Transforming into a Resilient Digital Business Requires a Data Strategy

During the COVID outbreak, I have written extensively about the impact of the pandemic on regions and individuals. One of the unsurprising outcomes of COVID-19 is that organizations that were prepared and could transform into a full-time “data business” saw great advantages. Conversely, organizations who were not prepared and remained stuck in the old economy struggled mightily.

Grubhub: Data Company Disguised as a Food Delivery Firm

One firm (as we all know) that benefitted from COVID-19 was Grubhub. It’s revenues grew from $1.3B to $1.8B from 2019 to 2020, which comes out to around 38% growth. Their 2021 revenues are likely to be much larger as they saw Q1 revenue of around $550M. Why is this important to know? The leaders in this market segment made lots of money during COVID primarily due to their digital transformation preparation they did in the handful of years leading up to 2019.

Digital Transformation Approach made by the Food Delivery Service sector.

Here are a handful of things that the leaders in this sector thought wise before 2019 and turned into a win during 2020 and 2021. Grubhub in particular is known as a true champion of digital technology. One of the ways it sought to strengthen it’s partner restaurants is through its “Grubhub for Restaurants” data analytics services. At this Grubhub site, the company discusses data insights their partner restaurants can use to revolutionize their own businesses. They list a number of new metrics that can provide their partners with insights into potential areas of growth. Some of these include:

  1. Delivery Speed. This is an interesting metric to me, because it reflects the flow of goods from raw materials to the hands of the customer. In factories, it is common to build large value stream maps that detail all of the value that is added to raw materials through factory operations as the product makes its way through. This can reveal bottlenecks in the factory that fundamentally limit how much money one can make. Grubhub recommends to their partners that they research alternate routes or techniques to shave off minutes of their value stream. I’d imagine that if Grubhub were smart, they would also sell value stream data services to their partners to help them optimize. If they’re not, I ought to offer my services, as this is right up my alley!
  2. Average Order Size. This is another good metric that restaurants ought to collect consistently. It is a measure that can also increase cash flow and profitability, because it measures a company’s ability to upsell. Often, I’d suspect that the goods being upsold are higher profit goods like dessert, coffee, and drinks.
  3. Customer Reviews. I’ve noted that smart firms patrol their reviews carefully and collect these reviews as data, both to improve their performance, but also to demonstrate their business virtue. A respectful and thoughtful response to a bad review could well result in many times more business than one might expect. This data could also be aggregated together and clustered by artificial intelligence techniques like natural language processing to identify the types of feedback.
  4. Order Accuracy: This is another interesting metric. I suspect most restaurants or similar firms don’t collect this data assiduously, but I suspect a strong, good-faith technique to gain order accuracy feedback from customers could result in a really valuable data set. Perhaps offering drawings for free rewards for providing feedback on order accuracy would be low-cost and high-reward to the restaurant.
  5. Average Orders Per Day: This is relatively low-end data… I believe one could greatly improve on this data feature. At a minimum, trends in orders per day combined with other data features like accuracy and review results could result in a small predictive dataset. Ultimately this could be used to make fairly accurate predictions on business trends per day or week. This might help optimize costs like material and labor costs. Given time and information on a firm, I could certainly think up many more valuable data features to measure that could improve the results of these kinds of predictive analytics.

Data Transformation through Data Strategy

Grubhub had a data strategy and collected data for years before COVID hit. This allowed them to make better and faster business decisions when the emergency arose. Companies without a solid data strategy (measuring important, high information data as a matter of doing business) may do fine when the sun shines and skies are blue, but often lack resources to deal with crises.

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