Highlights:

  • Motif Analytics says the platform is more user-friendly than general-purpose analytics tools primarily focused on other types of data science projects.
  • Users engaged in data science projects that demand only limited precision can opt for a trade-off, sacrificing some accuracy for enhanced query performance.

Motif Analytics Inc., the creator of an artificial intelligence-driven data exploration platform, recently revealed that it has secured a USD 5.7 million seed funding round, with Felicis and Amplify Partners taking the lead.

The funding round also saw participation from the angel investor group InvestInData, which comprises over 50 engineering executives from prominent tech companies such as Netflix Inc. and DoorDash Inc.

Examining the sequence of events leading to a purchase can assist companies in pinpointing fresh sales opportunities. For instance, an online retailer might discover that two-thirds of the shoppers who purchase a particular product had previously interacted with a banner ad designed to promote that product. Upon receiving this information, the marketing team could consider increasing its budget for banner ads to stimulate purchases further.

The sequence of events leading up to a sale is frequently far more intricate. Before making a purchase, a user may undertake various actions, such as perusing product reviews and evaluating competitors. Accumulating accurate data about each step, readying it for analysis, and conducting it can present considerable technical hurdles.

Motif, headquartered in San Francisco, has engineered a platform to examine multi-step user journeys. As per the company, the platform is more user-friendly than general-purpose analytics tools primarily focused on other types of data science projects. Motif asserts that utilizing such tools can take days or weeks to map out users’ actions before purchasing.

“I haven’t met a head of growth or operations who was happy about their team’s ability to use company data in everyday decision-making,” remarked Misha Panko, CEO and Co-founder of Motif. “They try and get disillusioned with self-serve analytics tools, which promise one-click solutions and trivialize the complexity of cleaning, modeling, and interpreting data.”

Motif’s platform showcases an internally developed query language known as SOL. It’s positioned as a more straightforward alternative to SQL, the syntax most competing analytics tools use. Motif Analytics asserts that SOL enables the implementation of most “practical” queries in under ten lines of code.

The platform executes SOL code using a query engine that the company has also developed in-house. According to Motif Analytics, the engine enables customization of the precision with which the results of an analysis are generated. Users engaged in data science projects that demand only limited precision can opt for a trade-off, sacrificing some accuracy for enhanced query performance.

As the default setting, the platform conducts analyses in the cloud. Data science teams dealing with sensitive records can process them locally using a Local Mode feature. The feature harnesses the power of WebAssembly, an open-source technology integrated into Chrome and other popular browsers.

According to Motif, companies can utilize its platform to map out shoppers’ most common user journeys before purchasing. Furthermore, the platform employs AI to recognize the individual actions constituting a user journey and quantify their impact on sales. For instance, it can ascertain whether users who redeem a coupon code may be more inclined to complete a purchase than those who do not.

The company recently unveiled its seed funding round alongside the launch of its platform into general availability. The new capital will aid Motif in accelerating its product development endeavors. According to reports, the company plans to develop a drag-and-drop interface that removes users’ need to write queries manually.