The great value of data cannot be disputed. According to the National Restaurant Association, “Data is king. Restaurants will see new opportunities to apply data analytics to predict and capitalise on consumer demand and optimise supply economics.” This doesn’t just apply to restaurants, the same applies to all food operators and businesses.
Digital ordering gives companies access to detailed information about themselves and their customers. By looking at the analytics gathered via digital ordering operators can see which products are popular at what times, who their most loyal customers are and what their favourite meals are in granular detail, at a customer segment level, as well as overall trends.
Taking Preoday’s platform as an example. Every time a customer places an order with an operator (online or via its mobile app) information from that transaction is gathered and added into the database. The business can see what individuals have ordered, the route through which they’ve placed the order, the value of the transaction and the frequency with which the customer orders. At a higher level they can gather insight into spending trends across days, weeks and months; they can analyse the success of menu items, what’s selling best and when. How often do people order dish one each month compared to dish two?
The goal of data for food operators is to understand the “what” and “why” behind sales patterns and customer behaviour, as well as being able to make predictions based on insights. It can be applied to specific areas such as:
Data is essential for personalisation, and that personalisation can be used to generate effective marketing campaigns.
The information accumulated from customers using a company’s ordering platform can be used to segment them into individual and collective personas. Once done, marketing, specific to that persona can be put into place. For instance, customers that regularly order on Wednesdays can be sent deals that only activate on that day. Conversely, customers identified as only ordering vegetarian/vegan foods can be missed off any marketing sent out that featured images of, and offers pertaining to meaty burgers and steaks.
Example: An operator can see that at one of its locations there is a drop in sales every Wednesday evening. It can also see that a number of customers frequently order the Family Meal Deal on Mondays and/or Thursdays. Knowing this, the operator creates a discount for that meal deal, available only on Wednesdays from a specific branch. It then arranges for push notifications to be sent out to those customers (open to marketing) and deemed most likely to respond.
There is a degree of overlap between the creation of personalised marketing campaigns and the construction of loyalty programmes. Importantly, data and personalisation are required for both.
Using data to first segment customers, operators can send select customers invites to come and taste test new items at the business. By highlighting to the customer that their selection is a reward for the loyalty they’ve shown to the company, loyalty can be strengthened. Customers can be offered perks based on their average basket sizes, frequency of spend or product preferences.
If a digital stamp card is active on the customer’s ordering app, they can be encouraged to spend through promotions that tie to this.
Example: Data shows that the average spend in-store in £4.60. The business therefore offers loyal customers a stamp for every £5 that they spend in one transaction. When the card is full, the customer gets a ‘freebie’ or offer. This encourages the a) to spend more, and b) to spend repeatedly.
Once created, a loyalty programme generates further data, allowing it to construct increasingly accurate customer personas that, when applied to a company’s marketing and engagement strategy, help it to retain and meet the needs of its most important customers.
Through ordering data, it becomes possible to see what items sell, at what frequency and when. Over time, this enables patterns to be built which feed into stock forecasting. In March, soft drinks might sell particularly well. Around November, the number of hot chocolates purchased might leap. This knowledge may already be known in some form, but data builds on that information, providing numbers that allow a company to plan ahead and order accurate quantities.
Example: Data gathered at a reoccurring event shows that, on average, 500 units of menu item X are pre-ordered, but that most of these orders are placed in the last 24 hours before the event. If the company went off order numbers a week before the event it might run out of stock on the night. However, with prior data informing them, they know to allow for an uptick in purchases and to order enough stock, without running short or ending up with great excess.
As mentioned, data gathered via digital ordering shows an operator what menu items are most popular and when. This enables them to shrink or expand a menu based on the purchases of customers at different points within the year.
Variations can be experimented with. Different meal sizes and modifiers can be employed to test whether average basket sizes can be increased. Short-term promotions may equally be used to see whether ordering frequency can be improved – and whether this results in better net revenue.
The resulting data informs the operator of the success of the experiment – like A/B testing in content marketing – and whether to continue on with it. As the data shows which items are being ordered, and in what amounts, similar items can be added to the menu.
Example: An item appears to be selling well, however data analysis shows that customers are only ordering the item once and not choosing it again. Are customers not re-ordering because the taste is bad? Or do they prefer to experiment with dishes? Combining data analytics with customer feedback will solve the question.
The data an operator collects means they can recognise patterns in customer use. How many orders are likely to be received for 6 pm, which day of the week brings in the most orders?
Data is ideally used to make all sorts of necessary adjustments to day-to-day operations. For instance, it may be used when scheduling staff for peak and slow times. By checking employee schedules against peak ordering hours, shifts can be managed accordingly.
Example: Data gathered by a takeaway restaurant shows that orders are likely to be higher than average on the third Friday of each month. This allows the owners to instruct its kitchen staff to begin prep earlier than usual in order to cope with later rushes. By using data to optimise food preparation, later strain on the staff is eased and customers receive food on time and to the business’ quality standards.
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