How Near Intersects Physical and Digital Behavioral Data

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How Near Intersects Physical and Digital Behavioral Data

Los Angeles’ data intelligence platform makes France the beachhead in Europe. It is preparing to verticalize its offer, starting with retail.

In May 2022, Near announced his intention to list on the Nasdaq via Spac. Mentioned Goal: Raise $268 million for nearly $1 billion in capitalization. An amount that would be added to the $134 million in private investment raised in mid-2019 notably from Sequoia Capital and JP Morgan. The IPO is scheduled for the fourth quarter of 2022.

Founded in 2012 in Singapore, the martech platform has since relocated its headquarters to Los Angeles. In addition to this implementation, the headquarters are located in Bangalore, India and Paris. With 270 employees, Near chose the French capital to establish its European headquarters where it has approximately 25 employees. The group will have sales of approximately $50 million in 2021, an increase of more than 60% year-over-year.

Through partnerships with network equipment manufacturers and telecom operators, Near has worked over the past 10 years to build a comprehensive marketing database. “We identify population movements with respect for privacy,” said Anil Mathews, founder and CEO of Near. In total, the company claims 1.6 billion anonymized unique user IDs. Covering 70 million geographic locations in 44 countries. From there, the platform crosses consumer behavior information from its customers’ websites and applications with their behavior in the physical world consolidated into its database.

Predictive Deep Learning

Based on this dual stream of data, Near has built deep learning models to predict visitors’ actions or appetites based on their physical and digital histories. “To carry out this operation, we rely on our own data and on indicators from the customer’s CRM, linked for example to their purchases of such and such a product, and in such and such a store or on the web. universes cycle through a fully anonymized unique ID,” explains Anil Mathews. “Once the customer leaves the physical or digital store, the company doesn’t lose them. It knows which other stores they go to and if they are competitors, how often, how far they travel, what their geographic area of ​​purchase is, etc. ” In addition to hybrid analysis, Near also allows a brand to identify areas of high potential to find new stores.

“Now we are investigating how we can expand to other European countries that matter to the market”

Near presents itself as a data intelligence platform and claims an average revenue per customer of $300,000 per year. In France, Darty and Orange Bank are among the main references. To appeal to its customers, the company shows two differentiators: on the one hand, the value that its AI technology gives to data and on the other, the compliance of its platform with the data regulations of more than 40 countries.

To establish itself in Europe and in France in particular, Near offered itself at the end of 2020 the Parisian start-up Teemo, a specialist in drive-to-store targeted marketing. “Beyond localization and technology, Teemo has brought us his expertise in GDPR,” explains Near’s CEO. Since then, Near has set up a technical team in his Paris office with the mission of adapting his product to local market problems. “We are now investigating how we can expand to other European countries that matter to the market, such as Germany, Italy or Spain.” A development that could go through buyouts if Near finds the shoe on his feet, or organic growth if necessary.

Targeted Retail

One of the main target sectors is retail. “With the Covid, retailers had to start all over again to analyze the profile of their customers and prospects. This makes our platform a solution of choice for these companies. And this, both to understand behavior changes and to identify new ways to engage consumers. to the store in a process of continuous improvement,” notes Anil Mathews. In terms of R&D, Near plans to initiate a policy of verticalization for its offerings, starting, unsurprisingly, with retail. Other planned developments: Merging consumer-oriented marketing analytics data and location-oriented marketing analytics data into one platform.

Why go public at this stage? “By becoming a publicly traded company, we will gain the credibility needed for our international development. The operation will also provide us with resources to accelerate our policy of organic and external growth to quickly access markets where we are not yet present .”, projects Anil Mathews.

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