What do marketing attribution and predictive analytics tools do?
Gauging the relative success of each of your marketing tactics is important no matter which way the economic winds are currently blowing.
Marketing attribution and predictive analytics platforms are software that employ sophisticated statistical modeling and machine learning to evaluate the impact of each marketing touch a buyer encounters along a purchase journey across all channels, with the goal of helping marketers allocate future spending. Platforms with predictive analytics capabilities also use data, statistical algorithms and machine learning to predict future outcomes based on historical data and scenario building.
Marketers have always railed against the idea that it is impossible to tell whether, and what part of, their spending drives sales. Even when everything was analog, Nielsen set-top boxes and Arbitron radio diaries — panel and survey data — provided insight.
The dawn of digital media promised a brighter future, where we eventually could look at every sale and determine which touchpoints were effective at delivering ROI and which were wasted spend. It’s not nearly as simple or straightforward as it sounds, but we’re getting closer these days, even taking into account the deprecation of cookies and stronger privacy regulations.
Gauging the relative success of each of your marketing tactics is important no matter which way the economic winds are currently blowing. But when budgets are tight, as they are now with the economic uncertainty brought about by the COVID-19 pandemic, the prospect of eliminating waste is especially resonant.
What marketing attribution and predictive analytics platforms do
Most of the vendors in this space provide the following core capabilities:
- Ingest data from multiple marketing and communication channels (radio, TV, connected TV, phone, email, digital ads, website interactions, etc.) to capture information about customers’ and prospects’ interactions with a brand.
- Employ a variety of attribution models — single touch, fractional, algorithmic, etc. — that users can choose from based on their own business category and goals.
- Provide reports, including visualizations, that help marketers understand which marketing activities performed better, and why some were more effective than others. Users should be able to input their key performance indicators to enable the system to judge based on what is important to the brand.
- Integrate with a variety of martech and ad tech software, including CRM, marketing automation, customer service software, ad servers, demand-side platforms and the like.
Vendors differentiate by offering more advanced capabilities that include, but are not limited to, the following:
- Sophisticated data modeling that allows for the amalgamation of multiple different models and types of data into a unified whole that provides actionable insights.
- Relationships with so-called walled gardens and other data providers that allow for the augmentation of existing profiles and campaign metrics.
- Machine learning and artificial intelligence capabilities that analyze historical or incoming data and proactively offer suggestions for future campaigns or for modifying ongoing campaigns in real-time.
- Orchestration capabilities that let marketers act on the analyses by tweaking targeting, creative or other elements based on their conclusions.
Let’s dig a little deeper into what these platforms do.
Types of media measured
While all of the vendors offering marketing attribution and predictive analytics solutions are able to ingest data from a variety of online and offline sources, each will have their own integrations — these will likely differ in terms of frequency of updates, reliability and depth and breadth of data ingested. Additionally, vendors may have specialized expertise in certain verticals or marketing channels.
Attribution models employed
To be able to integrate and understand data across multiple channels, vendors typically employ a variety of models beyond traditional first-touch, last-touch or weighted attribution.
The more types of data the solution ingests, the more sophisticated their modeling must be to provide an accurate overview of what’s happening in marketing campaigns.
Reports and visualizations
Vendors should offer highly customizable reports and visualizations that allow marketers to understand the impacts of, and the relationships between, their different marketing touchpoints. Since this data is as complex as the campaigns themselves, synthesizing this information into easy-to-understand visualizations is a big challenge, and how vendors deal with this hurdle says a lot about the ultimate utility of their platform.
For vendors in the marketing attribution and predictive analytics space, integrations are critically important and are some of the main drivers of the product’s value. Besides integrations with various ad tech and martech platforms within an organization, these systems link up to bring in media consumption data across websites, social media platforms and traditional media sources.
Data modeling and analysis
Many of the vendors in this space have developed their own proprietary “unified” models that allow them to synthesize the different types of data gathered from marketing initiatives. While deterministic data — actual behavioral information that may be tied to an individual profile — is considered to be the most accurate, there are cases in which that level of granularity isn’t available, and others where merging profiles will speed up the analysis time without negatively impacting results.
Relationships with “walled gardens” and other data providers
Besides the deprecation of cookies on the web, one of the biggest challenges marketers face these days is a lack of visibility into so-called walled gardens like Facebook, Google, Apple and Amazon, each of which gather data tied to a user account in a closed system. To combat this, and to add additional data points that yield insights, vendors forge relationships with these publishers and providers, strengthening their offering overall.
Machine learning and artificial intelligence
The more sophisticated vendors apply machine learning and artificial intelligence to their analysis to arrive at insights into marketing effectiveness, and some are beginning to focus on automatically delivering suggestions for next-best actions.
The intent of gathering and analyzing all of these sources of data is to allow a marketer to make decisions about their strategy and tactics going forward. So it makes sense that many platforms integrate with DSPs, email marketing tools and other execution systems, allowing marketers to manually, or even automatically, act upon that information — sometimes in nearly real-time.
Benefits of using marketing attribution and predictive analytics platforms
Marketing executives today are under increasing pressure to prove the ROI of their marketing activities, at the same time as channels and devices are expanding, customer expectations are higher than ever and privacy concerns are limiting and restricting access to data.
Adopting a marketing attribution and predictive analytics platform can address some of these issues by providing benefits like the following:
- A big-picture view of your marketing efforts. Rather than operating in silos and running your email marketing completely separately from your social media, your event marketing, your print advertising etc., you can feed data about all of your efforts into a single system and make a more holistic assessment of each element’s effectiveness.
- Accelerating time to insights and next-best action. Marketing attribution and predictive analytics tools are continually gathering data about customer and prospect behavior as they interact with marketing messages, allowing marketers to more quickly identify changes that need to be made in terms of audiences, inventory allocation across publishers, creative optimizations, etc.
- Speeding the process of acting on insights. Many marketing attribution and predictive analytics tools are tied directly to DSPs and bid management tools, allowing users to set in motion the decisions they’ve made based upon the performance analysis.
- Reporting to help justify marketing decisions and potentially save money. Marketers are more than ever under pressure to prove that their expenditures are having a positive impact on the bottom line. These solutions can help provide the information the C-suite is looking for. Other potential benefits include cost savings enabled by identifying wasted spend and better ROI gained by redirecting budget from ineffective programs to those where there’s unexploited opportunity.
- Ability to future-proof data loss. The rise of consumer privacy in the eyes of regulators and technology companies is already impacting marketers’ ability to measure their activities. Partnering with a marketing attribution and predictive analytics provider allows you to let these vendors — which have long been grappling with this issue and depend on its resolution for their survival — take care of this problem on your behalf.