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CRM for ROI : CUSTOMER RELATIONSHIP MANAGEMENT – ORGANISE FOR MAXIMUM ROI, i.e. RETURN ON INFORMATION! 1 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 2 business reasons for exploiting data. 2a 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. SEUGI 17 June 1999 1 3 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 4 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 5 starts, we used decision trees to identify that the event of an intermediary’s resignation SEUGI 17 was almost always followed June 1999 by a significant increase in 2 6 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. SEUGI 17 June 1999 3 7 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 8 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 9 back to the bottom line results. The key to profit is to find the right person to contact at the right time through the 10 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. SEUGI 17 June 1999 4 I find Al Dunlap said it quite aptly: ‘These days, there are two types of 11 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 12 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. 13 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 SEUGI 17 June 1999 5 14 - not to show that you can be very clever using these new tools. Don’t frighten 15 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 16 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. 17 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:- 18 ‘Generating repeat business is the key to profitable clients’. Evaluate the environment 19 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 SEUGI 17 June 1999 6 20 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 21 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%… SEUGI 17 June 1999 7 22 Where we are able to generate leads to a household where we have several 23 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. 24 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. SEUGI 17 June 1999 8 25 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 26 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. SEUGI 17 June 1999 9 27 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 28 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 29 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 30 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… 31 ‘Nothing is ever as simple as it seems’ …..and to implement a data mining model into production is not as simple as it seems. SEUGI 17 June 1999 10 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 32 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 33 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. 34 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. 35 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. SEUGI 17 June 1999 11 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 36 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. SEUGI 17 June 1999 12 37 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 38 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 39 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 40 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 41 individual clients. We can’t wait to demonstrate the growing profitability achieved from our marketing interventions – truly organising for maximum ROI! 42 Thank You. SEUGI 17 June 1999 13