Founder Playbook · Sub Club by RevenueCat
10 tactics from Taylor Wells
News Corp's Data Strategy Can Teach Small Businesses
Watch the full episode“Data in itself is not a value add to the business — collecting it, storing it, having it is not value to the business. It's whether or not you're actually using it and turning it into meaningful insights that impact the business. Not even insights just for the sake of insights.”
Collect What You Will Act On — Everything Else Is Noise With GDPR Risk
Collecting everything feels safe — you can always analyze it later. In practice, it creates GDPR exposure, inflates analytics costs, and drowns your data team in noise. Taylor Wells frames data collection as a question of action: for each event, ask 'what decision would this inform and when?' If the answer is unclear, don't collect it. The discipline to not collect saves more than the habit of collecting everything ever could.
“KPIs — don't overthink it. A KPI's usefulness goes down proportional to the amount of time you spent thinking about it. The best ones are just like: how many people use the feature, how many dollars through the feature. If a KPI you need a PhD to design, you probably need at least a masters to understand it.”
Simple KPIs Beat Complex Ones — If It Takes a PhD to Design, No One Will Use It
Complex KPIs look rigorous but fail in practice because no one except their creator understands them well enough to act on them. Taylor Wells observed teams designing elaborate composite metrics that were technically precise but practically ignored. The inverse: the KPIs that actually changed behavior at Disney+ were the ones a PM could explain in one sentence. Clarity of metric beats theoretical accuracy every time.
“When data is driven by a business strategy that informs a data strategy, things are incredibly effective and you're able to really extract a lot of value and learnings out of data. Companies that go out and hire a lot of data scientists but don't really know what they should be focused on — that can work but it so often doesn't.”
Business Strategy Must Drive Data Strategy — Not the Other Way Around
The most common data failure pattern: hire smart people, collect everything, and assume insight will emerge. Taylor Wells argues data only creates value when it's answering specific questions the business is already trying to answer. Start with the decisions you need to make, work backwards to the metrics that would inform those decisions, and only then build the data infrastructure to capture them.
“Catching something early on could be 1x of your cost and then 10x if you're fixing it by clean up downstream — because now you've got new data coming in that you'd have to continually add new iterative processes to fix — and then lastly at the last mile maybe 100x more cost if you have bad reporting that are actually telling the wrong things.”
Fix Data Architecture Early: Upstream 1x Cost, Downstream 100x Cost
Bad data compounds like debt. Taylor Wells worked on Disney+ from the ground up and saw this pattern at enterprise scale: a misnamed event or wrong tracking structure that costs an hour to fix at launch costs ten hours mid-product and a hundred hours — plus wrong business decisions — at the reporting layer. The principle applies to solo indie developers: spend half a day thinking through your event taxonomy before you ship.
“Instead of tracking 'download clicked, download started, download completed', if you're focusing generically you can say 'button clicked' and give keys to those buttons — ensure there's structure in how that's built. Now when someone comes and says 'we need to know when they hit download and then pause and then share' — well, either way those were clicks of those actions and I've mapped that behind the scenes.”
Build Generic Modular Event Tracking to Avoid Rewriting Your Data Stack Later
Disney+ built Glimpse — a generic click-stream event structure where every interaction is a 'button_clicked' with hierarchical keys, not a bespoke named event per action. When product added new features, the data infrastructure didn't break. Taylor Wells recommends this for any product: define a taxonomy of generic event types with keyed metadata, so new product features get tracked automatically without requiring new instrumentation.
“I would generally lean towards use off-the-shelf, use open source things like that early on. But with a clear evaluation structure around when you cut over to other things — and a culture that both acknowledges and accepts that it's okay that things may not be congruent over time when you switch.”
Start Off-the-Shelf but Pre-Plan Your Cut-Over Checkpoints Before You Scale
Most companies make the build vs buy decision once and treat it as permanent. Taylor Wells recommends making the decision with pre-agreed checkpoints: if we hit 10M users, we evaluate switching; if costs exceed X, we evaluate building. Disney+ had Adobe as a backup while building Glimpse internally — and within weeks of launch ($1K/day internal vs $33K/day Adobe), could confidently switch. Decision-making frameworks beat reactive pivots.
“I was tasked with writing an email each day by noon that just had three bullets — and up to two sub-bullets within each. Just an email that said: here are the three things that you should be focused on that happened the day before. And then suddenly dashboards become absolutely irrelevant frankly.”
The Best Executive Dashboard Is 3 Bullet Points in a Daily Email
Disney+ President Michael Paull found dashboards overwhelming and disconnected — too much data showing the same patterns without surfacing what mattered. Taylor Wells solved it with a daily 3-bullet email that synthesized the previous day's key signals and anomalies. The format forced prioritization: only insights that would change a decision made the cut. For small apps, the same principle applies — pick 3 metrics that actually drive action, ignore the rest.
“At a prior publication, when asked what their audience was — who are your readers — they would have strong opinions based on the assumptions of the executives. But when you went back and said 'what is this based on?' — it's often no, it's just 'that's what I built it for originally and I'm assuming that's who my audience is because no one said otherwise.'”
Your Actual Audience Will Surprise You — Validate Assumptions With Data Early
Every founder has a mental model of who their user is — and it's almost always wrong in at least one material way. Taylor Wells saw this pattern repeatedly: executives defending audience assumptions that had never been validated by data. At Disney+, over 50% of viewers were single adults on a platform assumed to be for families. Building the habit of asking 'what is this based on?' early prevents six months of product decisions aimed at the wrong person.
“You have this content that is being consumed heavily by a subset of users and they are religious about it — but you've made it nearly impossible for them to pin that as a rewatch once they've completed it, or for them to find similar kid content. Your root problem is actually because of a broken process with how the profiles were set up.”
Buried High-Completion Content Is a Hidden Revenue Unlocking Opportunity
Bluey had near-100% episode completion rates and high rewatch frequency on Disney+ — but it was buried in a last row, 25 rows down, with no way to pin it after watching. Taylor Wells used completion data to argue for surfacing it prominently. The lesson: completion rate and rewatch rate reveal which content users would pay to keep accessing. If you find any feature with anomalously high engagement buried in your product, move it front and center before building anything new.
“In the first couple of weeks we were able to show that the Glimpse events that we'd built were running us around $1,000 a day in total — like end-to-end cost — and I think Adobe was costing us about $33,000 a day. And so we were able to quickly say let's rip this out because the data is reliable, it's working, we're confident.”
Disney+ Internal Tracking at $1K/Day Beat Adobe at $33K/Day — Within Weeks of Launch
Disney+ built Glimpse — a custom internal clickstream event architecture — with Adobe as a backup. Within days of launch, at 10M users on day one, the cost gap was 33x: $33K/day for Adobe vs $1K/day for Glimpse. The internal system won on cost, data ownership, and GDPR compliance. For most small apps the economics don't justify building; the lesson is to have cost evaluation checkpoints pre-agreed before you scale into vendor lock-in you can't afford to exit.