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Having a lot of data is great - we're all curious to see different kinds of analyses. But what to do with it? How can we turn data into action?
In this article we break down some of our most useful analyses, and most importantly, what actionable recommendations you can take from them.
We'll discuss:
Customer service: knowing each individual customer's preferences and using that to provide a better service experience to keep them coming back
Menu optimization: deciding which items to leave alone, which to change, and most importantly how to change each
Capacity planning: understanding the expected throughput of each hour of each day to better plan staffing and purchasing
Customer service
Understanding the insight
Customers return to places they feel important and valued. That's why great restaurateurs and operators remember names and usual orders of loyal customers.
However:
it takes a few visits before you remember customers, their names, and their orders
all this information is in the individual's head - if they ever leave, so does this data
Papaya solves both those challenges. As soon as a customer (who has created a Papaya account and used Papaya with you before) returns to your venue, they will appear on your Today dashboard under "Repeat Customers Dining Today", as below.
In this example, we can see that "Laurie" at table 4 has been here 11 times before, the most recent time being April 14th.
On the right, under "Repeat Customer's Favorites", we can also see that Laurie's favorite items are the Iced Americano (which she has ordered 9 times) and Grilled Vegan Onion Soup (which she has ordered 6 times).
How to action the insight
As stated at the beginning of this section, customers return to places they feel important and valued. What better way to make them feel important and valued than:
making sure to personally welcome them and thank them for being a loyal customer - maybe even using their nickname
offering them one of their favorite items (on the house) as a thanks for being a loyal customer
The old-school way of rewarding customers is to use "come 10 times, get a free drink" cards. While the impact to your operations and bottom line may be the same, the relationship you create with your customer is completely different. If a customer "earns" a free item, they come to your venue feeling entitled to the reward. This doesn't make them feel valued or important, it just makes them feel like they've saved some money.
In contrast, offering them something that they don't expect creates a pleasant surprise that makes them feel like you care about them - maybe they even feel like they need to be even more loyal and tell everyone about the amazing service at your venue. It's night and day when it comes to the relationship you build with your loyal customers.
Menu optimization
Understanding the insight
All restaurants need to periodically review their menu and make necessary changes to try to optimize performance. It's not just about improving sales, it's about avoiding the massive opportunity cost of disappointing a customer who happens to order one of your items that does not give them the ideal experience.
Most restaurateurs combine the art of their menu philosophies with the science of sales data. It's usually some form of a popularity vs profitability matrix, and often involves names of farm animals.
That is often a great starting point, and it can usually tell you which items are doing great (high popularity AND low sales/profitability) which you shouldn't change, and which items are doing very poorly (low popularity AND low sales/profitability) which you should probably remove from your menu. But what about the items that are high popularity low profitability, or low popularity high profitability? What do you do about those? Can they be fixed?
A simpler method is to look just at sales performance, removing the worst sellers and keeping the best sellers. But what about the middle performers that you want to fix - how do you fix them? That has been gut-feel and guess-and-check - until now.
Thanks to our unique customer-level and item-level data, Papaya can finally tell you why an item is selling the way it is.
In the Menu tab of Insights (in the image below) you will be able to see the sales of each item (bubble size), and what the sales are driven by: Order Penetration or Repeat Order Rate. Hover over each bubble to see the details.
Order Penetration represents how popular an item is to your customers, showing how many % of orders contain the item. Repeat Order Rate represents how satisfied customers are with the item and the role an item plays in bringing customers back to your restaurant. Together, these metrics let you see whether an item’s performance is attributable to customer willingness to try the item, or customer desire to order it again. Every item is ranked compared to the other items on your menu and sorted into top/bottom 50% of each metric. This data is based on the previous 90 days.
How to action the insight
What does each color mean and what action do we recommend you consider taking?
Stars (green): Highest trial and repeat order rates. Don't change anything about these items - your customers love them!
Under-promisers (light orange): Repeat ordered often once they are tried, but not many customers try them. This suggests they are niche or don't look appealing on your menu, but over-deliver once a customer does try them. Consider changing the name, description, picture, or price of these items to realize their high potential.
Over-promisers (dark orange): Tried often but seldom repeat ordered. This suggests they look very appealing on the menu, but don't deliver what the customer hoped for. These items can be dangerous because they could disappoint a customer with high expectations. Consider adjusting the recipe or presentation so the high number of customers who are exposed to these dishes end up loving them.
Dead weight (red): Neither tried often nor repeat ordered once tried. Consider removing from the menu, as these items are taking space and attention from performing items, and leaving the customer unsatisfied and less likely to return.
Capacity planning
Most restaurateurs have a good grasp on when their usual peak hours are, and estimate staffing and inventory plans accordingly. Papaya takes out the guess-work and enables you to do that much more accurately.
In the Trends tab of your Insights, you will find the peak hour analysis, which gives you the average throughput (average number of items ordered) per hour-of-the-day per day-of-the-week. You can play with the date filter at the top to see how this differs over time and to make sure you use the most relevant time period (eg the last festive season, or the last payday week).
Of course, with Papaya taking care of orders and payments, you shouldn't need to increase your front-of-house staff too much! But in the kitchen, staffing could be tracked pretty closely to these numbers, as throughput in terms of items is a pretty good proxy for amount of work. Do make sure to check the category sales contributions during the time you plan to make staffing changes, as if the items are mostly drinks that require very little preparation, you don't want to be over-hiring in the kitchen.