Despite two-thirds of DMA survey respondents listing a desire to optimize media mix as their reason to prioritize attribution, last-click attribution is still the most popular method of tracking marketing performance. Time and time again, we have proven the inability of last-click attribution to effectively optimize performance. (Read this blog post to learn why.) But challenges, including insufficient data, fragmented oversight, limited expertise and inadequate technology, keep many marketers from taking the steps toward implementing cross-channel attribution.
We still believe multi-touch, cross-channel attribution is the future of tracking marketing performance, because it allows marketers to calculate and demonstrate the value of everything they do. This year, we predict three advancements will take us further along the path of cross-channel attribution adoption.
Advancement #1 Boosting Cross-Channel Attribution Adoption:
Marketers Will Become Increasingly Comfortable with Cross-Channel Attribution Techniques & Technology
Simply put, marketers can no longer justify budgets without delivering quantifiable marketing ROI. As a result, more and more marketers are prioritizing marketing measurement and attribution. With this additional attention, the industry should become more comfortable with the language and techniques required to accurately measure marketing success.
Measurement adoption has also resulted in new attribution tools entering the marketplace. Google introduced Google Attribution in 2017. This tool helps marketers view the full path to lead generation and optimize the media mix to generate a greater volume of leads. Using Google Analytics, Sparkroom (a proprietary marketing technology of Digital Media Solutions, LLC) offers complete transparency of pre-lead to post-conversion lead lifecycle. The cross-channel attribution functionality within Sparkroom allows marketers to optimize for more bottom-of-the-funnel, CRM-type conversions instead of earlier, less-reliable milestones like a pixel fire on a web page.
Advancement #2 Boosting Cross-Channel Attribution Adoption:
Blockchain Will Heighten Advertising Transparency
Early 2017, Procter & Gamble’s Chief Brand Officer, Marc Pritchard, issued a call to arms for the advertising industry to clean up the media supply chain. Pritchard announced that P&G would require accreditation by the Trustworthy Accountability Group (a joint initiative of three advertising associations) for any entity involved with its digital media buys. Pritchard later explained to AdAge the moment that sent him “over the edge” was when he was told by an agency executive that “there are many companies, including your competitors, who are ‘leaning forward’ and spending billions with us without that measurement.”
In addition to verification of buys, with limited media transparency, performance tracking has been challenging for advertisers. “There is an immense problem around disparate data sets, especially when these numbers are used to justify ad spend and return,” explained Ken Brook, CEO of MetaX, a blockchain ad platform.
Blockchain technology, famous for its ability to facilitate buying and selling of Bitcoin, can possibly be used to solve the big data monopoly issue and tackle the transparency challenges across numerous industries, including advertising. Blockchain technology uses “blocks” of new record transactions to log events. As blocks are developed, they are added to the chain of records, also called the ledger. The ledger is public, resulting in complete transparency. As explained by Yuyu Chen in DigiDay, “with blockchain, the intermediary is a robot, not a human, so people can trust it with their data.”
As of today, blockchain technology is too slow for the real-time bidding of digital advertising. But the IAB Tech Lab is seeing if it is a possible solution to reduce advertising fraud. “The shared vision,” according to Nadav Dray of AnyClip, “is that media buying will be based on a new, decentralized open-source network. This network will provide advertisers greater levels of trust, security, fraud detection, and the overall transparency they demand.”
Advancement #3 Boosting Cross-Channel Attribution Adoption:
Artificial Intelligence and Deep Learning Will Hasten the Speed of Multi-Touch Marketing Campaign Optimizations and Power-Boost Predictive Targeting and Modeling
While the act of attributing value for marketing touches is focused on the past, the objective of marketing attribution measurement is to positively impact the marketing performance of the future. Attribution models with predictive capabilities learn from prior results and calculate forecasts to identify the optimal media mix for the future.
Machine learning allows marketers to calculate more data and additional scenarios at a faster rate. “The method we use most often,” detailed Dan Cox, the Head of Product at Conversion Logic, “is called reinforcement learning. In reinforcement learning, the machine learning system interacts with its environment. Based on the feedback (penalty or reward), it updates itself to maximize or minimize a defined cost function.”
As machine learning gets faster and more intelligent, it’s able to calculate supplementary media mix scenarios and make smarter, more accurate predictions. But Brian Baumgart, CEO of Conversion Logic cautions us to keep our expectations in check. “In martech, and especially in marketing performance measurement, AI [artificial intelligence] is headlining claims of superior analytics precision, automation and unrivaled predictive capabilities. As is usually the case,” Baumgart continued, “the realities of what is possible and the resulting benefits are quite different outside the hype machine’s distortion field.”
Deep learning is a machine learning technique that processes data using multi-layered neural networks modeled after what many researchers believe to be similar to the basic human brain learning algorithm. Deep learning can be very powerful and is able to reduce the learning time and increase the accuracy of outcomes when dealing with large amounts of data ― especially pattern recognition. Although available for decades, deep learning is only recently usable and useful due to the increase in computational power and capacity.
From bridging the gap between data demand and supply to advancing automated discovery and prediction, a lot can be accomplished with AI and deep learning today. We are only in the infancy of this revolution, and we expect the future impact on many aspects of human life including marketing will be profound. In the words of Andrew Ng, Former Chief Scientist at Baidu and Co-Founder of Coursera, “Just as electricity transformed almost everything 100 years ago, today I actually have a hard time thinking of an industry that I don’t think AI will transform in the next several years.”
Google products have exploded with deep learning over the past couple of years, and we anticipate this will continue in an exponential fashion for all products until artificial intelligence can handle all products and tasks.
With marketers regularly being squeezed to produce more with less budget, marketing attribution is becoming a necessity. Increased comfort, transparency and speed should increase the adoption and the predictive capabilities of marketing attribution. As marketers and scientists, Team DMS looks forward to the data-driven, AI-powered future.
Are you ready to adopt cross-channel marketing attribution?
Are you looking for technology to help you visualize the pre-lead to post-conversion path of your customers?
Sparkroom performance marketing technology launched multi-touch, cross-channel attribution in early 2017, and the features and visualizations are continually being advanced to provide a more robust picture of marketing performance. Click here to read about our most recent press release regarding Sparkroom enhancements. Or request a demo of the award-winning Sparkroom technology here.
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