There are two problems facing marketers today. First, there's more data than ever before. Second, it's changing quickly, with different types of data, including social mobile and video. It can be overwhelming and time consuming to get through.
That's where Datorama comes in. It's a SaaS-based, big data management platform for advertisers and ad agencies, which uses machine learning and artificial intelligence to make it easier to upload data, and to categorize it.
The company closed $32 million in Series C funding, it was announced on Monday. The round was led by Lightspeed Venture Partners. Marker LLC and Eric Schmidt's Innovation Endeavors also participated in the round.
Datorama had last raised funding almost two years ago exactly, when it closed a $15 million round. This lastest funding brings Datorama's total raised to $50 million. Previous investors include Cedar Fund and Innovation Endeavors.
Founded in 2012, Datorama helps marketers connect all of their data sources together, to form a single source. That leads to more efficient reporting, better decision making and total control over their marketing performance. The company's Marketing Integration Engine uses artificial intelligence to automate the process by which marketers connect all of their online and offline data sources from advertising, marketing, sales and CRM technologies.
It also provides TotalConnect, a data integration capability, allowing its customer to create API-like connections with any data source containing marketing, sales, service, commerce, or financial data; and API Connector Library, which provides an extensive selection of pre-built data connectors to integrate your data from popular marketing applications, databases and Big Data platforms.
"Marketer trying to deal with all that data, and get a good understanding what's working and isn't, it's huge operational problem, and almost impossible to solve with current tools," Ran Sarig, co-founder and CEO of Datorama, told me in an interview.
"Our solution was to build a software as a service platform, which could be used by non-tech users, meaning analyts, agencies or publishers, to onboard their data with machine learning. They don't need to understand coding or databases. They just throw the data at the platform, and it will be able tell that this is CRM data, or that is is display ad data. It's all onboarded into one source."
For a marketer that is using multiple different technologies to bring all their data together, this can not only save them time, but improve their marketing outcomes as well. Recently, in a commissioned study by Forrester, consulting on behalf of Datorama, Forrester discovered a Total Economic Impact benefit over $9.8 million for a single enterprise customer, while total analyst hours saved per year was 2,600.
More importantly, campaign conversion rate increased by 25 percent, and the marketing spend saved and repurposed was 14 percent of the budget.
The company has so far onboarded thousands of brands, hundreds of agencies, publishers and technology companies. Global customers include L'Oréal, Foursquare, Yahoo! JAPAN and GoDaddy.
Datorama says that it will use the new funding to expand geopgraphically. Some of the countries it is currently looking at as potential new markets include Brazil, China, and India. It will also be going deeper into the countries it already has a presence; last year it expanded to Germany, and the year before it launched in Signapore and Japan.
That means expanding its team in those countries as well, and it expects to double its 160-person team in the next 18 months.
"Basically, what we're looking for, when working with internationL organizations, is a local presnece on the ground. That means sales, and business development, to add value to those clients. We want to have an international overview, along With local support for local markets, so we'll increase our footprint to add that personal touch and interaction," said Sarig.
Mostly, though, the funding will go toward accelerating its research and development in the artificial intelligence field, in order to elevate its machine learning capabilities. That is what differentiates the company from its competitors, said Sarig, and he wants to go further with the technology, expanding beyond only sorting the data, but actually being able to predict what clients should do with it.
"It makes us unique. Using this technology to help sort all these data sets is great, but we've reached a state where thousands of brands are now using it. So, what's the next phase? Maybe the next step is about using machine learning and artificial intelligence to start delivering automated insights," he said.
"Right now clients are getting answers to question they've asked, with statistical models. What if we can tell you, 'I have something for you that you didn't ask me about'? We could tell clients who have invested for the last three weeks in an area that might have shown interest and engagement, but didn't work out. Now we would have had to build a report, ahd start looking at data, but we should be able to scan everything, deploy some advanced AI tech, and give insights into areas the clients haven't looked into yet."