Is Machine vision all that it is made out to be?

Helping our retail software client define its unit economics for image recognition software in the lead-up to a funding round.

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Our Client’s Problem
Our client – a startup image recognition software vendor with a focus in the retail and CPG verticals – was being told by its Series B potential investors that it needed to do its due diligence on its unit economics and how other companies price their solutions to end customers. At its core, our startup client was facing issues scaling up its product market fit and had no idea whether their idea was wrong (i.e., not scalable because of lack of demand) or whether other vendors in this domain were in a mad rush to grab market share through low prices.

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Our Brief
Our client wanted us to look at how other vendors in their domain priced their solutions. They wanted to know how these vendors charged retail customers for pilots, what their upfront costs looked like, how much they charge for installation fees, what hardware costs were being covered by vendors, and what their monthly SaaS fees looked like.

Based on this, we were also tasked to derive unit economics for image recognition technology that would allow our client to price their own product and adjust their revenue and cost projections they were making for investors.

 

Our Method
At 4D, our four founding values include: Depth, Verifiability, Transparency, and Innovation. We recognize that each of these are necessary for the success of our clients. In this case, we recruited and incentivised four ex-executives from two leading image recognition software vendors that had go-to-markets in the retail and CPG space. Together with these executives, we formulated a “consultant roadmap” – essentially an excel sheet where we required them to provide us with the necessary raw data such as upfront costs, hardware, and SaaS costs by store size and products installed.

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The key to our method, of course, is to verify everything and to be transparent with our clients. By bringing two consultants on-board per vendor and ensuring that the client has a say over who was brought on board while maintaining absolute client and consultant confidentiality was key to the success of this project.  

 

Client Outcomes
Based on our due diligence, we were able to tell our client that there is no “hard and fast” pricing schema that is well defined per store size / type, per retailer, per category, etc. as was originally assumed by its staff. 

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All of our consultants confirmed with us that while vendors charge upfront costs, installation fees, hardware costs, and monthly SaaS fees for their projects, these costs are on a case-by-case basis and will vary according to a variety of factors including store size, number of stores, complexity of the categories involved in the install (chocolates and liquor vs. fruits and vegetable, for example), and KPIs to be deployed. 

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This was not good news for our client. Another piece of bad news for our client was that the monthly SaaS fees and unit economics that we were able to derive based on the feedback from our technical consultants was 30% - 50% lower than what our client was projecting in their pitch deck metrics. This actually confirmed what many potential investors suspected – that our client needed to pivot its sales process and/or reduce its revenue expectations significantly, and therefore, accept a lower valuation.  

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In the end, our client was able to raise a successful Series B, providing its investors with more realistic revenue and cost projections that were validated by our due diligence methodologies. Key to this was its sales and pricing pivot. Our client's go-to-market was much more believable from an investor perspective, back by our evidence-based approach to due diligence. 

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