Finding reference customers for a SaaS product

I've analyzed 482 founder interviews [1] (most of them from IndieHackers, which are all small and medium-sized businesses) and here's the key thing I found about acquiring your first 6 "reference" customers:

Aim for acquisition channels that "do not scale". Channels like:

- Reddit/FB Groups/any other online community. You can post once or twice in a a particular community to get feedback. If you want to "scale" your efforts (i.e. post every day), you're likely to be deemed as spammy and get banned. This is both good and both. Bad because you cannot "scale" this acquisition channel (compared to Facebook Ads, for example). Good because, for this very reason, these channels are open to new entrants. Compare this to something like:

- FB/Google Ads. Yes, you can "scale" these channels, but so can your potential competitors. These people are usually well-funded startups that are willing to invest $500 to get a customer (although the customer will pay $50/month), knowing that many of them will stay for 10+ months, when the company will eventually get a positive ROI. For competitive markets, this "waiting" factor spans to over a year, sometimes two. As an early-stage company, can you allow yourself to wait 15 months to get a positive ROI from your ad efforts? Probably not.

Of course, there are always exceptions (if you bid for a narrow/high-intent keyword with not much competition, like "screenshot API" - this comes from a real founder interview [2]), but in general aim for acquisition channels that "don't scale".

[1] https://firstpayingusers.com

[2] https://www.indiehackers.com/interview/building-a-hobby-proj...

Peter Kazanjy's "Founding Sales" is a good resource for figuring out how to get those initial customers and all the steps involved with it. I am halfway through it and really appreciate all the details it provides and doesn't hand wave any part of it.

If you're pre-product, identify a solvable problem that your target customers are facing. Many problems can be solved without a scalable solution/product for a few customers. Hack together a presentable solution/deliverable. Call it your beta program and do it for free. Set KPIs and collect metrics on your successes. Leverage those successes as references. Now that you've used those successes to get some money and build a team start releasing versions of the solution and convert your beta users to paid customers at a discount and incentives them with more discounts for any new paying customers they can refer. Have them post on linkedin for you, do press releases and most important cultivate and treasure those initial relationships. You will need to call on them again and again.

If your SaaS value proposition is compelling enough, the right person will be generous with their time and they will be passionate about the feedback that they give you. If you can't find a customer willing to do this, that's either telling you that you have the wrong customer or wrong product. Often the incentive for them is solving the pain itself. I had a few customers talk about how my SaaS was going to save their marriage. I would not skip this step - product discovery and validation is the core of what Marty preaches.

It’s always about who you know. Ask friends. Ask that non-technical businessman you know that has a Rolodex of 10,000 business contacts for some names. The personal touch is useful in the beginning.

this is a great question. i’ve talked to quite a few people about this, and having some reference customers is super valuable. easy ways are to make a hypothesis about who at what type of company would really need your software, and then use LinkedIn, cold calls, emails, referrals to get through to them.

it is definitely important to go through this process, just because this is defining the market and gets you to a repeatable model of acquiring customers.

I tweeted a thread[0] addressing how we do it. The evolution is as follows: I joined the company when it was a tiny consulting team [as employee number 4] building custom machine learning products for very large organizations. i.e: from problem definition, to data acquisition, model building, application writing that allows using these models, and even allows their domain experts to train models with new data.

Now, these projects take a toll on the team, especially when you have people working on different projects simultaneously. The consulting mode has worked for seven years in which the company delivered many projects in many sectors using many techniques. [energy, transportation, employment, telcos, banking, retail, advertising, communication, etc.]

After doing that, certain patterns emerged as we hit road bumps and learned lessons the hard way when it comes to machine learning projects in the real world, with actual stakes. This drove us to start building a machine learning platform[1][2] that takes away the overhead and enables a small team to ship product, deliver value, and do it fast.

We build upon the knowledge we acquired these years and build this in the platform. For example, we enable automatic model/params/metrics tracking and one click deployments because the cognitive load on our data scientists to track experiments was huge, and they didn't necessarily remember to do it, or didn't do it in a similar way. They also had to ask someone who could deploy a model to deploy their models, and this person could be working on something else [bottleneck, social relation].

As we are building this, we also interact with our clients and prospects, some of which are at the leading edge of machine learning and have internal teams, but are suffering from these problems.

So we're working on this to:

- Enable our consulting "arm" to deliver these projects fast

- Enable other people to do that as if they had a team, reducing the barrier to entry as these lessons were learned the hard way [time and money].

Any of the projects we already have shipped could be abstracted and offered as a SaaS to other similar companies in a sector. For example, customer churn for a telecom company. Market forecast. Next best offer. We're choosing to focus on the platform for now, but you can easily see how you could do it were you to choose one project your were paid for and abstract it to other clients.

One important thing is: keeping the conversation open with these organizations. Starting small with a contained specific problem just to get in, and then expanding from that small specific business use case either to expand the tool's capabilities, or to offer it as a SaaS to _other_ customers.

- [0]: https://twitter.com/jugurthahadjar/status/131066829330549965...

- [1]: https://iko.ai

- [2]: https://www.reddit.com/r/learnmachinelearning/comments/je0pm...

Pretend it exists and try to sell it. Everything else is w/e