We need to be able to communicate complex data outcomes to the rest of the C-level in a way that is compelling, understandable and relatable.
Welcome to the martech multiverse. Everything old is new again; martech is innovating in leaps and bounds, yet attribution remains a thorn in the side of the vast majority of CMOs. Currently, only one in four marketers is confident they can quantify ROI, which certainly helps explain why 70% of CMOs expect to invest more in marketing analytics and attribution over the next year according to Nielsen.
Marketers are feeling the pressure. Data complexity and fragmentation plague CMOs as the sheer volume of martech in play makes the accurate calculation of ROI on campaigns, channels
The traditional linear funnel has shattered and today, a customer journey might be dozens or even hundreds of touchpoints deep. Forget omnichannel – across devices, networks and locations, brands are expected to omnipresent. All of that martech is enabling marketers to get in front of their customers where and whenever they’re searching (and with more personalized content than ever before). Tracking and measuring outcomes, however, remains difficult.
“Accurate attribution is the single largest issue we have as marketers … digital metrics were held out as our savior, but instead, they have only led to more confusion” Simon Bell, professor of marketing and director of The Centre for Workplace Leadership at the University of Melbourne, told Deloitte.
Before you throw another technological tool on the pile, let’s have a look at how you can come to grips with what you already have – and realize its full value.
Moving beyond first- and last-click attribution
“Marketers that still employ a ‘last touch’ attribution model on campaigns will most likely assign credit to a media partner that is not delivering value,” writes Technology Business Research in a pre-release draft of their new report about advanced attribution tools.
Ideally, first and last click won’t be an attribution model option for you at all; instead, they’re simply metrics within a more robust, holistic attribution model. In Chief Marketer’s recent B2B Outlook survey, cost of conversion was cited as the metric that mattered most in attribution, followed by the amount of time to convert, channel, first click and last click.
How can you move beyond the minutiae to more accurately see the big picture and measure the ROI of your marketing and media spend?
This is a multi-touch attribution model that gives each touchpoint along the path to purchase an equal share of the credit. It allows for analysis of campaigns and accounts for multiple interactions, but you lose the ability to optimize for specific channels when all are considered equal.
With linear attribution, every touchpoint gets the same weighting. If you had identified social media, email, direct, organic search and paid search along the customer journey, each would get 20% of the credit for the sale. In reality, this could seriously undervalue the influence of your email efforts while artificially propping up the value of your social.
Time decay or U-shaped attribution
These two methods use time as a measure of the value of interactions. Time decay attribution gives less credit to earlier touchpoints. As the customer moves closer to conversion, the value of each interaction increases.
In U-Shaped attribution, on the other hand, the first and last touchpoint are weighted heaviest and the rest share an equal share of a smaller amount of credit. For example, Google Analytics assigns a value of 40% each to the first and last touchpoint. All others in between share a 20% weighting.
The problem with time-based attribution models is that these assumptions are really just a guess at what motivated consumers most. While every touchpoint gets at least some acknowledgment, the amount of credit for each can be seriously skewed as a result of this sweeping assignment of value. Should a social media ad as first touch be given the same weighting as the email that drove the customer in-store to purchase?
Algorithmic attribution is driven by data, not assumptions or predetermined rules. This model uses machine learning to build on the analysis and learnings of previous campaigns. Each touchpoint is analyzed and the attribution model evolves and becomes “smarter” as a result.
This is the basis of Google Attribution, launched in 2017. Marketers can capture a snapshot of an ad’s contribution with data drawn from Google Analytics and Google Ads, then layered over with machine learning. In an early case study, Google imported the analysis into Google Ads and used it to power an automated bidding strategy. As a result, the test hotel brand saw a 45% increase in revenue from generic search campaigns, 22% better ROAS from generic search campaigns, and an increase in overall cross-channel revenue.
CMOs in search of a more sophisticated strategy might choose to design their own custom attribution model, ideally incorporating the machine learning element discussed above. This enables marketers to make better use of what they know about their customers’ journey.
A cruise company dealing in high value, luxury experiences for example likely knows that their customer has a longer, more complex path to purchase. You might choose to assign a higher value to earlier touchpoints, as customers will spend more time with the brand they’ve already selected consuming content to help them prepare for the experience.
Or, you might have a higher customer LTV and a great repeat business. In that case, you want to give new customer acquisition and retention of greater value.
What CMOs can do to improve attribution right now – and going forward
Whatever model you choose, make the best use of it by continuing to hone and evolve. Attribution is not a “set it and forget it” calculation; it’s a living, breathing understanding of your customer relationships that will grow and change over time. Here are a few ways to make your attribution efforts more successful:
Develop more advanced modeling. Strive to accomplish a model that learns and adapts to your customers’ needs as they evolve. Embrace machine learning.
Support your technology investment with the right people. CMOs must be backed by a team of data-savvy, strategic marketers and analysts who can interpret results and activate data.
Get friendlier with sales. Those with tightly aligned sales and marketing departments see 36% higher customer retention and 38% higher sales win rates. Marketers need to understand which content and campaigns are driving the best results and leading to sales. Who better to ask than sales themselves?
Measure success based on business outcomes.
It’s time to dig deeper on attribution. Tomorrow’s leaders are putting the technology, people and processes in place to more accurately measure performance and attribute successes to the right channels and campaigns. Are you in a position to do the same?
Opinions expressed in this article are those of the guest author and not necessarily Marketing Land. Staff authors are listed here.