Incrementality & Attribution in Marketing – How to Measure Advertising Effectiveness with Changes to Digital Privacy
The recent changes in privacy and cookie laws have made it challenging for marketers to accurately measure the effectiveness of their digital advertising efforts. As a result, many marketers are struggling to demonstrate to stakeholders that their marketing budget is being spent in the right way and driving incremental revenue.
Measures of Success in Marketing
While data can be a valuable tool for making informed decisions, the lack of data can make it difficult to prove the effectiveness of advertising. However, it is important to note that research from the Ehrenberg-Bass Institute has shown that when brands stop advertising for an extended period of time, sales often decline by over 16%. Therefore, it is important for marketers to find alternative ways to prove the effectiveness of their advertising efforts, even in the absence of data. Read through as we answer these questions on what marketers should do when we have limited data.
For marketers, the key metric of success is often referred to as the “marketing funnel,” which measures the progress of leads through the different stages of the sales process. The stages of the funnel are derived from the famous AIDA Model – Attention, Interest, Desire, Action.
The ultimate goal of the marketing funnel is to convert leads into customers, and the metric used to measure success is typically the conversion rate, or revenue or the number of leads that become customers. Other key metrics used by marketers include customer acquisition cost (CAC), lifetime value (LTV), return on investment (ROI), and customer satisfaction.
Getting marketing budgets approved is becoming more difficult by the day. CFOs and other executives want evidence that marketing budgets will create value for the company before making heavy financial commitments. These days, if you can’t prove your campaigns will drive real revenue growth, you might not get the budget you want. This is one of the reasons why we need to incorporate an incrementality measurement so that we can prove the marketing effectiveness to stakeholders.
Incrementality & Attribution in Marketing
In the world of marketing and advertising, it’s important to understand the difference between incrementality and attribution, as well as how to measure each. Both concepts are related to the effectiveness of different marketing tactics, but they are not the same thing.
What is Incrementality?
Incrementality is a measure of how much a particular marketing tactic or campaign is driving incremental revenue or sales. In other words, it tells you how much more revenue or sales a campaign is generating above and beyond what would have happened without it. Incrementality is often used to assess the effectiveness of different marketing channels, such as email marketing, search advertising, or social media advertising.
What is Attribution?
Attribution, on the other hand, is the process of assigning credit for a sale or conversion to different marketing touchpoints. Attribution allows marketers to understand which touchpoints are most effective in driving conversions, and to optimize their marketing strategies accordingly. Attribution is often used to assess the effectiveness of different marketing tactics within a single channel, such as different ad creatives or targeting strategies.
It’s important to note that while incrementality and attribution are related, they are not the same thing. Attribution helps us understand how different channels are contributing to sales or other business outcomes, while incrementality measures the additional impact a campaign has over the natural outcome.
What is Incremental Lift
One key difference between incrementality and attribution is that incrementality often requires a controlled experiment, such as a randomized controlled trial, while attribution can be done using observational data. Additionally, while incrementality measures the impact of a campaign on a business outcome, attribution helps to understand how that impact was achieved.
With incrementality measurement, you’re trying to determine how much additional, non-native demand your marketing or advertising campaign creates for your products or services. The difference between the native demand and the non-native demand generated by your campaign is the “incremental lift.”
Measurement of Incrementality & Attribution
Now that we are clear on incrementality and attribution, let’s take a deep dive into the measurement of these two. Marketers measure incrementality in business sales through experimentation and testing. This typically involves running controlled experiments, such as A/B tests or randomized controlled trials, to isolate the impact of marketing efforts on sales. Attribution is measured through various methods:
- Last-click attribution: This type of attribution assigns all the credit for a conversion to the last click or interaction the customer had with the brand before making a purchase.
- First-click attribution: This type of attribution assigns all the credit for a conversion to the first click or interaction the customer had with the brand.
- Linear attribution: This type of attribution assigns equal credit to each touchpoint in the customer journey.
- Time decay attribution: This type of attribution assigns more credit to the touchpoints that occur closer to the conversion.
- Position-based attribution: This type of attribution assigns the most credit to the first and last touchpoints in the customer journey while distributing the remaining credit proportionally among the other touchpoints.
While attribution methods are built into analytics platforms to simplify day-to-day decision making, incrementality is more of a custom measurement. Businesses can also use MMM (Marketing Mix Models) or MTA (Multi Touch Attribution) to measure incrementality.
One common method of measuring incrementality is called the “holdout” method, which involves randomly dividing the potential customer base into two groups: a treatment group that is exposed to the marketing campaign, and a control group that is not exposed. The sales generated by the treatment group can then be compared to the sales generated by the control group, and any increase in sales can be attributed to the marketing campaign.
Another method is the “matching” method, which is also called propensity score matching Propensity score matching, which estimates the sales that would have been generated without any marketing efforts by finding a control group that is similar to the treatment group in terms of observable characteristics observable characteristics and using that as a comparison.
Incrementality can also be measured using more advanced statistical methods such as Machine Learning algorithms and survival analysis that can account for more complex dynamics such as customer lifetime value and customer retention. The choice of measurement method depends on many factors such as the nature of the marketing effort, the availability of data, and the desired level of precision.
Using the MMM Model for Measuring Incrementality
Marketing mix modeling (MMM) is a statistical technique that uses historical sales data to estimate the impact of various marketing inputs (e.g. advertising, promotions, pricing) on sales. This technique is widely used in marketing research and strategy to understand the effectiveness of different marketing tactics, and to make data-driven decisions about how to allocate marketing resources.
MMM uses a combination of statistical techniques, such as time-series analysis, econometrics, or machine learning, to create a model that predicts sales based on past marketing and other inputs. The model is then used to estimate the impact of different marketing tactics on sales, and to make predictions about the likely impact of future campaigns and initiatives.
MMM is used by businesses of all sizes and across a variety of industries, including consumer packaged goods, retail, pharmaceuticals, and financial services. It is particularly useful for companies that have a large amount of historical data on sales and marketing activities, and that want to make data-driven decisions about how to allocate marketing resources.
Here’s what it entails:
- Collect historical sales data: To begin, you will need to gather a large amount of historical sales data, including data on your marketing campaigns and initiatives, as well as data on other factors that may impact sales (e.g. economic conditions, competitive activity).
- Create a baseline model: Using this data, you can create a baseline MMM that predicts sales based on past marketing and other inputs. This model will serve as a benchmark for evaluating the impact of future campaigns and initiatives.
- Identify the campaign or initiative to be evaluated: Next, you need to identify the specific campaign or initiative that you want to evaluate for incrementality. This could be a new product launch, a promotional campaign, or a change in pricing strategy.
- Simulate the counterfactual scenario: To measure incrementality, you need to compare the sales generated by the campaign or initiative to a scenario in which that campaign or initiative did not occur. To do this, you can create a modified version of the baseline model that simulates the counterfactual scenario. For example, you could remove the data of the specific campaign or initiative from the historical data used to train the model.
- Compare the predicted sales: Using the modified model, you can predict sales for the campaign or initiative under the counterfactual scenario. By comparing these predicted sales to the actual sales generated by the campaign or initiative, you can determine the incremental impact of the campaign or initiative.
- Validate the model: Finally, it’s important to validate the model by comparing the predicted results from the model with the actual results. And then use the model to predict future sales and compare it with the actual sales to see how well the model is performing.
The goal of incrementality analysis is to determine the true return on investment (ROI) of a media campaign by isolating the incremental impact of the campaign from other factors that may have influenced sales or revenue. The importance of incrementality is crucial to a business and to demonstrate to stakeholders that advertising and your channel mix is working.
- Marketers need to find alternative ways to prove the effectiveness of their advertising efforts, such as using incrementality measurement.
- Incrementality is a powerful measure of how much a particular marketing tactic or campaign is driving incremental revenue or sales.
- Attribution is the process of assigning credit for a sale or conversion to different marketing touchpoints.
- Marketers should focus on key metrics such as conversion rate, CAC, LTV, ROI, and customer satisfaction to measure the effectiveness of their marketing efforts.
- Data is the most important key in decision making and keep testing until you find out what works for your business.
Our Marketing Intelligence team offers a full range of services to help you measure the performance of your marketing efforts. Contact us today to find out more.