Why Behavioural Data and Behavioural Analytics Have Become the Secret Ingredient for Product Led Growth!

Why Behavioural Data and Behavioural Analytics Have Become the Secret Ingredient for Product Led Growth!

SaaS businesses, more than any other, is dependent on the customer. On his likes, dislikes, buying prowess, even whims and fancies. When companies discovered this and User Interface (UI) and Customer Experience (CX) overtook other measures of SaaS success, the way business is done shifted from ma

Behavioural Data and Behavioural Analytics for product led growth

SaaS businesses, more than any other, is dependent on the customer. On his likes, dislikes, buying prowess, even whims and fancies. When companies discovered this and User Interface (UI) and Customer Experience (CX) overtook other measures of SaaS success, the way business is done shifted from market oriented one to a product led one. This brought its own challenges beyond just delivering cracking products – how to optimize users’ ROI, increase loyalty and renewal, up retention, reduce churn… how to keep the customer happy with your services so that his customer journey is sans any bottlenecks, and he stays with you for life.  

But how to measure happiness, experience? For instance, can a Customer Success Manager (CSM) forecast retention and revenue rates based on users’ behaviour? How to measure the effectiveness of your onboarding strategy? Can any of the above be bettered? 

Enter the SaaS dragon – data. Data has always been SaaS superpower. And the increasing need to monitor user progression has given rise to tools that can analyse user actions and patterns that can affect your product roadmap, revenue goals. So much so, that now behavioural data and behavioural analytics are considered the secret ingredients for product led growth. 

But what are behavioural data and behavioural analytics? 

At the very basic level both are interlinked methods of mapping your entire customer base’s engagement with your product or services.  

Behavioural data: This is the automated process of raw data collection for the purposes of further analyses. So, each time a customer logs in, swipes over services, and navigate a site or an app, it gets recorded. CS teams can then see this aggregate data to understand common patterns of usage, or look at specific journeys or user flows. Behaviour data is essential for behaviour analysis 

Behavioural analytics: This is the tool SaaS companies swear by to gauge digital user actions within your product. It helps organise, segment and make sense of your raw behavioural data. This helps you map each user’s behaviour with your product, right from the clicks to his entire journey. This analytics data helps you figure out what the users like and don’t like on your site, service or app, bottlenecks if any and then make adjustments accordingly to make your product more valuable to them. 

What makes behavioural analytics a SaaS growth success mantra? 

What is the one biggest worry of SaaS businesses and their marketing teams? “Are customers using the product at all?”, “If yes, then how is their experience?” “Are they happy with our services?” “Are they ready for an upgrade?” Behavioural analytics can provide inferential answers to all these questions by deducing customers’ needs and demands. 

Without analytics, collation of behavioural data is a waste. Without analytics, CS teams can only rely on insufficient demographic data, or what’s called ‘vanity metrics. So, as Streaming, Sharing, Stealing: Big Data and the Future of Entertainment co-author Michael D. Smith puts it, “If a company wants to personalize its service to users, it needs their behaviour data. A streaming movie platform can’t know that a user loves horror films, for instance, simply based on their age, gender, or nationality.” 

Behaviour analyses can help companies come up with detailed and segmented metrics that provide an insight into factors such as 

  • Where do users get stuck – the bottlenecks within the software/service 
  • How do they react to investment/upsell messages? 
  • Did they like your latest feature changes? 
  • Which ads on your site were most effective 
  • What is the time period between first click till conversion? Can you improve on that?  
  • What can you do to make your product more successful for the customers? 

The above questions can be modified – and you can set any parameter for analyses. And all of it can be used to enhance UI and CX. That is why, even though the computing of the raw event data is automatic, most companies struggle to put the data into effective use to read their customers’ needs and take action accordingly. What makes a behavioural analysis such a complicated effort? It usually harks back to a lack of clear vision on the company’s part.  

  • Do you have clarity on the path you want your user to take? 
  • A clear product roadmap? 
  • An achievable set of short- and long-term goals? 
  • Market demands and product change requirements? 
  • Are you looking for revenue generation? 
  • Or an increase in customer satisfaction? 

If yes, then analysis is easy. “Analysing generic data doesn’t magically produce answers to unidentified problems,” Drew Hendricks, a technology writer for Inc. Teams, write in this blog; “Companies must first identify what they want to achieve and write down the paths they expect users to take. Only with pre-set expectations can teams identify whether users are deviating from the ideal path and redirect them.” 

What goes into a successful behavioural analysis? 

The first step is to know that behavioural analytics is a combination of various business analysis tactics applied to two major areas of behavioural data: customer segmentation and event tracking. This gives the SaaS service provider a total picture of what his customer looks like, his habits, financial status and even his moods. With this data at hand, you can enhance service or product features to tie in with users demands. And when your product roadmap apes/anticipates user actions, you can align product development, marketing, launch and upsell strategies accordingly, and provide a customer multiple success journeys within your product. 

Successful behavioural analysis tips 

  • Select the right goals and metrics: To figure out whether your users are reaching the right goals, select KPIs that indicate progress in that area.  
  • Define customers’ desired outcome: What are the most common path users can take to reach their goals? All user journeys should end in the most desirable outcome 
  • Tracking the right events: The most common mistake is to track everything. This needs to nothing but data clutter. So, use the user flow data to demarcate the specific events you need to track. Involve all your teams for this. 
  • Set unique user identification tracker: Since users exist across multiple platforms in the cloud world, it’s not easy to track unique users because one user can appear as multiple people. So, assigning a unique identifier that doesn’t change across devices and platforms is essential: this can connect their touch points along multiple journeys.  
  • Take action, implement analytics: Use the analytics API to integrate with your product/service. You can update the analytics as you go along, but if you don’t implement you’ve analysed for nothing. 

Remember: Data never lies 

Even though user behaviour is not defined by your product’s abilities alone, it does well to use the behavioural data to measure the effectiveness of KPIs (Key Performance Indicators), and tweak it accordingly. For PLG companies, product is the source of growth and revenue generation. So, the product is the single source of data collation. And data, if analysed correctly, will give them all the answers they need to grow and expand.  

Studying customers with segmentation, and separating them based on user characteristics and behaviours can help build impeccable customer profiles, use funnels and cohorts to customise products further and make marketing more profitable for each segment. Behaviour data can help you find the most profitable customer journey, thereby increasing customer lifetime value, reducing churn, and also Customer acquisition costs. 


Great SaaS companies have great customer success stories, which they have achieved with effective behaviour data collection and analytics. They know how their customers were acquired, what had them renew, why did they churn, and they tweak their application’s functionality serve their customers better than their competition by understanding them. They use behavioural analytics to boost conversion and revenue, capitalise on opportunities, tailor marketing efforts for optimal ROI, speed up digital transformation and finally, for the most important aspect of cloud businesses: to combat fraud.  

In short, behavioural data and behavioural analytics have the potential to help SaaS businesses create a fab digital presence by engaging with customers, by tracking their decision-making methods, their perceptions, motivations and priorities, and keep them coming back. Behavioural Analytics is the Brahmastra using which you can reach the right customers with the right products and services at the right time, real-time. 

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