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.
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Advertising Effectiveness
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.
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