Many of our clients come to us frustrated with making sense of all the marketing data coming at them from multiple data sources.
When you’re wrangling data from Salesforce, Google Analytics, Hubspot, and more, it’s easy to get overwhelmed — especially when none of the platforms “talk” to each other.
We know how much pressure there is to figure out how to measure marketing campaign effectiveness when you are the one held accountable for the budget.
But there is a solution.
You can get data from different platforms to talk to each other. You can figure out how much money you made (or lost), split into categories. You can prove to your boss the return on investment (ROI).
And you don’t need complex algorithms or systems to do it.
This article will teach you how to:
- Improve digital marketing effectiveness measurement.
- Set up clean reporting and data attribution (we have two case studies).
- Track and accurately report on ROI, split by online versus offline sources.
Data Clarity is Like Building A House
What is a “multi-channel attribution model”? It’s a fancy way of saying “a way to make sense of all the data.”
More specifically, it’s about identifying who or what gets credit for a sale. Was it social media? Was it employee referral? Or was it a mixture of sources?
There are many complicated models, but we will focus on one. This model will solve your main problem on a higher level: how much money you made through online versus offline efforts or SEO versus paid.
To start off, think about data clarity like building a house. To build an enduring house, you need to start by building a strong foundation. Otherwise, anything else you build will crumble over time.
Caption: If you don’t have a strong foundation to organize your “house”, it will crumble.
Since our agency works mainly within the B2B sector, we start organizing data at the lead level. (For B2C E-Commerce, it might start at the sales level.)
Decide on one easy, high-level way of categorizing people starting at the lead level, such as:
- Online vs. offline (what we’ll use in this example).
- Organic search vs PPC leads.
- Website leads vs. trade show leads.
When we report on activities, we start at the lead level and measure by the actual dollar amount of each closed deal. This enables us to:
- keep the data from getting messy.
- trace where our successes came from.
- measure the most important metrics for the business.
Decide on the Timeline Flow of How Data Is Passed Through
It’s important to understand how different platforms work together and the order that data moves from one place to another so you can decide on the best lead scoring process. Here is an example of how marketing data can be passed through:
- You collect 200 emails from data entered into web forms on your website.
- 150 of those are pushed to Hubspot because 50 emails are fake or wrong.
- 100 of those are pushed from Hubspot to Salesforce because 50 are not qualified leads yet.
Without a good qualifying system, you can end up with a mess of unwanted leads in Salesforce. This can make data clarity as messy as a windshield during a snowstorm and it can make your sales people’s lives a living hell.
Make Sure All the Data is Connected Properly (There’s Always A Way)
It’s important to make sure all the platforms you are collecting data from are set up and working together. Without accurate data, the rest doesn’t matter.
We always double check to make sure that leads coming through are being tracked and marked correctly.
If you’re using Hubspot, we highly recommend that you use the Hubspot Intelligence tool. It lets you pull information through and pass it to other platforms like Salesforce. And it displays the contact history of a Salesforce lead in Hubspot.
Many platforms nowadays have built-in integrations and instructions that will help you connect data.
Data Clarity Case Studies: How Our Clients Made Disparate Platforms Talk To Each Other Effectively
Measuring marketing performance comes down to understanding the technical set up, taking action to set up integration and reporting, and delivering specific evidence of ROI.
Here are two success stories with our clients that walk through this process:
a) How We Used Data Clarification To Prevent A Financial Company From Axing An AdWords Initiative By Proving It Generated A 406% ROI
The first client we want to showcase was on the brink of eliminating AdWords as a marketing channel because they thought they couldn’t accurately prove ROI.
One day, the Director of Marketing said he did not feel like AdWords was sending quality leads based on the type of sales leads he was getting.
He was scheduled soon for his annual meeting with his boss to decide the marketing budget for the next year, so it was important we proved AdWords was worth the team’s time.
His boss was sure to ask, “What are you getting me with the $45,000 we gave you for PPC?”
The Director was considering turning off AdWords and asked us to disprove his theory with data.
Fortunately, we tracked and scored leads in a way that told us which ad or piece of content they came from.
For example, if they closed 13 deals, we could tell 11 came from AdWords and find out which keyword caused the conversion.
Their process for leads was similar to the earlier example: ads to web forms to Hubspot to Salesforce. Hubspot was set up to properly score and qualify leads so that only the right ones were passed to Salesforce.
Since the Salesforce data was properly organized and traceable, we were easily able to find the info we needed to support our hypothesis. We quickly calculated the number of PPC leads that resulted in business opportunities and showed this to him.
We found that we spent $26,596.11 and received 102 paid form submission leads that year.
59 of those went to Salesforce and 6 generated opportunities. The opportunity value was $285,500, with an expected revenue of $108,000 — a 406% return on investment.
He was pleased with this data and was convinced that our AdWords services were still worth his time.
b) How A Large Event Set-Up Company With A Wide Range of Customers Made The Data Easy to Understand
Another client of ours sells products and set-up for a wide range of events. Their customers are broad, ranging from smaller lawn companies to international, award-winning sports stadiums.
As you might imagine, it is easy to have messy data with so many different kinds of customers.
We set up their data flow so that it starts with ads or organic search traffic, goes to web forms, and then goes to Salesforce. They don’t use a marketing automation platform.
To start off, we defined different service categories to organize leads by to keep tracking accurate.
Because of how we organized and tied data together, we are able to dive deeper into the data and connect the dots. When we did, we discovered some useful insights.
For one category (Power Transmissions), we discovered that leads only found us through AdWords. Therefore, we learned to avoid wasting time with SEO for that service line.
Also, we found that phone calls were the conversion medium that brought in the majority of the profit through our AdWords efforts. So we learned to focus on calls.
By having information properly organized and passing through each system prescriptively, we can find out what’s working, double down on that, and reduce the time we spend on inefficient activities.
You’ve just learned the secrets we use for effective digital marketing analysis at WebMechanix.
In summary, you can improve data clarification and prove ROI with a few steps:
- Build your account like a house: start categorizing at the lead level for a strong foundation.
- Understand how platforms work together and map out the data flow.
- Integrate the tech so that disparate platforms talk to each other.
- Report deals by the dollar amount for easy tracking and monitoring.
- Ensure that you can drill down into data and trace results back to the source.
We feel your pain. It’s easy to get overwhelmed with managing all the data. But your problem can be solved with simple but effective marketing automation and clean lead scoring.
Here’s what’s most important:
Think of easy ways to categorize leads and then, start organizing at the lead level. From there, you can look deeper into each group to see what’s working and what’s not.
Now, we want to hear from you…
Let us know in the comments below what you’re still struggling with. Did we forget to mention any big data clarification issues that you’d like more help with? We’d be happy to continue the conversation.