For C-level executives such as the CMO, generating a positive ROI is often part of the job description.
It’s their responsibility to allocate the marketing budget to activities that ensure a steady inflow of customers.
As customer journeys become complex, figuring out where to focus attention is tough.
Questions like, “How much does an ad campaign really influence prospects?” or “How does one channel compare against another?” repeatedly come up.
Some believe that each activity has a role to play in the complex maze that is ROI.
And some think that placing your eggs in a few baskets exacerbates your losses somewhere else, leaving you no better off.
In recent years, the attribution model has proven otherwise. This ROI measurement model allows marketers to directly measure how a marketing activity influences revenue.
In-depth data from Google Analytics is pulled into tools like Excel where rudimentary ways of calculating conversion rates are used to plan marketing activity months into the future.
To show you the shortcomings of this approach, we’ve covered four marketing attribution metrics that you can’t track with Excel.
Read all about it below.
1. Understand Where the Money Makers Really Enter Your Funnel with Historical Attribution
When fully mapped, the customer journey reveals itself to be a multi-step process.
It’s incredibly rare for someone to pass through your conversion funnel in one sitting. Even if they do, the chances of them making a purchase are almost nonexistent.
People usually pass through a number of “touchpoints,” or events, to complete the purchase journey.
On that journey, the first touchpoint is given a lot of credit.
It’s what generates awareness and leads up to all subsequent interactions and sales.
Examples of first touchpoints include exposure to Google search results or paid ads on platforms like Google and Facebook.
From here, subsequent touchpoints focus on warming leads using lead magnet downloads, product demos, or phone calls with your sales team to bring them closer to making a purchase.
On average, it takes six to eight touchpoints to convert a prospect.
If we were to visualize this process in its entirety, it may look something like this.
With multiple first touchpoints, it can be hard for marketers to figure out where to devote their attention and money.
Should you spend more or less on SEO? What about PPC channels?
Extracting data from Google Analytics into Excel, hoping to find the answer, isn’t of much use.
At best, Excel can tell you where (and how many) prospects enter your funnel from a specific channel and what the conversion rate is.
This information isn’t enough to base investment decisions.
A more robust marketing planning tool is required—or at least one that can tell you from which touchpoint your buyers enter the funnel.
Let’s look at an example to understand this better.
Pretend you have two “first” touchpoints—Facebook, and Google ads. You get a lot of impressions and traffic from Facebook ads, but not AdWords.
Conventional wisdom would tell you to invest more in Facebook and cut back on Google.
Is that really the correct decision, though?
If we were to look at historical data (existing customer journeys), would it back up our decision to invest in Facebook ads just because most people came to know of our brand that way?
It might, but the opposite is very likely.
You might find that most people who actually bought from you were first exposed to your brand via AdWords and not Facebook ads.
In fact, most of the hits from Facebook may even lead to dead-ends.
This insight can be game changing when planning which activity or combination of activities to invest your money in.
It allows you to speak with confidence that money spent on certain activities will be recovered in ROI.
2. Disclosing the ROI and Impact of Every Channel
Retargeting, or remarketing, is a popular online advertising technique and channel. Let’s use it as an example.
When running retargeting campaigns, how do you track their impact?
Built-in analytics features in retargeting tools obfuscate the truth so that they can claim their influence on any positive outcome. This is why it’s important to consider a third-party attribution provider, one who’s vendor and platform agnostic.
A SaaS company may notice two individuals show interest in a subscription plan, but decide not to purchase when it’s time to close the deal. Or the SaaS company is interested in targeting open opportunities to keep brand top-of-mind, or engage prospects from open opportunities with new offers and content.
The marketing team runs two retargeting campaigns. One format uses dynamic ads on Facebook, whereas the other uses Twitter.
How would they measure which platform performs better?
Starting with goals, if the goal is revenue and positive ROI, then an attribution model that gives revenue credit to touchpoints driven by retargeting ads and campaigns can help you measure accurately and automatically, without the fuss of exporting data to a spreadsheet.
3. Discovering Purchase Intent
If your goal is to drive engagement, you can derive insights with the right tools. Current reporting standards will give you an answer using absolute numbers, but won’t provide the level of purchase intent your visitors have.
Tools like Bizible predict how likely a conversion is by analyzing touchpoints. They call this predictive engagement score. For example, how often the prospect visits, what content they engage with, what channels they come from, and even sales activity (email engagement and phone calls with sales). If you’re running a lot of retargeting, you may see predictive engagement scores go up, a sign that you are driving engagement and activity with retargeting.
And this scoring system helps you understand how engaged prospects are with your site and content, and can help you target warm or cold prospects with different types of messaging or offers.
This is done by looking at both historical data and machine learning (more on this next).
As a result, you’re able to optimize your budget spending on activities that make a positive impact.
4. Answering “What If” Scenarios with Machine Learning Algorithms
Perhaps the most useful feature not accommodated by Excel is “what if” scenarios.
As busy individuals, decision makers must be quick on their feet and assess whether an activity or channel is worth investing in.
Even if an activity seems worth investing in, it must be evaluated thoroughly.
Analysts are often hired to construct forecasting models to help visualize how changes to the existing marketing plan may affect future revenue.
Of course, these models carry the same limitations and biases we’ve mentioned previously.
A better model would be to assign revenue amount and probability to every touchpoint, and create forecasts on demand based on revenue goals inputted by the marketer. This better model would also be based on historical performance of each channel to create channel specific forecasts. This allows you to create revenue forecasts based on a model that gets updated real-time across every channel you’re actively spending on. This creates forecast metrics that are more precise compared to looking at the collective average of all channels.
Studying the impact of change at the individual level provides a more accurate foundation to build your case for making large-scale changes.
This is nearly impossible to do with Excel. Fortunately, a marketing planning tool like Bizible is fitted with machine learning and algorithms that let you plug and play around with predictive analytics.
If you’re wondering what the result of a change somewhere in your collective marketing plan will be on predicted revenue, Bizible will tell you in seconds.
When tasked with important, time-sensitive decisions, you’ll have the data readily available to help you.
For example, you’d be able to justify an increase in spending on social media ads over offline channels like conferences because the algorithm factors everything in.
In short, the benefit here is your ability to judge the value of a change of spend against expected ROI.
Make Smart, Not Blind, Changes to Your Marketing Plan
Top-level management are tasked with big responsibilities.
It’s not uncommon for their decisions to influence the direction, health, or even survival of the company.
In such circumstances, it’s only fair to arm them with the best tools on the market.
Excel can no longer be categorized in this list.
With a continually increasing volume of available data and advancements in machine learning, marketers deserve a tool that helps them make smarter decisions.
Let’s recap how tools like Bizible help:
Learn which channel your customers use to enter the conversion funnel. With help from historical attribution data, you can invest in the channel that brings you buyers and not just awareness.
Measure the ROI and revenue impact of every channel.
Discover purchase intent using “predictive engagement scoring” to learn about prospects and target them accordingly.
Predictive analytics is becoming less a luxury and more a necessity. With activities becoming meshed and intertwined, every change has sweeping effects. Bizible uses machine learning to measure and inform you how a change may play out.
Marketing is undergoing significant industry-wide changes.
As more and more activities move online, every activity is measured and data point collected.
Coupled with improvements in technology, it’s now expected for budgeting to be based on hard facts rather than creative guesswork or inklings.
It’s time to move beyond Excel, and embrace the new generation of smart analytics tools.