Vereday

Curated Product Recommendations from a community that shares your values.

The idea

A browser extension that pops up values-aligned product alternatives (e.g. sustainable, minority-owned, made locally, etc.) for you to switch to as you’re shopping online, curated from reviews by your friends and other consumers who share your judgment. In the long-run, this curation model could extend to any long-tail shopping criteria that consumers seek advice for (e.g. “gluten-free”, “good for beginners”, etc.)

Consumers have more options than ever to choose products that don't make them trade off between being good to use, and good for the planet. BUT they do experience a problem discovering and filtering through their options to pick one that actually works for their needs and tastes. They may ask friends or check online forums, but it often isn't efficient or effective. Vereday was designed to scale your extended network to receive and give trusted advice on better-for the-world products, and deliver personalized recommendations at a tie-breaking moment in the online purchase journey.

Here's a link to our old website with out running list of vetted products. The extension is no longer maintained but providing a screenshot of how it worked below:

Competitors / Substitutes

I mapped out the competitive landscape when we were ideating (~2020/2021):

Post launch, we came across more startups (ongoing or zombied) that were trying to solve this same problem of helping people find a green product alternative, whether it was through mobile app, web content, browser extensions etc. One of the more notable ones was Finch 

Our differentiation angle

Existing solutions required too much finding and filtering, so we designed the product to generate trust and delight

• Intervening just before the moment of purchase with personalized recommendations. Because values (like sustainability) are typically a tie-breaker in purchase decisions, vs. a primary driver (durability, cost), Vereday's browser extension format allows us to pop up and intervene right at tie-breaking moments, when consumers are already looking to buy an item in particular and open to comparison

• Recommendations come from people you know or follow. The more Millennials/Gen Z are served paid influencer ads, the more they value authentic reviews from a relatable friend / consumer who actually uses the product and shares similar assessment criteria

• We only show 3 suggestions, personalized for values/features you said you care about most, removing choice fatigue

Amount raised (if any)

Received a term sheet for $400K investment for 7% - team disbanded and declined

Duration from Start to End

1 year

Team size

3

Challenges and what you'd do differently

Product Market Fit

• The biggest insight / blocker is that even if we provided curated, vetted recommendations to the user (thereby removing the research part of their journey to swapping into a more sustainable product), it was still a lot of mental effort for the customer to make the switch. To further lower that barrier, we needed to get massively better at predicting / customizing what each unique consumer prefers, as well as boosting the credibility of the information listed around recommendations

• We found that while many people were excited to try Vereday and give feedback, few to none were using it consistently repeatedly. Also, some of the most hardcore sustainably-minded consumers we tested with preferring to thrift and buy second-hand (by passing online shopping entirely to reduce GHGs), or do their own very extensive web research. So we targeted this curation tool for the less zealous, but still do-gooder group - it was also a risky target audience because they'd also be more likely to give up buying sustainable if things got inconvenient

• When we first started populating our database "better for the world" products, we needed to pick a product category/niche to focus on and build userbase around. We chose personal care and home goods (e.g. candles, shampoo) because they were relatively inexpensive and not overly personal, so presumably more easy to switch. Example of other categories we considered but chose not to start in:

• Food and household consumables like toilet paper (often bought in-store or via Instacart, as opposed to via web like Amazon)

• Maternity goods (lots of interest in "non-toxic", but the values-driven brands tended to be much pricier than conventional)

• Fashion (involves more personal taste / fit elements to finding the right match)

• One challenge with the personal care / home goods category was that our learning cycles for how users were responding to / using app was long, because even if someone got a swap they liked, they may not buy something else in the same category for another few weeks. So we further sought out users that are going through a life stage change, e.g. moving houses, since they'd be more likely to make multiple purchases. Still, we did not get to a convincingly high number of search/swap data points

• Validating that a brand really does live up to the values it claims is challenging. You can sink a LOT of time into this. Our initial shortcut was filtering for third party certifications (e.g. Leaping Bunny, EWG) but smaller brands required first hand research, and it's hard to really know if the small company is meeting labor / production practices it claims without auditing their supply chain directly. Investing in this would mean a strategic choice to become research-oriented (and non-profits doing this work already exists) and/or in some cases hiring technical experts to validate claims. Incidentally we did see other startups with similar goals to us go down this path

Team / Partners

To build the product into something truly compelling, we increasingly needed expertise to build out a social graph, predictive algorithms, image recognition and matching, and we did not have the expertise in our team for this. Users wanted to see that many people that they either knew, knew of, or resonated with were using the same product. Also, a LOT of other situational factors other than just "values" and product attributes predict whether a consumer decides to buy something in any given moment. What we'd have to build is an ecommerce / ML / predictive purchasing company, and me and the team did not have the right technical or industry expertise, nor were able to find the right people to bring on.

Business Model

Our plan was to make money off lead generation fees for brands (cost per swap x swaps / mo x number active users) and at a future date providing data analytics to brands at a monthly subscription fee. One challenging thing is that once someone has made a switch, that revenue for the switch won't be recurring, so there's a finite number of switches a consumer will likely make. And even in a success case, given how often people buy a certain category of product, they wouldn't be searching / switching that often, maybe ~2x a month.

Some VCs had the view that we'd have difficulty going from 10k users to 1M users since the target audience may be too niche - this risk is more true if our focus is on values like sustainability, and less true if we expanded beyond social-impact values and into criteria like "gluten-free" or "good for beginners". As an interesting data point, through interviews with brand partners, they suggested we have at least >100K active users making swaps consistently before there would be any value in data / insights.

Notes above shared by co-founder / CEO Catherine Lee