Marketers use a number of statistical and analytical techniques to measure the effectiveness of their marketing investments, which can cross diverse channels like print, TV, promotions, digital advertising and search marketing. Marketing Mix Modeling (MMM) has long been used by companies across industries such as consumer packaged goods, retail, telecoms, financial services, travel and hospitality, automotive and others to provide a strategic view of how each major investment contributes to sales, profitability and brand growth. With digital marketing budgets growing rapidly in recent years, Multi-Touch Attribution (MTA) has become an increasingly popular alternative. MTA allows marketers to gain a detailed understanding of how different online and digital marketing touchpoints influence the consumers path-to-purchase and conversion rate.
However, a number of factors are prompting a shift in the way marketers approach both techniques. New privacy laws, changes in the digital landscape and better access to data science and computing firepower are all changing the options available to marketers. In this post we’ll review some of those changes, and how they appear to be impacting measurement preferences and approaches in the industries we work with.
Walled Garden Approach, Data Regulations are Changing Media Company Behaviour
MTA has been extremely popular over the last several years. It enabled brands to measure digital marketing effectiveness with very granular and precise user or household level data. The ability to link individual ad serving and content viewing with individual conversions is at the core of MTA, and a critical ingredient for its successful deployment. This necessarily requires sharing of user-level data between media companies, MTA providers and brands.
However, due to increasing user privacy and competitive advantage concerns, many large media companies (through whom a majority of digital marketing investments are now channelled) have adopted a “walled garden” approach. They do provide data on the campaigns they run. But they no longer share the granular user-level ad serving data required to fully fuel MTA.
Another issue is GDPR (General Data Protection Regulation), which came into effect in the EU in 2018. Similar laws are now in place in some US states, including California. These laws have clearly impacted data privacy practices, not just in the EU and US but globally as well. Both brands and media companies have significantly tightened their rules around sharing ad serving data, along with the personal data usually required for MTA.
Together, these factors have significantly impacted the value of MTA. Many brands are now considering other options, like MMM, as a better way to measure the effectiveness of their digital and non-digital channels alike. Marketers still want the convenience and accuracy delivered by MTA. But they need a multi-channel view unconstrained by MTA’s current limitations.
Advertisers are Reluctant to Share User-Level Data Externally
Several media companies, especially large ones, provide outsourced marketing effectiveness solutions, including MMM, as a value-added service to their clients. The stated goal is usually to help advertisers determine campaign effectiveness and Return on Investment for their media investments. These services still require advertisers to share some sales, customer and other data with those media companies. And again, due to competitive and regulatory concerns, many advertisers are now more reluctant to provide this data .
Additionally, both digital brands and data-savvy traditional brands often have their own data science teams, along with significant investments in cloud computing and data analytics to support their work. These investments allow companies to analyze their own data and keep it in-house. Companies with these tools are already exploring the option of running MMM, and other marketing analytics, in-house where possible. In addition to avoiding the sharing of sensitive data, they are able to reduce external expenditures for any work they can do internally. As the world economy adapts to reduced consumer demand, these economic advantages will become even more important.
Brands Want More Cost-Effective, Intuitive MMM Solutions
Brands that historically relied on MMM typically restricted themselves to running it less frequently, and only for their largest brands and markets. Even without concerns around sensitive data, MMM projects have traditionally been time and resource intensive, making them prohibitively expensive to scale across the organization or update regularly. Stakeholders may be forced to use results that are 8-12 months old, and focused only on the largest markets. The ability to provide greater scale and frequency is essential to making any MMM program a truly effective option.
Faced with these challenges, marketers are now exploring automated options that can deliver speed, scalability and cost effectiveness in-house. These systems allow data science teams and marketers to scale MMM programs with less effort, and cover more of their overall marketing budget without unacceptably high analysis costs or limits on scalability. The ability to run models in-house and on-demand also gives decision makers the flexibility to develop ‘lite’ MMM models as they encounter new questions or need to change priorities. This adaptability is a strategic advantage for any brand that can achieve it, especially in uncertain times.
These factors are rapidly changing prevailing views on the best way to measure marketing effectiveness. Viewed together, the trend is towards taking the measurement process in-house and implementing integrated, intuitive solutions that are faster, cheaper, and more scalable. This new thinking can help brands adjust by protecting competitive sensitive data at a cost more brands can justify.
Analytic Edge’s Demand DriversTM solution offers brands the ability to run MMM and other predictive analytics in-house, using a proven, integrated process. The goal of the system is to offer marketers the cost, scale and speed advantages they need, and to provide an alternative to both MTA and traditional MMM approaches. Demand DriversTM is already used by a number of global brands to achieve faster, more responsive marketing decision making.
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