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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