POPMENU was supposed to be Instagram for food, where people would share their experiences online. Like FOODit, the framing around the individual dish over the restaurant to cuisine took precedence.
Users would search their area or 'pop' items they enjoyed to share with others. While the idea was popular among the team a business model needed to support it.
Without menus there were no dishes, without dishes nobody could 'pop'. Restaurants needed menus and so the business started selling online menus to restaurants.
POPMENU took their idea out to restaurant manager's and listened to their pain points. They would meet restaurant owners and talk about their restaurant while they were pounding yam or cleaning crockery.
Owners were tired of Yelp's aggresive monetization of their data which didn't necessarily lead to more people through the door. They wanted a way to claw back their customers, but they didn't have the time.
Most exciting dishes came from the small independent restaurant concepts that were crafted by the hands of their owners. These concepts were full time jobs of those who managed them and they had a problem.
Big chains were able to market themselves far bigger and far more effectively than them. Even platforms like Yelp would bleed restaurants of their marketing budget just to be alongside others competing for attention.
POPMENU realised they could use the dish data to automate promotions and increase consumer attention with their platform. It was built in such a way that no technical or marketing expertise was required because POPMENU did it for them.
Finally the little guy could budge in on the space previously dominated by the big chains.
The POPMENU story started with a bunch of friends, passionate about discovering food, and some ideas that need longer in the oven.
Fast forward to present day and POPMENU is a US coast-to-coast supplier of SaaS to the restaurant industry. Their story champions the value of listening to customers and being passionate about the problems, not the ideas.
Some of our early mock-ups focussed on understanding the consumer. An algorithm would be employed to gauge users tastes by asking them to rate 10 carefully selected dishes.
The questions would allude to users preferences to proteins, spice, consistency and more. This creates a strong basis for a recommendation engine that can be further refined as users 'pop' future dishes during their user journey.
Menu layouts are one of the interesting challenges. Having different layouts avoids all restaurants having the same 'feel' when the product reached scale. What we're essentially doing with different designs are run constant multi-variant tests and try and take learnings from each theme to apply on the other while maintaining their uniqueness.
We had a collection of hypothesis about how users would engage with the platform and one of the strongest signals we saw was in the time users accessed the services.
Looking at our analytics we learned that while desktop did spend longer on a page, it wasn't by anywhere near as much as we first thought. The theory of users carefully researching menus before a visit didn't seem to add up.
We also saw mobile traffic really spiked Friday to Sunday and as we understand it these were people looking to check a restaurant just before they intended to go out as the hourly traffic on weekends steadily builds into the early evening before quickly dropping off.
During the week we saw desktop traffic matching mobile until post lunch. When mobile streaks into the lead. Again folks are out and looking for places to eat and not at work planning ahead.