Testing, Testing: The Economics of Social Conversions, Part 2
By Morgan Lucas
With so many variables at play, how can we dissect our data to give us new insights as to what is actually driving conversions and, ultimately, ROI? How can we measure all the way through the customer journey in order to truly understand what’s working, rather than making biased assumptions based solely on first or last click attributions. After studying Economics & Applied Mathematics, I understand the value of truly grasping the effects of campaigns in order to provide insights that can be built on for optimal growth. In this series, we will approach social analytics from an economic standpoint to understand where we can more efficiently allocate our resources and increase conversions. Save your time and effort for tactics that have proven effectiveness in driving ROI.
In Part 1, we focused on creating a multiple regression model in order to better understand what factors are the best predictors of social ROI. We used five key variables involved in the customer journey, including 1) awareness 2) engagement 3) consideration 4) conversion and 5) customer retention. We analyzed how these variables play a significant role in driving ROI, and further, how this data can be useful in your testing. A solid understanding of this model is an essential component of diving deeper into your analytics and providing a more efficient allocation of your resources. In doing so, you can more accurately evaluate the impact that social is making on ROI and compare it across different spectrums. By allocating more resources to what is working and less to what is not working, you can achieve better and faster results.
We will now take a closer look at how you can transfer your understanding of the model in order to gain the best possible insights available. That being said, if you would like a comprehensive understanding of this approach, please read the first part of this blog series.
The goal in marketing is to achieve the greatest possible results with the least amount of effort, achieving maximum efficiency. Therefore, we want to reduce bias as much as possible without sacrificing efficiency. Omitted variable bias presents itself in some form in almost all models. In order to reduce potential bias, we must isolate certain variables or run tests on uniform samples. However, it is nearly impossible to perform a perfectly unbiased test without incredible effort and wasted resources.
Let’s say you want to achieve maximum precision with minimum effort in order to establish a deeper understanding of your campaign data; you’re looking to find the most probable and insightful explanations. Control group testing is the best method to achieving that goal.
In control group testing, we can control for unobservable differences between the samples that could influence ROI by approaching it with a difference-in-difference strategy (fixed effects). By comparing the change in certain behavioral variables of social media users to non-social media users, we can control for unobservable differences, and thus, reduce potential bias.
Control Group Testing
Use this resource to create your own ‘social user’ segments in Google Analytics: https://trks.it/ev8UT.
The manner in which you analyze your data is very important. The most effective way to approach this is to use your data to answer questions. Are users who are coming from social media engaging on the website at a higher rate than users who don’t visit your social channels (i.e., spending more time, viewing more pages and coming back more often)? Are users who engage with your social media driving more conversions than others? Specifically, which conversion points are social users reacting more favorably to? What are the patterns and trends? How does this correlate with your posting structure? Structuring useful conversion points provides a strong source of data and allows for ROI measurement. Look at whether users coming from social media are converting at a higher rate than those not coming from social media.
Look at the effects of a marketing campaign on both your social audience and non-social audience. Are users on social media reacting more favorably and driving more conversions that those who do not interact with your social media channels, indicating a higher quality audience and higher potential for ROI? In order to compare the effects between both samples, obtain both pre- and post-campaign data for your social user segment and non-social user segment. Measure the difference between pre- and post-campaign conversion rates for both samples. Finally, compare the difference between social users and non-social users to see which audience deserves more advertising spend. You can perform this analysis between many other different audiences, channels, and campaigns.
You can further analyze users and campaigns with link tagging. However, you must have a defined and consistent tagging structure in place. You can use Google URL Builder or TRKS.IT as link tracking tools to see what type of users, content, and distribution channels are driving the most conversions. Link tagging makes it possible to test and measure the effect of different variables on social media ROI.
In sum, it is important to look at an economic view of your campaign results, all while ensuring efficiency by avoiding time-killing tactics. Ultimately, the primary goal of digital analytics is to achieve the greatest possible results with the least amount of effort by putting more energy into what is working and less into what is not working. It cannot be stressed enough: work smarter, not harder!
- Thank you very much for taking the time to read and comment on … by Morgan Lucas
- I thought the most interesting part was “Omitted variable … by Robert Middleton