What is Unified Marketing Measurement?

Unified marketing measurement (UMM) is an approach to marketing analytics that combines the aggregate and person-level insights offered by attribution models into one holistic measurement. The integration of these various marketing analytics and models provides a comprehensive view into the success of marketing campaigns and their overall impact on driving conversions.

Marketing Evolution Dictionary

Modern omnichannel campaigns span both digital and offline media. As such, marketers need to understand in real time how each campaign, on each medium, drives conversions. Moreover, marketers need visibility into how these ads resonated on an individual level to understand the optimal path of the consumer journey as well as optimal messaging. Unified measurement provides the metrics allowing marketers to optimize spend in campaign.

Unified marketing measurement works by correlating aggregate data, gained from methods such as marketing mix modeling, with the person-level data offered by multi-touch attribution. Marketers are then able to discern which messages are the most impactful on the individual level, while still considering the broader marketing context and external factors.

Effective unified marketing measurement requires an advanced marketing analytics platform that is capable of distilling mass quantities of data into digestible metrics. Additionally, marketers must be able to perform quality analysis of this data to glean actionable insights.

The Role of Unified Marketing Measurement in Modern Marketing

Today’s consumers have learned to tune out marketers, unless they are providing a message and service tailored to their needs in the moment.

Media mix modeling (MMM) and multi-touch attribution alone cannot provide the real-time, granular insights required of successful omnichannel marketing.

UMM will play a crucial role in modern marketing. As such, marketers who have not already must build a strategy to implement UMM and a unified analytics platform into their practice.

This will require:

  • Conducting a data audit
  • Certifying your team in the UMM software
  • Increasing how often you evaluate metrics for in-campaign optimizations
  • Choosing which data should and models should be fed into the UMM platform
  • Using “test and learn” to facilitate continuous discovery

Benefits of Unified Marketing Measurement 

Unified marketing measurement allows markets to use multiple attribution models for the most comprehensive campaign insights, and normalizes that data to provide a holistic view into campaign efficacy. From there, marketers can ensure they are making the correct optimizations to each message they produce.

Core benefits of UMM are:

Online and Offline Data Visibility: UMM provides insights into offline campaigns alongside those for digital campaigns. This allows marketers to understand the role offline interactions play in driving conversions.

Person Level and Aggregate Data: UMM allows marketers to combine person level and aggregate data to understand individual buyer journeys within the broader context of market trends.

Integrated Data View: UMM aggregates and normalizes disparate data sources, allowing marketers to leverage the insights collected by each of their attribution models.

Real-time insights: Advanced UMM platforms can provide real-time insights and analysis, allowing marketing to pivot mid-campaign to optimize spend.

Measurement Challenges for Modern Marketers

In the past, marketers relied on offline tactics, such as print, radio, and television to reach consumers. They largely subscribed to the method of distributing as much material as possible to find consumers wherever they were. This was relatively easy to measure. Marketers could run a magazine ad for a week, and if sales in that area increased, the ad was effective.

However, as digital became more prevalent and consumers savvier, marketers realized they needed granular analytics that track consumer engagements, rather than just conversions.

As marketers have tried to refine their marketing tactics over the years, they have consistently encountered a few common challenges:

Real-Time Campaign Optimizations: If a campaign is under performing, marketing teams need to learn this early on. These insights will allow them to make the necessary adjustments to the messaging or medium to increase engagement and ROI. However, many attribution models cannot offer real-time analytics. For example, MMM, requires data from the fully completed campaign as well as several years of back data. Additionally, many analytics platforms cannot work through the large quantities of big data produced by digital campaigns to provide the in-campaign insights needed to reach consumers with the right message, on the right channel, at the right moment.

Incomplete Attribution Data: There are multiple attribution models, many of which lend themselves better to certain types of campaigns. For example, last-touch attribution would be applied to a digital campaign, not television ads. However, no single attribution model can measure every contribution to a campaign’s success. This means marketers must utilize multiple models to get an accurate representation of the customer journey and the touchpoints that played the biggest role in conversion.

Furthermore, relying too thoroughly on one model can result in a variety of attribution biases. This means your marketing team might be making optimizations based on inaccurate data, and actually reducing ROI and reach.  

Isolated View of Marketing Performance: Marketers now leverage various attribution models to get the best understanding of online and offline campaign success, however, this data is largely isolated. Marketers need a measurement that can aggregate and normalize the data collected by separate models to provide an integrated performance report from which to derive insights. 

Traditional Attribution Models Fall Short 

There are three common attribution models on which marketers rely. While they all play a role in modern marketing measurement, each has its shortcomings.

Media Mix Modeling: Media mix modeling is an attribution model that focuses on aggregate data, not person-level data. MMM looks at the impact a marketing campaign has on the ultimate goal, relying on long term data collection – looking at years’ worth of campaign data to provide insights. MMM offers strong benchmarking insights for campaigns due to its it long term nature, and can also provide insights into the role of external factors in marketing effectiveness.

MMM insights are valuable, but cannot be relied on alone in today’s omnichannel environment. Marketers now require person-level data that shows user-level engagements (clicks, impressions). Moreover, the fast pace of the digital world does not accommodate the long-term data collection.

Multi-Touch Attribution: Multi-touch attribution was the answer to the need for person level insights. Multi-touch attribution looks at the impact user engagements have on a goal, allowing marketers to see which touchpoints a consumer interacted with before taking the desired next step.

Multi-touch attribution is not effective at measuring offline campaigns, and can be subject a variety of biases that can skew campaign data.

Single Touch Attribution: Two types of single touch-attribution models are popular in digital campaigns. These are first touch and last touch attribution. These measurements ascribe full attribution to the first or last touchpoint a consumer engaged with before converting.

These models do not factor in the broader customer journey, including additional touchpoints or offline messaging.

Questions to Consider When Implementing UMM

The questions marketers should ask when making a move to a unified measurement will largely focus around how to set up the processes and get buy in, and how to select the correct marketing analytics platform for their specific goals. 

Implementing the Process:

  1. Who are the stakeholders in this operation and how should their roles be defined?
  2. What is the goal and what metrics will we use to measure success?
  3. What are the factors what will impact optimizations and what are our critical data sets?
  4. How often do we perform in-campaign optimizations and how often do we need to?
  5. How will we account and optimize for branding?

Selecting UMM Software and Tools

  1. What modeling formula functions are used?
  2. How granular is the data offered?
  3. Can it measure offline ROI?
  4. From where does the tool pull consumer data?
  5. How quickly can the tool distill data into actionable insights?
  6. How does the platform / incorporate branding?

Additional Unified Marketing Measurement Tips and Resources