The purpose of this paper is to demonstrate the methodology used to identify accounts that have a propensity for cross-selling in a B2B SAAS industry. Data spanning across various categories – Demographics, Client Relationship, Product usage & Transaction pertaining to customer accounts was considered to ensure 360-degree visibility of each account. A probabilistic model was built to predict the account propensity (trained on accounts that had shown historical transactions of cross-sells for a given product). Performance of the model was gauged on parameters – F1 score, precision & recall. Ultimately, the model with higher recall with reasonable precision was shortlisted (with the sole intention to increase the coverage on potential customers that are highly likely to buy other products). This exercise was done with 5 products and separate models for each product were built, to create an ensemble of cross-selling models. The accounts were further bucketed in high/medium/low based on their probability score. These results were presented in a dashboard along with additional key information around the accounts (such as industry operating in, region, tenure, etc.) for the stakeholders to make better sense of the cross-sell accounts. Several marketing and sales campaigns have been initiated for the high propensity accounts.
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