Predictive Analytics Helps Build a High Performing Retail Sales Force

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Marketing practitioners today rely on multiple channels to reach their sales goals and customer-facing employees, whether in-store, in-person or on-phone, play a critical role in meeting revenue targets. If these customer facing roles aren’t fully engaged with marketing efforts and ready to exceed customer expectations, retailers risk falling short of goals.

Valiance Solutions recently worked with a leading Fashion retailer to explore ways to improve the performance of the company’s Personal Stylists, a team that serves as fashion consultants for the retailer’s clientele. The team had under-performed for several years and struggled with the challenges of attrition: as some of the longer tenured stylists left the company, their replacements weren’t performing at acceptable levels. So the retailer turned to Valiance to understand how to they could improve their new hire process using predictive analytics.

Valiance developed a set of sophisticated machine learning & statistical models using a “Champion vs. Challenger” approach to ensure the most accurate and implementable solution for the retail client was identified. The results were profound.  After completing the modeling exercise we found that for every 100 agents the company would hire randomly, only 15 would be among the top performers. Meanwhile, with the help of predictive analytics, 56 of every 100 new hires were expected to be top performers. The Valiance model helped improve the hiring process by over 270%.

Education Matters, Sociability Does Too

Significant variables from the modeling exercise pointed to findings that weren’t altogether surprising but are critical to measure in the new hire process. For our client, level of education matters, so a college graduate has a greater chance of becoming a top performer than someone with only a high school degree. In addition, we found that the applicants “level of sociability” matters a lot, too. For example, being involved in outdoor activities with friends was positively related to the likelihood of an applicant becoming a top performer. These factors, and many others, helped our client reshape the process followed to hire the right stylists.

The Business Result

Successful implementation of any predictive analytics project requires strong collaboration between the business partners and the analytics team working on the project. Thinking creatively about how modeling output can transform existing processes is critical to get the most value from the work. In this case, the retailer completely changed their hiring process to evaluate candidates for the Personal Stylist role based on their ability to build relationships and the likelihood to contribute meaningfully to the business.

 As a result of the work, our retailer client:

  • Significantly boosted their key ‘revenue per stylist’ KPI due to the better talent that joined the team
  • Reinvigorated the existing Personal Stylists on the team who were now expected to perform in the top 10% but were recently missing their sales goals
  • Created unique strategies to improve performance for under-performing agents.
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