How to Squash Your Data Anxiety and Build Top-Tier Marketing Data
How to Squash Your Data Anxiety and Build Top-Tier Marketing Data
Building a Solid Marketing Data Foundation: The 4 'I's for Success
In the last post, we covered each tier of Ward’s Marketing a marketing data maturity framework.
If you were starting at a company with zero infrastructure—or just going into somewhere new to you—we put together the hierarchy of how things should be ordered.
Great!…How do you get there from zero?
In this post, we’ll go over the 4 ‘I’s to achieve the top tier of marketing data capability.
The 4 I’s:
Inspect: Assess Current State
Ingest: Define and Gather Individual Data
Include: Analyze Group Behaviors
Implement: Develop Models to Forecast Trends
1. 👁️ Inspect: Assess Current State
Inventory Your Data: Start by taking stock of the data you currently have.
Identify Gaps: Learn what all departments want and areas for improvement.
Data Quality Check: Ensure that your existing data is accurate and reliable. Cleanse any dirty data to avoid GIGO issues.
Start by assessing your current state
Before you know where improvements need to be made, you need to know what your environment looks like.
Start with an inventory. Learn about your existing data sources. Some common ones include CRM systems, social media platforms, web analytics, and email marketing tools.
Tip: Don’t be afraid to ask people what they use.
You’ll often run into datasets that are outdated or unused, but haven’t been archived. Asking around will give you a better idea of which ones are active and updated.
Those relationships will also help you later on.
Figure out what features people want—but don’t already have. Talk to the finance teams, marketing, sales—anyone you can talk to. Once you have a list of those features, start prioritizing them.
Ignore people who want more complex analysis at this point. You’re pointing a finger in the wind—don’t overcomplicate it. Conduct a data quality check. Assess the accuracy, completeness, and reliability of your data. For example, does marketing REALLY have 50,000 newsletter subscribers? Could some of those be old emails, bots, or something else?
This might involve cleansing your data to remove duplicates, correct errors, and fill in missing values, ensuring that your dataset is ready for deeper analysis.
2. 🍽️ Ingest: Define and Gather Individual Data
Establish Key Metrics: Define and track essential KPIs like LTV, CAC, and AOV.
Implement Tracking Tools: Use tools like Google Analytics, CRM systems, and marketing automation platforms to gather and analyze data
Pick the right tools for the job
Once you have a clear understanding of your data's current state, move on to building your foundation.
Establish key performance indicators (KPIs). Common KPIs are Customer Lifetime Value (CLTV), Customer Acquisition Cost (CAC), and Average Order Value (AOV).
Standardize the metrics: Create a data dictionary that outlines the definitions and structures of your data. Ensure that all departments use a common data language to avoid conflicts.
[Optional] Implement relevant tools1 like Google Analytics, upgraded CRM systems, and marketing automation platforms to gather data consistently to where it’s needed.
3. 👨👩👦 Include: Analyze Group Behaviors
Segment Your Audience: Use your foundational data to create meaningful—not too many—customer segments.
Analyze Channel Performance: Regularly review the performance of different marketing channels to optimize your strategy.
Run Campaign Analysis: Evaluate past campaigns to understand their impact and improve future efforts.
Work on the the improvements
With a solid foundation in place, move on to the second tier.
Collect and analyze group statistics.
Create customer segments: Use your foundational data to create customer segments based on demographics, behavior, and purchase history. Identifying distinct groups within your customer base allows for more targeted understanding of customer needs.
Regularly review channel performance using metrics such as Cost Per Acquisition (CPA) and Return on Ad Spend (ROAS) to optimize your strategy.
Know (and let your finance lead know) that all attribution numbers are a directional estimate. Don’t let them grill you about “But Google X” or “And Twitter Y” without explaining the probabilistic nature of their data.
Regularly evaluate marketing campaigns to see if they worked in the way you expected.
Tip: An unsuccessful campaign can still teach a lot.
Don’t paper over or throw those lessons away.
Reviewing campaign effectiveness—even for failed campaigns—will help you improve future efforts.
Document the limits of your data: Recognize any biases or gaps in your data, such as recency bias or incomplete tracking. “Data-driven” doesn’t mean ‘only listens to numbers’. Don’t let your leadership make bad decisions off of unstable data foundations.
4. 🎯 Implement: Develop Models to Forecast Trends
Develop Predictive Models: Work with data scientists or use predictive analytics tools to start forecasting future trends.
Optimize Your Media Mix: Use media mix modeling to allocate your marketing budget more effectively.
Perform Sensitivity Analysis: Test different scenarios to see how changes in strategy might impact your results.
The final step is to advance to predictive analytics, the third tier of our marketing data hierarchy.
Use predictive analytics (or work with data scientists) to develop models that forecast future trends, such as customer churn or purchase propensity. This proactive approach helps you anticipate customer behavior and adjust your strategies accordingly.
Use media mix modeling to manage your spend across various channels, ensuring maximum efficiency and impact.
Tip: Conduct a sensitivity analysis
It’s useful to understand how changes in variables (like price or advertising spend) affect your outcomes. This allows you to identify the most sensitive areas of your strategy and make necessary adjustments.
Estimate the future value of your customers based on their past behaviors to focus on high-value segments and develop strategies to increase their lifetime value.
By following these steps, you can improve your marketing data structures, transforming your data from a disorganized source of anxiety into an actually useful asset.
Start from the bottom and build up confidently, keeping your balance at each step and building systems that support more sophisticated analysis and drives real-life results.
This is literally always harder than you think it is. Don’t implement if you’re on the fence about something. It should only be slam dunks.