The world of analytics took a turn for the best in the last years, and 2022 looks even more promising.
The global product analytics market size is expected to reach $29.7 billion by 2027, rising at a market growth of 20% CAGR during the forecast period. Such high growth is fueled by the need for companies of any size to understand customer behavior and generate more growth from their data.
All of this, within the context of having more data available than ever before, a non-linear buying journey with more touchpoints, and the need for vendors to act with urgency and optimize products faster.
The future of product analytics is bright ✨ for every vertical; here are some key trends 🗝️ that we anticipate the B2B SaaS and mobile app 📱 verticals will face in 2022 and beyond.
1. A shift to algorithm-based analysis for more advanced analytics 🕵️
It seems quite easy, today, to collect basic metrics, like:
How many active users do I have?
What is the retention of my signups?
All of these KPIs are pretty much covered by any product analytics tool on the market.
But these metrics alone only give the status of the business. They don't tell product teams how to improve their products. So that's why a lot of product teams still rely solely on customer interviews to identify growth opportunities.
The truth is that the data is capable of answering very advanced questions, like:
What’s the difference in activity between people that churn and people that upgrade?
Which combinations of marketing channels are most likely to give me the best quality customers?
To get such answers, you need to perform advanced analysis 🕵️ The way to do that today is to go through consultants or data scientists who download the raw data from the product analytics tools and use advanced scripts to get the results they need.
We believe we’re going to see a shift from query-based analytics, where we simply go and query the data to get a visualization, some metrics, and dimensions, to an algorithm-based analysis, where you take multiple data sets and apply algorithms to find correlations, influence, or even causation.
In 2022, we'll see more of such algorithms emerging, and more consultants being used to find low hanging fruits in the product analytics data.
2. The emergence of opinionated analytics 👏
Basically, what product analytics tools 🛠️ will start to do is offer faster insights driven with specific frameworks built by data scientists. It's like crowdsourcing data science for very specific situations. So when you need to find a very specific answer, you'll be able to go to said tool which will automatically apply an algorithm on your data, and the answer will be generated instantly for you.
The challenge we’re seeing many companies run into is too much data, that’s too complex to customize reports and build queries for, unless you are a data scientist.
Business people want to understand insights fast, and in a self-serve model ⚡
Opinionated analytics tools provide an easier learning curve and more consistency.
While they don't offer a lot of customization opportunities, it’s ultimately fine because the results most companies will get will be far beyond what they were once receiving through query-based analytics, where they had to figure out what data to query, how to correlate it, and how to make sense of it.
We believe strongly in this trend, and that's why InnerTrends is an opinionated product analytics tool with pre-built reports, offering product managers and growth hackers the ability to get advanced analysis fast, with insights they can rely on 📈
3. Product analytics - a hub for other departments, more closely linked with web analytics and customer success.
Product analytics is about analyzing what people do inside the product. But what a lot of product teams have noticed is that the quality of the people that sign up has a huge impact on how the product is used; how well these people are educated about what the purpose of the product is and how it can help them will all have a influence over whether or not the person will be onboarded, if they will retain, and so on.
So marketing attribution starts to be very important for product analytics - bringing the product and marketing teams together 👥 - especially in growth team structures, and helping them make sure that they’re bringing the highest quality leads, while also making sure the product delivers on the promises that the marketing team is making to the audience of the business.
To take it even further, product analytics starts to join product management and customer success teams together, where the focus is on retention and revenue.
As we move towards product-led growth strategies where self-service makes up a large percentage of the growth, the customer success team will need to work with the product usage data - especially when it is enriched with market attributes:
How big is the company that’s using the product?
How many users from that companyare getting into the product?
What are the roles of the people that get into the product, and how engaged are they?
Which sections of the product do they use most?
4. Account-based product analytics to be offered by default.
B2B SaaS businesses are account-based; however, few product analytics tools allow account-based tracking by default.
We believe this is going to start changing because analyzing user-based activity for a B2B SaaS business can lead to wrong interpretation of the data.
5. Higher data accuracy achieved with product analytics linked to the source of truth 🔎
Teams will start to rely more and more on their product analytics to understand:
What needs improvement?
How can we make that improvement better?
And in order to get there, they need their data to be more accurate.
85% data accuracy was considered “good enough” a few years ago. Now, people want to achieve higher accuracy because they are starting to use the data in the targeting 🚩 So we’ll likely see the emergence of server-side tracking, or backend tracking, for at least part of the data in the product analytics tools, because it can offer 100% accuracy ✅
Basically, product analytics will be linked directly to the source of truth for the data of a business. And that's very important because:
💡 More teams will start to trust the data in their product analytics tools.
These are the main trends we see happening in the near future, and we believe all of them will accelerate based on the data that we’ve collected and analyzed from our customers on what they are asking and what they need the most.