Marketing has changed almost beyond recognition in the last decade. In an age of bigger and faster data, consumers are driven by knowledge and have more choices across an ever-increasing number of channels. More so than ever before, marketers must truly embrace a multi-channel mindset of sending the right message at the right time and through the channels consumers most prefer.

Meeting the demands of today’s omni-channel consumer requires a well-integrated offline and online marketing strategy. However, many marketers are limited to using lists from common data sources that provide zero competitive advantage across online channels. Data siloes across different departments can also hamper success. In fact, it’s not uncommon for different groups within the same department, such as social and digital marketing groups, to work with disparate systems of consumer data.  

While many companies are increasingly investing in marketing technologies and digital solutions to meet these changing dynamics, one of the biggest challenges continues to evolve around big data. Although big data can translate into big revenue, many companies are unable to fully utilize its potential. In fact, 85% of Fortune 500 organizations will be unable to exploit big data for competitive advantage through 2015, according to Gartner.

Data-as-a-Service is Changing the Game
Data-as-a-Service (DaaS) is completely changing the game, fueling customer acquisition and retention strategies for marketers across all industries. DaaS is a process that leverages the modern data ecosystem and real-time data analytics to create a customized “always on” dataset.

DaaS fuels marketing by combining a company’s first-party CRM (customer relationship management) data with real-time triggers and Hard-to-Find-Data (HTFD) sources to deliver better targeting and a stream of in-market consumers. Completely different from list buying, DaaS combines three types of data which are uniquely customized to each company:

  1. Foundational Data: Internal data combined with additional demographic and firmographic enhancement and Hard-to-Find-Data.
  2. Onboarded Data: Offline data transformed into addressable online identities.
  3. Fast Data: Real-time behavioral data.

 

Foundational Data
Foundational data refers to what a company’s customers and prospects look like at the moment – a 360-degree view. This foundational data set comprises internal data, demographic or firmographic data, and Hard-to-Find specialty data sources.

Internal data includes anything sitting in a data warehouse, CRM system, or other sources that have been integrated into a marketing database. Examples of internal data include customer service records, transactional data, credit card purchases, or email.

Adding additional demographic and firmographic data establishes a more comprehensive customer and prospect profile. This includes demographic data, such as age, health interests, marital status, net worth, occupation, religious affiliation, and more. Firmographic data may include annual revenue, D&B credit rating, legal code, number of employees, primary business address, SIC, and years in business.

A powerful advantage of DaaS is the ability to add unique and Hard-to-Find-Data to a foundational data set. These highly targeted data assets have been aggregated from hundreds of big data sources, going well beyond third-party lists. As an example, these may be highly specialized sources of furniture or fashion interests, or spend data on specific businesses by categories.

To really understand the potential of these HTFD 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:

  1. 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.
  2. 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).
  3. 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 personal preferences, likes, “check-ins”, shares, and comments users are making.
  4. 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.
  5. 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.
  6. 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.

Onboarded Data
Data onboarding is all about bringing offline data into the online world. Offline data, such as customer service calls or point of sale transactions, is information that sits in a company’s CRM.  While this data is great for traditional marketing segmentation and targeting, it doesn’t address the digital needs and behaviors of customers and prospects who are online.

As part of a data onboarding process, matches are made between offline data and online user profiles. Data such as a phone number, email address, name, or a physical address are used as identifiers. These identifiers are then matched to online cookies, creating a universe of digitally addressable prospects and customers.

Fast Data
Fast data is the continual processing of big data in real time to generate insights for immediate action. As data is created, such as click-stream data, social media posts, purchase transactions, mobile GPS signals, or sensor data, these events occur almost instantaneously -- thousands of times per second.

Fast data aggregates event and behavioral-driven data to determine purchase intent as it occurs. These moment-to-moment insights are crucial for today’s enterprises and play an important role in targeting in-market consumers and businesses to generate ROI. Just imagine the competitive advantage in having exclusive knowledge about who is actively searching for products you (or your competitors) sell. Examples may include social purchase signals, such as people posting to social networks such as “Excited about the new baby” or “Taking a family vacation.” Or these may be discretionary purchase power signals, such as customers and prospects who are securing new credit sources, selling and buying cars or planning to move residences.

As an example, a specialized DaaS dataset in the automotive industry may look something like this:

DaaS structures these data set to create an “always on” source of in-market consumers. Companies can deploy marketing campaigns through multi-channel programs or customized ads and messaging can be sent directly to a company’s customers and prospects through a digital marketing platform.

For years, organizations have been reliant on their internal data or data enhancements from list brokers. This is stagnant data compiled from third parties. DaaS on the other hand is transformational in nature -- a revolutionary way of mining today’s massive data sets to find qualified prospects in the market now for what a company is selling. DaaS is the next leap forward in the modern data ecosystem, fueling competitive marketing advantage in new and exciting ways.

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