Case study: Installed Base Product Recommender for leading Enterprise Software Company
About the Client
The client is one of the top 10 Enterprise Software providers with a revenue of over $4 billion. It operates in all major territories with a portfolio of over 300+ products from platforms like Mainframe, DevOps, Security and Agile Management.
The objective outlined by the client was segmentation and profiling of the installed base to identify prospects that could be targeted for new business, cross-sell or up-sell.
BRIDGEi2i partnered with the client to profile and segment accounts that could be leveraged to identify new and cross-sell/up-sell opportunities. Algorithms were deployed to generate recommendations that could help the sales team achieve sales targets.
The sources of data considered by BRIDGEi2i’s data engineering team were:
- Primary Internal Data- Obtained from multiple source systems; contains all customer contracts/installed base data
- Secondary Internal Data- Customer data mapped with account details such as segment, Region, Industry etc., complemented by revenue, license type etc.,
- Primary External Data- Data sourced from D&B providing firmographic information on the installed base
BRIDGEi2i’s data scientists and machine learning experts from AI Labs built a sophisticated descriptive model. Three independent tenets for profiling customers such as loyalty value, product value and customer value were computed. Principal Component Analysis (PCA) was utilized in quantifying the strength of each dimension in discriminating installed base.
BRIDGEi2i’s proprietary AI accelerator ‘recommender’ was deployed for segmentation of installed base and that helped the sales teams with right leads to focus on at the right time.
Recommended hand-picked existing contacts for sales effort over a large list which the sales teams typically follow-up with, saved significant time and resources.