Its been seven months since Twitter officially made the 140 to 280 character limit shift, and by all indications they have pulled it off spectacularly. I remember the outrage when the move was first announced – many Twitter diehards felt the idea was superfluous and would overnight dilute its ethos of forced brevity, painstakingly built over a decade of toil. Trolls had a field day – people were imagining what Trump @280 would do to world peace!

But the changeover happened, and data shows it is working significantly better than before. No one’s complaining – to the contrary, Twitter seems ever more useful. My own usage of twitter and the time I’m spending on it has since jumped significantly. And interestingly enough, the Twitter stock is up almost 100% since the rollout (though that doesn’t necessarily imply causation!).

Betting your Company on a Single (Product) Decision!

If one were to reflect on this – we’d realize this move was unparalleled, something I’d describe as “The Mother of all Product Decisions”. I can’t think of any other example, where a company’s future was bet on a single (product) decision. Twitter is a public company, a globally loved service, and in early-mid 2017, its growth was plummeting, and the stock was tanking. Wall Street analyst types were baying for the CEO’s blood. If the 280 character transition were to have bombed, it would be a public fiasco, possibly leading to the CEO’s removal. But to everyone’s relief, the transition went off smoothly.

This set me thinking about the broader implications of Twitter’s decision from a product management standpoint – where did the conviction for their decision come from, how did they back this call and finally pull it off? And more importantly for lesser mortals – what are the lessons product managers/owners can derive from this very special happening?

Where do Product Ideas come from?

In general, product people are confronted with ideas, opinions & suggestions all the time. They come flying thick and fast, from left, right & center – when you’re at the coffee shop or water cooler, in meetings or focus groups – they keep pouring in via emails, text messages, Twitter, social media, customer support et al.

Should I build that new feature, or just copy my competitor? Do I need specific data to back my product assertions, or that two year old user research I read in the newspapers would suffice? Do you trust your guts, or just listen to your boss (and in the process, make him happy)? These questions have no perfect answers – but what would help is to have a mental model or a decision framework that provides a structured way of looking at the flow of incoming product ideas.

Kipling’s 6 Honest Serving Men as a Product Management Decision Tool

I’ve always been a big believer in Rudyard Kipling’s 6 Honest Serving Men Principle – “I keep six honest serving-men (they taught me all I knew); Their names are What and Why and When And How and Where and Who”. Throw it at any problem, and it always gives you a good starting point to analyze any situation and build your thoughts & arguments upon.

I’ve frequently fallen back on it to analyze product situations and have rarely been disappointed. With time and experience, I’ve had this realization that (as applied to product situations), there are 2 “men” of those 6, that tend to matter more than others – “who” & “why”. This might be surprising because you’d expect “what” & “how” to be paramount. “What” & “how” are important no doubt, and usually get called upon to explain the “content”. But “who” & “why” have something else going for them – in many situations, they capture the context better than the others. And as they say – “Content is King, But Context is God“.

6 ways Product Ideas get Originated

To help product managers & owners add structure to their thinking, and slot the barrage of incoming feedback/ideas into a logical, repeatable mental model, here’s a simple framework (focused on the “who” & “why”). Most incoming product ideas can be slotted into one of the six originating points – each of these would require a different approach to analyze and act upon.

– One Person’s Opinion: Imagine meeting your boss at the water cooler, where he casually shares an idea. Or one of your investors mailed you his thoughts on the future product direction. Or it could be just about anyone else giving you their point of view – all of these are examples of what is essentially a One-Person’s-Opinion. The way to deal with this – take the suggestion at face value and then put it through the rigours of your own decision making process (refer: LinkedIn CEO Jeff Weiner’s post explaining OPO, which is where I borrowed the term from!).

– Multiple Pointers: Suppose three different customers asked you for a particular product feature. So now you have “multiple pointers” about something which seems to have a strong case and merits further exploration.

– Vocal Minorities: Vocal minorities are tricky phenomena to deal with, specially at early stages of your product’s evolution. This might be a (numerically small) set of users who love your product, and have very strong opinions/suggestions about it in a very vocal/loud way. (I’ve personally had bad runs with vocal minorities during SlideShare’s early years, some of whom ping me [read pester:)] on social media even to this day!) The problem – they feed off your psychological biases, and often become catalysts in driving the product in undesirable directions. Needs careful, mature handling. (Suggested reading – Stop listening to just your vocal minority, work to understand your silent majority instead).

– Insights: Sometimes your idea’s genesis lies in statistical data, market trends or customer research. Or it’s something your competitors did so successfully, copying them makes sense. Example – read this excellent case study of Bira, the Indian craft beer and how its founder identified the optimal product attributes, because he had gained deep insights from importing foreign beers into India.

– Derived Decisions: These are product decisions you make based on past internal experiences. You might have tried various experiments in the past which didn’t work, and that shapes your future conviction about what to do (or not do). Example – think Google, and how it has virtually given up on building new social applications. It’s likely that given their previous (numerous) failed attempts at social products, they have come to the realization that their company is engineering centric, doesn’t “get” social, which is best avoided.

– Hunch: Hunch is like your sixth sense about what works or doesn’t work. It’s a rare skill to have, but if you have someone on your team with a good product hunch, that’s a VERY valuable resource. The best example of hunch off course is Steve Jobs with his telling quote “A lot of times, people don’t know what they want until you show it to them.”

If I had to stick out my neck on the Twitter case, I’d imagine it was a combination of derived judgements & specific product insights, while ignoring the (opposing) vocal minorities. They must have had real data (maybe A/B tests or something else thereof) why the 280 character limit would work. If Twitter were still a small startup, one could have attributed it to its (brilliant) founder’s hunch. But given it’s a public company and high on public/political visibility, unlikely this was just a hunch. Its fair to assume that in large organizations, such pivotal decisions would need the backing of objective data-points and scientific measurements.