Why Product Analytics Is More Important Now Than Ever Before

May 9, 2021

Why Product Analytics Is More Important Now Than Ever Before

and why you need to be investing in it

I believe Product Analytics will be the next big thing in the data space. It might not reach the dizzying heights of Data Science and I doubt it will ever get a “sexiest job of the 21st century” article published about it on Harvard Business Review (HBR) but I’m certain it will make an equally impactful contribution to any company that invests in it.

Product-Led Growth (PLG)

Product-led growth is a relatively new concept coined by Open View Partners who describe it with the following definition:

“Product led growth (PLG) is an end user-focused growth model that relies on the product itself as the primary driver of customer acquisition, conversion and expansion.”

The core philosophy being, you remove the barrier to entry and get users to sign up for the product (acquisition). This, in turn, results in the user trying the product (activation) and falling in love with it (retention). You can see where I’m going with this. If users love your product, they’ll share it with their friends, families and colleagues (referral) all of which leads to money (revenue). Big deal, I’ve just described the AARRR model, right?

On paper, sure! But the big difference is the mindset on acquisition, activation and retention. Companies like Spotify and Slack achieved growth by removing the barrier to entry using a freemium model which allowed users to sign up and try the product. But it was because of the razor-sharp focus on the end-user that they were able to develop platforms that people loved and were willing to pay for. The product became the acquisition strategy.

Traditionally, companies have thrown huge sums of money into acquisition through marketing-led growth (MLG), they may have even tried to optimise the sign-up process, but ultimately, they sucked at making products people loved and then sat around wondering why they weren’t activating and retaining customers. I’ve seen this first hand!

Despite my examples, PLG is not limited to SAAS companies using freemium models. You can also offer trials or highly subsidised first orders if you’re a subscription service. If you can’t do that, you can make the sign-up processes seamless by removing friction. But going back to the previous paragraph, if you don’t make a product that people love you’ll trip up where it counts — activation and retention.

Enter Product Analytics

Many startups and even established organisations are realising that MLG is unsustainable on its own. PLG is non-optional and companies are investing in Product more than ever before. Product Managers are given actual autonomy in delivering what’s best for the user and not a to-do list from marketing teams. Product Directors, VPs and CPOs have more operating budget to build out cross-functional product teams. With this growing investment in Product, accountability becomes critical for Product leaders and quantifying the return on investment is inevitable.

This is where the need for Product Analytics begins to arise and why it’s more important now than ever before to start building Product Analytic capabilities. Investing in Product can no longer just be about hiring Product Managers, Designers and Engineers. Analytics should form part of the acceptance criteria — is this feature useful for our customers? Is the code bug-free? Can we measure its value?

But the Product Analytics team is not your run-of-the-mill analytics team. Product Analysts need to go beyond extracting data and crunching numbers, they need to:

Understand the customer
Quantify the user experience and behaviour
Identify core and proxy metrics
Build a measurement plan
Find opportunities
Design experiments
Communicate clearly
Aid decision making and discovery
Make recommendations
Steer roadmaps
Support in setting OKRs
And above all else, prove growth through product

Closing remarks

Behavioural data does not provide black and white answers like transactional or marketing data so even if data is easily accessible, non-analysts might draw incorrect conclusions, find false positives or assume correlation equals causation. Product Analysts will spend a significant portion of their time in the grey areas, trying to separate the signal from the noise. They’ll be emotionally invested in the product, but remain unbiased in the measurement.

If you’re building a business case for a Product Analytics team and you need to provide an ROI, then remember this: