The Thesis
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The Thesis

Who will manage my meals?

Source

The Opportunity

While shopping journey and logistics for grocery and restaurant delivery have been streamlined and optimized, the overall journey of a user solving for their meals is still broken and fragmented.

The graphic below provides a simplified view of the broad decision and process steps customers undertake to solve for their meals. Most users rely on a mix of cooking, delivery and dine-out for their meals.

Weekly meal planning decisions

Let us superimpose existing solutions already available on this chart.

Existing solutions address chunks of the process but no one addresses the entire journey

We can immediately notice a few things:

  1. Some steps have not been sufficiently addressed (e.g. creating the actual Ocado basket for ordering)
  2. All these solutions are fragmented, and don’t digitally speak to each other
Who is solving for actually adding items to cart, or choosing a delivery service?

This leads us to the current state of affairs, and key customer pain-points. (note: my focus here is more on grocery delivery to solve for cooked-at-home meals — rationale to follow).

Creating a grocery-cart is manual, and tedious.

Converting meal-plans into a grocery shopping list is a purely mathematical exercise. There is no reason why it shouldn’t be automated — and many apps do that. But the real hard problem lies in actually adding those items into a cart on a grocery-delivery app.

But if you’ve ordered from Ocado, you know the pain of choosing from a confusing long range of choices (organic chicken, grass-fed chicken, free-range chicken, best-value chicken, filleted chicken) and SKU sizes (250g, 500g, 900g, 1000g, …). In reality, choosing the right SKU is a simple heuristic that the customer repeatedly applies for each item, depending on what they are solving for (cost, value, dietary needs, organic produce, prep-time). Imagine having to do this manually for every single item in a grocery list solving for 21 meals in a week!

Managing this process manually could also lead to over/under-stocking and the related issues around last-minute grocery runs or wastage.

Moreover, building a grocery cart is not a linear process — rather, it’s an iterative one. if a critical ingredient (say, mussels) is out of stock, I need to change the menu for that day (from spaghetti vongole to spaghetti marinara), which has an effect on my other SKUs as well (change quantity of garlic, basil and tomatoes)

Some studies suggest, that:

80% households spend >1hour per week on meal-planning and grocery ordering!

Choosing from a several delivery options is complex

The past year has seen a slew of grocery delivery companies, offering different combinations of assortment-delivery time-cost-quality.

However, this makes the task of optimizing the grocery order between different services extremely complex. You might want to optimize for delivery time for fresh items, cost for staples, assortment for gourmet ingredients. This is exactly the kind of problem that a computer and AI were built to solve!

At the end of the day, consumers are not looking for more choice, but an optimized right-answer for them. There is potential value to be created if a service can offer that.

Ordering a week’s supplies mean ordering from several different providers

While I have mostly covered grocery delivery earlier, in reality, a week’s order might also involve food-delivery or meal-kits like gousto. Traditional shopping lists don’t cover that. The task of translating a meal plan to orders across several of these platforms still falls on the customer.

All of today’s solutions are built considering meal-planning, shopping and cooking as an individual activity

The success of peloton, the millions of cooking handles on instagram, and reality shows like masterchef & big bake-off indicate that there is a strong “social” element to food, that has yet not been fully explored

The underlying factors

Several trends support the timing and need for solutions to the problems I mentioned above:

  • The pandemic has increased penetration of online grocery delivery, thus expanding the set of users who might find this service useful
    59% of UK consumers shopped online for groceries in 2020, with 7% of current online grocery shoppers using online services for the first time. Online grocery shopping now makes up 11.4% of all grocery sales.
  • There amount of choice in grocery delivery has increased significantly, with almost all possible combinations of time-cost-assortment-quality covered at reliable customer-experience
  • As share of meals cooked by users increases, carts will grow larger (or frequency will increase), compounding the problem involved in grocery planning and ordering
source: here

Size of the opportunity:

  • >11% of UK’s $200Bn+ grocery purchases are currently online, and that’s the first potential market for this service
  • Potential to get early traction from the more health-conscious users; considering this base to be at least 10% users, that shows a market potential of at least $20Bn in annual sales in the UK
  • If the scope expands to cover all meals including food-delivery and meal-kit delivery, the market becomes even larger (UK food delivery market = $11.4Bn in 2021)

Scalability & Long term defensibility

A reason why I am really excited about this space, is that it has the potential to take and retain ownership of the customer (away from the delivery companies). If that happens, such a company could capture a significant share of the value it creates.

Integrating into the meal-planning and grocery ordering habit of the customer (through an easy-to-use UI and a customized content engine) could mean high stickiness and high switching costs. This could improve even further if a company is able to integrate a meaningful social dimension into the solution, and act as a powerful moat.

The largely software (+partnerships) play and asset-light nature make it a potentially high ROCE and non-linearly scalable solution in a geography. The asset-light nature and high stickiness could mean high LTV and could potentially even support higher-than-usual CACs for food/grocery delivery.

I am excited to see a few players making inroads in this space.

Companies of interest:

LollipopAI

Description:

  • Recently launched (2021) London-based platform for meal-planning and automated intelligent grocery ordering. Their aspiration is to be a one-stop platform for users when they think of solving for their meal needs.

Product/ solution:

  • While I have not tried the product myself (#650+ in the queue despite applying all referral codes), I am really impressed by their customer-first thinking based on snippets of different interviews and news articles. While the current focus is on building a meal-planning platform with inbuilt recipes and automated grocery ordering, they are already thinking of cook-along aids etc. which enable customer fulfill their aim of solving for their meals.
  • I think they also realize the importance of getting the UX absolutely right the first time, and hence the strategy to do waitlists and limited-size alpha/betas.Of course, this strategy has been helpful in creating an initial following for Monzo as well!
  • They have partnered with BBC Good Food for recipes and cracked a major partnership — Sainsbury’s for deliveries.
  • While the product is currently simplistic, they plan to add more consumer-preference driven selections (e.g. organic vs non-organic, whole vs already chopped veggies, premium vs affordable SKUs) slowly reducing the human-involvement element to close to zero.

Funding

  • Lollipop has raised (an undisclosed amount of) preseed funding from JamJar and Speedinvest and relevant angels (e.g. Ian Marsh of HelloFresh)

Traction

  • As of today, there are a little over ~1100 people on the waitlist. I assume at least 10k people have already signed-up (because they had offered free premium accounts to the first 10k sign-ups, reasonable assumption that the waitlist only starts after they’ve hit 10k). However, this seems like it’s purely on the basis of the one PR article and posting in a few forums.
  • The big traction indicator though, seems to be on the partnership side, through the Sainsbury tie-up. Their ability to attract users will depend heavily on the assortment and delivery options available.

Team: Founder Tom Foster-Carter has a stellar startup resume with the latest being his COO role at Monzo. His ability to attract top grocery bigwigs as investors, and ink partnerships with Sainsbury’s at alpha-stage is just additional early proof. Co-founder and CTO Chris Parsons brings along valuable prior experience from his own startup and other companies.

Overall thoughts:

  • LollipopAI seems to have all ingredients in place — a proven founding team, right partnerships (Sainsbury’s and more in pipeline), a lot of consumer interest.
  • They also seem to have a compelling broad vision, that expands beyond just grocery ordering to being the destination for solving for solving for meals (cooking, grocery, meal kits, food-delivery)
  • To top it all, there are early indications that they will also include a social element
  • By relying on partnerships for delivery, they’re following the “asset light” model which makes the space so exciting for investment.

Jow

Description:

Paris-based platform started in 2017. Provides meal-planning and automated grocery cart creation and ordering.

Product/ solution:

  • Elegant product built to quickly understand the customer’s needs and situation by asking questions on family size, existing kitchen appliances etc.
  • They’ve build a personalization engine which draws upon the recipe library to suggest the best suited meals
  • Also includes video-tutorials for people learning to cook
  • A look at the reviews indicates strong affinity for their recipes offering but major challenges with their ordering and logistics.

Funding: $7Mn in 2018 led by Stride.vc with Caterina Fake and Jyri Engeström from Yes VC, and other angel investors. No news of any subsequent fundraise.

Traction:

  • Android reports over 1Mn downloads for the Jow app (compared to the popular meal planning app Paprika which has just 100k downloads).
  • Jow has managed to sign up several French retailers (e.g. Carrefour)

Team: Founder Jacques-Edouard has had entrepreneurial experience building “enthusiast” and digital marketing and consulting.

Overall thoughts: Excited to follow the progress of Jow, as there is the possibility to draw parallels between Paris and London grocery retail set-ups.

Jupiter.Co

Description:

  • SF-based startup founded in 2020 as Tala and then subsequently renamed. Provides an end-to-end solution focused on meal planning, automated grocery basket creation, as well as own delivery.

Product/ solution:

  • Similar to other services, it creates a meal plan based on dietary requirements and then builds a grocery basket from it.
  • The distinguishing feature though, is that they also manage delivery themselves, instead of relying on partners (unlike Jow and Lollipop)
  • The service started off as seemingly aimed at a fairly affluent population who value the convenience more than anything — they have a $45 monthly fee and inflated item prices. However, they have removed subscription fees and claim to match prices with offline retail stores.
  • They also introduced social shopping in May2021 trying to create a community buying experience.

Traction:

  • Exact traction numbers are not available.
  • They are currently operating only out of the SF bay area.

Team: Team of 4 Stanford alumni and YC graduates. Have very diverse backgrounds and experience working well together.

Funding: $2.8Mn seed round by Khosla Ventures and nfx

Overall thoughts:

  • While the proposition is very interesting from a customer’s perspective, Jupiter might face many challenges due to the decision to manage their own delivery.
  • Own delivery is usually infrastructure heavy which could down expansion and also impact ROCE significantly.
  • It would also be interesting to see how they manage to compete with established supply chains of existing retail and delivery players.

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