The increased demand for lead generation has led to the use of many different tools and channels. However, without backend data in a company’s CRM tool or campaign management software, it can be difficult to know what is really working.
Backend data integration with digital marketing campaigns is a gamechanger in terms of performance.
But what can you do if the conversion data from the backend is missing or unreliable?
How can marketers improve lead quality and increase profitability if they don’t know which keywords and demographics are most valuable?
Despite not having the best data passback with your digital advertising platforms, this post will guide you through some indirect approaches to improve for lead quality in Google Ads.
First, let’s go through why this is so critical.
Why Optimize For Margin Or Down-Funnel Events?
Before we go into tactics, let’s make sure we’re all on the same page on why this is important in the first place. What’s the danger in focusing your efforts on a single lead event or transaction?
This is a diagram that our firm uses to explain why it’s important to combine backend data with advertising strategy and decision-making.
While this example focuses on B2B marketers, B2C advertisers would often follow a similar path to revenue.
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For example, a Click > Add To Cart > Start Checkout > Create Account > Complete Order cycle might be used in B2C ecommerce.
Closed Loop submitted this image in December 2021.
As seen above, there are various steps on the way to income, including Clicks, Leads, MQLs, Opportunities, and Closed/Won.
Most marketers nowadays are aware that optimizing for the lowest cost per click/website visit will result in low-quality, fat tail keywords that have little influence down the funnel.
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However, because of the technical challenges of combining backend and frontend data, I often see marketers stop optimizing at leads.
It’s not a huge thing, right? Reconsider your position.
Here’s an example of how moving your attention down in the funnel may have a big impact:
Closed Loop submitted this image in December 2021.
When analyzing based on leads, Campaign A seems to have considerably superior results.
Lower in the funnel, however, the disparity expands to the point where Campaign A’s cost per sale is more than 5 times that of Campaign B.
So, what am I going to do about it?
Before focusing on lead quality, don’t wait until the optimal state solution is in place.
It might take some time for offline data to be loaded into Google Ads and other networks.
While such integration is being worked on, you can still take important measures to start shifting the needle in the correct way.
Let’s take a look at the ideal data state before we go into those phases.
Backend Data in Its Ideal State
The optimum situation for optimizing toward backend data consists of the following elements:
- To send the platform identification (GCLID, FBCLID, etc.) into your CRM account, hidden fields are set up on all of your website and native lead gen forms.
- OCT (Offline Conversion Tracking) has been completely integrated into Google Ads and other supporting channels (Microsoft Ads, Facebook, LinkedIn).
- Each conversion point has its own set of values that have been computed and allocated to it.
- In-platform value-based bidding is now available.
- Backend data mixing across channels through a daily, automatic CRM export to provide cross-channel, full-funnel reporting (assuming not all ad platforms you’re using offer data passback).
While this may seem simple, given the necessity to include stakeholders from many departments, I have found that many marketers take a long time to get to this position.
While the ideal state is being worked on, here are some concrete measures you can take to optimize for down-funnel occurrences.
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The Low-hanging Fruit (Stage 1)
Keyword Intent as Perceived
When optimizing for lead generation, the search phrases that generate such lead will be of varying quality.
If you have a restricted budget and want to boost down-funnel outcomes, you should analyze keywords (and campaign budget allocation) based on perceived intent as well as tangential quality signals like engagement signals transferred in from Google Analytics.
Consider reducing exposures on words with low perceived intent or weak tangential signals if share of voice (SOV) is lower than optimum for highly performing, high perceived intent phrases.
Don’t be alarmed by increasing cost per lead figures.
Consider implementing a “intent multiplier” for leads driven by particular keywords and audiences if you find terms with greater perceived intent despite higher cost per lead.
Pro tip: When making modifications, use labels so you can simply filter for changes made at certain times.
This will allow you to rapidly update that data set in the future (e.g. If your monthly budget increases and it makes sense to activate a tranche of keywords previously paused).
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The Click Must Be Pre-Qualified
According to marketing 101, the greater the CTR, the better your targeting and message are matched.
However, in order to improve CTR, one should not optimize naively.
When it comes to improving the quality of your leads, one of the simplest levers you have at your disposal is your ad wording.
Consider the characteristics of a high-quality lead, and then design your advertising to appeal to those characters.
For example, if you’re a B2B marketer targeting corporate IT prospects in the Retail industry, your headline should include phrases like “Enterprise IT Solution for Retail.”
For certain searches, tailoring will reduce the relevance of the ad.
However, you’ll have more money to spend on audiences that are more aligned.
You’ll eliminate out those that aren’t excellent matches, such as SMBs and manufacturing firms, by explicitly specifying who your product or service is for in the content.
Make use of Audience Layers
Advertisers may choose from a variety of affinity, in-market, comprehensive demographic, and bespoke audience choices in Google Ads.
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You may manually bid up or down (manual bidding) or include or exclude people from your RLSA ads by using audience layers.
Stage 2: Use first-party data to build an audience
Whether or not your CRM is linked to your advertising platforms, you may still extract customer and prospect details that are very important to you as a marketer.
Here are three methods to make the most of your data.
CRM Data for Nurturing
Lead nurturing programs in display/programmatic, YouTube, social, and search may increase down-funnel lead quality, particularly in sales funnels that last longer than a few days.
The work of a marketer does not end at the lead stage.
Throughout the buyer’s journey, an organization must remain front of mind.
Marketers should collaborate with sales to determine which leads in their database have potential (typically using a lead score system).
Then, using ABM, make sure they and the organization’s purchasing committee are constantly bombarded with brand and thought leadership material to keep you front of mind and increase your authority.
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Marketers should also work with sales to identify prospective prospects (using a lead score system) and nurture them with content marketing to retain brand recognition and industry authority, including the organization’s purchasing committee (through ABM).
While Google does not provide ABM solutions, you may use the Microsoft Audience Network, LinkedIn, and other providers to target particular firms and roles inside those companies.
Using CRM Data to Create a List
To seed Similar To/Lookalike audiences, most ad networks provide list submission alternatives (by phone, e-mail, or mobile app ID).
You may target folks who have traits comparable to your most valuable lists by thinking through your list upload segments (e.g. top customers).
On the other hand, you may submit lists of low-quality prospects and customers, then remove them from your targeting (all bid methods) or lower your price if you’re utilizing a manual bid strategy.
That list isn’t required for explicit targeting. It may also be used to get insights into your consumer base or as a starting point for creating Similar To or Lookalike audiences (read on!).
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CRM Data for Target Modeling
Both Google Ads and LinkedIn include audience intelligence tools that may help you uncover additional audience categories that correlate with your best and worst customers, in addition to expressly targeting and/or generating a lookalike-based audience via a list upload.
Go to your Shared Library > Audience Manager > Your Data Insights in Google Ads.
You’ll be able to choose an audience (upload, pixel, or YouTube-based) and compare it to a control group (e.g., US population; Bad lead list) on a variety of metrics, including age, gender, parental status, geography, device, and, most crucially, Google affinity and in-market segments.
Using a “Closed/Won” list, here’s a glance at that report:
December 2021, Google Ads screenshot
December 2021, Google Ads screenshot
Once you’ve gathered some data, you may determine how to best use it to your efforts. This might be accomplished by bid modifications, value regulations, RLSA inclusions, or exclusions.
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TL;DR: Use Customer Match uploads to feed your customer data to Google Ads (and beyond), then use it via inclusions, exclusions, and attribute modeling.
While you can manually transfer lists to platforms, technologies like Zapier, Salesforce Advertising Studio, and Liveramp may help you automate the process and boost match rates.
Stage 3: Incorporate Conversion Values into Bidding
OCT-based conversion points + value-based bidding are the ultimate grail to aim for. Using value signals in your bidding choices may be a net positive for performance even if you don’t have OCT data.
Here are three ways to getting the most out of your conversion value.
Step 1: Give each conversion point a value.
When you’re just starting started, don’t stress about giving the system the right value. The idea is to set settings that guide the algorithms in the appropriate direction.
These numbers should be calculated using the value multiplied by the conversion rate from the action to the transaction in the future.
Step 2: Incorporate Value-based Bidding (Max Conversion Value/tROAS) into your strategy.
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When you initially start out, your tROAS objectives should be equivalent to the CPA of your legacy bid strategy.
The aim is to transition to value-based bidding without creating unnecessary volatility, and then tweak the tROAS target to improve efficiency and/or scalability.
Use Value Rules in Step 3
This tool, which debuted in Google Ads in 2021, lets you add, remove, and multiply any conversion value depending on audience, device, or region.
Consider this scenario: I want to reach out to corporate IT decision-makers, but I don’t have OCT, so I don’t know what drives performance beyond the lead.
Even without knowing the backend data, I know I want the algorithms to:
- If you’re in a Similar To audience, bid higher depending on on-site engagement.
- If Google classifies them as In Market: Enterprise Software, up your bid.
- Bid higher if you work for a large corporation.
- Bid higher for those in San Francisco, California.
- If they work for a tiny business, they should offer a reduced price.
This is how it appears when translated into value rules:
December 2021, Google Ads screenshot
With value rules, you can instruct the algorithms to display advertisements to higher-quality prospects using tools like Google Ads Data Insights, combined with insights from LinkedIn Detailed Demographics reports, first-party personas, and customer data.
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Putting the Pieces Together
Integrating first-party data to guide audience growth and bidding should be a high priority for you.
Although incorporating backend conversion data through OCT might be difficult, it is a worthwhile goal to pursue.
While laying the basis for direct data passback, keep these strategies in mind while improving lead quality without using OCT.
Additional materials are available at:
Brovko Serhii/Shutterstock/Featured Image