Marketers Know Even More About Offline Behavior Than You Think
Last year, I wrote a series of essays outlining the growing impact of data on the local technology industry. An explosion of cloud-connected devices, I argued, was spawning a new dataset of offline behaviors — what we called local data — that could help local marketers, sellers and economies better compete with the ecommerce industry.
The thesis is in the process of bearing itself out. Innovation around local data accelerated last year as digital marketers, in particular, looked to develop products to help convince so-called offline marketers — essentially, everyone not selling remotely — move spending from traditional media to the web. Some of the largest internet publishers — Facebook and Google among them — have embraced local data as a means of measuring the impact of advertising on their properties.
As we approach Street Fight’s second annual Local Data Summit on March 5th in Denver, I wanted to take some time to outline the ways in which local data differs from other types of information and offer some insight into some of the ways these datasets are influencing strategy in a few key sectors.
Looking beyond the “big data” monolith
In the private sector, the value of “big data” is already accepted as near-gospel. Gartner found that nearly three quarters of corporations surveyed have or plan to invest in data initiatives over the next two years. But last year, two large-scale data breaches, in both the private and public sectors, thrust the data industry into the mainstream consciousness.
Subsequently, the bulk of the conversation about data has largely played out under the “big data” moniker. We tend to talk about data analytics as a monolith — and, to an extent, that makes sense: cloud computing is, by definition, a movement toward centralization. However, as data becomes part of the mainstream business discourse, it’s critical that we begin to discuss “big data” as an array of applications implemented across industries — each of which presenting its own unique challenges.
At a high level, “local data” consists of information that can answer any questions a business or consumer may have in the process of buying or selling goods or services locally. A consumer profile, developed from online search behavior, may tell a marketer where a consumer goes online; but web data often says little about purchase intent than where a consumer goes in the real world.
For marketers, that may mean analyzing behavioral data about consumer’s local shopping patterns: where do they shop, what have they bought, what are they looking for in the next week? For consumer-facing companies, that often means collecting data about businesses: what businesses sell which products and for how much; are they quality businesses; when are they busiest?
Connecting the dots, online and off
Part of the “bigness” of the local data is its reach beyond the Internet. Cloud storage now allows business to shovel every bit of information, whether it’s payment data from a vending machine or sales data from a store, into a database without the concerns of scale and cost implicit in more traditional forms of data storage. Businesses can create connections between systems without causing a massive technical headache.
The ability to make sense of disjointed datasets is at the heart of what’s driving the growth of the “local” dataset. Online, everyone operates under a single, unified environment, making data collection far simplier. But in the real-world, we interact with hundreds of separate, siloed systems creating a complex trail of information that is often left unseen. And without links, our transitions — when we move from one part of the shopping process to another — often go unmeasured.
Mobility is helping technology companies create threads between these datasets. Index, a startup founded by two early Google Wallet executives, uses the data generated from consumer’s smartphones to help improve the models the company developed to create consumer profiles for retailers based on credit card data. The company also uses mobile devices as a way to deliver applications that put those data to use — namely, in the form of delivering discounts and suggestions in-store.
Marketers have also turned to both mobile location and payments data as a way to develop more efficient attribution models that account for local spending. In January, Facebook released a new attribution product, Lift, that compares the buying behavior of users shown an ad against an independent control group. The key to project is that Facebook can now access offline purchase data through a partnership with Oracle-owned Datalogix, and does not need to just rely on users who click on an ad an immediately buy a product online.
We are seeing innovation in local business data as well. Seattle-based Porch raised $65 million in January to expand its home services site, which has exploded due in large parts to its data collection efforts. The company has aggregated project data — who built what for how much — from hundreds of thousands of service providers. Then, it uses that information to help consumers pick the right professional based on experience, expertise and cost.
Over the next month, Street Fight will take a deep dive into the ways in which brands, marketers, and technology companies are using local data to improve existing products and create new ones.
Steven Jacobs is Street Fight’s deputy editor.