Data can provide insights into customer or audience behaviour that allow for more effective targetting. In this article, we look at how clustering algorithms can find patterns in customer behaviour, and how these patterns can help design segment-specific marketing actions to attract and retain customers.
Posted 2014-11-04 15:02 under social network analysis, law, organization
A while ago, we worked with a leading law firm to help them become more customer-centric. A key component of this work was understanding the networks of partners within the firm, and how natural working relationships mapped onto the formal organization structure. To do this, we used existing data and social network analysis.
Posted 2013-10-17 14:45 under collections, customer service, optimization
Optimal staffing levels depend on service demand, which is volatile. The problem is complicated by the cost of not meeting service, which can vary by call type. In this article, we'll look at and play with a live monte-carlo simulation of staffing levels in a payment collections department.
Posted 2013-10-08 17:12 under insurance, marketing, data, optimization
Insurance companies (and many other types of businesses) send out large numbers of quotations to prospective customers, and success hinges on encouraging a large proportion of these customers to accept these offers as soon as possible. This article describes an approach we have been thinking about for doing just that.
When seeing MarketPulse in action, many people have asked me about how response curves work, and what they really mean. In this article, I'll show you a few examples of marketing response curves, and explain how they relate to the real world, and how they reflect the Return on Marketing Investment (ROMI) for different campaigns.
Many businesses send targetted special offers to their customers. Doing this in an optimal way is hard, but valuable. Here is an animated demo that illustrates some ideas around how to optimize the targetting of offers, combining both mathematical optimization techniques (computationally expensive) and rules of thumb (quicker but less precise).
People are very good at detecting patterns, if the data is presented to us visually. Here are a couple of recent examples where we have used visualization to help clients rapidly find patterns in data sets, leading to insights that would have been elusive using purely analytic approaches.
I frequently work with teams that analyse fairly large datasets with nothing but Excel. While you can get quite far with Excel, there are other tools that allow you to extract meaning from data more quickly, and automate repeated steps in the process. In this blog entry, I'd like to discuss some the tools that I use, and explain where they are appropriate.
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