The COVID-19 pandemic has reshaped how consumers interact with and purchase from retailers, restaurants and service providers. With digital as the new normal, consumers are buying new categories online. They’re purchasing from new retailers. And they’re increasingly opting for direct-to-consumer businesses.

Across the board, consumers are interacting with companies through a myriad of digital and physical channels – with countless moments influencing their decisions on when, what and how to buy. It’s changing the way brands and companies are thinking about and crafting more dynamic and interactive customer experiences. For data scientists like me, it’s also generating a plethora of new signals and data points. A new series of data environments and platforms has arisen to change how marketers store, analyze and evaluate each individual touchpoint to optimize not just marketing investment but also the “next best consumer experience.”

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Traditional Media Mix Models have gone the way of the dinosaur. A new wave of digital attribution and personalization models has given rise to near-real time, consumer event-level optimization.

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I’ve been around long enough to remember when marketing mix modeling (MMM) was the gold standard for quantifying and evaluating what was working from a marketing perspective. In the early 2000s, we relied on a top-down statistical modeling approach that looked at the return on advertising investments across TV, radio and print at the weekly and regional level. Funny enough, we even captured the growing pace of digital investment through one variable we called “The Internet.” CMOs used these marketing mix outputs as their annual scorecard and separated insights and recommendations from customer lifetime value modeling, digital measurement reports and customer propensity modeling.

These traditional Media Mix Models have gone the way of the dinosaur. A new wave of digital attribution and personalization models has given rise to near-real time, consumer event-level optimization.

Today, our approaches, data environments and technical knowhow have accelerated almost exponentially. We’ve realized the shortcoming in having so many siloed, infrequent and lopsided data science models, and we’ve worked tirelessly to unify our techniques to provide true, holistic customer-level tracking. Marketers have come to realize that the unifying factor must put consumers (and their data signals) at the core while providing more real-time actionability and full, omnichannel data-driven experiences.

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These new holistic attribution and personalization platforms delve deeper and enable marketers to generate more detailed answers to questions like these:

  • Who are the right customers to pursue? What are their unique attributes and needs?
  • How do we design optimal consumer journeys that lead to incremental conversion? What is the best combination of digital touchpoints and handoffs?
  • What’s the right digital content (e.g., static, video, organic) to present in the moments that matter? What content will leave a lasting impact on consumer motivations?
  • Who are the partners (e.g., Facebook/Instagram, Google, Twitter) that can help bring those customer journeys to life?
  • Where are we wasting dollars reaching the wrong audiences? How could we cut costs without compromising results?

Making the move from static, rear-view-mirror performance reports to a real-time, automated ML/AI-enabled gauge of what’s working (and why) requires a strong commitment to data science – and a willingness to change how you’re thinking about using data. It also requires an organization to find a partner with the expertise to guide you in creating a customer data architecture, along with the partnerships and platform to help you execute against it.

Consider the power of an attribution model platform that enables you to:

  • Track Marketing ROI (MROI) in real time, including providing performance insights and campaign efficiencies; regional assessments based on user-level conversions; product and unit-level eCommerce conversions; and a drill-down capability to view insights at a most granular level.
  • Analyze product/segment costs per action (CPAs), including performance across tactical dimensions; product-level planning and cross-channel assist vs. direct conversion analysis; and frequency and trending across critical digital and business key performance indicators (KPIs).
  • Plan for spend and frequency, so you can measure customer journey trends, quantify the impact across key user segments, analyze individual touchpoint impacts and identify conversion vs. non-conversion paths.
  • Achieve keyword-level granularity for deriving insights from keyword-level category and consumer engagement and for analyzing detailed organic and paid search trends and key indicators of brand demand.

The structural shift in online media consumption and purchasing behavior places an even greater premium on digital MROI. With a sophisticated attribution model platform, CMOs can meet the moment – more precisely understanding ideal customer journeys and focusing on digital channels and partners best able to support them.

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See more Customer insights.

 

Michael Bregman

Managing Director – Applied Intelligence, Solutions.AI for Marketing Lead

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