Riding the Waves: How The Elliott Wave Theory Mirrors the Product Management Lifecycle

First of all, I should mention that this isn’t going to be a boring article—I think.

And yes, I heard you groan after reading the title.

Things like “theory” and “wave” sound a little too “mathy” for some people, and I understand. It isn’t going to be boring, so read on.

First things first.

A little about me… I have something of a background with crypto and blockchain-related stuff. This means that I’m familiar with technical charting concepts like the Elliott wave theory and whatnot.

Useless information for now, really. But stick with me, okay?

Another thing to know is that I also started taking this product management course that Pendo put together, a month ago.

Finally… I don’t think I’m shit at expressing my ideas through words.

Meaning, I think I’m a fairly decent writer.

Taking The Course, And What I Learned

The first (and most obvious) thing that stood out to me during the course was the Product Management Lifecycle.

Pendo defines this as a framework for “conceptualizing, building, launching, and iterating on software products” from start to finish.

This means that every product manager has to follow a pre-defined set of steps from the initial idea for a software solution, to the building, to the launch, and even the improvements after launch.

Sure, product managers can chuck the product management lifecycle thing into the bin, and launch products haphazardly (this is called the feature factory model, BTW).

However, following these steps is a proven way to make sure that any product performs as well as it can on and off the market, in terms of revenue and post-launch market share.

Pendo also draws these steps out as illustrated here:

The product management lifecycle

Source: Pendo

Product managers need to make sure that designers and developers follow a predefined set of steps, from defining a clear business outcome to understanding the pain points of the market, to validation, to actual building, then launch and so on.

There are different versions of this lifecycle though.

Other sources on the internet put out the product management lifecycle as “Ideation, planning, design, development, launch, maintenance and sunsetting”.

In a way, both of these versions say the same thing:

Don’t just jump into developing and launching a product without planning.

You’ll likely set millions of dollars on fire, waste the time and energy of several developers/designers, and possibly get fired in the end.

This Is Where It Gets “Sciencey”

Remember I mentioned I had a background in financial analysis and such?

While taking the course, I noticed similarities between an interesting technical (financial) analysis tool, and the product management lifecycle:

The Elliott Wave Theory.

You don’t have to know much about Ralph Nelson Elliott and his complicated theory.

Just that it suggests that the prices of assets (like Bitcoin for example) don’t just go straight up (or down) during rallies and dumps.

Instead, they follow a five-wave pattern—sort of a zig-and-zag, where the price hits $2, reverses to $1, rallies again to $4, reverses to $3, and so on until it hits $5.

This explainer from Investopedia does a great job of clearing things out.

The Elliott wave theory

Source: Investopedia

So what does this have to do with the product management lifecycle?

Well… (most of) everything. Just stick with me.

Remember how we said something about “launch and sunsetting (or death)” in the life of a product?

It turns out a product’s journey almost perfectly aligns with the Elliott wave theory, from the moment it hits the market (launch) until its sales start to decline (sunset).

I’ll explain what I mean:

Launch and Introduction (Wave 1)

This is the first wave of an asset’s price increase, or a product’s launch.

Imagine you’ve just launched a shiny new product—a donut analyzer app, maybe, that takes a picture of a donut and predicts whether it might be tasty or not.

And the market absolutely loves it!

Everyone has their phones out, taking pictures of donuts at restaurants, sharing screenshots of their donut predictions on social media and whatnot.

We even have donut influencers popping up and doing free reviews of your product, beause “it’s just too good”.

Wave 1 completed, four to go.

Growth and Early Adoption (Wave 2)

The next wave is a downward facing one.

After launch, the market takes a break from donut analyzing. Your product’s market share is still pretty good, and people still whip out their phones from time to time.

However, your app’s usage metrics aren’t as good as before, and feedback starts pouring in.

This is wave 2.

You take this feedback, add a few updates to the product, and reenter the market.

Rapid Growth and Market Acceptance (Wave 3)

This wave is the most important and the longest uptrend among the five.

The new update to your donut analyzer app is fire, and the market loves it once again!

Instead of just donuts, users can analyze rice, drinks and even akara if they felt like it.

More people download your app, and sales skyrocket again.

Then comes the fourth wave, which is of course, a downward trend again.

Maturity and Peak (Wave 4)

The thing about hype is that it often fades.

In this phase, your app is still as good as ever, but sales have slowed down. More people now prefer the good ol’ way—buy a donut and take a bite first, rather than whip out a phone for another round of pastry analysis.

Also, more companies are starting to realize the potential of food analyzing apps, and are starting to apply this tech to cars, houses, etc.

You need to work harder to keep your project relevant.

This phase is the maturity and peak phase, and is marked by lower demand—which is fine, it happens all the time.

Saturation and Decline (Wave 5)

All good things must come to an end, right?

If your product falls from wave 4 into wave 5 without a major table-turning event, you might see the final (and possibly weakest) surge in activity.

After this, numbers start to decline.

The market is saturated, and newer donut, house and car analyzers are popping up.

At this point, you, the product manager might need to re-strategize and either launch a newer and even shinier product, or add a game-changing update to the original.

Either way, Donut Analyzer 1.0 will inevitably hit the corrective waves (A, B, and C), which signal the end of the product’s lifecycle.

Its only hope is a newer and better Donut Analyzer 2.0, or a different product altogether.

Overall

While the Elliott Wave theory is merely an analogy, it isn’t a one-size-fits-all way to predict how a (software or non-software) product might perform.

Some products last much longer than others in the market. Some products stop being relevant at the third wave, and others never even get past the first wave (Meta’s Threads app is a good example).

We have seen this on several occasions through the years.

Take smartphones for example, like IBM’s Simon Personal Communicator from 1994.

While this phone seems wildly primitive compared to the iPhones we have today, it was all the rage back then.

It featured a touchscreen, email capabilities, and basic apps and likely raked in millions on launch (wave 1).

Considering how we now have newer, shinier phones in the makret today, this phone likely went through all the other stages of “Elliott Wave PM Lifecycle”, with brands like Apple, Samsung and Xiaomi dominating the scene (wave 5).

Understanding how a product moves from launch to sunsetting, and applying the Elliott wave theory to better anticipate trends might be a great way for any product manager to predict, improvise adapt and ultimately overcome in any market 😉👍.

So, uh… I hope you read until the end 😁, and I hope to see you in the next one.