AI in Retail: Beyond The Hype and Into Reality for Store Operations.
The Top 10 AI use cases that Retail Operators at every level told us would transform their daily work. AI for Retail Operations is...
I joined a panel last week at the Restaurant & Bar Tech Live event at the ExCel in London and identified three main themes – and three main takeaways – that emerged from our debate.
It’s a tough time to be in the restaurant business. Demand is flat, rents are high, food and labor costs are going up and competition is increasing as non-branded concept restaurants flood the market. Technology is no longer a nice-to-have: it is critical for keeping up to speed and holding ground in a shifting landscape. For those in the sector, it can feel like a race against competitors to avoid falling behind and losing market share.
With such a plethora of new software, tools, and apps available, how should restaurants prioritize their tech investments?
The average restaurant manager interacts with about 17 different systems on a daily basis. Evidently it is hard to resist shiny new tech, with its promises of wild and wonderful benefits, but having so many individual tools ultimately leads to inefficient ways of working, despite the antithesis being the goal.
It is critical for restaurants to avoid this slippery slope. Investment needs to focus on tech that:
Most tools built for the restaurant industry are point solutions, designed to target micro pieces of the business. They can be great at what they do, but take on too many with overlapping features and functionality and it can become complicated, time-consuming and even detrimental.
Behind all the gadgetry, the fundamentals of any great restaurant remain the same. Instead of relying on a plethora of tools, restaurants need to focus on one or two that tackle the core business needs. Tools should only ever free up the time of talented managers to apply their expertise, not take up their time, juggling finicky and disconnected applications.
This need to be focused and selective when it comes to technology choices is especially true for the bigger chains. While the ‘eating out’ market in the UK remains flat, big restaurant chains are being outcompeted by small, independent ones – this year, 2% of branded restaurants have been replaced by non-branded new concepts. But scale doesn’t need to be a hindrance, as long as they recognize the risk and accept that they can’t run 20 restaurants the way they ran one.
While smaller restaurants need to focus on getting the basics in place (systems for POS and staff scheduling, for example), branded chains can turn their attention to the big stuff – i.e., executional excellence. With their wealth of multi-site data, coupled with the right tech platform to digitalize the workflow, optimization of operational processes becomes possible and ongoing performance improvement can be embedded into the business.
Being big is no excuse for being clunky. Software like Quorso means that complex, multi-site restaurant chains can behave as nimbly as the little guys when it comes to analyzing and understanding their rich data, then translating it into practical and measurable actions at every level and every location.
As well as harnessing deep data to become more efficient, big chains also have the benefit of shared learnings across their restaurants. However, technology comes into play here as well. Typical point solutions tend to operate within individual sites, so they don’t benefit from the enormous amount of knowledge that can be gleaned from looking between sites. Harnessing these dispersed learnings and sharing best practices across multi-site businesses is common sense – and yet it is incredibly hard to execute at scale, without the help of purposely designed technology.
Quorso is built specifically to handle portfolio businesses – not just to help them analyze and take effective action on their data, but also to measure the impact of those actions and automatically build the company’s playbook to rapidly share what works. That said, SMBs should also consider embedding tools like Quorso early on, so that they are set up for success and ongoing performance improvement, even once they become unwieldy, (i.e., 20+ locations and 200,000 data points to manage).
On the topic of sharing learnings, and to wrap this up, here are the three main takeaways I gleaned from the discussion –