The multichannel, big data future is here now. If you haven’t already, you need to get smart fast about the data you’ll need. Data to collect, data to ignore, data to store. It’s a daunting task in this time of exploding channels and devices and media—all of which are throwing off more data each year than was ever created in the history of the world up until this minute.
Data is the heart and soul of any marketing technology delivering multichannel interactions with customers. Marketers need to have a clear and distinct data management roadmap if they want to pave the way for relevance for their customers.
Technologists and marketers need to agree on a clear vision for bringing together both online and offline data that delivers a full view of the customer. This means product purchase, marketing and digital usage data, plus a way to enable consumers to express their preferences, so that you are able to respond to those preferences.
Here are five importantdata management milestones to keep your multichannel strategy on track:
1. Build Knowledge into Your Database
Over time, each campaign will generate new insights and those insights will be operationalized by creating what we call “knowledge assets”—business rules, scores, transformed variables and other new types of data that will be used to drive targeting and relevance in marketing programs. The next step is to turn insights into action by creating a “knowledge layer” for your database by integrating a business intelligence (BI) toolset to the database that fits the abilities of your marketing users (with a good balance of standardized reporting and query capability to fit most needs)
This BI reporting platform will be ever-evolving as more users become more sophisticated and continue to ask more and more complex questions of their database. Marketers should build a roadmap to a robust BI layer that can be highly actionable and provide consistently improved marketing results.
2. Master Data Management
“Address hygiene” is a critical component for successful marketing and goes well beyond removing duplicates in your database. What “addressable” really means is changing rapidly. What if you only collect an email address, Twitter handle or device ID, but no full name or street address – will you want to use that in the future? Of course. Merging online and offline data means rethinking the traditional data model and is an area of great innovation right now. Standards of online/offline addressability are just starting to be laid down and will change dramatically in the years to come.
Building a marketing database means integrating consumer data variables from multiple internal sources. Without a thoughtful way to determine the best source of each of these variables, it becomes very difficult for marketers to use the data with confidence. Creating business rules about best data use practices is an important part of any marketing database or marketing automation build.
3. Data Integration Planning
Consumer behavioral data is complex, and you will want to work closely with your technology and analytics groups to make practical recommendations about bringing in new data fields as your analytics or pilot programs prove which are most effective in generating marketing results.
If you want to reach outside your customer base to acquire new prospects, you should evaluate a national consumer database, or a national file for businesses. Consider the usefulness and productivity of having quick access to additional data that could give you speed to market and stronger programs.
Marketing response and campaign data are often not re-integrated to consumer databases for many reasons. But as you plan your campaigns and plot out your campaign measurement, you’ll want to determine which channel data—and what types of response data—would ultimately make the most sense to integrate back into your data warehouse.
4. Identify New Data Opportunities
You will want to build a plan for finding and acquiring missing but important data that can be used to support marketing programs. As part of a data management roadmap, you should have a plan for adding new data over the course of time as it becomes practical. A gap analysis would help map out requirements for campaign analysis and ongoing nurturing and triggered response programs. Some of these data types may include:
· Response Data
· Preference Data
· Survey Data
· Enhancement Data
· Prospect Data
· Model scores
· Web visit data
5. Create a Data Management Infrastructure Plan to Support Analytics
Consider the analytical environment you will use for campaign analytics. Timing of data delivery to this environment will be critical, and new response data may come out of the campaign that has no current home in your database—but you still want to use it for campaign evaluation. An analytical data mart could bring together existing and new data for modeling and campaign analysis, as well as data from new sources such as web activity and telemarketing data that might be difficult to integrate into the database, short-term.
These are just some of data issues to consider as you look to maximize your investment in multichannel campaigns, but they will keep you grounded amidst the flash and flurry of the promises you’ll hear about new tools and new channels. Find the right data, and get great results.
Martha Bush is senior vice president of strategy at SIGMA Marketing Group.
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