How to break data silos for customer success

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Today, most software-as-a-service (SaaS) companies maintain multiple internal tools to effectively manage different customer touchpoints, like support.

While these platforms work well in their own space, they lag behind in one key area: Data access. Essentially, the tools keep relevant customer data siloed, creating a major disconnect for teams looking for insights to power customer success (CS). This hits repeat purchases, upsells, cross-sells and expansion from customers — affecting the net revenue retention (NRR) metric of the company.

Velaris’ customer success platform

To address these challenges, London-based Velaris offers a customer success platform that brings together complex customer data from different tools on a single-pane dashboard and uses artificial intelligence (AI) on that information to produce insights for CS teams. The company just announced it has raised $4.7 million in a seed round of funding.

“We have a flexible stack with high-performing analytical services enabling flexible near real-time querying on various types of datasets. The product utilizes NLP [Natural Language Processing] and Predictive(AI/ML) algorithms to extract early signals enabling actionable insights,” José Fernández-Castaño, co-founder of the company, told VentureBeat.

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Velaris connects data streams from touchpoints such as email conversations, product usage, renewal dates, support tickets to create a unified view of the customer. Then, using this information, users can create business dashboards and reports with insights to identify critical trends and forecast the net and gross retention for the future. These insights empower teams to target customers with the right kind of nudges and expand business with them.

Automation for nudges

While insights prove critical, the job of Velaris doesn’t end at this stage. The solution also includes automation to act on the data signals. For this, the team has to build a scenario flow utilizing a drag-and-drop interface. This way, it easily automates renewal emails, tasks, playbooks, surveys and actions for specific events, such as the creation of new accounts, or segments like unhealthy customers.

“We not only bring and generate the relevant intelligence, but we arm the reps with tools to do their day-to-day job easier, including project management capabilities, orchestration of automated cadences, communication features, etc.,” Fernández-Castaño noted.

The company started last year and claims it has already seen fivefold growth since then. 

“Velaris helped us immensely in structuring our CS operations. The platform has enabled us to unify important information from disparate data sources, remove the silos and create an actionable 360-degree view of our customers,” Anuj Sharma, head of customer success at Vector AI, said about the product. “With smooth onboarding, great service and an excellent product, we’ve already started to see its value. We’re very impressed and happy with the switch we made.”

Plans to expand AL and ML

With this round of funding, which was led by Octopus Ventures, Velaris plans to expand its AI and ML engine to take the intelligence piece to the next level. The company will also use the capital to develop its R&D, product and go-to-market teams.

The investment comes at a time when more and more SaaS players are looking to double down on their retention efforts. During the pandemic, companies with a deep focus on retention came out as the ones surviving or, in some cases, even thriving.

As a result, several players supporting retention efforts, such as HeadsUp, StepFunction and Gainsight, have come to the fore and seen increased support from investors. Velaris claims to differentiate itself from them with its intuitive UX and UI, as well as the ability to make complex data combinations and visualizations without needing an analyst.

According to Allied Market Research, by 2030, global SaaS is expected to grow at a CAGR of 18.8% and be worth around $700 billion, up from the estimated $121 billion in 2020.

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Shubham Sharma