‘Factual is a behind-the-scene player providing Points of Interest data’

‘Factual is a behind-the-scene player providing Points of Interest data’

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In an exclusive interview to Geospatial World, Bill Michels, SVP, Factual talks about the location platform as an enabler of personalised mobile experiences and how Factual is poised to be the best provider of global location data.

What is the core business model of Factual?

Factual has a suite of products covering contextual relevance, built up from a dataset and all our products are set atop the datasets. Our “Global Places” is a shared global dataset curated by us. It covers over 65 million local businesses and points of interest in 50 countries in about 40 languages with extensive smart phone penetration, including India.Bill Michels, SVP, Factual Inc

What is your mode of building up a data stack?

We have a data stack built over several years starting from 2007. We take billions of data inputs that can come from many sources about places, businesses, landmarks, general points of interest — any sort of data that you would need for a local search product.

Do you also buy data from companies like TeleAtlas or source it from the mobiles?

We don’t buy data. It is all our own. The data is pushed into the agile machine of Factual that continually improves as it sucks in all this data. On the other end, comes a very clean structured data set of these locations.

When you say data, what kind of data are you indicating at?

We, basically, mean points of interest data and facts like name, address, phone number, coordinates, categorisation, website, and other links like a Facebook or a Twitter page. We get billions of inputs that we collapse down into structured data, which is roughly about 65 million place entities worldwide. This dataset constantly grows. It is changing all the time with new things coming in. We are learning more and constantly adjusting that data set. Then we licence that data set out to prepare various applications. The search engines are probably one of our first customers. Among some of our notable customers are Microsoft's Bing and Yelp and others who duly attributed us.

How many themes do you cater to with regards to points of interest?

In terms of categories of places, there are hundreds of categories with over 470 different nodes in them. These include airports, malls, businesses, geographical landmarks and anything that people would look for in a mapping use case, local search use case or mobile search use case.

Having had so much of data, are you also looking to create a service model or some kind of solutions?

We have other products that sit on top of the data. We have tools that help people crunch and organise their data. This data could be worked for enterprise companies and financial services organisations that want to understand the places data. We also have couple of products that help mobile companies build contextual, personalised experiences for applications.

But we don’t have consumers of our own. We only like to provide platform to lots of mobile developers to build their own products on top of this. We don’t create consumer applications. And, we don’t sell advertising. It’s good to think of us as curators of a very large mobile data set. We often work with advertising technology and mobile advertising technology companies.

So are all these points of interest geo-coded?

It depends. Our data are all geo-coded. Some of these inputs are not necessarily geo-coded. We have tools and processes to learn geo-codes and we can also license geo-codes. Every point, considering our primary use cases are mobile developers, has a geo-code.

When you say Microsoft Bing is a customer, they are also data providers themselves. Do they source data from you?

We don’t know the specifics of where they get their data from. But we are attributed in Apple maps. For Bing, we have announced that we are empowered to contribute to lots of countries to help inform their local search products.

Who comprises your partner mix?

Our partners could be entities who use or license our data for mobile applications or a search engine. However, they are not individuals but companies. In addition to licensing our data, they may even have data of their own, which they might like to share back. For instance, they may have good data sets of a hotel in Europe, which they would contribute back to us. We are not guaranteeing that we have selected data that way but we are taking that in as another signal.

What type of partners you are working with in Asia?

In Asia, we have data sets of India, Malaysia, Thailand, Hong Kong, South Korea, China, Taiwan, Japan, Australia and New Zealand. We also worked with a few local companies in these geographies. We, however, don’t have sales people in these countries. Sometimes, we have people here who buy licenses for data and then offer it to companies in these countries for developing applications.

What is your revenue model?

Our revenue model is licensing out the data as well as the profiles and information that we learn about. We learn about the mobile devices. When we get details from our customers about a user (which isn’t personal information), say, some of their identifiers and the coordinate information (basis the application running on the devices), then we provide various profiles on that device: behavioural, geographical and demographical profiles that our customers can use to personalise user experiences.

Any new initiatives coming up from you…

We are always improving our data. On top of that we continue to build new profiles. In next couple of months, we will be announcing some new customer partnerships and developmental improvements of our products.

How do you see the demand going up for this kind of data?

With growing number of mobile devices and increasing mobile applications, there is an ever-growing interest in this segment. Our prime customer base derives from application developers and mobile advertising technology companies.

Are you enabling augmented reality players with such data?

Certainly someone who is building augmented reality can use our data to help inform their product. We are like scaffolding where someone puts everything else around it. We provide infrastructure for someone else to build their own consumer experiences or advertising products.

Considering that you have access to so much data, are you looking at directly providing services to individuals and consumers.

No. We consider ourselves behind the scene which is what makes the data better.