In our current climate, SaaS companies need a new approach to big data. The same old processes aren’t working anymore for employees or customers, and new trends are emerging as customers demand more from their SaaS products. In order to stay relevant and competitive in the evolving SaaS marketplace, organizations must find innovative ways to utilize big data to understand their customers, their needs, and their product’s unique positioning.
Here are three major big data trends in the SaaS industry that you need to know about now to keep your company ahead of the curve.
Trend #1: Increased emphasis on product-led growth
One major strategy that has gained momentum within the SaaS industry in 2022 is product-led growth. Product-led growth is a SaaS strategy that uses the product itself to drive new business and customer retention. According to a Gainsight report, 91% of SaaS companies are planning to increase their investments in PLG strategies in 2022, and 47% say they’re making efforts to double their investments in PLG strategies.
PLG strategies are effective because they enable customers to test-run products before they commit to an investment. This approach also makes it easier for customers to experience different types of software and transition between products. Of course, in order to execute an effective PLG strategy, SaaS companies must utilize big data. Product usage data and customer data are paramount for an effective PLG strategy because they provide essential insights on the customer journey. They also help provide signals on when to intervene for upsell and expansion opportunities.
With the insights mentioned above about the increased focus on PLG, it’s clear that big data will become even more important than ever before in the SaaS industry. Companies not only need access to this data to build successful strategies, but they also need to be able to draw conclusions from it to communicate the value of their product with customers.
Trend #2: Reliability is still a challenge across the board
As PLG strategies grow in popularity and SaaS companies become more reliant on data, the issue of reliability still remains. Gathering data throughout the customer lifecycle is one thing, but gathering accurate and reliable data that can actually make an impact is an entirely different challenge. Although tons of new tools have emerged in recent years that aim to improve data quality, data still isn’t as reliable as it should be in many cases—it’s often disconnected, outdated, and limited. This challenge of reliable data inhibits SaaS companies from telling unified, comprehensive stories to their prospects and customers, which can ultimately damage business.
In order for SaaS products to reach their full potential, teams not only need access to accurate raw data insights, but they also need a streamlined and consistent way to interpret that data. To make this possible, organizations must employ tools that simplify data insights for employees and customers alike. This is especially true for customer-facing teams like customer success and sales as they work to prove the value of their product.
Trend #3: Data accessibility and efficiency are non-negotiable
We’ve established that PLG strategy is gaining popularity in the SaaS industry and that access to reliable data is more important than ever. With this in mind, product usage data and customer data not only need to be accurate, but they also need to be accessible by all necessary parties.
Data is incredibly powerful, especially for customer-facing teams. It can be used to build business growth strategies and cultivate better relationships with customers. However, if this data isn’t easily accessed by the people who need it most, it loses its potential for impact. In order for data to be fully utilized, it needs to be:
- Cleaned up and translated into a streamlined format that allows for deeper analysis
- Housed more efficiently, so that large amounts of data can be stored and crunched in seconds
- Simplified from its technical, complex form so that more people can use the data to draw conclusions, create actionable plans, and incorporate it into their tactics
Fortunately, several tools have emerged in recent years that aim to make all of the above possible, but many SaaS teams are still stuck in silos with disparate systems. Platforms like Salesforce are home to large amounts of data that can be accessed by non-technical users, but these individuals often still need additional support and time to convert Salesforce data into actionable insights. While AI has the potential to fill this gap in the future, it still has a long way to go. Ultimately, to remain competitive in today’s landscape, SaaS companies need to empower even their non-technical employees to access, use, and draw insights from customer data.
It’s clear that big data is essential for developing overall strategies as well as the day-to-day tactics of customer-facing teams, but data for just strategy creation is not enough to retain customers. In order for SaaS teams to stay competitive in our current economy, they need to be able to easily sift through data to tell comprehensive, at-a-glance stories about the value of their product. However, many teams are unable to pare down data without extensive support from technical team members, which limits overall scalability and productivity.
Staying ahead of the trends
Moving forward, the next step for SaaS teams is to find an easy way to enable their non-technical team members to translate obscure data into clear, compelling stories. By making big data more accessible and reliable in this way, they will be able to stay ahead of the curve, build successful strategies, keep operational costs low, and retain customers year after year.
About the Author
Nikola (Nik) Mijic is the CEO & co-founder of Matik. Prior to Matik, Nik worked in various roles and companies that focused on helping customer success teams retain and grow customers. At LinkedIn, he built internal tools that helped them leverage LinkedIn data in their presentations and be more efficient while doing so. Nik was the first non-engineering hire at Bluenose Analytics, where they built a platform that used predictive analytics to engage at-risk customers and identify drivers of churn.
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