Subscribe to DSC Newsletter

Data-as-a-Service Dictionary: 5 Terms You Need to Know

daas

Data-as-a-Service Dictionary: 5 Terms You Need to Know

A few months ago, we posted this Ultimate Beginner’s Guide to Data Quality and Business Intelligence for those looking for a crash course in how high quality data is the foundation for improving customer experiences, engaging consumers across channels, and more. As a follow up for those looking to widen their Data-as-a-Service knowledge even more, here’s a quick overview of 5 terms commonly referred to in DaaS.

1. Data-as-a-Service (DaaS)

daas

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 the company is selling. Distinctly different from list buying, these data sources are a highly customized marketing asset versus disconnected, one-time use prospect lists.

Here’s a more basic version:

Data is everywhere, and it’s here to stay. Every second people leave behind trails of information in different places- social media, purchase history, online search habits, etc.- and all of this information predicts their future behavior. Specifically, this information can be used to predict their next purchase. DaaS finds the clues that indicate an in-market, and therefore high quality, prospective consumer and delivers their information to your marketing systems or channel partners in real time, so you can market to them before they may even realize they’re ready to make a purchase.

Consumers leave clues. DaaS connects the dots.

2. Siloed Data (or Data Silos)

data siloes

Although it may seem odd to borrow a term from agriculture, a data silo is really just a database that is completely separate from other data sources. The danger of data silos is that they don’t allow for communication between departments which can lead to missed opportunities and customer service blunders. For example, in the image below, one department may send outreach to consumers from the green silo based only on contact information, without checking the blue silo to see if they have purchase history with the company.

This leaves the consumer feeling unappreciated and frustrated with the unnecessary marketing when they are already a consistent customer to the company. By integrating data silos into one comprehensive database, marketers can avoid these faux pas and assess marketing strategies with a clear view of the big picture, not just bits and pieces. 

data

3. Fast Data

fast data

Fast Data is the continual processing of Big Data in real time to generate insights for immediate action. It’s the choices, opinions, and events that are taking place right now that are driving your consumer to their next decision. These moment-to-moments insights are crucial, because they 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.

Fast data is compiled of digital footprints left by consumers such as searches and social media posts as well as transactional actions such as purchases, financial deposits or withdrawals, flight reservations, and loan requests. All factors give marketers the competitive edge of knowing what their consumer’s probable next step is.

4. State of Change Data

In marketing, timing can make a huge difference in your campaign’s success. You may have the perfect message for the perfect target, but if delivered too soon or too late after an integral life event can fall on deaf ears.

The two main types of consumer states of change are life changes and asset changes.

life change data, asset change data

Examples of life changes are marriage, divorce, new job, new child, or moving to a new city. Examples of asset changes would be purchase of a new car, boat, or house. Consumers follow a path of fairly predictable life stage events, and since asset changes often follow closely after life changes the two are closely related.

Marketers with access to state of change triggers can significantly increase the likelihood of attaining a new customer by reaching out at the opportune moment. Once analytic tools identify signals that imply change of state in life or assets, this data is enriched by appending it with consumer profile databases to provide a comprehensive view of the prospect, and the best way to move forward with marketing to them.

5. “Dirty Data”

“Dirty Data” is inaccurate information, and it’s a big problem. Dirty Data is the number one reason for CRM Failure.  When customers are sent inaccurate outreach they don’t feel like a most valued customer, and can leave them doubting your records competency. Or, it may leave no impression at all as many times consumers with incorrect contact information will not receive intended communications at all.

According to a 2014 survey by Ascend2, 36% of marketers say that low quality and inaccurate data is the biggest obstacle to marketing automation success. Even if the information was input correctly, data decays at a rate of 2% per month. On average, every 30 minutes 120 business addresses change, 75 phone numbers change, 20 CEOs leave their jobs, and 30 new businesses are formed. (Source: D&B The Sales and Marketing Institute)

Read more

Views: 1610

Comment

You need to be a member of Data Science Central to add comments!

Join Data Science Central

Follow Us

Videos

  • Add Videos
  • View All

Resources

© 2017   Data Science Central   Powered by

Badges  |  Report an Issue  |  Privacy Policy  |  Terms of Service