Measuring cross-channel performance is difficult.
In fact, according to a recent eMarketer study, 34% of marketers surveyed still rely on individual channel analysis and channel-specific metrics to measure the success of their marketing campaigns.
Even more shocking, 35% admit to not using any sort of robust measurement technique.
Add to the complex digital measurement landscape all the different media platforms and sales channels (in-store, online, mobile, and digital) and it’s no wonder so many marketers haven’t been able to wrangle all the right pieces for measurement.
On top of it all, just managing the deployment of all your loyalty programs and customer dimensions (like purchase frequency, demographics, and psychographics) make keeping track of customers and measurement even more complicated.
The Measurement Solution: Marketing Attribution
This is where the latest buzz is all about. Marketing Attribution is the practice of tracking and valuing all marketing touch points that lead to a desired outcome.
Within attribution there are many subcategories:
- channel and media performance,
- ROI analysis,
- customer and audience analysis, and more.
The types of models used for attribution depend on the channels being included and analyzed.
Keep in mind that attribution isn’t a one size fits all solution. The right models need to be connected with the right data inputs across all channels.
Forrester’s research recommends that “customer insights and marketing professionals adopt an approach called unified marketing impact analytics (UMIA), which effectively blend the best aspects of attribution and marketing mix modeling. This approach allows companies to truly measure marketing’s entire value and identify the most effective ways to optimize customer interactions.”
Three Types of Marketing Attribution Measurement
So what’s the difference between these analytical approaches?
Let’s explore the various types of attribution measurement.
1: Cross-Channel Digital Marketing Attribution
This user-level approach generally uses a machine-based algorithm that tracks and assigns credit for every cookie-based consumer touch point.
Because it’s often easiest to track data from digital marketing, it’s also one of the first areas that many marketers start their attribution measurement journey.
Many ad servers and site analytics platforms already have some existing attribution modeling capabilities as part of their offer to track line item performance.
More advanced attribution solutions (generally third-party vendors) incorporate additional marketing factors such as:
- offline marketing,
- sales data,
- customer segments,
- weather, and more.
Attribution allows you to understand your marketing down to the user level, accurately measuring the impact of each touch point and tactic.
By gaining insight into how individual channels are performing against one another, you can figure out where to move dollars for the best return on your marketing and ad spend.
2: Marketing Mix Modeling (sometimes called media mix modeling)
Marketing mix modeling (MMM) is the more established practice of analyzing years of historical data (traditionally offline data) at a more aggregated level than digital cookies can allow to predict future sales and revenue.
MMM helps you look across all your marketing influences at an aggregate level, including factors like competition, to compare against your “business as usual.”
It’s often common to see two different models used for MMM and attribution within one brand, but the two work together to provide insight.
A study conducted by Forrester found that more than “9 in 10 respondents were using some form of MMM tool, and 49% were using an attribution tool they either built or bought.”
In recent months, there are more emerging solutions that blend both attribution and marketing mix modeling together to enable marketers to make strategic planning decisions and precisely measure individual-level interactions in near real time.
After all, as marketers, we need both the high-level, multi-year view of revenue and sales that marketing mix modeling provides, and the intricate, user-level based insights from attribution.
To create the best possible plan, make sure data-driven attribution insights are used to inform MMM activities.
3: Online to Offline Attribution
We can’t forget the need to measure the various sales channels that many marketers must understand.
Whether consumers are buying in-store or online, they’ve been exposed to a variety of your brand’s messaging along the way. The consumer journey travels both online and offline, desktop to mobile to outdoor and can seem impossible to understand and track down.
The rise of people-based marketing however allows attribution and marketing mix modeling providers to bring in these sales channels as another, and possibly the most crucial, data point for analysis.
It takes user-level exposure data from attribution and high-level sales predictions from MMM and makes it truly tangible and concrete, since you can then understand exactly how and where sales are coming from and why.
The Who of it All
Once you, as a marketer, can connect the dots between your marketing attribution touchpoints, you finally want to be able to tie that back to your customer dimensions.
Deciphering this path to purchase by capturing and combining exposure and transaction data with people in mind will add a whole new level of insight.
Plus by incorporating sales data from stores and CRM systems, you not only have your revenue indicators but also your customer segments. The who of it all.
While there is no absolute right answer for all marketers on which measurement solutions to pursue and in what order, it is incredibly important to make sure you are beginning to think about the variety of information at your fingertips.
Otherwise, you’ll continue to optimize with one eye closed.
If you’re interested and want to read more about cross-channel attribution, take a look at our 9 Steps to Implementing Cross-Channel Attribution.