In our previous post, we wrote about StoreDNA being prominently featured in the Chalhoub Sustainability Report.
This time, as part of our series on the optimization of retail with StoreDNA, we are discussing technology-assisted sales staff allocation.
To learn more, we sat down with Uroš Lekić from StoreDNA to carefully discuss their work on retail staff optimization and obtain a better understanding of the efficient processes they enable through their StoreDNA Platform.
Uroš, why is staff allocation critically important?
Store staff have always been among a brand’s most prominent ambassadors. Sales staff daily face shoppers who walk into their retail spaces - be they loyal or incidental customers, and present a necessary final link in delivering memorable service and shopping advice. Shopping experience and advice will certainly stay the main reasons for which a footfall shopper will continue valuing physical shopping and visiting stores. We need to stay committed to elevating this service.
Proper training of sales personnel aside, some of the pitfalls important to avoid are the lack of staff in store when they are needed on one end, or too many free sales staff in the store on the other, both a consequence of non-optimal staff allocation. First will result in missed sales during traffic peaks and jeopardise continuing shopper interest, the other even worse - it also encompasses avoidable operational cost of staff in store during traffic dips. We are fortunate to work with many of the world's best retailers and brands, and in spite of decades of their retail DNA and experience, many of our partners still struggle in this regard.
How does technology help here?
People counters installed at retail stores’ entrances provide granulated traffic data. POS systems record sales, and retailers are also digitally planning staff schedules. By merging these, traditionally siloed, data streams, in an automated, accurate and meaningful way, the technology opens a potential for creating new and useful insights.
Here at StoreDNA, we have derived a measure of optimal sales staff capacity - “staff minutes per visitor” ratio, and we believe that balancing it across stores, seasons, weekdays, and hours within, is one of the very effective and objective methods for efficiently driving optimal staff schedules. This crucial analysis is automatically embedded in our Platform, and when our clients look at data collected over several weeks of a season, they stand a good chance to quickly identify inefficiencies and opportunities for improvement.
Recently, you meaningfully improved staff allocation using insights you obtained through StoreDNA’s Platform. Could you guide us through this process? What are the most significant findings?
StoreDNA’s Platform is automated to collect footfall, sales and staff presence data in retail stores without any additional effort of analyst teams, and designed to present actionable findings directly to operational managers, thus also saving on cost of analysis.
Since sales staff planning is typically done per weekday, our Platform averages information per every of seven weekdays and every of opening hours for a selected relevant period of time. The tool then correlates values like allocated staff time per visitor, conversion rate and traffic, so that users easily identify disbalances and make educated decisions on the weekdays in which more staff is needed in the store, and those where more staff time does not necessarily add to increases in conversion.
In the Platform screenshot above, one can directly conclude that relatively least staff time is offered to store visitors on Thursdays, Fridays and Saturdays. Bubble size denotes the highest average traffic per weekday and the conclusion would be to test with adding more staff on those days (which also offer the highest potential for sales gains). We could hypothesize that this retail should plan their ideal schedule around 15 to 16 minutes per shopper; different retail brands will certainly offer different nature of service, so this measure is by no means a general statement. A caveat to mention here as a misconception which we often witness: retailers most often already allocate more staff on the high traffic days, and before such analysis they believe that sufficient is already done in this regard. It gets easily uncovered, though, that on those days they still offer relatively the least of their sales staff time per customer they serve.
We also often see savings opportunities. One could argue that more staff time per visitor on Sunday, Monday and Tuesday in the example above does not drive higher conversion. This retailer can then examine if reducing capacity on those days would really lead to hampered sales. Another caveat: the first step in optimizing is always testing the changes in a structured way and closely monitoring the results before final implementation. Another tool in StoreDNA’s Platform captures such testing results, also automatically.
Next step is balancing the scheduling along the opening hours, and in the example below we observe a typical Thursday for a selected period at the same retailer.
Platform users can easily observe the correlation between the Conversion Rate and staff capacity, which indicates the opportunity to increase store’s performance on Thursday afternoons by adding staff hours. The above analysis is also automated in the StoreDNA Platform.
Can you finally share some results with us?
Results from such changes can be indeed very impactful. At some of our clients we have witnessed up to 15% impact on conversion rate, often with no increase in operational cost of staff. When translated to increases in the top-line, impacts can lead to gains in millions of dollars on a year base. One of our clients reported results in operational improvement at only one of their portfolio stores accounting for a 4000% return on investment in our tools.
As their key objective, besides Staff Allocation, StoreDNA offers decision-ready insights for the improvement of Visual Merchandising, Storefront Design, and KPIs across all regions. Want to know more? Get in touch here.