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- "TIMi" is part of the "TIMi suite".
TIMi is a predictive analytic software. It builds predictive models. For example, TIMi is able to predict if an individual will buy your product or not. TIMi is also able to explain why somebody bought your product.
Fields of application of TIMi :
- Finance: Risk scoring, Fraud detection and CRM (Customer Relationship Management)
- Telecommunication: CRM
- Retail: CRM
- Healthcare: CRM
Here are some classical problems that can be easily solved with TIMi . These problems can be B2B problems (i.e. "Business to Business" problems) or B2C problems (i.e. "Business to Customers" problems):
- 1. Propensity-to-buy Models - B2C - banking,telco,retail - cross-selling/up-selling
With TIMi, we can easily do cross-selling. Let's assume that you have a database of all your customers. Some of them have a credit card, some of them have a life insurance. You can use TIMi to create a model that predict if a customer is willing to buy a credit card. On the same database, you can also use TIMi to predict if a customer is willing to buy a life insurance.
When you have several products to sell (in this example: a credit card and a life insurance), you can compute the probability of purchase for each product and only present to the customer the product with the highest probability of purchase.
- 2. Propensity-to-buy Models - B2B
Let's assume that you are working inside a bank. You have a dataset that describes all the companies in Europe or America. Such information are directly available here, or here, for example. You can use TIMi on this dataset to create a model that predicts if a company is willing to accept a leasing proposition from your bank.
Real Business Case: Usually the acceptance rate of leasing contract propositions is around 4 to 5% (when you are lucky). On a real case, using TIMi, you can increase the acceptance rate to 35%.
- 3. Probability of Default - B2B - banking, risk assesment, Healthcare
You can use TIMi on the same table as for the previous point to create a model that predict if a company will go bankrupt within the forecoming 6 months.
Real Business Case: TIMi is able to detect with a confidence of 95% that a company will go bankrupt within 6 months.
- 4. Propensity-to-buy Model - B2C - retail
Let's assume that you possess a large table that describes all the customers in your country.
Let's now assume that a company X that is selling cars is coming to see you. They want to know the best leads to sell their cars. They have the name and address of the people that already have bought their cars. You can easily use TIMi to create a model that predict if an individual is willing to buy a car from company X.
- 5. Churn Prediction - B2C,B2B - Telco,Banking
Let's assume that you are working inside a telecommunication operator in the mobile phone department. You can use TIMi on your cutomer database to create a model that predicts if a customer will churn (.i.e. change to another mobile phone operator).
- 6. Optimal resouce allocation - B2C - retail
Let's assume that we have to decide in which geographical region to install a new agency for your company. We have to estimate the business potential of all the regions and choose the best one. TIMi can find the best site for you.
The are many more applications of TIMi: please explore the solutions section of this website to know more about how TIMi can help you solve your business problems.
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