Home CompaniesB2C Local Meal Service Pivots into a Machine Learning-Driven Personal Chef Platform

Local Meal Service Pivots into a Machine Learning-Driven Personal Chef Platform

by Muriel Vega

Offering ready-to-eat meals delivered to your door same day, Real Meal Delivery was conceived to capitalize on the massive growth of the tech-enabled food delivery industry. While they successfully made it through a Techstars Atlanta stint, the startup struggled to pin down a viable scalable model. Now the team has decided to pivot to get the customer experience exactly right. Enter Saige — a personal chef platform that uses machine learning to learn your food faves and delivers custom meals to your doors overnight (to avoid the infamous Atlanta traffic) twice a week .

While most meal kits focus on exposing you to new ingredients, Saige wants to learn exactly what you like to help you enjoy your favorites after a long day. Once it learns your preferred recipes, it can introduce similar ones. Better yet, you can customize meals per family member based on dietary restrictions and favorite (or not-so-favorite) ingredients.

“The early signs are very positive,” says CEO Pat Pow-anpongkul. “Customers like the set-and-forget nature of it and the fact that we do the decision-making. One of the biggest opportunities for improvement, which we’re starting to tackle, is making this feel more like a personal chef versus just another meal delivery service.”

In his Real Founder Lesson column, Dave Payne praised Pow-anpongkul for his initial foray into the meal service industry as a best-to-market company vs. a first-to-market one. Now, Payne, along with Michael Tavani, is helping the company pivot into the more focused product of Saige via their entrepreneurs program Switchyards Studios. Talk about a dream team.

Here, Pow-anpongkul talks more about his decision to pivot despite great customer feedback, how Techstars taught him to look ahead and build a strong growth model, and how Saige employs machine learning to keep your food cravings happy.

You had a strong customer base with your first business model. How did you arrive at the decision to pivot? 

We believe that the best consumer companies have two primary elements: strong word-of-mouth growth and high usage cadence. We feel lucky to have built Real Meal Delivery almost from word-of-mouth. However, customers weren’t using us enough for us to feel good about building a sustainable business. Since usage cadence is driven by having a product that customers inherently love using, we realized that we needed to take a second look at our business and ultimately landed on Saige, which is our take on the personal chef.

How did your stint at Techstars help you make this decision? Did you feel like you learned the right tools to pivot as a founder?

Techstars teaches us to be long-term value creators versus short-term optimizers. One tenant of long-term value creation is to think of growth over the course of years instead of months. We were happy with the size of our business, but we realized how hard we were working drive growth each month. Though, we knew that it would be risky in the short-term to revamp a product experience that had many raving fans, we ultimately decided that it was riskier not to pivot the business.

Now you’re working with Switchyards Studios on your pivot. How’s that going?

Michael Tavani and Dave Payne were Techstars mentors and our relationship got much closer through the program. They believe in building a product that’s 10x better than what currently exists. Knowing how difficult that is to do in actuality, we began working with Switchyards Studios in November 2016. It was 6 months of close collaboration that led to the launch of Saige. I continue to believe that not enough B2C founders in Atlanta take advantage of their expertise.

How will this new on-demand model work, from the consumer side? 

We believe selection and choice are a negative attribute for dinnertime. What we learned from Real Meal Delivery is that asking consumers to make a choice (place an order) every time they want dinner creates a lot of friction. So it’s not just about us doing the cooking, it’s also about taking away the decision making. That’s where Saige personal chef comes in. Just like a personal chef, we learn about you through our online profile. We then use a combination of human expertise and machine learning to recommend the right meals for everyone in your family. You do the work just once upfront and can just set-and-forget while we deliver meals that are right for everyone in your household.

Who is your new target audience for your new model?

The audience is exactly the same – busy families and couples. The fact that we grew Real Meal Delivery via word-of-mouth meant that the concept really resonated with folks, but we needed to get the product experience right. It was through listening to the feedback of these existing customers that led to our a-ha moment. And look forward to continually improving the experience and solving dinnertime pain for them.

You will be concentrating on favorite foods vs. newer foods for your consumers. How does machine learning play into that in your platform?

Yes, we believe that the eat-at-home meal is habitual. Our job is to figure out what your favorite meals are. Imagine how delightful it would be if we knew what your 10-15 favorite meals were and just rotated through them such that every meal is one that you love.

The machine learning helps us to take your profile information and come up with recommendations faster. Since food is emotional, we’ll always have a human element to the recommendations. But just the sheer volume of data needed in order to come up with the right meals for a large group of customers requires some help from technology.

Your new business model will allow you to scale more rapidly. What’s your expected market impact and goals? How will the revenue model change, if any?

We’ll continue growing and honing the Atlanta market. Our viewpoint is that last-mile delivery companies like ours hardly ever fail because they expanded to slowly, but I could name dozens that went under for trying to launch new geographies too quickly. We’re moving to a weekly subscription, but our ability to scale is impacted more by the improving usage cadence metrics based on a better product experience than the subscription revenue model itself.

What are some lessons you’ve learned as a founder during this time that may help other entrepreneurs?

In hindsight, I would have focused way more on refining the product and brand experience. Early on, we knew that usage cadence wasn’t quite where we needed it to be. In order to stem that, we tried all types of growth hacks and revenue model changes. Ultimately, it’s the great foundational product experience that we should have continued to refine and improve upon.

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