In our experience, structured customer development work is right up there amongst the most valuable things a founder can do in the early days of their startup. Once you have an idea that feels strong, it’s imperative to speak with customers about it. But good customer development is tough to do. It takes a long time, think 10-20 hours of interviews plus preparation and digest time, and conducting structured interviews with strangers is outside the comfort zone of many founders. So lots of entrepreneurs skimp on this vital piece of work. That’s a bad decision. It leaves you flying blind when with a little hard work you can be seeing clearly.
Let me use the Jobs To Be Done (JTBD) framework for product development to explain why.
Reams have been written about the JTBD framework. I will give you a high-level summary here, but if you are building products then I recommend spending some time with Google to find out a bit more.
The core idea of the JTBD framework is that customers use products or services to do a job for them (in the US many people say they “hire” the product or service to do the job, but that doesn’t translate too well into UK English so I prefer “use”).
For example, I used my bike to do the job of getting from home to work this morning. I could have used a bus or a tube, but I used a bike.
That job breaks down into component parts. I have to access the mode of transport, pay for it, travel, dispose of the mode of transport at the other end and maybe get from the disposal point to my final destination. With my bike that breaks down to getting it out of the bikeshed in my front garden, no payment, cycle for 15 mins, put it in the bike rack in our office garden, and then walk five metres to my desk. If I was getting the bus the breakdown would be walking to the bus stop, paying with my iPhone, sitting on the bus for 30 mins, getting off the bus, and walking 200 metres to my desk.
Each of those component parts has associated outcomes that I’m looking for. Some of those are functional and others are emotional. Taking the travel component of the job, the functional outcomes I’m looking for include speed, comfort, exercise and predictability whilst the emotional ones include safety, anxiety, and consistency with my identity as an active person.
Those component jobs could be broken down further and then there would be outcomes associated with each job at the next level of detail down the stack. Part of the art of using the JBTD framework well is picking the right level of detail to work at.
The work I’ve described so far is a desk exercise. That’s valuable, but the real insight comes from talking with customers to discover what’s important to them, how satisfied they are with their existing provider, what needs to change and how much they would be willing to pay. The focus should be on the outcomes they want and their answers will tell you what features you need to build and where the opportunities for differentiation lie.
Before you start you will probably have a gut feel for the answers customers will give you to these questions. I originally titled this post “Two compelling reasons to do structured customer dev” because unless you do it you won’t know whether you are right or wrong until you’ve invested the time and money it takes to design your MVP, build it, release it and find some customers. Now that we all follow lean development methodologies mistakes are much cheaper than they used to be, but they are still a hell of a lot more expensive than 10-20 hours of customer dev.
Common mistakes which lead to wasted effort include building a feature to drive an outcome that isn’t important to customers and not realising that an outcome provided by competitors is important for customers. This second one is particularly dangerous as it will result in low conversion and might lead you to the erroneous conclusion that your point of differentiation isn’t resonating (a false negative).
The second reason to do structured customer dev is that the interviews can yield insights which drive marketing. An example from Photobox. They sell personalised photo gifts, photo books, mugs, calendars etc. Using the JTBD framework they established that one of the jobs they do for their customers is help them remember when a birthday or anniversary is coming up and they need to give a gift. Through the customer interviews they discovered that remembering and not forgetting had very different emotions associated with them. Remembering something is nice, but not remarkable, whereas not forgetting means avoiding all the anxiety associated with forgetting something important and letting someone down. They used this insight to change the subject line in some of their reminder emails. The message moved from “remember XYZ” to “don’t forget XYZ” and the response rate was much improved.
I could go on for ever about the importance of customer dev work. It really does make a huge difference. The reason many founders skimp on it is that the benefits often seem a bit nebulous. I hope they seem less nebulous now.
A quick shout out to Dave Wascha from Photobox who was kind enough to spend time with the Forward Partners team on Monday educating us about the JTBD framework. His talk was the inspiration for this post.
Max Niederhofer recently published this chart showing European exits. As you can see there’s been impressive growth in sub $100m exits, but the story with larger M&A exits and IPOs is less compelling. As I wrote last week our ecosystem is making great progress, but clearly, if we are to keep growing then at some point we need to see an increase in large exits.
The good news is that we can reconcile the facts that we have an increasing number of great companies with the fact that the number of large exits isn’t going up: great companies are staying private for longer. Witness mega rounds by companies like Transferwise and Deliveroo that in years gone past would have had to IPO to raise that kind of cash or, as was more often the case, sell to a larger company that could finance their growth.
This ‘staying private longer’ phenomenon isn’t just a European thing. In the US companies are raising amounts of capital previously only possible through IPO with much greater frequency than they are here. Whether that’s a good or a bad thing is debatable (private companies have less scrutiny and therefore lower costs, but arguably the scrutiny makes them more disciplined) but the important point here is that it’s skewing the exit data. That said, if LPs are to keep making new commitments to fund, they need to get cash back soon, so this trend can’t continue forever.
This week I’ve been at the SuperReturn/SuperVenture conference in Berlin. It’s the biggest European gathering of venture capital fund managers, private equity fund managers and LPs, the institutions that invest in both types of funds. I’ve been going off and on for the last ten years and the good news is that the tide is definitely turning in favour of European venture.
That said, we’re coming from a place where there was very little interest amongst LPs in European funds. For many years our asset class, which is “European venture” was at the bottom of their priority lists. I remember vividly one year, I think it was 2012, when a placement agent (industry jargon for a broker that helps raise private equity and venture funds) had surveyed 83 LPs. He had given them each three votes to cast across about 15 different asset classes. Of the 249 available votes, only 5 were put against European venture. European VCs fundraising at that time were fishing in a very small pond…
As I say though, things have been getting better for a while. The logic in favour of European venture was always strong. VC investment per capita is lower than the US (it’s now 33% lower) and enterprise spend on technology here is much higher as a ratio to VC investment than it is elsewhere in the world. Efficient market theory has it that money should flow into that void.
The problem has been that many LPs lost money in European venture in the 1999-2000, were nervous about making the same mistake again, and wondered if there was a structural reason why venture capitalists seemed to be less successful here than in the US. Structural reasons mooted included an absence of serial entrepreneurs, insufficient venture capital to scale businesses properly, lack of ambition and the fear of failure.
However, whilst money didn’t flood into the void, it did trickle. Governments around Europe played their part, funding the EIF and domestically here in the UK the British Business Bank, and a few brave LPs were prepared to walk where others feared to tread.
And with that capital, a few entrepreneurs succeeded against odds that were much tougher than they would have faced in the Valley. They became serial entrepreneurs, attracting more venture capital into the market, enabling us to fund businesses more aggressively, which in turn drove returns higher. We entered a virtuous spiral and if there was ever a lack of ambition or too high a fear of failure nobody is talking about it anymore.
That virtuous spiral has been turning slowly for a while now and the result has encouraging growth in capital invested into European startups and raised by European venture funds. Different data sources vary, but I think the Dealroom data you see in the chart below is pretty close to the truth.
However, what most of us in the industry would like is for the virtuous spiral to turn faster. I think we could comfortably deploy more capital into more companies and grow them more aggressively and reach bigger outcomes without the market getting overheated.
As I wrote recently to an active investor in venture, the input metrics of funds raised and dollars deployed are very healthy, but we don’t yet have published written evidence that all this activity is translating into great returns for LPs. The fact that commitments to venture funds are rising implies that the returns are there, or at least that LPs believe they are coming, but we haven’t yet seen that in industry stats.
What we do have now is active venture LPs saying publicly that they are making good returns from European venture and that those returns are getting better every year. We heard that at SuperVenture this week, and that’s a first for me in 18 years in this game.
We also had LPs who have historically invested in private equity but not venture questioning whether it was time for them to make a change.
So I think the chances are good that the growth in our ecosystem will accelerate. I can’t remember feeling this optimistic about our collective prospects.
I just read ‘Need More Time’? Guideposts For Tech Founders Going To Market When No Market Exists which is full of great tips for what they call ‘pre-chasm’ enterprise startups. The term ‘pre-chasm’ is a nod to Geoffrey Moore’s 1998 classic Crossing the Chasm and refers to companies that may have sold to early adopters, but haven’t yet found a way to sell to the mainstream. Getting sales going in those early years is terrifically challenging and requires great product and great sales. There are lots of common pitfalls that founders fall into and the whole post is well worth a read, but I want to highlight two sections which cover mistakes that in my experience many founders are prone to making.
- Over-value conversations and even deals with large enterprise customers. Here’s how they put it:Surely people paying you money for expertise is a strong signal you’re heading towards product-market fit? The twist is: In much-hyped new technology areas, before there’s a big market, it’s not uncommon for startups to close high dollar PoCs and even some large contracts simply because companies are happy to be educated by startups.This practice is particularly common in fintech.
- Believe that channel partners will accelerate sales. Here’s how they put it:I see this play out in new markets again and again: Pre-chasm enterprise startups throw time and resources at indirect sales channels (including OEMs, etc.) in the hopes that someone else’s sales team can do a better job than your own. Or, assuming it will accelerate sales, they will spend a lot of time with technical or channel partners … [but] rarely will indirect channels for enterprise devote real resources to help push someone else’s product to market. As for VARs, they typically only provide fulfillment in the early days (and if you’re really lucky, deal registration for qualified leads) because they aren’t structured to carry pre-chasm products — i.e., pitching, educating, hiring the right sales force. They’re good at distributing things where there’s already an educated customer base.Nine times out of ten (or more) direct sales is the only way for early stage startups.
Google is pushing hard to make artificial intelligence as easy to access as cloud computing, building services that reduce both costs and the technical skill required from users. To my mind, there is a strong parallel with how Amazon Web Services made it easier and cheaper for companies to build web apps.
Throughout 2017 they made steady advances, releasing Google Cloud Machine Learning Engine and grew Kaggle, their community of data scientists and ML researchers, to more than one million members. They now have more than 10,000 businesses using Google Cloud AI services, including companies Box, Rolls Royce Marine, Kewpie and Ocado.
And now they are introducing Cloud AutoML, which promises to:
Help businesses with limited ML expertise start building their own high-quality custom models by using advanced techniques like learning2learn and transfer learning
Google seems to be in the vanguard, but Amazon and Microsoft are pursuing similar agendas. For AI startups this means that machine learning expertise will become relatively less important whilst access to data and customer understanding will rise in significance. Given that ML expertise has been in short supply we can expect to see a sharp rise in the number of high-quality startups using machine learning to make better products (as distinct from low-quality startups that claim to use machine learning but don’t really). It also means that the opportunity space will tip towards applications and away from infrastructure.
At Forward Partners we’ve had “Applied AI” as one of our two focus areas for investments for around six months now. We define Applied AI as anything that mimics human cognition (that’s the AI bit) in an application applied to real-world problems using well-understood technologies. We insist on ‘well-understood technologies’ because as an early stage investor we want to be funding companies that can get products to market in predictable time-frames rather than what I sometimes unkindly refer to as research projects. From our perspective, services like Cloud AutoML are great because they add to the available suite of well-understood technologies and therefore increase the range of startups we can back. This is a trend we can expect to continue as Google, Amazon and Microsoft offer more services in this area as they compete to keep people inside their ecosystems.