Forrester Q&A: Unified Marketing Impact Analytics

 

Q: What is unified marketing impact analytics (UMIA)?

UMIA is defined as a blend of statistical techniques that assigns business value to each element of the marketing mix at both a strategic and tactical level.

UMIA uses an ensemble of marketing analytics approaches to gain a full view of marketing performance. It considers all marketing and media impact and non-marketing effects, such a pricing fluctuations, economic factors, and competitive intelligence.

Q: What is the value/benefit of UMIA?

Marketing mix models and attribution have the same goal: to measure marketing effectiveness more accurately by assigning business value to each element of the mix. Traditional mix models measure aggregate channel performance but fail to measure tactical marketing program performance. Conversely, attribution models measure user interactions across digital and some direct response programs, yet they fail to measure traditional media, internal, and external effects. UMIA uses multiple marketing analytics approaches to measure marketing performance across the entire life cycle. This provides marketers with a complete, holistic view of marketing performance at the customer level. This cannot be achieved using singular measurement approaches

Additionally, UMIA provides multiple benefits to marketing initiatives and customer-based strategies. Specifically, UMIA provides more guidance on evidence-based marketing budget allocation, measurement of cross-channel effects, and deeper path-to-purchase insights.

What types of companies are adopting UMIA?

Companies that put data at the center of their business strategy are embracing a more unified measurement approach. We find that financial services and hospitality (specifically hotels) are looking at ways to align their advanced analytical models to better understand the holistic impact marketing has on business drivers.

What are the technology and data requirements for UMIA?

UMIA uses several methods to create a single view of marketing performance, which is essential to understanding cross-channel behaviors, like the relationship of mobile app usage to an in-store purchase. To create UMIA analysis, marketers must blend customer-level data, commonly found in digital-cookie-level information; traditional direct response data; and aggregate-level media performance data. Further, UMIA requires connecting different marketing channels across devices and platforms, such as connecting a mobile device user with their browser-based activity. This can be achieved by using deterministic or probabilistic identity approaches and matching marketing and media across devices, channels, and tactics. Finally, once a stream of customer-level activity is created, marketers can apply varied methodologies to measure holistic marketing performance.

How do you get started with UMIA?

Firms must define the overall measurement objectives and clearly articulate what action your marketing team will be willing to take based on these insights. Work with your partners, including technology providers, agencies, marketing service providers, and consultants, to audit your marketing and media data. These partners must have capabilities to synthesize data into a singular UMIA model. Create data governance standards to ensure your marketing and CRM data is high quality. And finally, engage with your data scientists to determine a marketing analytics course, identify different ways to measure marketing impact, determine stakeholders, and continually manage marketing measurement in the future

How can I improve my bottom line by consolidating data silos?

Consolidating data silos has multiple cost savings opportunities, customer insights benefits, and marketing benefits. Data consolidation allows firms to eliminate first- and third-party data waste and leakage, helps firms create a source of truth, identifies which data sources are being utilized, and eliminates data redundancies. It identifies how much time and effort is required to manage different data sources, providing an opportunity for businesses to streamline data support.

On the customer and marketing insights side, data consolidation helps firms create a string of customer interactions along different types of purchase paths. This enables marketers to identify the right message, frequency, and sequencing of campaigns. Further, it provides firms with more insights as to the motivations — both marketing and non-marketing related — that have an impact on customer choices. Brands can now anticipate needs and identify unmet needs, creating interactions that stimulate a deeper customer relationship.

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