- "Propensity-to-buy" models
- Probability of default models
- churn Models
- Predictive models for "Strategic & Tactic decisions"
- Customer-Life-Time-Value forecasting
- Direct ROI simulation
- Optimal resource allocation & planning
- Next Best Activity/Offer/product
- Text Mining
- E-mail campaign optimization
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Let's assume that you want to sell the same product in three different shops S1, S2 and S3. You want to know how much sales forces you should invest in each shop. To know that, you start building one "propensity to buy" model. Thereafter you can easily compare the optimal "sell rate" computed by the predictive model for each shop to the actual "sell rate" that is currently achieved.
For example, let's assume that the shop S1 has the potential (as estimated by predictive technique) to sell your product to 10% of its customers. If the current "sell rate" of the shop S1 is only 5%, it means that the shop S1 could use some help. You should remove some salesmen from the other shops S2 and S3 and put them in shop S1. In opposition, if the current "sell rate" of the shop S1 is 20%, it means that the shop S1 is over-exploiting its pool of customers.
The same reasoning could be applied on other variables than the "Shop" variable. For example, you can create (with TIMi, it's easy: it only requires one mouse-click) some intuitive charts that directly tells you if you are over-exploiting customers in a given "age range" or in a given "geographical region".
With TIMi, you can easily see, on intuitive charts, where your "commercial potential" is and then you can:
- Organize optimally your resources.
- Fix reasonable commercial objectives for your different salesmen/shops/locations.
Next: Next Best Activity/Offer/Product
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