As your company becomes increasingly data-driven, it can be easy to get caught up in markers of success such as leads, bookings, or site visits. But what about the most important metric to your business—revenue? In this tip sheet, Connect Marketing to Revenue with Performance Measurement, you'll learn how to: - Gather clean, complete data - Bridge the gap between marketing, sales, and service - Increase the scope and capabilities of your attribution strategy

1. Connect Marketing to Revenue
With Performance Measurement
Even as the fields of marketing and analytics progress toward more focused, data-driven insights, it
remains common for marketers to focus predominantly on marketing-specific metrics—things like
leads, bookings, site visits, event sign-ups, etc.
Don’t get me wrong. All of these constitute important milestones in customer experiences with your
brand, and they are critical in driving conversion as well as promoting ongoing customer engage-
ment and loyalty.
The problem? None of these events show up on an income statement. In the end, it’s revenue and
profitability that really drive businesses forward. So how can marketers demonstrate that their ac-
tions directly impact their organization’s key financial metrics?
The answer lies in performance measurement, an approach to attribution that focuses on inves-
tigating the revenue impact of all your marketing, sales, and customer retention efforts across the
entire customer lifecycle.
Assembling and analyzing the right breadth and depth of data to effectively gauge revenue im-
pact in each of these areas can seem daunting, so we’ve laid out four steps to help you move your
business toward effective performance measurement.
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Step 1: Gather clean, complete data.
Often, when we talk about performance measurement, our minds jump to the attribution models
involved. But before worrying about advanced attribution modeling, it’s best to start with the foun-
dation.
In reality, getting to accurate, actionable attribution insights is usually much more of a data prob-
lem than simply a model problem. Implementing an advanced attribution model—such as a cus-
tom heuristic or even machine-led model—can be helpful, but only if the data being fed into that
model is accurate and complete.
Before you analyze your data to begin calculating ROI, it is best to ensure that data is clean and
reliable. The legacy approach to most organizations’ data lifecycle typically looks like this:
Using this process can help your business begin to attribute the economic impact of your efforts,
but it is not without limitations and inefficiencies. What this process lacks is upfront data standard-
ization and unification. As a result, the cleanse, normalize, and combine phase of this model is often
time-intensive, expensive, highly manual and less capable of giving you critical insights right when
you need them.
The most efficient way to collect clean and complete data looks more like this:
You can attempt the first step of the above model by employing rudimentary UTM builders or cre-
ating campaign tracking IDs using a series of complex spreadsheets. However, attempts to stan-
dardize data in this way typically lead to different tracking methods across teams or campaigns
and different levels of granularity being used for each channel—so you are never truly able to sys-
tematically standardize all the data.
The only way to truly standardize and unify your data is with an enterprise-wide taxonomy—ideally
with built-in governance and automation—that ensures standardization before data is collected.
This allows you to skip the cleanse, normalize, and combine phase of the data lifecycle and achieve
a quicker time to value, enabling you to see the revenue impact of your efforts in time to adjust
strategies and drive greater growth.

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Step 2: Bridge the gap between marketing, sales, and service/support.
Unfortunately, revenue is not something that is automatically generated every time a brand inter-
action occurs. Revenue is accrued upon a customer’s final conversion and, more importantly, as
that customer continuously returns to or renews with your business. For that reason, performance
measurement requires understanding customer experiences that take place not only during the
marketing phase, but also during the sales process, and through the ongoing customer lifecycle.
The Marketing Phase
During the marketing phase, it is best to track customer interactions across not just paid, owned,
and earned touchpoints, but also online and offline experiences, and then unify these interactions
to clearly see which brand touchpoints most significantly impacted conversion.
The Sales Phase
Many of a customer’s most critical interactions with your company, such as interactions with sales-
people, phone inquiries, or attendance at company events, happen during the sales phase. By
gathering data from your CRM platform, e-commerce platform, or anywhere else you track sales
data, and unifying it with marketing data, you can form a more holistic picture of what is and is
not working. Then, you can use that knowledge to improve both sales and marketing efforts and
achieve greater alignment across these departments.
The Service and Support Phase
Understanding not only what gets a customer to convert, but what gets a customer to continue
converting is the cherry on top of performance measurement. By tying together marketing and
sales data with data from service, support, VoC, and product experience technologies, you can
more fully understand what efforts are helping your company to nurture loyal customer relation-
ships that drive ongoing revenue.
Unifying data across each of these phases may seem daunting, but is necessary to take the next
step in achieving data-driven insights—and is a much more approachable task when you use
automation to lessen your manual workloads. A performance measurement solution can help you
achieve holistic insights on the end-to-end customer experience by integrating data from your
marketing, sales, and service/support phase technologies, bringing those insights together in one
place.

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Step 3: Expand the scope of your attributes
One of the best ways you can enhance your attribution strategy is to move past the standard,
mostly aggregated dimensions of traditional UTM parameters (channel, source, campaign, term,
and content) and expand the number of attributes you are tracking. Most businesses are only
tracking at best 4-5 metadata dimensions, when in truth they can and should be employing doz-
ens! You can begin this process with the following steps:
Define your business questions. What do you hope to understand about how your company’s
efforts are driving (or not driving) revenue? Are there specific channels or phases of
the customer journey where you could better understand the ROI of your efforts?
Lay out the specifics of what you hope to gain from enhanced attribute tracking.
Use those questions to pinpoint important attributes. After establishing your business questions, it
is important to pinpoint the specific metadata that can help you answer those questions
and add them to a unified taxonomy. Don’t be afraid to track inventive, focused attributes
such as call to action, image type, or even font color.
Collect all the relevant metadata. You can try to track some additional attributes by recording
them in a shared spreadsheet. But the more effective approach is to automate wher
ever possible (by taking advantage of available APIs and implementing an enterprise
performance measurement solution) and establish clearly-governed, manageable
workflows to supplement that automation.

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Step 4: Increase your attribution capabilities
Once you’ve gathered clean, complete data, bridged the gap between marketing, sales, and ser-
vice/support, and expanded the scope of your attributes, you are ready for the final puzzle piece in
understanding how customer interactions contribute to financial success: your attribution model.
Most attribution strategies focus only on the experiences of users who convert—but these custom-
ers typically account for only 3% of a company’s site visitors. Ignoring the other 97% can hinder your
ability to correctly understand which strategies are working and which are not.
For example, imagine you are looking at conversion data and you see that every customer who
engaged with a specific piece of marketing content ended up purchasing from your brand. Sounds
great, right? But what if you then discover that every site visitor- including the 97% that did not
convert- encountered that same piece of content? Suddenly, that content doesn’t seem nearly the
surefire winner you had originally thought it to be.
A performance measurement solution can further increase the depth of your attribution strategy
by running machine-led attribution on all your customer data, including both those who convert
and those who do not, so you can more clearly see the revenue-contributing impact of your efforts.
Achieving Effective Performance Measurement
Today’s most data-driven companies are using performance measurement to help them un-
derstand the ROI of their efforts across all phases of the customer experience. With an enterprise
performance measurement solution like ObservePoint, you can unify all your data upfront and track
dozens of focused attributes, so that you can more effectively attribute revenue credit to your mar-
keting, sales, and support strategies. With your focus set on the financial impact of everything you
do, you’ll be ready to drive overall business performance.
To learn more about how you can get actionable insights in real time, schedule a demo today.
Hint: No business should be held hostage to a single, fixed approach of view-
ing its data. In addition to machine-led attribution, ObservePoint’s perfor-
mance measurement solution also offers a variety of single- and multi-touch
rules-based attribution models, giving you the flexibility to leverage whichever
model or combination of models is most appropriate to answering the busi-
ness question(s) at hand.
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