Media and Entertainment
Veretech Holdings, a Hearst Company is a leader in providing automative shoppers with instant, accurate trade-in value for their current vehicle and free credit score estimate. In turn dealers capture high quality sales leads. Veretech’s products apprear on over 700 dealer websites of 20 major automotive manufacturers including GM, Ford, Nissan, Totota and BMW. Veretech assists over 3 million automotive shoppers in their buying process with over 300,000 purchasing a new vehicle.
Veretech offers its auto dealer customers various products incl. trade appraisal, credit score estimator, and prospect follow-up. Customer cancellations are monitored and managed by Veretech’s dealer support reps (DSR) via retention programs. The management closely reviews them to gauge customer satisfaction and revenue impact.
The DSRs manually generate reports (in Excel) from CRM systems to measure customer retention programs. These reports are shared with the management, OEMs, and car dealers on a monthly basis.
However, the cancellation rate had continually crept up leading to significant concerns. Veretech faced key challenges in analyzing the underlying drivers:
- Report generation was manual, and not optimized for performance and insights
- Up to 40% of data was erroneous and out-of-sync across the system
- Terminology mismatch occurred due to multiple, manual data entry mechanism
- Limited IT resources were allocated to product development with a busy road map
- Analysis capabilities and report delivery mechanisms were limited
Hence Veretech turned to LogicMatter. The objectives were twofold: one, to automate generation of cancellation reports of post-sales programs; two, to develop insights and better understand the rationale for cancellation.
LogicMatter used VISPER, it’s unique, agile engagement model, to systematically, iteratively build a simple, flexible, and manageable solution.
First, the data from sales database, CRM, and excel reports were collected and integrated in an ODS (operational data store). Error reports were now quickly and automatically developed to identify data inconsistencies and terminology mismatch. They were then used to correct the data in the source applications and systems to improve data quality.
Secondly, a unified, consistent, actionable information model was developed to reflect the data and workflow in the real world. The data from the ODS was transformed and stored in a data warehouse. This enabled longitudinal reporting, on-demand aggregations of data, and supported sophisticated reporting and analytics.
Finally, automated, self-service analytics was developed. The DSRs were able to access reports, trends, benchmarks, and execute ad-hoc queries to answer questions such as “which dealers are cancelling and why” to gain better insights into cancellation and retention.
- Track historical trends, benchmarks, and seasonal variations
- Gain deep insights by using what-if scenario analysis
- Reduction in data errors from 40% to less than 5%
- Consistent terminology improving communications
- Quick, automatic generation and sharing of reports
- Fast decision-making and reaction times due to shortening of cycle from data collection to report generation
Ultimately, both the management and front-line DSRs are informed and mobilized to quickly and confidently take data-backed actions to drive customer retention and sustain revenues.