Here’s the story of how partnerships and collaboration can lead to long-sought solutions – of how an independent nonprofit member organization finally found an answer to their challenge in elevating their sophistication processes, thanks to the dedication and hard work of the Blend360 team. We introduced a new approach to the client's long-standing concerns via our statistical expertise in Bayesian models and R coding language. Following that, we ensured constant communication and collaboration to get the Client’s team comfortably acquainted and receptive to the new processes, ultimately taking their statistical models to new heights.
The Client, an independent, nonprofit member organization, was looking for ways to improve the sophistication of their statistical models. They intended to build hierarchical Bayesian models in R coding language but, unfortunately, did not have the expertise to do so.
In addition, the Client wanted to replace their SAS-based frequentist models to improve their modeling process on consumer customer survey data and improve their B2B insights product experience.
To resolve this, Blend360 utilized statistical expertise to provide the required Bayesian conversion by model and in R coding language.
As for replacing the Client’s SAS-based frequentist models, Blend360 initially replicated the Client’s SAS-based solution results using R-based solutions. We then enhanced the solution based on client feedback.
Moving forward, Blend360 held weekly status check-in meetings. With presentation decks, we helped explain each step in the complex conversion process to the client team to ensure they fully understood and were receptive to the new processes. We evaluated the clients’ automobile product consumer survey data in our use case.
Blend360 created a new approach that had been a long-standing priority for our Client - elevating the sophistication of their processes. We helped the Client revise survey analysis methods and solved some technical problems:
• For models where no problem was reported in the sample, they can rely on other similar models to estimate a more realistic problem rate.
• Because of computational flexibility, they can add additional features to models to, for example, investigate the effect of 'age' or 'mileage' on the reported problem rates, which helps them isolate them even more.
Based on satisfaction with the results, engagement was extended to incorporate additional critical variables into the process.
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