Data is everywhere. The number of unique data sources out there is larger than any one organization can wrangle. And make no mistake – these big data sets are thesource of competitive advantage for companies across all industries. But finding the right information from today’s huge data ecosystem is a hurdle that more than one marketer has yet to cross.
DATA-AS-A-SERVICE GIVES MARKETERS ACCESS TO BIG DATA
We have all heard of Big Data, but how many of us have heard of Data-as-a-Service (or DaaS)? Still a relatively new concept, DaaS is primed to make a huge entrance in 2015 – it is the game changer that is completely revolutionizing marketing.
DaaS is a service approach in which unique and Hard-to-Find Data (HTFD) assets are sourced and structured to deliver a constant stream of qualified prospects, including a company’s own customers, who are actively searching for what they are selling.Distinctly different from list buying, these data sources are a highly customized marketing asset versus disconnected, one-time use prospect lists.
Marketers analyze the data and crunch the numbers – but how many can really say they know which consumers or businesses are in market for their products and services? Or how many companies are overly dependent on modeling what they thinka prospect or customer may do versus having real-time insights into their actual behaviors?
DaaS empowers companies with knowledge – not guesswork – for sustainable and immediate revenue.
HOW BIG DATA CAN EMPOWER YOUR MARKETING
A powerful advantage of DaaS is the ability to source hard-to-find data that has been aggregated from hundreds of Big Data sources. These data sets are highly targeted and go well beyond third party lists.
To really understand the potential of these unique data sets, it is important to understand where all this data is coming from. The information being generated from Big Data can be segmented into six specific categories:
- Web Mining: Data compiled by mining the open web. This includes automated processes of discovering and extracting information from Web documents and servers, including mining unstructured data. This can be information extracted from server logs and browser activity, information extracted about the links and structure of a site, or information extracted from page content and documents.
- Search Information: Data available as a result of browser activity tracking search and intent behavior. This data also identifies digital audiences through onboarding (matching consumers to their online IDs).
- Social Media: The average global Internet user spends two and a half hours daily on social media. A vast array of data is available on hashtags, keywords, personal preferences, “check-ins”, shares, and comments users are making.
- Crowd Sourcing: This is collective intelligence gathered from the public. Data is compiled from multiple sources or large communities of people, including forums, surveys, polls, and other types of user-generated media.
- Transactional: Data that is created when organizations conduct business, and can be financial, logistical or any related process involving activities such as purchases, requests, insurance claims, deposits, withdrawals, flight reservations, credit card purchases, etc.
- Mobile: Mobile data is driving the largest surge in data volume. It isn't only a function of smartphone penetration and consumer usage patterns. The data is also created by apps or other services working in the background.
DaaS provides highly specialized data assets – HTFD - that have already been minedfrom these Big Data sources.
Some examples include:
- Data collected on residential and commercial building permits 88 million residential and commercial building permits, 155 million inspection records, and 7 million contractors in the U.S.
- Comprehensive healthcare data (doctors, dentists, other prescribers, their practices, clinics, hospitals, etc.)
- Directly measured Digital Footprint Data™, that includes web pages surfed and email and digital activity (the majority of web traffic)
FURNITURE RETAIL USE CASE
FurnitureROITM is a DaaS product for furniture retailers. It has five main data components:
1. Identify New Prospects with DataMentors’ Consumer Database
The first component is to identify new prospects within DataMentors' consumer database. This database includes 250 MM consumers with 300 different data elements, including age, income, home ownership, recent home buyer, and much more. Marketers can choose the data fields that are most relevant to their target audience.
2. Target Millennial Consumers with Multi-Channel Messaging
Next, marketers can select and receive speciality data on target audiences. For example, a furniture retailer may want to target millennial consumers and send them offers through social media. We are able to find data on 42 MM millennials. Then, we can segment these consumers by their income, proximity to store location, and home-owner or renter status.
Lifestyle interests, such as the social media channels they were active on and their home improvement and decorating interests, can be used to create and send relevant offers to them through the channels they prefer to use. Rich contact data, including email addresses and mobile number, are included with these prospects so a retailer can reach out to these consumers in multiple ways.
3. Send Offers to Consumers Who May Soon be In Market
Another key aspect of DaaS is using innovative web mining technology. Web blogs, forums, online listings, and more can be mined for valuable data on who may be in-market for specific services or products. For furniture retailers, pre-movers and new movers are identified from their listings on craigslist ads and real estate directories. This real-time data is gathered across a comprehensive network of websites and includes information such as new rentals, houses sold, geography, income level and more. Once these consumers are identified then retailers can send them real-time offers.
4. Social Signaling Data: Boost Customer Acquisition Through Social Prospecting
FurnitureROITM monitors social media for furniture purchase signaling, such as “excited about the move”, or “looking for a leather couch”. Users on twitter may reveal key information that can identify them as in-market for specific pieces of furniture.
For example, Alex Johnson may tweet, "Looking for patio furniture but I'm not sold on the color." Or, Eric Mitchell may tweet, "We are looking into buying a piece of furniture for our board and card games. This should be fun!" This data can be utilized by retailers to send these consumers offers in real-time.
5. Onboarded Data: Digitally Addressable Dataset for Real-Time Messaging
Companies can enhance their offline databases, which usually includes contact information and transactional history, with online data on their customers. Offline data, such as customer name or e-mail address, will be matched with their corresponding online IDs, such as social media handles or profile urls and web browser cookies, so that customers are digitally addressable.
Through DaaS, these customers can be monitored online for social media and web browsing activity (i.e. keywords, search terms, tweets) that meet specific purchasing signals.
DaaS can essentially be applied to any industry, not only furniture. By sourcing Hard-to-Find-Data through DaaS, you really don’t need to have a huge data repository to find the right information. The right information is sourced for you and delivered directly to your CRM or digital marketing systems.