some cases where there was an
automatic 100% take up. These special cases depended on special policy
conditions which might have distorted the model.
Looking at success rates by branch or sales unit we found that the variation is
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enormous – a factor of 10 between the most successful and least successful
teams – This clearly shows that the opportunities weren’t used by some area.
This lable shows two top branches, two middle ones and two bottom ones!
To analyse the true opportunities we should look at the results of those people
that actually use them . So to make sense of the results we should probably limit
our analysis to branches in the second quartile.
When we cut the campaigns into a number of segments we find that there are
still significant variances between segments – although less substantial than the
difference between top and bottom branches.
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When we segment by age and gender the larger sample allows us to identify
more segments and we can see that there are some subsets, older female in this
case, which fail the hurdle rate when viewed independently although the bigger
segment passed the test. Conversely the age bias in the results suggest that
some even younger clients could have been selected although they were
discarded because of the grouped data used in the initial model.
The actual impact of the magazine intervention was also not even across all
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categories – Obviously some of the illustrations appeal more to young men.
Having all the learnings we must of course go back to the original model to refine
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it… define new target markets … test new interventions… measure the results …
redefine the model and generate ever improving results an profitability from our
information.
To summarise - over the past 18 months since we first started using Enterprise
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Miner we have progressed from quick wins using decision trees to the building of
generic scoring models using the full suite of statistical models supported by the
system.
The recent coupling with the FUTRIX OLAP tools has allowed us to start to refine
the generic models to discrete sub-segments. Next we will move on to scoring
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individual clients. We can’t wait to demonstrate the growing profitability achieved
from our marketing interventions – truly organising for maximum ROI!
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Thank You.
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