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Insight

Food Algorithem

17 July 2021 - By Elie Merhy

Generally, you could describe an algorithm as a set of rules that guide a computer on analyzing facts into information for specific applications. People can expand their knowledge with the information which comes from the processed data or facts. 

 

You may be wondering how algorithms play their roles in your daily life. Well, as a user of social platforms, mobile services, and food delivery, algorithms are employed by service providers to present you with offers that match your plant-based preferences. When you were a child, all you had to do to develop your palates was to try everything you were presented with. Consequently, you developed preferences as regards food and an aspect of your personality would be created following this exploration.

 

These days, we can discover new tastes and recipes from the comfort of our homes. However, some algorithms enforce the options we're presented with on our quest for new delicacies. We need to ascertain whether our options are being expanded as we explore or our known tendencies are being reinforced with minor changes. While algorithms promote content based on the things we like, they may also be tricking us into forming biases that reinforce our predilections. You may wonder how we will truly enjoy new experiences, content, and dishes when there is a purposeful stifling of the 'unknown'.

Streamlining Interests and dealing with confirmation bias

Algorithms play different roles in our lives as we go about our daily activities, such as listening to music on our favorite platforms and viewing content on social media. Although there are several in-depth factors that we may not understand concerning the workings of these algorithms, we can identify fundamental principles that guide their functions. 

Image by Courtney Clayton

For one, there is a common objective shared between media and content algorithms which is to increase the interaction time of customers. The more time users spend playing, watching, and interacting with platforms, the better satisfied they seem. In turn, that can boost advertised sales. The main principle that helps these algorithms to keep people engaged is that they rely on our demographics, past activities, location, and other facts to show us what we would probably like. It is quite likely that we're being bombarded with preference-reinforcing content that will eventually lead us down a rabbit hole.

 

Although there is nothing life-threatening about these algorithms, the previous year witnessed people grow wary of recommendation engines. People can see how easily they will get cornered into a short-sighted view of food novelty by completely relying on these algorithms. 

 

As regards food media, it has become a trend for viral moments on social media platforms to replace traditional taste-making efforts. Last year, 100% vegan chicken pieces were the hottest cooking trend, and it is highly speculated within the plant-based community that it will be replaced by vegan fish this year. Although popular during the quarantine, these trends were launched on my feed, and not in a restaurant. Now, there have been 440.5 million views attracted by #dalgonacoffee on TikTok, which makes it the most viewed food trend in recent times and subsequently prompting the plant-based community to make its vegan version with plant-based milk. 

 

However, several other culinary niches get ignored for every Dalgona uploaded. Some examples of such trends are the ingenious vegan pancake cereal, the vegan tortilla wrap hack, and other fascinating culinary innovative programs from various vegan influencers like Zacary bird who has amassed thousands of followers or the turnip vegan, and many more.

 

Restaurants can use our data (which can be for creating our digital beverage or food personas on social media like Instagram, Pinterest, or TikTok. We can imagine how much evolution our palates will undergo as we get more exposed to food & beverage content as part of our feeds.

How Restaurant A.I. has Expanded

Last year, businesses had to adapt their operations since most restaurant experiences moved online. Since we have all come to terms with the relevance of the digital ecosystem to both traditional and virtual companies in the industry, it is expected that there will be rapid innovation across several brands. 


The drive-thru is a great example of the creative outcomes from the collaboration of a profitable pandemic real estate and food tech invention. McDonald incorporated Artificial Intelligence into their drive-thru two years ago and now the brand's AI investments have exceeded $300million. What that signifies is that innovation labs in Silicon Valley are beginning to take big food seriously. As time passes, the objectives for restaurant AI closely resemble those of retail companies, social media platforms, and content providers. And ultimately, they aim to boost the accuracy of the prediction of their customers' preferences.

 

For instance, the McDonald’s drive-thru uses a license plate reader to log your order as you opt-in. The AI-infused innovation uses the data to design your recommendations for future visits. That means, you can order a Big Mac today, and they will suggest it to you on your next visit before you even order. 

 

However, how can restaurant AI adjust their preference-reinforcing suggestions as we change our eating patterns as time passes? One thing that the world has learned from big tech in 2020, is that we may alienate and disillusion ourselves when reinforcing our existing taste or predilections. We need to reconsider if we are utilizing restaurant AI to the best of its ability by just letting it recommend what we’ve had before. With continuous digitization of restaurant interactions, AI can play a more valuable role which is adding a human element to the experience.

Restaurant A.I. has promising prospects for incorporating age-old hospitality and discovery into consumer service.

It has now become a trend for people to expect their app for ordering goods online to suggest an upsell. This concept is also not new to traditional restaurants where servers subtly make recommendations by casually mentioning an ideal addition to a meal (a side dish, beverage, or finisher). We typically attribute this to hospitality in a restaurant setting as we appreciate the server for anticipating our needs. On the other hand, we see such intrusions on the apps as an obvious endeavor to boost their check average.

 

By focusing on closing the gap between anticipating wants and needs and predicting offers based on history,  restaurant AI has to meet the challenge to carve out a meaningful space within hospitality. For instance, coffee shop regulars can certainly appreciate having their order ready before they request it. The ideal balance between familiarity and discovery is yet to be struck by restaurant  AI irrespective of their level of sophistication. Instead of explicitly requesting an order, the algorithm can take the next step by analyzing whether it's a day for sticking with the familiar or trying out new tastes.

 

Beyond the level of the item, the preferences and tastes of guests can be better understood. The proficiency to utilize such opportunities to learn of customers' preferences and make recommendations at the level of ingredients can boost loyalty with guests. What that means is that the platform will go beyond recommending a specific sandwich and perhaps provide opportunities for consumers to exchange the typical goat cheese for cheddar. This deepens the level of understanding that the brand will have about their consumer's needs. 

 

With all of this in mind, we can imagine the prospective contexts, such as predicted trends in consumption. Traditionally, guests are better served through relational cues, environmental conditions, and enforcing control on the pulse of consumer trends. What's the next boundary for AI to cross? It has to be discovering how to emulate the “best server ever”.

 

As we connect to our guests digitally, we need to stay competitive and innovative. One way to do that is by moving away from promoting LTOS and giving customers what they already like. What makes our relationship with diners so special is that they satisfy our natural craving for that hospitality element. That's why there needs to be forecasting data that's focused on the future, with data on consumer behavior.

 

You may wonder if there will ever be a digital platform's version that will recommend some soup or special vegetarian recipe when it detects that the weather is cold outside. Will it recommend some exquisite wine as a reward for a productive week? Will this always be a grey area for businesses who keep suggesting if their guest wants side dishes or meals? All we can conclude is that we need to transform our objectives to the point where business success is measured in terms of the quality of hospitality and not upselling.