Jenya Edelberg is the CTO of martech consultancy The Destiny Lab.
Marketers measure return on investment (ROI) to evaluate the effectiveness of their campaigns and allocated resources. Calculating ROI on marketing spend requires careful consideration of various data points and upfront strategy.
What makes the calculation process complicated is data points and attribution of monetary value in customer journeys. The proliferation of marketing solutions related to data collection, processing and reporting, on the one hand, helps marketers understand users and customers better. On the other hand, chief marketing officers (CMOs) and marketers, in general, are challenged with figuring out what data is relevant for ROI calculations.
The basic ROI formula considers net profit generated as a direct result of the marketing efforts and costs. Marketing expenses are straightforward in connection with advertising, content creation, personnel, applications and other spending closely associated with the marketing activities. What is not apparent, though, is the generated profit.
This is why it is important to carefully attribute value and collect appropriate data before the marketing campaigns begin. This can provide stakeholders with a real picture of the impact of marketing.
Marketing ROI Rule 1: Measure What Matters
Until recently, attribution data played a significant role in evaluating the success of various campaigns and allocating revenue to specific marketing channels. Examples of attribution models and how value is assigned:
• Last Click: This model attributes all conversion values to the last interaction.
• First Click: In contrast, the first click model assigns all the conversion credit to the first interaction.
• Linear Or Multi-Touch: The linear attribution model distributes credit for the conversion equally across all ad interactions in the conversion path.
• Data-Driven: The data-driven attribution distributes credit based on the historical data of your specific conversion actions.
Marketing mix modeling is now gaining popularity in place of traditional attribution as it allows for a more accurate measure of marketing ROI and its impact on sales. One should consider applications that provide data management, predictive modeling, marketing mix modeling software and consulting to leverage insights across multiple channels.
Marketing ROI Rule 2: Clean Data Equals Clear Results
Data is everywhere; analytics and reporting are all around us. We collect all kinds of data points, and we need to collect as much data as we can, as it seems. While we need analytical data to find correlations between various activities and, consequently, generate profit, we also potentially collect data that will not help us.
Here is an example of what can happen when the analytics provider changes how the data is tracked. The old set of data is no longer comparable to the new set. The problem is that the old set cannot be converted to a new one as it has different criteria for how it was collected and tracked. This creates a barrier to the continuous year-over-year comparison and does not reflect compared efforts over time.
The decision about data collection and how it can be used now and in the future must happen at the strategy level so it is clear what information will be needed to make decisions. More often than not, data collection occurs later in the process, which does not account for the efforts or the lack of effort put in before the current dataset.
Marketing ROI Rule 3: Know What You Want
Each specific company has to decide what should go into calculating its marketing ROI and what criteria should be considered for measuring success. Instead of starting at the point of what data is telling you and what results you are seeing in the analytics applications, set the parameters first.
The activities that should be accounted for when validating marketing impact are the ones that go into the marketing mix: product, price, place, promotion, packaging, positioning and people.
Then, to understand which marketing metrics will be most important, identify your critical questions and arrange them based on their importance. These rankings will serve as a reference for data requests and will aid in determining where to allocate resources. It will provide more clarity on what should be used for KPIs.
Data and media mix modeling tools can enable organizations to convert data into predictive models. This creates opportunities for setting goals and explaining how various factors impact the outcome of marketing efforts.
At the end of the day, marketing activities exist to bring value to customers. Marketing departments should start communicating their value to the stakeholders in a language backed by data science. Marketers should also accurately picture customer engagement across multiple channels and how these channels contribute to the generated profit.
Proactive data collection strategies like these should set a foundation for a better marketing strategy and higher impact overall.
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