Marketing Automation Analysis:
Foundations for Great Analysis
One of the most commonly cited benefits of implementing a top marketing automation system is the opportunity to improve your organization's ability to analyze marketing effectiveness. This is indeed possible, and provides a powerful advantage to organizations who are successful. However the final outcome of great marketing dashboards rests on a strong foundation of data. Ensuring that the data you need is available, cleansed, and normalized is critical to generating the analysis you need.
In evaluating a marketing automation software investment, it's crucial to look at this overall stack and ensure that you will be able to access the right sources of data, keep that data clean, and then build an analysis framework on top of that. In this post, we'll look at the key foundational elements of good analysis.
Data Sources
The first part of this foundation is having the data that you need. Without the right data, analysis is impossible, so this forms the first key area to evaluate, even before looking at any reports or analytics. The following examples give you a sense of the types of data you might need available within your marketing automation system in order to perform certain types of analysis:
Product Purchase History
An analysis of whether individuals who bought product A are more responsive to messages about product B, or product C, would require you to integrate and store information about product purchase history within your marketing automation system
Social Media Effectiveness
Understanding the effect of your social media efforts on buyer behavior requires you to track all key social media interactions with both known and unknown web visitors, and associate them back to purchase activity
Offline Activity
Analyzing the effectiveness of offline activities, such as tradeshows or lunch seminars will require you to capture and track that information (registrants and attendees) within your marketing automation system
Each of these data sources is continually changing, so in thinking about your marketing automation choice, you'll need to ensure you have the needed integration capabilities in order to get any data that resides in external systems into your marketing automation platform.
Data Quality
The next foundation for marketing analysis is data quality. By this, we mean the ability to manage duplicates, cleanse, and normalize data. As marketing data is continuously touched - by web forms, list uploads, event registrations, and white paper downloads - it is imperative to have a process in place to manage this data quality inline.
Some key aspects of data quality that can affect your ability to effectively analyze your marketing efforts are:
- Duplication: identical or similar records of individual contacts or accounts need to be identified, matched, and merged. Without this, any measurements of lead count, campaign response, or segment size will be inaccurate or misleading.
- Completeness: in many cases, the data in a marketing database is not complete. Records may be missing values, or the data in them is not up to date and accurate. If analysis relies on these field values, it will not be effective until that data is available. The ability of your marketing automation system to do progressive profiling, and gradually add needed data to contact records is crucial in ensuring that you have the needed data completeness in order to properly analyze your marketing effectiveness.
- Normalization: Data within a field, such as title, role, industry, or geography must be normalized to standard values in order to effectively analyze it. For example, if analyzing the effectiveness of a campaign in driving response from senior executives, if job titles are not normalized, and "Vice President" is written as "VP", "V.P.", "VP of", "Vice Pres" or "Vice President", your resulting analysis will be almost impossible.
Great marketing analysis rests on a foundation of great data. The ability of the marketing automation software you select to obtain the needed data, and maintain it in a state of continuously improving data quality is key to your success, and should be a key part of your analysis in making your selection.
