Advanced Analytics applied to Marketing and Sales

Very few leaders in the business world discuss the need to exploit their existing data to make decisions and incorporate advanced analytics into their processes. In commercial and marketing functions we are used to seeing the “Spotify algorithm” (the ‘Fans also like’ tab, or automatic playlists based on what you have previously heard) applied to B2B and B2C.

No wonder that Customer Clustering is the paradigmatic use case of predictive analytics. By searching for patterns in your customers’ purchases, you can characterize them into groups and calculate the probability of success of an offer of a certain product to each of them. If a significant group of your customers with certain common characteristics who have bought A, B and C have also bought D, you can recommend product D to all those who share those same characteristics and have already acquired A, B and C, with good estimation of the probability of success that these recommendations would have.

However, this is not enough. The moment in which you advance a proposal to a client is also important. If you have collected data of your past commercial actions, you can extract guidelines on when and how (channel, frequency, number of actions, etc.) the contact with the client should be carried out.

However, once you have categorized your clients by product, and you have defined these actions, you must assign and plan your commercial actions, which is not obvious because your resources are not infinite and you will want to use them in such a way that the return is maximized. Without a specific plan, you will not be taking advantage of the information provided by the data.

Not all actions cost the same in time, money and effort, nor are all your commercials the same, with the same specialization, preferences and availability. Mathematical Optimization (and in particular Linear Programming) allows finding the allocation of resources that optimizes a variable while respecting a set of constraints, and you can use it to identify the optimal actions in terms of return and allocate them, with names and surnames, to your sales force considering all its characteristics, availability, specialty area, etc.

The result is a maximum impact and viable business plan that allocates a series of specific actions on certain clients to each member of your sales force, with certain advantages thanks to their speed: it is possible to explore alternative scenarios very quickly, and it can react quickly to changes.

If you want to know how your organization can benefit from mathematical optimization, contact us at info@baobabsoluciones.es

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