With increased competition on retailers, large stores, retailers and companies started to think of new ways to increase their sales and strategies to minimize their retail costs.
So far, stores used traffic data to analyze the traffic growth, conversion rate and basket value that gives indicators on the marketing effectiveness, store layout or salespeople performance.
An interesting metric and optimization that is still underexplored is using traffic data coupled with other historic data like sales and conversion to predict traffic trends and patterns and use those predictions to improve labor planning. Thus having more sales people on the shop floor during peak hours which results in higher conversion and upsales and minimizing the headcount during off peak.
Such optimization resulted in 6.15% boost in sales from recovering lost sales that lead to 5.74% improvement in profitability. Those numbers are based on a study conducted via “Pennsylvania State University” and “University of North Carolina” on large retail chains to identify the extent of under-staffing in retail stores and impact on sales and profitability.
While these numbers may seems small however when applied on large turnover they result in millions.
Understaffing or overstaffing occur due to several reasons like lack of visibility of historic data, forecast errors and business and labor law rules and constraints.
- Rules: employers need to provide the shift length, hours per week and month, breaks… to their employees either due to life consideration, business or labor law rules. Employees would like to have their preferences into account like days off, morning or evening shifts… all those rules makes the problem hard to solve
- Unpredictable traffic: store traffic is unpredictable thus leading to forecast errors thus resulting in staffing decisions.
- Bias: biases are part of human nature and essential to proper functioning in our daily life. However such biases may conflict with business decisions.
Impact on the business
- Overstaffing: could lower profits because of high labor cost
- Short term under-staffing effect: leads to customers leaving stores without making a purchase (lower conversion) or without upselling them.
- Long term under-staffing effect: leads to unsatisfied customers thus switching to competitors do to inadequate service or expressing their negative experience on social networking
- Employees turnover: badly created employees roster results in lower employee satisfaction and thus high turnover which impacts both staffing cost and customer service quality.
AZPlanner is a solution that automates the prediction of customers trends and patterns using machine learning and create a retail employee schedule that allocate the right salespeople at the right time using artificial intelligence search algorithms.