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How Dodo Brands Use AI to Avoid Delivery Meltdowns (and Win Customers)

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Khushbu Raval
Khushbu Raval
Khushbu is a Senior Correspondent and a content strategist with a special foray into DataTech and MarTech. She has been a keen researcher in the tech domain and is responsible for strategizing the social media scripts to optimize the collateral creation process.

How Dodo Brands use AI to keep their pizzas hot and customers happy. Learn how they use machine learning to optimize delivery operations and avoid peak-hour meltdowns.

Business leaders can look at technology to solve certain business challenges. But be prepared to take on new challenges that come with it, especially if people, processes, and legacy systems aren’t aligned. 

When choosing partners and investing in technology solutions, Andrey Filipyev, Chief Data Officer at Dodo Brands, says, “The optimal approach is to appoint someone within the company responsible for integrating the transformation and comprehending the business. This person will develop the AI development strategy and strive to achieve the objectives. Following this, the company can collaborate with outsourcing firms or construct an in-house team, but having an expert in charge of transformation integration and business understanding is crucial.”  

NextTech Today asked Filipyev how enterprises could use data to drive innovation and product design. Here’s what he had to say;

Excerpts from the interview;

Develop an information system to automate

Let me explain our business model. Dodo Brands operates via a franchise model in 17 countries, and we have an extensive network of partners to whom we provide our business model and technologies. Since the company’s inception, our headquarters has been developing its information system to automate all store processes, enable order creation via mobile app, and facilitate order tracking.

Establish the success parameter for your business

Our goal is to provide excellent food service to our customers, and the quality of our service is determined by various parameters such as product quality, delivery speed, and convenience of communication channels like mobile apps. Delivery speed is a crucial parameter that directly affects customer loyalty. When customers order food for delivery, they expect it to be delivered quickly, at a reasonable temperature, and a good price. However, during peak hours when the kitchen and roads are overloaded, maintaining a high level of service becomes increasingly challenging.

How AI drives price optimization

We can use two strategies to handle this challenge of peak hours with dynamic prices: increase the prices or decrease the number of order peaks. Many examples of increased costs during peak hours can be seen in ride-hailing services through apps.

At Dodo Brands, we decided against raising our product prices to solve the same problem. Instead, we utilized machine learning algorithms to identify peak hours and offer discounts to customers who order before peak hours. For instance, if a customer orders pizza a little earlier, they can enjoy an extra discount on the product while still receiving the same quality. This approach reduces the number of orders during peak hours, thus reducing the store’s workload and allowing us to maintain a high level of service quality during peak hours. 

This B2C business model is a win-win situation for our customers and us.

How can an enterprise overcome common mistakes?

One of the most common mistakes is assuming that developing an AI model will solve problems and yield quick results without a clear vision of how it will transform your company.

To ensure the success of a machine learning project, it is essential to include the following elements in the development plan:

  • A detailed description of the process where the model will be applied, how it will be transformed, and by whom.
  • An understanding of how many iterations of model improvement are feasible.
  • Metrics that will indicate when the AI module has achieved success.
  • Recognition that developing, deploying, and maintaining a machine learning model in production are distinct activities with unique skill requirements. 

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