Portmeirion Ltd. has analyzed over a billion data records and counting.  Our staff is very comfortable with the largest data sets, but we’re just as effective on smaller ones.  Managing, manipulating, sometimes wrestling your data, we can provide you incredible insights into your customers, subscribers, residents, or patients.  And Portmeirion can do this without any distraction to your IT staff.

Leveraging our deep big-data competency, you will be able to uncover hidden customer segment info, understand buying and usage behaviors more clearly, and strengthen your ability to forecast and grow.

Example 1: Retail Sales Promotion Optimization though big-data analysis of daily sales by store.  Our retail client felt sure that their newspaper flyers were driving extra sales, but they did not have any idea how much.  Fluctuations in sales routinely happened because of seasonal, event, and regional dynamics as well as promotions, and no significant effort had been applied to teasing out these various sales drivers.  Our solution involved looking at the entire (and quite large) set of sales records over a number of years, aggregating on a weekly basis.  Through our analysis, which included a complex regression analysis bolsters by a number of statistical tests, we were able to isolate the lift effect of the flyer and identify which product categories benefited most from this promotion.

Example: Retail Sales Lift


Example 2: Customer Record-Based Analysis at a global telecommunications company.  Generally, a segmentation analysis will draw on a sampling of customer and competitive data, often collected via primary research.  In this particular case, our client did not want to perform any incremental market research since they’d just completed several rounds of customer research and were rightly wary of “customer fatigue”.  Fortunately, in the telecommunications environment where literally millions of records of data are generated and stored each day, we had access to a rich big-data set we could leverage, without the need for extra primary research.  Handling close to a hundred million records, our big-data team was able to tame this gigantic data set and draw actionable, first-time insights out of it.