Before deciding on an attribution methodology, such as a simple model, multi-touch model, or data-driven model, figure out how big the attribution problem is that you're trying to solve.
Judging by the evidence of blogs, articles, and conferences, campaign attribution is still a hot topic for today's marketers. A quick search for the topic on your favorite search engine spawns a mix of content and technologies devoted to the subject. But is campaign attribution a problem for you? Is it something that you should be investing your analytics dollars in understanding and leveraging?
First of all, let's look at the types of attribution methodologies typical available today. I categorize these in to three main types, broadly in order of complexity and potential cost.
Simple models are those which only take into account one touch or interaction in the customer's path to purchase. The typical simple model used is the "last click" model (i.e. the marketing channel of the converting visit) and although it's railed against by many, it's still probably the most frequent used model due to its simplicity and the fact that it's ingrained in both marketing culture and marketing technologies.
The alternative is the "first click" model, which credits the first interaction with the conversion and is potentially combined with an attribution window that will only consider the first interaction within a certain timeframe, e.g. the previous 30 days, 60 days, etc.
Multi-touch models give credit to all channels or interactions in the path to purchase. The amount of credit given is determined by some kind of weighting scheme, of which there can be many flavors. Examples include linear schemes where every touch point gets the same credit, and decay schemes where the further in the past the touch point occurred, the less weight it gets.
Simple models and multi-touch models are what I call "deterministic" models. Someone, somewhere decides what model to use. That decision may be informed by some kind of insight or knowledge of customer purchasing behavior but at the end of a day a human makes the call and says "this is the attribution methodology that we're going to use."
Data-driven models are just that - they are determined by an analysis of the data, usually involving some form of statistical modeling. There are a variety of different data-driven methodologies, including regression modeling and machine learning methods. More recent developments include applications of game theory into understanding the contribution of each channel to the overall marketing mix.
These models are inherently going to be more accurate (when done well) than simple models or multi-touch weighting models in understanding the contribution of each marketing channel to performance. Because they are based on analysis of the underlying data, they will reflect the reality of the contribution that different channels are making rather than someone's perception of the way that the marketing system works for their particular business.
The challenge with any modeling-based approach is that the analysis and interpretation of the results can be complex, time-consuming, and costly. Therefore, there has to be a return on investment (ROI) on the development of these more complex attribution methodologies. So, how do you know if you have an attribution problem that needs or justifies these more sophisticated approaches?
Understanding the Path to Purchase
The key to understanding whether you have an attribution problem big enough to worry about is understanding the way that customers buy your products and services. Are the customer journeys simple and straightforward or are they typically quite complex? Typically, if you look at the paths to purchase, you will see that a significant number of these journeys are quite short. At a recent conference, some metrics presented by Adobe suggested that on average half the journeys were simple "one touch" paths to purchase, i.e. customers either bought on their first visit or they didn't.
Given that half the journeys are simple journeys, that suggests that in some cases the proportion may be much higher while in other cases the paths to purchase are more likely to be more complex. In my own experience, the proportion of simple to complex (multi-touch) journeys can be quite high. If this is the case, one issue might be that you have data problems because you're not tracking journeys across different devices and touch points, meaning that you are not getting a complete picture. But let's assume that's not the situation and that indeed the majority of purchases happen with short journeys, in which case you don't really have an attribution problem. If the vast majority of conversions happen on a single visit then it doesn't matter which model you use, they're all going to give you the same answer!
Scale and Complexity of Marketing
The other factor to consider in determining the potential ROI from advanced campaign attribution is the scale and complexity of your marketing activities. Naturally, if you are working with significant budgets it will be easier to justify more investment in understanding attribution even if your customers' journeys are usually simple. The sheer volume of budget that can still be optimized around the remaining complex journeys will still be worth it.
Simple and other deterministic attribution models are often available "out of the box" these days. There is an increasing trend toward the development and use of data-driven methodologies, which I applaud. However, before leaping in that direction, just check out how big the problem is that you're trying to solve.
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Neil Mason is SVP, Customer Engagement at iJento. He is responsible for providing iJento clients with the most valuable customer insights and business benefits from iJento's digital and multichannel customer intelligence solutions.
Neil has been at the forefront of marketing analytics for over 25 years. Prior to joining iJento, Neil was Consultancy Director at Foviance, the UK's leading user experience and analytics consultancy, heading up the user experience design, research, and digital analytics practices. For the last 12 years Neil has worked predominantly in digital channels both as a marketer and as a consultant, combining a strong blend of commercial and technical understanding in the application of consumer insight to help major brands improve digital marketing performance. During this time he also served as a Director of the Web Analytics Association (DAA) for two years and currently serves as a Director Emeritus of the DAA. Neil is also a frequent speaker at conferences and events.
Neil's expertise ranges from advanced analytical techniques such as segmentation, predictive analytics, and modelling through to quantitative and qualitative customer research. Neil has a BA in Engineering from Cambridge University and an MBA and a postgraduate diploma in business and economic forecasting.
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