Today, customers are exposed to advertising everywhere— while listening to Pandora, browsing Facebook, on their smartphones when they are checking last night’s scores, and of course during their favorite TV shows. This advertising has a strong effect on the decisions and purchases customers make. But how do marketers make sense of what advertising channels are working? How do they decide which channels are the most influential in driving purchases? This is where response attribution comes in to play to actually measure the influence marketing channels have towards driving sales.
Companies often sit on a rich wealth of data they have collected about their marketing efforts—but they’re not quite sure how to measure the influence from those efforts. Are the millions of emails sent out to a customer base as influential as the paid search advertising being done? Is the catalog someone received the driving force behind the sale before a customer went directly to the vendors site to make the purchase? Simple attribution models like last touch, arbitrary business rules, or even the more complex fractional allocation just don’t cut it anymore. These methods are either too basic to handle the complex mix of marketing we encounter or they introduce bias which may skew attribution results. Customers make complex decisions about purchases that need to be attributed properly in order to gain the best understanding and intelligently build effective marketing campaigns.
The marketing world needs a response attribution solution that can handle the complex mix of marketing delivered to customers. If there was only one channel to market with, the solution would be simple. But in today’s world it is not that simple. Customers are seeing advertising from tens of channels and may use hundreds of touch points before an actual purchase is made. There needs to be a real understanding of how various channels perform. Traditional models don’t produce strong enough results; they’re simple and can be highly inaccurate.
We solve this problem by capturing and using all marketing touch points along with customer history (both purchase and promotion).
I recently wrote a paper on the subject, Intelligent Cross-Channel Response Attribution: A Next-Generation Approach to Evaluating Channel Contributions to Sales. The paper highlights:
- Why Cross-Channel Response Attribution Matters
- Three Legacy Approaches
- Next Generation: The Intelligent Cross-Channel Attribution Model
- Advantages of Intelligent Response Attribution
The Intelligent Response Attribution model uses a much more robust, data driven approach than basic models. One of the advantages is that it drills down to every customer touch point used during the buyer’s journey. This granular view is then aggregated for each purchase and marketing channel to provide a final attribution of sales for all channels. The model not only includes all customer touch points that have occurred throughout the buying process, but also leverages the customers’ historical behaviors and demographics to aide in measuring a channel’s influence. Capturing and using the historical behavior is important because it will help identify the attributes that make email a more effective marketing channel for some customers than others. At the heart of the solution are advanced data mining methods used to accurately measure how influential specific channels are, which in turn provides an unbiased data driven approach to measuring influence for marketing channels.
What are your thoughts about response attribution? Share your comments and questions!