How understanding your product’s maturity curve can save your business
Don’t go the way of Blackberry or Nokia. Using datascience to understand how your product matures can save you from corporate death
It is not the strongest of the species that survives, nor the most intelligent that survives, rather that which is most adaptable to change.
Every new product that’s introduced to the market has a maturity curve. It starts with the initial euphoria of fast growth, the glamour of being the new belle of the ball and the adoration of users.
But pretty soon, the shine wears off. The product begins to lose steam. It has a steady, faithful user base, but eventually newer entrants to the market take over, and the process repeats.
Everyone knows this. But the biggest issue frustrating product managers is the lack of data to foresee. There isn’t a lot of product specific data out there to help product managers make data-driven choices. Most design gurus (like IDEO) often advise product managers to get out there and test it (which is management speak for getting your own damn data ☺). But even IF you manage to do that, the sample size is going to be ridiculously small.
So what’s a product manager gonna do?
We subscribe to Alon Halevy’s thesis that an abundance of data can be unreasonably effective at making predictions. We don’t think that you need to follow a theorized model of the product maturity curve. Instead, you can (and should) use the data at-hand as proxies for the insights you need.
Product maturity curves can be inferred from numerous market signals. Category benchmarking is an excellent way to predict or infer your own product curves using data from a basket of similar products.
Here’s a great way to do this: Lets take, for example, the Blackberry Z10 and the Apple iPhone 5S. Both products were released in 2013, look somewhat similar and share similar performance specs.
Here’s sample JSON output for the Blackberry from the Semantics3 API:
Similarly, here’s a sample JSON output for the Apple iPhone 5S from our API:
Through our database, we track a number of market signals for e-commerce products, which are used to calculate our proprietary ProductRank, which in turn seeks to measure the absolute popularity of a product in our vast catalog against everything else in the database.
High ProductRank (i.e. a rank of 1) means that a product has very high volumes of sales, while a low ProductRank (i.e. ∞) means that a product has low sales overall. Taken over time, the decay of a ProductRank can be used as a proxy for product maturity curves.
As this chart makes abundantly clear, although it’s pretty evident that the iPhone 5S dominates the Blackberry, what’s also interesting is how its ProductRank decays over time. The iPhone goes through several phases when its ProductRank jumps up, then decays with multiple “sparks”. But more importantly, it takes much longer for its ProductRank growth to flatline.
The Blackberry, on the other hand, has far fewer “sparks”. Not only that — apart from the couple of times its rank spikes, its ProductRank decays pretty rapidly. It’s pretty evident that the Blackberry Z10 wasn’t a very popular product, with both the CMO and COO being ousted over its failure.
By using both products as extremes, you could actually get a very reasonable estimation of how well your product might perform over a similar product cycle, if it belongs to the same category, and shares similar product characteristics. If you get data over an even longer time line, and do it for a higher number of products (i.e. even more data), your estimations would only get more accurate.
Published at: November 03, 2014