There’s a paradox in retail: we want thinking machines and programmable people. We’re giving autonomy to screens, equipment, and all manner of sensors, and taking it away from our frontline staff. We prize machine learning and human rigidity. The increasing cost of labor and the shrinking of microprocessors (both in size and price), have led to this paradox, and the rise of big data only promises to exacerbate it. The problem is, data answers questions, but won’t tell you what questions to ask – you’ll be overwhelmed by all of the data you’ll receive from kiosks, appliances, cameras, or apps. Despite advances in technology, the most capable sensors and the most responsive interfaces are still your workforce. Your staff must be critical partners and sensors of change.
For instance, during a NOBL project with a national restaurant chain, casual conversations with servers revealed that fewer customers were buying their signature dishes, and were instead opting for their weekly specials or limited-time offers. A year later, after the lure of LTOs and coupons waned, the company paid for a comprehensive study which just confirmed servers’ observation – customers had indeed moved on from their bedrock products, and the company had surrendered lucrative territory in their original category. That same client also spent months testing digital interfaces in their stores, and even though they continued testing for nearly a year, the waitstaff observed almost immediately that customers were ignoring the devices in favor of their own. Our client wasn’t unaware of their staff’s observations, they were simply – and with good reason – unwilling to make strategic decisions based on only a handful of anecdotes. But if they had had a process for collecting observations and insights at scale, they could have saved themselves months and millions of dollars in lost sales and costly studies.
In response, we’ve developed a process we call “Big Sensing.” It’s the cousin to big data, and it uses human observation and intuition to direct analytics and analysis. It allows our retail clients to collect unfiltered observations and hypotheses directly from their front line staff in a fast, streamlined manner. Managers can then look across stores to determine both short and long-term strategies to test.
Here’s the process we follow with our clients:
- Once a month, each store manager should lead a small group of their customer-facing staff (5-7 employees in total) in a rapid 15-minute standing workshop. Often, this can be tacked onto an already occurring shift meeting or corporate update.
- The workshop starts with one simple question: “What changes have you noticed among our customers?” Give employees two minutes on their own to silently jot down their observations before sharing. Make special note of any shared observations.
- Coach the team to state their observations as either something increasing or decreasing, such as foot traffic is up, coupon usage is down, people seem in more of a hurry, ‘I see customers wearing more of Brand X these days,’ returns have increased, the parking lot at a competing restaurant is increasingly full of cars.
- Don’t debate or even discuss their observations, just write down and note any observations shared by multiple team members.
- Next, for each observation noticed by multiple team members, ask the group to generate hypotheses as to why conditions have changed, with the prompt, “Why do you think this has changed?”
- Examples could include: Competitors are running a big sale, the mall we’re located in is less popular now, an Instagram celebrity endorsed our competitor, store cleanliness is an issue.
- Again, don’t debate or even discuss, just capture the hypotheses generated by the team and then adjourn the meeting and thank the team for their input.
- After the workshop, the store manager should send a short read out of the workshop to a regional manager. The store manager can also call out any of their own observations and list any proactive measures they plan to take (e.g., addressing store cleanliness).
- Regional managers can then collate the observations and hypotheses from their stores and funnel the findings to corporate headquarters with a draft of potential strategies as thought starters.
- Examples of strategies could include partnering more with social influencers, moving our footprint out of indoor malls, increase our spend on coupons temporarily.
- These hypotheses can be paired with those back at headquarters and fed into a process of validation using the data the organization has at hand.
Big Sensing allows you to scale up and compare qualitative insight quickly, just as you would hope to scale up quantitative assessments from each store. Moreover, it creates more value from your existing staff, in a relatively short period of time with a simple exercise.
Bud Caddell is the Founder of NOBL and has been focused on systems design for over 15 years.
This article was written by Psfk Op-Eds from PSFK and was legally licensed through the NewsCred publisher network.