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The World’s Largest Database Using Ambient Intelligence

There is a term that we often use at Nanalyze to describe how our online activities leave behind a breadcrumb trail that companies use to track our behavior: data exhaust. It refers not only to every mouse click or swipe left on our devices but where we go and when, not to mention every social media post about our AI-powered vacations. Companies usually vacuum up this data exhaust to fuel retail analytics that can be used to automate retail store operations or even to prevent shoplifting. Or such data can be used for surveillance, such as the case of Dataminr, a startup that mines Twitter feeds using algorithms. More recently, we covered San Francisco startup Geoquant, which uses big data to assess geopolitical risk. Now we’ve come across a startup, Near, which claims to have built the world’s largest database of human behavior based on what it calls ambient intelligence.

The World’s Largest Database of Human Behavior

Click for company websiteFounded in 2012, Near is headquartered in Singapore with offices in San Francisco, New York, London, Bangalore, Tokyo, and Sydney. We briefly profiled the company in our article on Top-10 Artificial Intelligence Startups in Singapore in February. Since then, it raised a $100 million Series D this month – all of which came from London-based investment firm Greater Pacific Capital. Never heard of them? Us neither. They’re a bunch of ex Goldman Sachs lads who setup their own investment firm with a focus on India’s growth and apparently other opportunities as well. Some of the brand name investors behind Near, which has pulled in $134 million to date, include Sequoia, J.P. Morgan (JPM), and Telstra (TLS).

No doubt Jim – as well as marquee clients like Mastercard (MA), The We Company (formerly WeWork), and MetLife (MET), among others – is interested in Near’s global marketing database that reportedly stands at 1.6 billion unique profiles across more than 40 countries. (Facebook claims about 2.4 billion active monthly users, for comparison.) Near calls its platform Allspark. It leverages location data from mobile and WiFi carriers, data brokers that aggregate app activity, millions of smart devices, and even your Uncle Joe in order to ingest billions of real-world signals. Its machine learning algorithms are then able to build individual profiles that companies can use for marketing campaigns or to somehow enrich the customer experience using these alternative datasets:

Graphic illustrates how Near builds customer profiles.

Credit: Near

The real trick is that the whole operation happens in what Near refers to as a privacy-led environment that meets current privacy laws. In other words, everyone remains anonymous while still receiving personalized spam recommendations or other services from Near’s growing client list.

Ambient Intelligence and Human Behavior

Near refers to Allspark as its ambient intelligence platform. Ambient intelligence is basically the marriage of AI, big data and the Internet of Things, in which we live in an environment where our technology understands and responds to us in a highly individualized manner. The concept was actually first floated in the late 1990s by a venture capitalist named Eli Zelkha and his team at Palo Alto Ventures, which predicted that our devices would eventually merge into the everyday background of our lives, responding to our needs effortlessly and flawlessly. You’ll be relieved to learn that should all happen by next year, according to their original prediction.

The technology developed by Near is only one aspect of realizing this ever-present ambient intelligence that watches, learns, and responds. For example, an AI startup that we’ve covered before called Affectiva is developing algorithms to recognize human emotion. Further advances in computer vision will be key, as machines will particularly rely on visual cues to analyze and understand human behavior. All of this may eventually lead to some version of what is called artificial general intelligence, when machines can match or even exceed humans in thought and ability before enslaving us while serving our every whim. Or, at the very least, Siri should be able to come up with the right answer once in a while.

Case Studies Using the World’s Largest Database of Human Behavior

Near believes its Allspark platform can provide solutions to multiple marketing challenges. Let’s look at a couple of real-world case studies:

  • Near was hired by Virgin Active, a chain of health clubs nominally part of the Virgin Group brand, to help boost membership. Near created a database of Virgin Active’s target audience in Allspark using multiple data sets as part of a two-phase ad campaign. Those users were then targeted with dynamic mobile ads when they were within close proximity to a Virgin Active Health Club. After crunching the numbers, Near said walk-ins increased by 70% in the first phase, which further increased to 82% in the second phase as it refined its target audience, a group that apparently frequented cinemas and theatres, and enjoyed long walks on the beach. (We might have made up that last one.) At the same time, the average cost per walk-in dropped by 62%.
Heatmap shows targeted audience for promotional campaign using Near's global database of human behavior.

They know where you are. Credit: Near

  • For a quick-service restaurant chain in Thailand, Near used Allspark to create a target audience of 18- to 45-year-olds who had been spotted in or around the client’s or competitors’ stores over a 30-day period to promote a new dish. Near then targeted the audience segment in addition to the users seen in and around its client’s eight stores in real-time to drive walk-ins, which increased by 65% during the campaign. The promotion also revealed a few other tidbits, such as a higher number of women (66%) walked into the stores to try the new dish.

Competition in Location Data Marketing

While Near has amassed an impressive war chest – and could someday soon join the unicorn club of startups valued at $1 billion or more – it’s not the only company using AI and big data to build customized databases of customers. Let’s take a look at some of the more prominent competitors using location data to help boost marketing and support business decisions.

Click for company websiteFounded in 2008, Los Angeles-based Factual has raised $104 million, including a $42 million venture round less than a year ago, and its roster of investors includes top VC firm Andreessen Horowitz. The startup’s business model is similar to Near in that it is focused on applications related to location data, offering a suite of products that help businesses target products and services. One offering is called Factual Insights, which “helps the world’s top companies make data-driven decisions by using high-quality location data and real-world behavior patterns to understand customer intent, movements and footfall.” Its Location Engine, meanwhile, “enables mobile marketers and app developers to easily build location intelligence into apps to better understand customer behavior and build personalized experiences that drive user engagement.” In other words, user behavior is all about location, location, location. Factual’s customers include Amazon, Apple, BMW, Google, Facebook, and Microsoft.

Click for company websiteFounded in 2010, PlaceIQ is based across the country in New Yawk, where it has raised $52 million in disclosed funding, with Chinese tech giant Alibaba serving as one of its key investors. It also offers various location data products to help drive marketing and business decisions. In addition to general retail, PlaceIQ also works with third-party vendors to build datasets in other industries, such as media and TV viewership. It refers to the process of analyzing location data for consumer insights as location intelligence:

Location data is used to created location intelligence.

Credit: PlaceIQ

In partnership with Braze, a mobile marketing software company, PlaceIQ used its location-based audience and insights platform to target customers through a mobile messaging campaign for Urban Outfitters. The results: The retailer boosted conversions from its promotional outreach by 75% and increased related revenue by 146%, compared to messages based on customers’ expressed interests. In other words, we don’t know what we want until we want it.

Click for company websiteFounded in 2015, Cuebiq is another New Yawk startup gleaning market and business insights from location data. It has raised $33.1 million in funding from investors like Goldman Sachs, Thomson Reuters, and NASDAQ. Its location data analysis engine, Clara, does some of the usual tricks with building customer profiles and demographics by measuring foot traffic; providing insights into consumer loyalty; and determining how a business matches up to its competitors. Cuebiq also emphasizes privacy in its platform, using blockchain technology to ensure its products only use data from users whose consent can be verified on the ledger.

Click for company websiteWe return to Singapore for our final startup in this roundup: Founded in 2017, Lifesight raised $2 million in a Seed round in 2018. It claims more than 425 million user profiles and processes 3.6 billion data points per day, though its geographic range is limited to 16 countries in Asia presently. One of its customers, the venerable Col. Sanders of KFC fame, engaged Lifesight to analyze the success of a digital campaign in Malaysia. Below are some of the insights gleaned from the ROI analysis:

Metrics of Lifesight campaign for KFC.

Credit: Lifesight

Perhaps the most important from Lifesight: Pizza Hut controls the major market share in terms of footfalls among KFC’s main competitors. Coming soon to your neighborhood KFC: chicken-fried pizza. Now that’s technological progress.

Conclusions

There’s no getting away from the fact that location data is becoming big business thanks to big data and the computing power of AI. The five companies we’ve profiled here have already raised more than $300 million, and this list is by no means comprehensive. Near is the obvious leader of the pack, not just because it has amassed more money but because it has built the world’s largest database of human behavior – all while reportedly protecting privacy. Maybe a certain social network should take notes – or even consider a strategic acquisition.

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