In fact, towards the end of the pilot, our repair forecast model was scaled to all product lines and products (60 in total) and allowed our client to drill deeper into repair times at different levels – such as region and country. The models were not only more accurate, but could also quickly scale.
#SIGMA CLIENT RAT MANUAL#
In just 6 weeks, we developed a more algorithmic and automated model for repair forecasting, requiring very little manual intervention. Using our platform and processes, we quickly realized that manufacturing batch was used as a variable in the forecasts, when in actuality, the products sold in the market would be a much better cohort for forecasting expected repairs. But we found them to be heuristic and manual, making the forecast process both time consuming and hard to scale. The client already had forecasting models in place. The customer care group works on many problems – all related to customer experience.īut as we do with every client relationship, we began with a pilot – this one focused on repair forecast variations in just two product segments. We developed requisite model for identifying deficit transactions in a short period of time, resulting in high monetary impact for the client.Īgility – The fact that we are agile and could go the extra mile to make the relationship successful was a differentiator for them. The low cost solution framework, which we call Extreme Experimentation, won many accolades. The process of representing the problem, coming up with various factors around the same, and hypothesized caught their attention.
#SIGMA CLIENT RAT SOFTWARE#
Our problem solving approach using muPDNA TM software impressed the business stakeholders. In a very short duration, we were able to add value to the client’s business and hence strengthened our relationship. Here is what the client appreciated about us: We also partnered with the CMO’s organization to work on various initiatives around advertising and campaign effectiveness. Very soon, we were involved with various functions across the organization such as marketing, finance, CRM etc. The client liked our approach to problem solving and took us to multiple subgroups within the organization. They were able to figure out unsatisfactory transactions and suggested preventive measures to tackle them. The client focuses a lot on consumer satisfaction, which eventually helps in building trust for the services.īy engaging with us, the client saw clear ROI. The auto suspension policy of sellers on low trust score was estimated to have prevented a loss of $2.5 million. The business impact was clear – sellers under the refund program showed an average 6.5 percent lift in transactions. In addition to this, a framework was devised to detect and suspend highly abusive buyers. This refund program was hugely successful and got positive reviews from the customers.
Based on the trust score, sellers were identified and selected for a refund program. Next, we established a trust score for sellers, segmented them into various classes like loyalists, high growth and category anchors.
The client was very happy at this point as they wanted to go ahead and reduce BSE at least by 10 percent, globally. This was achieved by setting threshold for service levels like lead time, quality, refund status etc. In 3 months, we developed a robust analytical framework to define Bad Selling Experience (BSE). To start with, we tried to represent this problem using our muPDNA TM software, which helped us understand the issues faced by the buyers and sellers.Īlso, muPDNA TM helped us look at various factors such as addressing seller and buyer grievances, characterizing types of buyers, reducing bad seller experience etc.
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