3 ways to significantly increase the chances of your product’s success
Key practices you can establish today to help your product win
Fresh and innovative ideas are important. Backing these ideas with consistent and solid execution is indispensable. But, innovative ideas backed by solid execution without continuously collecting evidence is futile. Making an idea eventually work in peoples hands is a lot of work. Good news is that putting in this work early on and at the right cross-sections while building the product can save rude awakenings (and cold sweats!) later.
Here I share three things that I learnt over the years of shipping consumer products at scale, that you can audit and correct today to improve the chances of your product succeeding:
Don’t fall into the trap of a great proposal. Always see it for what it is. A proposal
Articulating the user pain in compelling and passionate words during early days of an idea is important. But, putting in the hours to talk to people and collecting evidence to validate this user pain being sold is what makes it real. This is not a statistical exercise. Walking in the shoes of just 3–4 users will start giving you insights that will ground your idea in reality. Get off your chair, talk to real people and ask questions to understand if the problem you have identified is real. Enough framing the proposal, step out and validate what’s being said.
Back in 2012, we were working on an e-learning site to help school children grasp science and mathematics concepts sitting at their home via interactive videos. We were generating good leads from our acquisition sources but hoped to see better conversions. We prioritised an effort to simplify the site experience based on a hypothesis that the site was cluttered (it really was). However, we soon realised that the more we simplified, the lesser subscriptions people bought. In short, our hypothesis was incorrect. Turns out, parents didn’t mind the clutter as most of it was testimonials and academic jargon, which lent credibility to the site. We had just spent months of design and engineering effort for something that worsened our metrics and could have been avoided by talking to people.
Make a case with top down sizing if you need to, but the reality is revealed only in a bottom’s up model
TTop-down opportunity sizing gives us a great start to sell your idea within the company. But, bottoms-up sizing is what make the estimations real and reveal the interventions you need to build into your product.
These are the kind of questions you need to ask for a bottom’s up mindset:
- How much traffic do we need and where is it going to come from? What can come for free and what’s the cost of acquiring the rest?
- How will users find and experience the feature or product?
- How qualified will different kinds of traffic be and how will conversion vary across sources? Are there other comparable features or products I can learn from? What can we piggyback on?
- How and why will users come back for this feature or product? How does that impact the estimations?
The trick is to build an estimation model with the components of what makes sense for your product, even if it has no numbers to start with, and then start filling it in one by one as details emerge. I have seen many teams stall estimation exercises while they wait for data, but the cost of waiting is too high, especially since the model is going to inform what you are going to build. By the end of the exercise, you will know which knobs to turn to make your product successful. Some high level examples of realisations that a bottom-up model might reveal and actions you might take:
- You realise that you need a consistent flow of traffic over time to try your product than what you earlier thought. You rework on ‘feature discovery’ and move it closer to existing user path by integrating it with an existing successful feature. You save your team’s valuable time by engaging with the dependency teams early on.
- You realise that your product needs higher than imagined conversion rate to reach the financial impact you were projecting. You choose to increase focus on potentially high converting sources of traffic ahead of time rather than an afterthought (surprisingly few product teams do this). You decide to work more closely with deeplinking and affiliates team to acquire high quality traffic and build the hooks into the product
- You realise that you need existing customers to use the feature more often than earlier imagined. Instead of relying just on the awesomeness of your feature to make users come back, you now send a notification to users as a reminder to come back if they start using your feature, but didn’t go all the way.
Getting a deep understanding of the impact that’s actually achievable will help you put in measures needed to either adjust expectations early on or put a plan in place to get there. The bonus of keeping your team’s motivation and energy alive as you help them graduate from opinions to data is an upside that you will appreciate. After a point, stop claiming numbers and start supporting them with bottoms up estimations.
Start validating your idea, journeys or designs with real people as soon as you can. Don’t wait for the perfect moment. There isn’t one.
Great well polished mocks sometimes helps convey an idea quickly. But, putting them in the hands of real people as early as possible is what brings those pixels to life and give them meaning. There is nothing more humbling than actual user feedback. The bonus of honing your team’s understanding of users is something that will pay dividends forever. Get to user interviews and usability studies as soon as you can. The first few studies will expose patterns that will punch holes in your biggest assumptions and help you course correct early on. Stop shining the mocks and start observing them in people’s hands.
Products struggle because we sometimes skim on the effort needed to evolve our hunches into actual nuggets of proof. Building a habit of gradually validating our assumptions at different stages of product development increases your odds of ending up in a winning place and actually delivering value. Confidence in a solution should be proportional to the evidence collected for it, if we hope to stand a chance against the confirmation bias hidden in proposals.
If we are to remember one thing, let’s remember to ask ‘How do we know that..?’ for each and every assumption made as we turn an idea into a real product. Keep it real.
