How to use post-campaign data to make your marketing better

In previous articles, we’ve explored the role of data-driven strategies in the planning and execution phases of marketing initiatives. Here, we’ll focus on using data to analyze and improve decision making after launching a marketing initiative. 

The post-campaign phase is crucial for understanding results and extracting actionable insights to inform future strategies. Without learning from past efforts, meaningful improvement is impossible. We’ll cover the importance of thorough data analysis, recognizing and overcoming biases and incorporating feedback loops to refine strategies — ensuring each campaign builds on the last.

What prevents more effective learning after a campaign? 

After all the hard work of planning and launching a marketing campaign or introducing a new initiative, the team quickly shifts focus to the next project. Success metrics and dashboards may be in place, but there’s little time to assess the last initiative holistically. Does this sound familiar? 

Let’s explore three common barriers that prevent us from gaining and applying valuable insights from our marketing efforts — and how to address them.

1. Incomplete data collection

Incomplete data collection can stem from many starting points, including gaps in tracking mechanisms, insufficient data points or overlooked metrics. For instance, a campaign might track only immediate conversions without considering long-term customer engagement or retention. This leads to an incomplete picture of the campaign’s effectiveness and can undermine accurate analysis. 

Without that full picture, it may be difficult to understand the broader implications of the effort. This may lead to either incorrect assumptions — and misguided recommendations— or missed opportunities altogether.

The specifics will vary greatly based on what type of campaign or effort you have launched. Here are a few steps you can take to avoid ending a successful launch with incomplete data:

• Ensure comprehensive data collection before you start by planning your data needs before the campaign begins. 
• Implement robust tracking systems that capture all relevant metrics across different channels and stages of the customer journey. 
• Use a combination of quantitative and qualitative data to get a holistic view of the campaign’s impact. 
• Make sure to regularly audit your data collection processes to identify and address any gaps or inconsistencies.

By preventing incomplete data before drawing conclusions with long-term impacts, you and your team will be set up for greater success and can fully enjoy the benefits of data-driven decision-making.

Dig deeper: How to quantify the ROI of data using decision playbooks

2. Making interpretations from biased data

Even with complete data, underlying issues can skew the analysis and the recommended actions. Bias in interpretation happens when conclusions are based on preconceived ideas or expectations, not objective analysis.

Another common scenario is when teams share common anecdotal references, such as “It always seems to happen this way” or “We’ve been doing it this way for years.” Such biases can influence interpretation and decision-making.

Different types of biases can affect your analysis. For example, confirmation bias happens when analysts focus on data that supports their beliefs, while selection bias occurs when only certain data sets are considered. Even how a marketing test is designed can be influenced by bias, affecting the entire outcome. These lead to flawed strategies and missed opportunities for improvement.

How can you and your team do their best to avoid introducing bias and having it guide your data-driven decision-making? Here are a few steps you can keep in mind:

• Start with a strong hypothesis. Use objective data analysis techniques and involve diverse perspectives in the interpretation process. Detail your objectives clearly before you begin. 
• It’s OK to get scientific. Employ statistical methods to validate your findings and ensure outliers or anomalies do not skew them. 
• Don’t be afraid to ask follow-up questions. Have team members review and question the analysis to encourage a culture of critical thinking and challenge assumptions. 
• Get a second opinion. Using third-party tools or consultants for an unbiased review can also help mitigate internal biases.

The above list is only a starting point. Educating yourself and your team on common biases can help reduce or eliminate them, leading to better decisions with your data.

Dig deeper: How to use decision intelligence to tackle complex business challenges

3. Failure to incorporate feedback loops

We’ve all been there. The elation — and exhaustion — after a successful launch or when a campaign wraps. It would seem the most important work is now complete, but it is only part of the story. The data you’ve gathered is a valuable resource for future marketing efforts. By ensuring complete data collection and avoiding bias, you’re now set up to make informed, data-driven decisions.

Not using campaign insights to shape future strategies is a common mistake, even among experienced teams. This often happens when data analysis is seen as a one-time task instead of an ongoing process. As a result, lessons from past campaigns aren’t applied, leading to repeated mistakes and stagnation.

The good news is that creating feedback loops is something any team can do. It requires extra effort and attention to past campaigns, even when the focus shifts to the next big initiative. Failing to do this wastes valuable time, effort and data that could improve future decisions.

Keep the following in mind to help avoid this trap:

• Establish feedback mechanisms and regular reporting cadences that ensure insights from data analysis are continuously fed back into the strategic planning process. 
• Create a structured post-mortem process after each campaign to document findings, lessons learned and recommended actions. Discuss the results and how they were planned, measured and analyzed.
• When planning a new initiative, review past, similar efforts as part of the strategic process. Find ways to incorporate learnings into your planning to test assumptions. 
• Avoid one-off improvements. Foster a culture of continuous improvement, where feedback loops are an integral part of your marketing operations. Support your fellow teammates in building a feedback loop and reviewing what can work better.

Dig deeper: How to avoid pitfalls of data-driven marketing execution

Summary 

After a campaign is over or a marketing initiative has launched, data-driven decision-making is critical to ensuring long-term success. Building on the success or lessons of your previous efforts is the best way to improve continuously. 

Becoming a data-driven decision-making organization requires commitment, the right tools and a culture that values data integrity and objective analysis. Embrace the power of data to drive your marketing strategies, and your organization will be well-positioned to achieve greater success in an increasingly competitive landscape.