How Offline Conversion Tracking Led to 74% Increase in PPC Revenue: Case Study

We show you how using offline conversion tracking can reveal new PPC data that’s crucial to your optimization strategy.

Many modern eCommerce businesses don’t just rely on online channels to generate sales. More often than not, these brands employ additional channels — like brick-and-mortar locations, pop-ups, and call centers — to diversify and maximize their revenue streams.

But, if you’re not tracking these channels correctly, you’re probably not employing the best digital marketing strategy for your brand.

In today’s case study, we’ll tell you about one of our clients who recently found themselves in the same situation. With 20% of sales generated through their website and a whopping 80% generated through a call center, they needed to get accurate numbers (and fast) to successfully optimize their paid search advertising strategy.

So, Inflow stepped in to make it happen — ultimately resulting in a 74% increase in Google Ads revenue.

The Client: A Wholesaler Operating on Seasonal Trends

For this case study, the client in question is a wholesale retailer offering a variety of products across all niches and interests (think: school supplies, pet supplies, etc.). Therefore, their store performance is highly seasonal as customer behavior changes throughout the year.

This seasonal nature was already reflected in our team’s PPC strategy. We commonly pulled back or completely paused certain Google Ads campaigns and continually adjusted bidding strategy all in the name of one goal: maintain a 4x non-brand ROAS on all eCommerce purchases.

As mentioned above, this client sold products in two ways: through website purchases and through phone call orders. But, when we came aboard, there was no way to identify exactly which call conversions were stemming from our paid search campaigns. This was a huge piece of missing data.

Without knowing how their Google Ads campaigns were affecting call center orders, our client would never have the full paid search ROAS picture. Sure, we might assume that if we were driving 25% of online conversion revenue through Google Ads, it would be the same percentage for call center orders, too.

But our client didn’t want to fly blind, and neither did we.

So, we decided to implement offline conversion tracking for Google Ads.

Our Offline Conversion Tracking Strategy

To fully optimize our Google Ads approach, we needed to understand exactly how many call center purchases were driven by our paid search campaigns — and what the value of those purchases was.

The solution: Use Google Click ID (GCLID) tracking to associate users with our paid ads.

Our client had long ago implemented a login requirement for users to add items to cart and complete a purchase. By working with their development team, we were able to add a hidden field in the signup form submission process. This captured a customer’s GCLID (if they came to the site from a paid search ad click) and would store it in the client’s CRM to associate any future call center transactions to their account. 

Now, our PPC team not only sees the eCommerce conversion data from our Google Ads campaigns, but also that revenue created through call center orders, too.

Google Analytics campaign report, showing "call center transactions", "offline revenue," and "offline ROAS" for campaigns "New - School Supplies - Low Return" and "New - 05 - School Supply - Desktop - Tier 1" and "New - 05 - Shopping General - Desktop - Tier."

Using that Data in Our Google Ads Strategy

Now that we had understood which type of conversions were related to our ad campaigns, it was time to revisit our paid search strategy, campaign by campaign. 

Early in the school shopping season (and prior to implementing our Google offline conversion tracking), we had broken popular school supply products into high-return and low-return segments. Those that were high return (binders, markers, etc.) had historically high ROAS, but had previously been cannibalized by high-search-volume, low-return products (notebooks, folders). 

To maximize our budget and stay within our 4x goals, we ended up pushing more budget toward the high-return (4.82x ROAS) items, rather than the low-return (3.51x ROAS).

But, when we started tracking offline conversions through our call center, we revealed data we hadn’t seen before: While our low-return products were underperforming online, they were absolutely crushing sales in the call center. 

Because these low-return products were typically ordered in higher quantities, many customers were calling in to purchase them, even if they had originally arrived at the site through our Google Ads.

The offline sales picture for our school supplies was completely switched: Our high-return campaign was only generating 0.1x offline ROAS, while the low-return campaign was at 25.37x offline ROAS!

Google Analytics campaign report, highlighting "offline ROAS" for "call center transactions," "order placed," "registered," and "transactions." Campaigns are "New - School Supplies" and "New - School Supplies - Low Return."

Now that we had these metrics, we knew pulling back on our “low-return” campaigns was a mistake. Even if the ads weren’t generating online purchases, they were in the call center. Our optimization strategy needed to reflect that.

This opened the door for us to reflect on our goals with our client. If we were driving this type of return offline, and our impression share wasn’t currently at 100%, where did we have room to push harder on bids and budgets for next back-to-school season?

We also used the data to consider which categories to rework ad copy for in the next year, to highlight a “Call to Order” CTA, including the client’s phone number.

The Results

Knowing that our actual ROAS was much higher than it had been reporting before tracking offline conversion actions, we had leverage to expand our budgets — and could make appropriate adjustments to drive higher revenue, both in the online and offline worlds. 

With that strict ROAS goal and budget, our campaigns had historically been losing out on impression share as much as 80% due to budget. But, with the new offline revenue data, we had the ad spend freedom to be there for more conversions — and, year over year, our impression share lost due to budget dropped by 78.23%!

Line chart showing search impression share from Nov. 1-30, 2020, and Nov. 1-30, 2021. Search impression share for 2021 averages around 75%, while search impression share for 2020 hovers between 30% and 10%.

With a better understanding of our offline conversion value, we approached our holiday season search campaigns with a different strategy and saw incredible results!

No longer strictly limited by our 4x goal, we scaled spend by 34.8% year-over-year, generating a 74% increase in Google Ads revenue.

Final Suggestions: Implementing Your Tracking Strategy

Moral of the story: If your paid search campaigns aren’t generating the eCommerce results you expect, don’t pull back or pause your campaigns without doing a little digging — especially if you have multiple channels through which shoppers can make their purchases.

If you’re looking to set up offline conversion tracking for your Google Ads accounts, we recommend you consider all your sources of revenue first. 

Remember the old phrase about assuming things? If you’re starting your offline conversion tracking from scratch, take all of your purchase channels into account. This means call centers, third-party sellers, brick-and-mortar locations, and more. The more attribution data you have, the more informed your strategy will be.

If you’re not sure how offline conversion tracking is affecting your Google Ads campaigns, our strategists are always here to help. We stand ready to audit your accounts, incorporate offline conversion data into your campaigns, and create a personalized strategy for your business goals and needs.

Get started by requesting a free proposal today.

Here’s an alternative to cookies for user tracking

Instead of having your analytics toolset read a cookie, pass a unique identifier associated with the user ID. Learn how to do it and keep it privacy compliant.

The post Here’s an alternative to cookies for user tracking appeared first on Marketing Land.

For over 20 years, website analytics has leveraged the use of persistent cookies to track users. This benign piece of code was a mass improvement over using a user’s IP address or even the combination IP and browser. Since it was first introduced, the use of these cookies has become the focus of privacy legislation and paranoia. So what alternative is there?

If your website or mobile application requires the creation of user accounts and logins, it’s time to plan to transition away from cookie-based tracking to user ID tracking. In simple terms, instead of having your analytics toolset read a cookie, you pass a unique identifier associated with the user ID and then track the user via this identifier. Typically the identifier is the login ID.

Preparing for advanced tracking

Step 1

Ensure that the user ID you’ve deployed doesn’t contain Personal Identifiable Information (PII). Too often, sites require users to use their personal email address as a login ID or event their account number. These are PII. If this is the case with your organization, then the trick is to assign a random unique client identifier to all existing accounts as well as for any future accounts as they are created. 

Step 2

Have your developers start to push the User ID to the data layer. This way, the variable will be there waiting for your analytics software to read it once you’re ready to implement the new tracking method. Check with your analytics software on the variable name for this element as it varies from analytics software to software.

Step 3

Create a new view/workspace within your analytics software and configure it to track users by their user ID. Most analytic packages will still set a temporary cookie to track user behavior prior to their login and then will connect the sessions. This way you can see what a user does on your site even prior to them logging in and what site visitors who never login do.

Benefits of tracking users by user ID

Improved accuracy

The use of cookies is flawed in many ways. If users jump between devices (from desktop, to mobile, to a tablet, or office computer to home computer) you can’t track that it was the same user. This generates inflated unique user counts.

What if a user clears their cookies (perhaps they’re utilizing antivirus software that purges all cookies every time the browser is closed)? Once again this leads to inflated user count data.

By tracking a user via their user ID, you’ll obtain a more accurate count of unique users on your site.

Cross Device Tracking

This is perhaps one of the greatest benefits of tracking users by their user ID. You can now see how users interact with your site and/or mobile app. How many use a combination of devices. Is there a specific preference for which type of device might simply be used to add to a shopping cart, only to have the order processed on another device?

Greater Analytics Insight

Armed with enhanced analytics data, new and potentially powerful insights can be harvested. With this new knowledge, you can better direct internal resources to focus and enhance the user experience and optimize the user flow for greater profits.

Real life examples

The following examples demonstrate the power of tracking users by their user ID. 

Overview – Device Overlap

The following image shows what percentage of accounts use which type of device and the percentage that use a combination of devices. For example, while 66.6% use only a desktop, 15.8% use a combination of Mobile and Desktop.

User Behavior – Device Flow

Reviewing the device flow leading up to a transaction can provide some of the greatest insights from this enhanced analytics tracking methodology.

While it might not be surprising that the two most common device (by number of Users) paths were Desktop only and Mobile only, what was surprising to me and to the client was number 3. While the device path of Desktop -> mobile -> Desktop is only experienced by approx. 3% of users, it accounts for approximately 8% of all transaction and over 9% of all revenue generated.

The minimal overall use of tablets was also a bit surprising. Of course the mix of devices does vary from client to client.

Assisted conversions

By dropping the use of cookies, the quality of the data of assisted conversions is significantly increased. For example, how many people read an email (can easily be tracked when opened and attributed to a user ID) on a mobile device, click into the site, browse around to and review the items that are being promoted (maybe add them to their shopping cart). Then think about it for a bit before logging-in later via a desktop to complete the transaction?

For example, from the above report, one can objectively assign a more accurate value to SEO efforts by examining the role Organic Search traffic played in generating sales. While a source of an immediate sale (in this case) from organic search generated traffic represents 1.3% of total revenue as an assist in the sales cycle, it played a role in over 10.4% of generated revenue.

Enhanced user insights

In this example, the client allows its customers to also have multiple logins for their account. Essentially a user ID represents a customer/client and not a single user. The client operates in the B2B world where multiple people within its clients’ organizations may require unique logins and rights (who can order, who can just view product details, who can view or add to the cart but not place an order, etc.). By leveraging both tracking by user ID and recording a unique login id within their analytics, these additional insights can be obtained.

user-breakdown.jpg

The above report not only breaks down revenue by division, but demonstrates how within different division users use the site differently. In region 1, there is almost a 1:1 relationship between user ids and login ids. Yet in Division 3, the ration is over 4:1, this means that for every customer there is an average over 4 logins being utilized in Division 3.

How can they leverage this data for more effective marketing? By understanding that within divisions there are differences, carefully crafted email marketing can be created to target customers differently with multiple logins vs. single account/login customers. 

A further dive into the data could also reveal which login IDs are only product recommenders (only view products) from those who make specific product requests (add to the shopping cart and never place the order) from those who only process orders and from those who do it all. Each one needs to be marketed to differently with different messaging to optimize the effectiveness of the marketing effort. It’s through detailed analytics that this audience definition can be obtained.

Is tracking by user ID right for me?

Making the decision to change how you track your users is a difficult choice. First, does your site/mobile app require users to login at a reasonably early part of their journey? This is ideal for e-commerce sites and sites where the vast majority of user interaction takes place after the user logins into the site/application.

If you’re running a general website with the goal to merely share information and generate “contact us” type leads, the answer to making this switch is no.

If you have a combination of a general information site plus a registered user section, then yes you might want to consider making this change and perhaps just for the registered user section.

If you do make this change, don’t stop running your other analytics views/workspaces that use cookies. Keep them running. By operating two different views, you’ll be eventually able to reconcile the differences between the two, plus it makes it easier to explain to those who you report to, why you’ll be reporting a dramatic drop in the number of users. Of course, when you first make the switch, all users will be first-time users so expect a major increase in new visitor traffic.

If you decide to make this change, don’t forget to review the impact of the change with your legal department. They will tell you if you need to update your privacy policy.

The post Here’s an alternative to cookies for user tracking appeared first on Marketing Land.