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SaaS Wars: Why data-led strategies can be a game changer for SaaS

What is data analytics? Although the inception of analytics can be traced back to the 1800s, the concept acquired a life of its own only in the 21st century, when big data became a buzzword – unmissable in any conversation. How often have we been told ad nauseam that data is the new oil?

Today, when we are asked ‘why analytics’ we have only a simple answer to offer: data analytics is a tool that supports good decision-making. And as any business leader or entrepreneur today can attest, creating opportunities and solving problems are a result of good decision-making. This is particularly crucial in 2023, when SaaS leaders are at the forefront of innovation and staggering levels of growth. But their work is only getting started – with the market becoming more competitive, these high-growth firms need to determine how to grow sustainably. So, how do these leaders stay ahead of the game?

Of course, no good decision arises from a single source of truth. Context, human expertise, and research are the cornerstones of business acumen. However, data analytics is the indisputable differentiator, with data-led SaaS firms growing by more than 65% annually. And it’s easy to see why.

Getting a head-start

Let’s start with the most basic motivation – integrating analytics with your product saves time and money. When leaders have timely clarity on redundant features, upcoming market prospects, and complex pricing structures, they can act swiftly to make decisions that move the needle. This could help the SaaS firm adopt relevant strategies and maximize value for customers.

For instance, Zeda and Heap Analytics both use machine learning to track and analyze feedback, sentiments, customer demographics, and revenue. These data points are then translated into insights which feed directly into product development, helping trace the product’s roadmap.

Insights discovery made easier

With data-led growth, companies are also able to relieve the burden placed on intuition and guesswork, and turn to research, context and analytics for answers. But these answers don’t always come from sophisticated reports and time series analyses alone. Analytics can go a long way in giving firms a complete and clear picture right from the data collection stage. This is more important for firms seeking to improve user experience and customer retention. By using feature tags and heatmaps, firms can track which feature of theirs is the most used – by whom, when, where, and why.

UI-centric platforms today deploy feature heatmaps to denote areas that users engage with the most using a color-coded system. Red indicates high activity where users spend the most time clicking or hovering, while blue denotes low activity.

Customer relationship management for risk mitigation

The final motivation is risk mitigation. There is no bigger threat to high-growth ventures today than churn risk. With predictive analytics and analyses, it is possible to identify operational inefficiencies and build a culture that puts the customer at the center. A recent concept that builds its culture around customer journeys is Revenue Operations, or RevOps. RevOps captures the entire revenue cycle by aligning go-to-market teams. By leveraging analytics, these teams can gain insights on market trends, buyer sentiment, and revenue outcomes.

At Tesser Insights, our RevOps team ingests weekly data from email campaigns, offline outreach, sales reports, outbound advertizing and social media feed into TI Pro. Although this data is often derived from unique sources, having a single repository to track and evaluate related metrics helps the team gain a comprehensive picture of our performance. Not only does it enable us to periodically finetune our approach, but also alerts us to which software is working well for us – and which isn't.

While there are plenty more reasons as to why data is the fuel that will fire SaaS engines, finding the right ecosystem to enable this transition can be tricky. For SaaS firms which seek to align disparate verticals and draw insights from isolated sets of data, using several analytics tools can feel akin to moving up the creek without a paddle. One way to simplify this uphill battle is by adopting an analytics ecosystem with capabilities that covers every tool of the analytics lifecycle. Such an ecosystem will not only yield opportunities for growth, but also establish a data foundation that can withstand the test of technology obsolescence.

If SaaS is here to rule, data analytics will only prolong its reign.


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