Data-driven hospitality for a national fine dining chain
2 min read
Improving dining service using data doesn’t seem that complicated. But with target users who are already experts in fine dining service, adoption and change management are keys.
Success would mean getting them to adopt a new tool, actually improving guests’ experience, and that experience to have a measurable impact on the business. And that’s what we were able to do.
CONTEXT
As we worked with our client through a data-driven digital transformation, at the heart of it was Guest DNA, which would enable new personalization, marketing, and more. Our idea was to create an app for guest management, built on top of the Guest DNA, that could provide their restaurants with deeper insights about their guests.
If we can provide a team of service experts—managers and wait staff—with actionable insights about their guests, can we help turn more people into “regulars”?
WHAT WILL BE MEASURED?
Check size
Visit frequency
OUTCOME
We designed a tool that complemented general managers’ existing processes for service prep, team communications, and reservation management. In year one, the program was on track to deliver a 1.5% EBITDA lift based on guests’ increased spending and visit frequency.
HOW’D WE DO IT
Selling a new approach
For our team and our client, delivering a new enterprise product on top of a data platform like this was largely new territory. I had to outline an approach that involved the right stakeholders, users, and consumers, while also meeting aggressive budget and timeline constraints.
Designing for behavior change
Our tool and change management process had a big important message to get across: spend money to make money. Managers previously had strict budgets for “gifting” guests with things like appetizers or a round of drinks. In order to have the customer experience impact needed to turn infrequent guests into regulars, managers had to increase their budget for this. Our data science determined that budget based on each location’s profile, then managers essentially had to spend that much.
This meant the solution not only had to give them visibility into how they were tracking that budget, but the insights on how to spend it had to be good enough to build trust with the managers.
Feedback loops & change management
This is a longer feedback look than typically experienced in digital products. Our solution delivered insights based on data, managers and staff had to adapt service plans based on those insights, and guests had to not only have a good experience, but change their spending and visiting behavior. Even “frequent” guests for a restaurant like this might be once every month or two. This certainly isn’t like measuring “daily active users.” So while we tracked the hard numbers, we also had to partner with each restaurant during roll out to understand the perceived value of our insights, their comfort level in changing their service, and their impression of how it went over with guests. We made many adjustments along the way to our recommendation algorithm, along with how we presented and captured new information based on how in-house staff was running their restaurant throughout the day.