InfoTech Marketing

Data Mining Applications

We build customer clusters from internal behavioral data, like recency, frequency, and monetary value.  We also evaluate purchase cycle, cohort groups, etc.

We implement one-to-one marketing by examining customer purchase patterns and ownership.  For instance, you can send specific offers, like up selling,  to customers based on their likely response. 

Through behavioral analysis, you can target your offers to segments most likely to respond.  A business-to-business rental company used our analysis to prioritize the equipment offered based on their customer's industry.

By examining pre- and post-data and treatment groups vs. controls, we can estimate the results from your advertising campaigns.  This is usually done on an area basis, but we've also performed this analysis on scanner data.

We assist companies in determining churn predictors in order to provide intervention to reduce churn. 

Grouping customers into deciles can lead to insights on how to build revenue.  For instance, if higher deciles order more items on each order, you may want to implement a program to boost order size.  Tracking customers as they move between deciles can also help pinpoint revenue retention problems and opportunities.

Market  basket analysis reveals significant cross-selling opportunities.  Amazon.com, McDonalds, and others have gained significant revenues by suggesting additional products to go with the initial purchase.

Analyzing customer locations can suggest additional site locations and changes.  For instance, a truckline closed certain locations and re-located others based on our site location analysis.  You can also use site locations to optimize your sales force assignments.

Every company has underperforming customers who can spend additional revenue with them.  Using multivariate techniques, we can estimate additional potential revenue from your existing customers who can then be approached through up selling and cross-selling.

Using time-series techniques, we can produce forecasts at a disaggregated level based on your internal customer data.  This can then be aggregated to the appropriate level for planning, budgeting, and other uses.

To see these in one affordable, easy-to-use package, check out Total Customer Analytics

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