Origin Story of a Venture-Backed, Acquired Startup

Decide.com was acquired by eBay on September 6, 2013. For those unfamiliar with Decide, we invented technology that predicted the future price of consumer goods. Thinking about buying a Samsung television? We’d tell you whether the price would drop in the next two weeks. We helped you decide when to buy.

Seattle, Late 2009 — For the past 18 months, my 3 co-founders and I had worked out of my brother’s cold cold basement, without pay or insurance. We’d built a handful of products that hadn’t gone anywhere. Our future wasn’t bright.

An Observation

Brian noticed his girlfriend (now wife) Jessica, had looked at the same dress on Nordstrom’s website several days in a row.

Brian: “Why do you go to that page every day?”
Jessica: “I’m waiting for the price to go down”
Brian: “Oh, we can build you something that checks it for you”

A Prototype

Brian told Ian about the conversation and by the following Monday they’d built a prototype. They showed it to Hsu Han and I during our weekly meeting on my brother’s couch. Without thinking about the normal startup criteria like market size, defensibility, differentiation (it would’ve failed all of them), we hopped in. It sounded like fun.

The prototype was simple, you entered a link to something you wanted to track, it would check the price every day and email you when the price changed.

It needed a little work though. Their initial prototype was a white page, a logo and a input box. Like Google. Missing were the customary one sentence value proposition and information on how it worked.

After taking 9 months to release our first product, we made a rule that any new product had to ship the following Monday. Regardless of how buggy, unpolished and how many features it lacked.

We renamed it PriceYeti (is it the right price yet?), made some small improvements and got it out the door.

Here’s what it looked:

Cutting Corners

To adhere to our “it must ship in one week” rule, we got good at cutting corners. For this project, we decided to not invest in the scraper despite its importance. Accurately tracking the price of a product was what the site was about. It’d be terrible to send an email, notifying someone that the price of something they wanted had dropped, only to have them find out it hadn’t. Over-promising and under-delivering is typically a bad user experience.

Unfortunately, building an accurate scraper that would work on any site would’ve taken weeks if not months. It would also require constant maintenance since websites are constant changing. It would’ve been a huge investment. We needed to know if this was something people wanted first.

The fastest way to write software is to be the software.

Instead we made a decent scraper and had users verify the current price. When the scraper detected a price change, rather than emailing the user automatically, it published the change to an internal dashboard. We (mostly Ian, thanks Ian) would check the dashboard several times a day, verify the price changes and send the emails by hand. It’s not a scalable solution but it got us moving immediately.

When a Bug Isn’t a Bug

For most sites our semi-automatic system worked. Someone would track a product from Costco’s website. We’d diligently check the dashboard for price changes, verify and send out the email.

It didn’t work that well for Amazon though. The system would detect a price drop from $500 to $450 but when we manually verified the price it would be $475. We thought there was bug but couldn’t find it. To help us troubleshoot we had the scraper save the page when it detected a price change. That way we could see the same page the scraper saw. If the scraper said $450 but the price on the page was $475 we’d know there was something wrong on our end.

That’s Interesting..

The scraper wasn’t wrong. When we manually looked at the page, the price was in fact $450. What’s going on?

Turns out in the few hours between the scraper detecing the price change and one of us manually verifying it, the price had changed again. The reason it was almost exclusively an Amazon problem was because their prices changed the most frequently.

We had no idea prices moved so often. We started collecting prices more frequently and graphing them over weeks. We found that prices even went up sometimes. We’d assumed, like most consumers, that prices either stayed constant or slowly declined as new products came out.

It was becoming apparent that when you bought something, not just what and where, was important. You could get the same product for significantly less, if you bought at the right time. If only there was a way to predict whether prices were going to go up or down.

It’s Good to Be Lucky

We’d met Oren Etzioni 2 years earlier while Ian was a student in one of his Computer Science classes at the University of Washington. He’d politely told us the idea we were working on at the time, data mining resumes, wouldn’t work. It’d only be useful if you have hundreds of thousands of resumes. How are you going to convince people to give your resume until then? Classic chicken or the egg problem.

2 years later, we were sitting in his office again. Telling him he’d been right but we’d found something else he’d be interested in. We showed him what we’d learned, that prices changed often, that people could save money if they bought at the right time, that we’d been wondering if it was predictable.

We couldn’t have found a better person to talk to about what we’d learned. Besides being a Computer Science Professor specializing in Artificial Intelligence, Oren was a startup founder. His last startup (Farecast) predicted the price of airline tickets, was acquired by Microsoft and became Bing Travel.

I can’t overstate how fortuitous this was. The only person who’d started and sold a consumer product based on predicting the price of something happened to be in Seattle and we happened to have taken classes from him.

He thought our findings were interesting. As he liked to say “I’ve seen this movie before.” But first he wanted more data. Specifically answers to the following questions:

  1. Does price volatility happen in many categories?
  2. Are the price changes meaningful?
  3. Are they reasonably predictable?

We spent the next few weeks gathering data to answer those questions. I’ve never been to graduate school but I imagine it felt a little bit like that. Checking in with a professor every few weeks, presenting your findings. Fortunately the answer to all 3 questions was yes. As we progressed, Oren joined as the 5th co-founder and the rest, as they say, is history.

Big Ideas Start Small

If you’re gonna take anything away from this story maybe it’s that big ideas start small. The idea that led to Decide was price alerts. A feature most shopping sites have. Not a change the world, kind’ve big idea.

And that’s okay. I see people try to brainstorm their way into the next big idea. Like you can sit down one day, decide you’re going to come up with the next big idea and will it into your consciousness. That’s not how ideas work.

You see, I think he better than anyone understood that while ideas ultimately can be so powerful, they begin as fragile, barely formed thoughts, so easily missed, so easily compromised, so easily just squished.

– Jony Ive on Steve Jobs

We wouldn’t have stumbled into price predictions for consumer products unless we’d built a website about tracking prices. Sometimes jumping down the rabbit hole is the only way to see how deep it goes. Most of the time won’t go very far but every once in a while you’ll find an entire world.

Postmortem of a Venture-Backed, Acquired Startup

Decide.com was acquired by eBay on September 6, 2013. For those unfamiliar with Decide, we invented technology that predicted the future price of consumer goods. Thinking about buying a Samsung television? We’d tell you whether the price would drop in the next two weeks. We helped you decide when to buy.

I’m obviously happy with the outcome. From my brother’s basement, we raised $16.5M, employed 30 people and most importantly built something people found useful. Only in my wildest dream would I have the audacity to think I’d build something that would be acquired by a company like eBay.

Although the outcome was positive, it’s helpful to reflect on the experience. Below are 3 lessons I learned from the experience. They’re not new but hopefully from within the context of a real startup they’re more tangible.

Scale Your Company with Your Product

We launched Decide.com on June 20th, 2011 with 20 people and 2 round of funding totaling $8.5M. That’s atypical, especially these days when investors want to see traction before investing. Instead of those resources helping us find market-fit it slowed us down.

We focused on company building:

Hiring. We hired 20 people in the 9 months following our first round of funding. That took lot of work. For every person you hire, you might interview 5 others, for each of those people you have a recap meeting with everyone that interviewed them, work on a offer for the person you want then sell them. That doesn’t take into account the work it takes to source candidates and fire people that don’t work out.

Organizational Structure. As we kept adding people, we had to figure out how they’d all work together. What teams should do what? Who leads those teams? How do those teams interact? Who needs to be in the meeting where we decide what we’re doing?

Once we got through those parts, it was harder to change directions:

Communication Overhead. The larger the team the more energy you need to spend communicating. Let’s say you want to change directions, something that happens often pre-market-fit. You have to convince everyone that there needs to be a new direction, what the new direction is and why this new direction will work when the last one didn’t.

Specialization. As companies grow, the people they hire become more specialized. If we had pivoted to target businesses (we talked about it) instead of consumers we’d need sales people (we didn’t have any) which probably meant we’d have to layoff other people (probably engineers) to hire them. This didn’t happen to us but it impacted some of our decisions so I felt it was worth mentioning.

Do whatever helps you reach market-fit faster. In my experience, that’s raising modestly (if at all) and keeping the team small. 2 or 3 people can get a lot done and is small enough where you don’t have to worry about structure or communication.

You wouldn’t scale your technology to support 10M users when you only have 100. Don’t scale your company that way either.

Hire Similar Minded People

Our hiring criteria can be summarized with 2 basic questions: (1) Are they great at what they do? (2) Do we like them? Turns out there should’ve been a 3rd question. (3) Do you generally agree with me?

We spent a lot of time debating rather than doing. What technology should we use? When should engineering get involved with the design process? What’s our design methodology? How long should our release cycles be? Should we even have release cycles? I could go on indefinitely.

Decide how you want do things then hire people that want to do things that way. There’s value in having a diversity of opinion but in a early stage startups, the benefits (moving fast) of hiring people that generally agree with you outweigh the benefits (diversity of opinion) of hiring people that don’t.

If you can’t hire anyone that agrees with you, re-evaluate how you want to do things.

Expanding Your Target Market Doesn’t Help You Find Market-Fit

We started with support for 3 categories (televisions, laptops and cameras) but expanded into all electronics within the first year. Looking for more growth, we talked about a few options:

  1. Expand into more categories – Be relevant to more purchases. Number 1 thing our users asked for.
  2. Expand beyond when to buy – Move up the buying funnel by helping people figure out what they should buy (product scores, reviews, comparison,etc.) in addition to when.
  3. Double-down on when to buy – Make it so good, if anyone is buying in a category we cover, they have to use us.

We ended up doing a combination of 1 and 2. We built features (product recommendation, sentiment analysis, etc.) to help people figure out what to buy then expanded our coverage into appliances, home and garden, babies and kids. We looked for growth by expanding how often we would be relevant (more categories, cover more of the buying funnel).

Most of our traffic stayed in electronics and our experience became thin. We were in a lot of categories trying to do a lot of things. I would’ve liked to see us do option 3, double-down on when to buy. That was the part of the market nobody else was playing in. Get that experience to be amazing then roll it out into other categories.

Expanding your target market, like we did by supporting more categories and building when to buy features, doesn’t help you get to market-fit. In fact, I’d argue it makes it harder because there’s more needs to satisfy.

Said another way, is it better to start with a small group of people who love what you’re building or a large group of people who are indifferent? I think you’d rather have the small group that love you. It’s easier to turn a small group of rabid users into a large group than a large group of indifferent users into a large group of rabid users.

Keep your product narrow and focused on the small group of people most likely to love what you’re building.

Something positive

I don’t want to end this negatively because there’s a lot we got right. I just tend to learn more from failures than successes. Maybe I should write another post about worked for us. For now, let me tell you about the my proudest moment at Decide, when we lost almost all our revenue.

When we created the first version of our mobile app, we put Amazon’s prices next to everyone else’s just like we did on the web. Apparently Amazon doesn’t allow you to do that on a mobile device but it’s ok on the web. They want you to use their app to check their prices.

We received a notice from them informing us we weren’t compliant and unless we removed it they’d suspend our affiliate account. We weren’t making a lot of money but that account probably represented more than 80% our revenue.

We had 2 choices: (1) Comply and remove Amazon prices from our mobile app (2) Not comply, keep the prices and lose our biggest revenue stream. Proudly and with very little discussion, we decided not to comply. Most people want to buy from Amazon. Not being able to show those prices to our users wasn’t acceptable. In the 2 years since we got that notice we never complied. When faced with a decision where we had to choose between revenue or our users, we chose our users. I couldn’t have been more proud.

If you want to see what I’m working on now checkout CoffeeMe, or as we like to call it Tinder for professional networking.

Hello Decide

This update is a little late but to borrow from my favorite show, “Good news, everyone!”. Our little startup was recently funded and is now Decide. Read the full announcement on our blog.

I’ve been asked by a few friends how it feels after almost 2 years without pay, working out of coffee shops. I’d like to give this rousing speech about the power of hard work and the spirit of entrepreneurism but it doesn’t seem apt. I’ll save that for some time later. It feels too much like a beginning for that kind of stuff. Still so much left to do.

Honestly the best bit about all of it is the quality of people we’ve been able to hire. Not only are they the best at what they do but they’re good guys. It’s one of those cliches that’s totally true, getting to work with talented people you enjoy is what it’s all about. I don’t know how I’ve managed to trick them into letting me work at their company.

I remember all the blog posts I used to read about startups when we were first getting started. I’ll have to do a post about common startup advice that I’ve found to be true or untrue.

If you’re interested in working at Decide let me know. We’re still looking for awesome people.