For marketers, marketing measurement is critical for determining campaign success, optimizing the media mix, and reducing wasted ad spend. With the proper attribution models, teams can identify the type of message that is or isn鈥檛 working, and then use those actionable insights to adapt and optimize future campaigns to increase ROI. However, with many marketers managing multiple campaigns across multiple media channels, accurate marketing attribution has become increasingly challenging, which is why many teams are turning to unified marketing measurement.
Unified marketing measurement (UMM) is an approach to marketing analytics that combines the aggregate data and insights offered by attribution models into one holistic measurement. The integration of these various marketing analytics and models provides a comprehensive view of the success of marketing campaigns and their overall impact on driving conversions. Unified measurement provides the metrics that allow marketers to optimize marketing spend in the campaign.
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 for the consumer journey as well as optimal messaging. However, as access to user-level data is increasingly restricted through things like walled gardens, browser tracking restrictions, and government regulations, it has become difficult for brands to successfully measure and optimize their marketing efforts.
Unified marketing measurement helps combat this challenge 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 a quality analysis of this data to glean actionable insights.
Today鈥檚 consumers have learned to tune out marketers, unless they are providing a message or service tailored to their needs in the moment. Media mix modeling (MMM) and multi-touch attribution (MTA) alone cannot provide the real-time, granular insights required for successful omnichannel marketing. This highlights why UMM plays such a crucial role in modern marketing and explain why. Marketers must build a strategy to implement UMM through a unified analytics platform.
This will require:
Unified marketing measurement allows marketers 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.
The core benefits of UMM are:
UMM provides insights into offline campaigns alongside those for digital campaigns. This allows marketers to understand the role offline interactions play in driving conversions.
UMM allows marketers to combine person-level and aggregate data to understand individual buyer journeys within the broader context of market trends.
UMM aggregates and normalizes disparate data sources, allowing marketers to leverage the insights collected by each of their attribution models.
Advanced UMM platforms can provide real-time insights and analysis, allowing marketing to pivot mid-campaign to optimize spend.
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. For example, marketers could run a magazine ad for a week, and if sales in that area increased, the ad was effective.
However, as digital marketing became more prevalent and consumers savvier, marketers realized they needed granular analytics, now known as marketing measurement tools, 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:
If a campaign is underperforming, 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 marketing ROI. However, many marketing 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. This makes it challenging to provide the in-campaign insights needed to reach consumers with the right message, on the right channel, at the right moment.
There are multiple attribution models, many of which lend themselves better to certain types of campaigns than others. 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鈥檚 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.
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.
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 (MMM) 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 鈥� looking at years鈥� worth of campaign data to provide insights. MMM offers strong benchmarking insights for campaigns due to its long-term nature, and can also provide insights into the role of external factors in marketing success.
MMM insights are valuable, but cannot be relied on alone in today鈥檚 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 (MTA)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, however, and can be subject to a variety of biases that can skew campaign data.
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.
When deciding what marketing metrics to track, it鈥檚 important to focus on KPIs that accurately reflect the success of a campaign and can be used to guide future strategies.
Here are a few key metrics needed to accurately measure marketing performance:
When marketers analyze the above metrics in a single model, they will gain insights that help determine what messages are resonating with the audience and which are falling flat.
The questions marketers should ask when making a will largely focus on how to set up the processes and get buy-in. It鈥檚 also important to consider which marketing analytics platform can reach their team鈥檚 specific goals. The platform should be able to provide reliable, data-driven insights, and must be able to understand and adapt to changes. Consider these questions when getting started with UMM and a comprehensive marketing performance tool:
In addition to the questions noted above, there are a few key things to keep in mind and look out for when choosing a unified measurement platform, including:
An effective marketing measurement platform should be able to optimize multiple KPIs at the same time. This makes it easier to establish and maintain a balance between factors like brand, performance, and sales, even down to a percentage basis.
A consistent view of the customer is critical to being able to optimize spend, increase engagement, and maximize satisfaction, otherwise, it can be extremely challenging to optimize the performance of your efforts. By bringing data together across all marketing touchpoints, brands can gain a clearer customer-centric picture of who their customers are and what they want.
Rapid shifts in the marketing environment and global economy have highlighted the need for flexibility across all aspects of a business, and this includes flexibility in your analytics solution. Brands need marketing analytics platforms that can separate relevant and irrelevant data, allowing them to more easily pinpoint and subsequently adjust the affected areas of their strategy as needed.
In an increasingly digital world, various software, data, and other technical issues are likely to arise. It鈥檚 important for brands to be aware of the level of service and expertise they will receive after deciding to go with a particular solution. Consider whether or not the platform offers the support needed to keep your organization鈥檚 marketing strategy up and running.
Consumer demographics are a critical component in being able to create a complete picture of marketing performance. This is what ties together all of the small pieces of consumer information that a brand has access to, offering more nuanced and contextualized insight into who your target audience is.
Your unified measurement platform should have an idea of what a complete data set would look like, and should be able to position smaller pieces of data into a normalized structure, cleaning and mapping the data throughout the process. By sampling a business鈥檚 representative data set, brands can gain additional clarity. From here, the model uses scenarios and formulas to find a logically consistent pattern, creating an accurate picture of the consumer.
After you鈥檝e carefully considered which unified measurement platform best suits your organization and picked your winner, it鈥檚 time to implement and activate. Using an organized, balanced approach to starting any new marketing initiative is always best, but it鈥檚 especially important with an all-encompassing tool such as a unified marketing measurement platform.
The following steps will help you find your path forward to a successful implementation:
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