It’s the data, stupid. If your organization is charging into the multichannel future, 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!
The heart and soul of any marketing technology you plan to use to deliver multichannel relevance for your customer is the data inside. 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 alike 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 channel usage data, plus a way to enable consumers to express their preferences, so that you are able to respond to those preferences.
Critical Data Management Milestones for Multichannel Campaigns
1. Build Knowledge into Your Database
Over time, each campaign will generate new insights and those insights will be operationalized through the creation of what SIGMA calls “Knowledge Assets.” Knowledge Assets are business rules, scores, transformed variables and other new types of data that will be used to drive targeting and relevance in marketing programs. We think about turning insights into action through the creation of a Knowledge Layer on the databases we build and/or help our clients manage.
This knowledge layer is an ever-growing component to the database, and marketers should detail a roadmap to a robust knowledge layer that can be highly actionable and provide consistently improved marketing results.
2. Master Data Management
- Address hygiene is critical for successful marketing, but what “addressable” really means is changing rapidly. What if you only collect an email or twitter name but no street address – will you want to use that in the future? Merging online and offline and rethinking the data model is happening all across the marketing spectrum today.
- Typically, consumer variables are often available on a database from multiple internal sources. When too much time is spent determining which is the best or right 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 the build-out of a data dictionary.
3. Data Integration Planning
Consumer behavioral data is complex, and you will want to work closely with your systems group to make practical recommendations about bringing new data into the database as that data is proven effective in generating marketing results.
- Prospect data is not always integrated with customer data. Consider evaluating a national consumer database, or a national file for B2B, and testing the usefulness and productivity of having quick access to additional data that could prove to create stronger programs.
- Marketing response and campaign data is often not being re-integrated to consumer databases for analytical purposes, and in the course of building campaign analytics, you’ll want a plan for determining which channel data, (and the 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 dealing with 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 form 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
- Knowledge Assets
5. Data Management Infrastructure Plan for Support of Analytics
- Consider the analytical environment you’ll need for campaign analytics. Timing of data delivery to this environment will be critical, and new data may be generated by a campaign that has no home on your database – but you still want to consider it in light of campaign responses. 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 in the short-term.
These steps just scratch the surface of data issues you’ll want to consider as you think about moving rapidly to multichannel campaigns, but what an exciting time for marketers!
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