Mperativ aims to leverage AI in new revenue analytics platform
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Today, Mperativ announced it raised $6 million in funding to launch a revenue operations platform. The company claims the platform will offer users time series data of the revenue supply chain and access to a bitemporal data warehouse.
The organization will use the funding provided by GFT Ventures, with participation from Heroic Ventures and Westwave Capital, to expand the company’s data science, engineering, sales, and marketing teams and to add new features to the platform.
The new offering is designed to provide organizations and decision-makers with a prebuilt, centralized solution for managing the demand engine of sales and marketing teams and monitoring the entire revenue supply chain. For instance, decision-makers can view the revenue supply chain across marketing and sales teams through a single screen, with searchable filters and time-series data that enables them to calculate how much they need to spend on generating leads to develop a specific amount of revenue.
This approach, Mperativ claims, means that organizations don’t have to spend time building their own demand engine or rely on ad hoc solutions like spreadsheets and disparate systems like CRMs, marketing automation, and business intelligence platforms to gather insights into the customer journey.
Addressing the disconnect between revenue-facing teams
Traditionally, executives have had limited transparency over the goals and performance of revenue-facing sales and marketing teams. According to Mperativ’s survey of 500 executives released today, 85% of executives report they can’t clearly map marketing spending to revenue.
“During my tenure at large, public enterprise companies, I repeatedly faced the same challenge. Without a common language or unified way of understanding the demand engine across all revenue-facing teams, it was impossible to align not only on success metrics but ultimately on a united revenue operations strategy,” cofounder and CEO Jim McHugh said.
Without a centralized demand engine, organizations instead deployed different analytics solutions in each department, leading to a lack of transparency over the revenue supply chain and preventing strategic alignment.
“Historically, marketing, sales, and customer success are misaligned around success metrics, making it impossible to understand how go-to-market investments drive revenue outcomes,” said Daniel Raskin, cofounder and chief marketing officer (CMO) of Mperativ.
In the past, organizations that have recognized this disconnect have had to create their own demand engines from scratch, investing heavily in consultants and developers to complete them.
Enabling predictive revenue analytics
The provider’s core offering enables organizations to build more accurate predictive analytics by gathering metrics on the revenue supply chain. This comes as more organizations are keen to leverage the power of predictive analytics, with the global predictive analytics market expected to grow from $10.5 billion in 2021 to $28.1 billion in 2026.
Mperativ is providing a solution that will provide organizations with a data model they can use to gather datasets on revenue, and turn them into actionable insights, while also offering a serverless bitemporal data warehouse, which they can use to enable AI solutions and process market data at scale.
Mperativ’s main competitors currently aren’t necessarily marketing automation and business intelligence platforms, but custom data warehouse solutions, such as Snowflake or Segment, that organizations can use to build a new product from the ground up.
Both Snowflake and Segment are established providers, with the former announcing revenue of $334.4 million earlier this year and the latter being acquired by Twilio for $3.2 billion in 2020.
What Mperativ says sets it apart from these custom solutions is that those products are custom with time-series data warehouses rather than offering a bitemporal data warehouse. This means they can capture activity over time but can’t compare planned revenue compared to what was actually generated.
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