"Knowing when 'power hours' occur is crucial for personnel planning"
Interview with Bill McCarthy, ShopperTrak and James Cook, Thomas Sabo
Store employees play a big role in ensuring that customers receive a superior shopping experience. This is why retailers need reliable shopper data to understand key shopping timeframes, including busy periods, and to adjust their staffing needs accordingly. In doing so, brands can ensure that the right employees are on the shop floor at the right times.
EuroCIS.com talked to Bill McCarthy, CEO of EMEA at retail analytics software provider ShopperTrak and James Cook, retail director at lifestyle brand Thomas Sabo, who use the software to manage staff schedules more effectively.
Bill McCarthy: "Retailers can use data to recruit staff in line with shifting trading patterns, including seasonality and promotional periods."
Bill, tell us a bit about ShopperTrak’s analytics solution. What areas does it analyse?
ShopperTrak’s retail analytics solution unlocks actionable insights about in-store customer behaviour. This helps retailers to optimise marketing and operations and increase sales. Insights derived from location-based analytics can inform multiple aspects of a customer centric strategy.
One of these aspects is customer engagement. Our solution enables retailers to understand how many people come into the store and when, which areas are generating the most traffic, how quickly and effectively the sales staff are engaging with customers and converting them into buyers.
Retail analytics also help to increase operational effectiveness by pinpointing strengths and weaknesses in staffing, planning and evaluating training and taking advantage of traffic analysis to facilitate rota scheduling and improve customer service.
Retailers are also able to optimise their marketing spend, determining the success of marketing campaigns by analysing traffic patterns and conversion rates. Factual data can be used to analyse which sites will benefit from promotional activities, using hard numbers to make existing and future campaigns more successful.
Ultimately, ShopperTrak’s analytics solution helps to benchmark performance. The technology allows retailers to tap in to diverse behavioural patterns and insights to evaluate why one department, store or country is superior or inferior compared to others. This empowers retailers with the knowledge they need to adjust strategy and share best practice across the network.
How can this data be used for personnel planning?
Most retailers experience ‘power hours’, or variations in traffic volumes by day of the week, or even hour by hour. Knowing when these power hours occur is crucial from a personnel planning perspective, enabling store managers to put their best staff on the shop floor during peak traffic times and to reduce staff numbers, schedule breaks or complete operational tasks during quieter hours. Retailers can also use data to recruit staff in line with shifting trading patterns, including seasonality and promotional periods such as ‘Back to School’ or Christmas.
If store managers know when visitor numbers are likely to spike, they can ensure resources are on the shop floor at the right times, enabling staff to devote their entire attention to customer service. Again, quieter periods can be used to carry out staff training, restocking or handling deliveries.
Why is improving the personnel management so important for brick-and-mortar retailers?
Today’s shopper demands a tailored, personalised approach and will waste little time hanging around if they can’t get the assistance they need or if they have to queue for long periods to make a purchase. Giving the customer the best possible experience when they arrive at the store, with staff in the right place at the right time, is therefore vital for a thriving business.
Using location-based analytics, retailers can analyse footfall to anticipate busy periods and seasonality patterns, as well as monitoring the effect that a new product in the store is having on traffic levels. This holistic view enables store managers to be proactive, planning staff rotas more efficiently to ensure that there is always a healthy assistant to customer ratio.
James Cook: "We have discovered that conversion rates drop at midday – deducing that this is because more experienced staff have gone to lunch and changing staff scheduling as a result."
James, how are you using ShopperTrak’s in-store analytics in your stores?
It’s important to gain a holistic view in our boutiques and get a good understanding of the trends happening day-to-day. At an individual store level we are using ShopperTrak’s platform to identify areas of opportunity, including the rate of conversion during certain trading hours or staff scheduling for peak trading times. It is also very useful to benchmark, making comparisons between our stores and developing business strategies to ensure that weaker stores can improve their performance against a group of similar stores.
We also use it to see the average transaction value between stores – this helps us to see if certain store assistants are making a difference to performance, when compared to our five product category mixes.
Finally, the actual footfall count is always useful to compare individual units versus those located in a shopping centre – the variance here is a strong indication of the effectiveness of our marketing initiatives, including visual merchandising campaigns.
Why is it important for you to manage your staff differently according to the location of the shop?
At Thomas Sabo, our stores fall into three main categories: concession stores, stores in shopping centres and high street boutiques. They each present different challenges and the teams in these stores have to acquire different skills suited to specific environments. For example, the way a customer is approached in a boutique will differ to how a customer is greeted at a counter in a department store.
We also see big differences across the locations of stores including the type of Thomas Sabo product being purchased. This means that some stores experience high volumes of lower spending customers, and in others, lower volumes buying more expensive lines. Given this, a 'one size fits all' approach simply does not work.
We also see a variance in the type of customer we get in different locations and on different days of the week – some stores have lots of family visits at weekends while others have more tourists. The closer we get to the data, the more effective the management of each individual store can be.
What benefits have you seen so far from the analytics software?
We have discovered that conversion rates drop at midday – and have deduced that this is because more experienced staff have gone to lunch so we have changed staff schedules as a result. We were also able to note that Sundays are one of our weakest days. This is because it is only a six hour trading day, which means that the strongest sales staff tend to work on Saturdays instead. Again, we have been able to change schedules to improve this. Additionally, we are now able to follow-up on weak conversion data with a mystery shopping exercise to highlight areas for improvement across in-store selling, arranging team training to help with this.
Rota-shaping is also significantly helped with the analytics software. We take away all anecdotal feedback and just deal with facts. We can also interpret the data easily to reflect different periods and then see if any of the changes we have made have had the desired effect. The final benefit lies with the ability to receive automated data at 6am - we do not have this presently with our other systems, so we get a good picture of the previous day's trading first thing in the morning.