Gousto’s approach to solving customer problems to create massive business value has radically changed in the last two years. We’ve learnt that Sean Ellis’s Growth Hacking is great in the short-term but destructive in the long-term. We’ve expanded our principles to reflect Brian Balfour’s better-defined “New Age Growth”, and adopted Product Thinking to unlock huge A/B tested conversion, retention and AOV uplifts.
The story and secrets of our evolution are grounded in the theories of great thinkers across the world. I’ll share these secrets here, with detailed examples of how we use them today.
When Boris Johnson announced the first…
I’ve been searching for a guide to help me run better development conversations with my team since I first became a manager two years ago.
In those early days, my development conversations were wooden and over-prescriptive. I’d spend hours preparing 5-page documents, scoring(!) people on ‘success factors’ the team and I had created, and generally telling, not coaching. At least I usually compensated my team with a pint at our local Wetherspoons after for putting them through all that.
So I began looking for ways to improve those conversations. The best articles I could find were inspiring and thought-provoking, but…
I believe that every team must always have a plan in place to improve both the team and ways of working continually. Otherwise, your team can never be on a steady path to becoming the best at what you do. Things like that don’t just happen by chance. And in the second half of 2020, senior leads in Product Management and I set out a first plan to get us there. You can find it here (disclaimer: I’m very proud of it, and the progress we made).
Talking in terms of outputs is one of the most straightforward changes you can make to communicate well at work. A simple reframing of what you plan on doing can be the difference between someone thinking you’re fully on top of things or confused and in need of help.
But what does it mean to talk in terms of outputs?
I’m worried that our most important project is at risk as one team looks like it might bottleneck others. What are we doing to address this?
Next week, I’m sitting down with that team lead to explore how risky they…
This post originally was published on mindtheproduct.
At most high-growth startups, engineering time is scarce, and targets are high. If you work on the wrong opportunity for a few months, you run a significant chance of missing your results. So how do you get the maximum amount of value out of a resource-constrained tech team?
Product manager Nicolas Hemidy and I put our heads together to work out the best solution. We had one core belief — if we always had a well-researched set of opportunities to prioritise, we would spend our time working on the right things. …
The one-sided vs two-sided debate has been one of the most fiercely fought of 20th-century. The feud began in the fifties when psychologists Marks (1951) and Jones (1952) squared off with Burke (1953) and Hick (1952). Jones believed one-sided was the only methodology required. Burke said it could lead to no real learnings and we must instead always use two-sided tests. I like to think they settled their dispute in a back alley outside a remote local university bar. But on paper neither side ever conceded.
At the end of 2019, the mood in the Growth team was low. The team had been working individually for long stretches, shipping impactful but smaller-scale CRM tests to hit our targets. The work was tedious, uncollaborative, and gave little room for creativity.
We did hit our end of year objective for driving increased orders, but it was at a cost. People were not excited by their roles. The siloed, non-explorative approach we’d developed was not sustainable. First, we were running out of valuable small-scale optimisations (a topic for another day). …
A room full of smart people with access to a boatload of information on the internet about A/B testing wouldn’t make basic mistakes when experimenting, would they?
In our case, absolutely. Our room full of smart people with access to a boatload of information made a boatload of mistakes in the early days of experimenting.
In 2019 one the biggest mistakes teams across Gousto kept on making was extending tests to try and reach significance, while using the same basic online statistical calculator and 95% significance threshold each time they extended. Experimenters would run a test for a pre-determined time…
Head of Product at Gousto