The Challenge in B2B Marketing
Business segmentation and targeting is a complex challenge for a number of reasons:
Business Accumin is a marketing and segmentation tool that allows the analysis and segmentation of B2B customer and prospect information at a geographic level unlike any other tool available in the market. The profiling techniques that it deploys are commonly used to great advantage in the B2C world. However the greater challenges faced in B2B sales and marketing mean such sophisticated techniques are rarely employed other than on a cost prohibitive bespoke basis.
Business Accumin couples the analysis of the UK Business Universe with an understanding of UK Postcode Sectors and individual Postcodes to create a view of the interplay between geographical and commercial factors which will ultimately influence business behaviour.
Because Business Accumin works at an individual postcode area it is highly efficient at matching individual business entities and ideal for any user who needs to analyse the universe of medium sized and smaller businesses. Utilities, telco providers, office supplies companies and automotive companies have all benefitted from this approach.
Business Accumin is the first geographical classification system for profiling and determining business potential. It was developed using the same techniques used highly successfully in consumer marketing.
Version One was developed by Clive Humby, the inventor of ACORN and founder of Dunn Humby. Clive Humby recently received the Lifetime Achievement Award at the 2014 Data IQ Awards.
Owned and maintained by Context4 Ltd, Business Accumin has been continuously redeveloped and improved by Dr Tim Drye, Data Scientist of the Year and widely respected Data Guru. Tim is responsible for the regular refreshing and improvement of the model, which is now on Version Five.
1. Use Bayesian Cluster Analysis to analyse the Business Universe by postcode to identify the shared attributes of businesses at the same geographic location e.g. similar size or business activity.
2. Using postcode analysis, identify and overlay information about the commercial geography, the key item being the identification of residential areas and their nature.
3. Incorporate other complementary data sets to create further insight e.g. is this a business hub, is this a blue collar or white collar area?
4. Create the classifications from the clusters. Derive from each cluster a unique description
5. Build the segmentation to reflect the descriptive labels of the clusters, ensuring that they capture every postcode area in the UK.
6. Work back from the individual descriptions to the postcoded business to validate and refine the model. Modify the groupings of postcodes to ensure that they reflect the descriptions.
7. Refresh the key datasets on an annual basis.