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Deduplication of Customer Information Using Fuzzy Based Data Workflow
Nov 09, 2020 | Case Study
About the Client
The client is a multinational corporation that provides high-performance networking & cybersecurity solutions that meet the connected world’s growing demands.
Business Objective
The client wanted to increase the efficiency of the subscription and maintenance contract renewal process. One of the levers was to improve the quality of End Customer information on expiring contracts.
Problem Statement
The client sold subscriptions and maintenance services via a multi-tier channel. The reseller passes along end Customer information to their distributor, and distributor to the client. Due to the differences in CRMs and ordering processes amongst the different channel partners, the data received by the client often had duplicates and incorrect customer information—E.g. Installed location instead of bill to location etc. The client’s sales team addressed this manually during the renewal sales process, resulting in a longer/inefficient renewal sales cycle and an inaccurate view of the pipeline.
The Approach
Scalefresh team helped optimize the renewal process by:
- Reviewing information about customers on contracts, using SAP standardized name and address
- De-duplicate customer information using fuzzy logic based on Levenshtein and Jaro distance models
- Developed a data workflow to recommend the best version of customer record based on their Annual Recurring Revenue (ARR), Total Contract Value (TCV), parent hierarchy, and headquarter information
- Update CRM customer master after validating with the channel partners and end customers
Tools used
- Knime
- Excel
- Snowflake
- SAP Business Objects
Business Impact
Helped the client achieve the following:
- Shorter renewal sales cycle time
- Accurate view into renewal opportunities
- Higher renewal rates and revenue
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