Marketers spend tremendous amount of money and effort to better segment their customers. This helps them target offerings that exceed customer’s needs and keep a step ahead of competitors. There are a tremendous number of data sources from which they can tap into including sales data, purchase history, market research, product reviews, demographic and psychographic profile information, advertising spend analysis and many others.
This information when analyzed, dissected, cataloged and categorized will help marketers target campaigns better, communicate messages clearly by segmented audiences and understand customer purchase and preference behavior.
There are 3 primary problems with the current manual process of segmenting customers.
First the data sources are not all the same, hence many tools and technologies are needed to “normalize” and de-duplicate them.
Second, analysis models and statistical correlation requires expensive expert resources with multiple skills including knowledge of industry, distribution models, sales channels, marketing, branding, statistics and analytics.
Finally analysis algorithms require process diligence and modeling knowledge which have to be constantly updated. |
