The Science Behind Successful Campaigns: Maximizing marketing effectiveness
Campaign analysis refers to the process of analyzing the performance and effectiveness of marketing campaigns. It involves analyzing the data and metrics to understand the impact, reach, and success of a campaign in achieving its objectives.
The fundamental goal of campaign analysis is to evaluate a campaign’s performance and effectiveness. This motivates marketers or stakeholders to choose sound strategic decisions. Overall, this leads to better campaign outcomes.
To ensure the effectiveness of any marketing campaign, we must examine and analyze key campaign data at two stages: before and after the campaign is launched. These marketing measurements can be described as:
- Pre-Campaign Analysis
- Post-Campaign Analysis
These analyses help in evaluating what is and isn’t functioning so that preventative steps can be implemented to improve campaign performance.
Pre-campaign Analysis:
A pre-campaign analysis is an essential step that is performed before starting a campaign that can help marketers to optimize their marketing strategies.
The steps involved in pre-campaign analysis are:
- Campaign Objectives: Clearly understand the goals and objectives of your marketing campaign. Determine what you want to achieve and get the characteristics of the targeted audience in order to fetch the exact data.
- Analyze Data: Gather relevant data based on provided characteristics of the audience segment. Perform data cleaning, and data preprocessing and then use this data to gain more insights.
- Set Key Performance Indicators (KPIs): KPIs are the metrics that help to measure the success of a campaign. Examples of KPIs are: signups, activations, click-through rate, payments, revenue growth
- Analyze Campaign: If there were similar campaigns in the past, analyze and identify the keynotes of improvement. Thus, identifying the success and failures of past campaigns helps in helps in optimization of our new or upcoming campaigns.
- Control Group Sizing: The purpose of control group sizing is to split the data into control groups and treatment groups. An appropriate size of a control group is determined based on different factors like sample size required for statistical significance, the level of confidence, baseline conversion rate, minimum detectable effect, significance level, and expected campaign size.
Control Group(A) — Users that don’t receive the campaign
Treatment or Variant Group(B) — Users that receive the campaign
Significance level — It is the probability of observing a difference between groups by random chance variation. Significance is usually set at 0.05 or 0.01. A lower significance level indicates a higher level of confidence required to reject the null hypothesis and make a claim of statistical significance
Minimum Detectable Effect (MDE) — It is a measure to determine the minimum difference that can be detected between treatment and control groups.
Post-campaign Analysis:
Post-campaign analysis occurs after the campaigning has ended. Analyzing campaign data and delivering insights to marketers allows them to prepare and plan accordingly for their future campaigns.
The steps involved in post-campaign analysis are:
- Metric: Understand what are the key metrics (KPI-key performance indicators) that you are going to measure. This could include the Number of Signups, Number of Activations, Increase in Revenue generated, Number of transactions, or other metrics chosen based on the campaign targeted.
- Clean and Prepare Data: Collect all the relevant data related to the campaign for analysis. Perform data cleaning and transformation to make it suitable for analysis.
- Comparing the groups: To understand the impact of the campaign, compare the campaign results (KPIs) of treatment groups with the control group.
- We can perform A/B testing on these groups in order to understand whether the results obtained are due to random chance variation or whether there is a significant difference in the results.
- If it is clear that the results obtained are not due to random chance variation, then you can compare treatment group and control group results and calculate incremental or relative lift.
- Stickiness: Let us assume that the conversion window is 15 days. Now calculate KPIs and incremental lift for collected data 15 days prior to the campaign launch or start date. Comparing results for a conversion window before and after launching a campaign, helps to understand more about the campaign performance.
A/B testing: It is also known as split testing or bucket testing, which is a method of comparing two versions (A and B) to determine which version is better. A/B testing helps to identify the most effective variations.
If the treatment or variant group outperforms the control group, it indicates that the campaign has a positive impact.
If there is no significant difference or the variant group performs worse, it suggests that the campaign is not successful.
Let’s explore incremental lift and relative lift in the context of signups as KPI
Incremental lift: Measures lift generated by a variation over the control group. Where it quantifies the incremental change in conversions.
Incremental lift here indicates that the treatment group resulted in 100 additional signups compared to the control group.
Relative Lift: Also known as percentage lift, it measures the percentage difference in conversions between two groups.
It indicates that the treatment group experiences a 20% improvement in conversions compared to the control group.
Insights and Recommendation: Summarize the findings from the analysis. Based on insights gained, provide recommendations to marketers or stakeholders for future campaign planning.
A post-campaign analysis is an iterative process that enables marketers to learn the audience behavior from past experiences and this helps to refine marketing strategies.
Application of ML models:
- Use ML algorithms such as clustering techniques to segment customers based on their behavior, demographics, and other preferences. This helps in targeting specific segments for future campaigns.
- Focusing on the targeted customers exposed to different campaigns and understanding their behavior or responses. Building ML models on these data can identify the factors influencing customer response and optimize future campaigns.
- Analyzing historical campaigns can help in gaining insights like fetching external factors (seasonality, holidays) that affect the campaign. Applying ML techniques to historical campaigns can help in tailoring future marketing strategies and also helps to predict future campaign performance based on historical data.
