It’s a new world out there, and obtaining user-level data for algorithmic, multitouch attribution just isn’t as easy as it used to be. Great minds have written at length about the effect that limitations on tracking users will have on the industry. The walled gardens build higher walls, browsers add more and more tracking restrictions, and regulation increases the obstacles marketers have in using data.
Taken together, it’s motivating an increase in clients asking me whether attribution has a long-term future. While that’s a topic for another day, the solution isn’t. I typically propose pursuing a combination of alternative – and complementary – approaches to attribution:
- Marketing Mix Modeling: MMM refers to software and services marketers use to evaluate their overall marketing plan and assign value to various marketing activities. MMM applies econometric regression techniques to estimate the aggregated impact of marketing activities on desired outcomes, such as sales or lead generation. Because it uses aggregate data, MMM is often called a “top-down approach.”
- Unified Measurement: UMA refers to the software and services that measure and inform decisions for both day-to-day optimization and longer-term planning decisions. Being able to support both types of decisions within a single measurement system is the key distinguishing characteristic of UMA. To deliver on that promise, many methodologies combine (or unify) a top-down MMM model with a bottom-up MTA model.
- Customer Journey Analytics: Gartner defines customer journey analytics (CJA) as tracking, integrating and analyzing how customers use a combination of available channels to interact with an organization.
- Rule-based Attribution Analysis: Basic attribution methods assign fixed weights to events along a sequence leading up to a success event. This approach to attribution analysis uses rule-based heuristic models such as linear, time-decay and opener-assist-closer methods. It does not depend on statistical algorithms, nor does it necessarily require collecting data at the user level. Most marketing analysts have the skill set required to perform rule-based attribution analysis in-house. Because the rules are straightforward, this analysis is easy to understand and explain to internal stakeholders.
- Content Optimization: Know when and where your consumers want to engage with you and have a deep understanding of your brand. This will enable you to optimize your content and provide the ideal message at the right time. An atomic approach and the right technology will allow you to scale your content marketing to support optimization (see: Embrace These 3 Key Trends in Content Marketing).
Re-examine your marketing plans to determine the right mix for you. For instance, an educational institution may employ mix modeling to evaluate the total impact of its marketing spend in online and offline channels, and supplement that with customer journey analytics to examine learner experience from name acquisition to enrollment. A CPG business likely uses marketing mix modeling to evaluate its brand awareness and the combined impact of online and offline data, while using rule-based attribution while campaigns are in-flight to get a directional idea for how to tie data to decision making and make necessary adjustments. A foundational exercise would be to reexamine how you’re collecting first-party customer data (see: What Marketers Need to Know About Customer Data, subscription required).
Multitouch attribution isn’t dead, so much as its power is increased by using the methodology alongside others. What complementary approaches do you use, and why?