CRMs are an incredibly powerful tool for PPC advertisers to improve account performance, but many times they get overlooked or not looked at at all.
These databases are clearly integral to internal marketing and sales efforts, but they can also be highly influential to PPC account strategy, optimization, and success.
This post is going to be a little bit of how to, but mostly a high level view of how CRM data can impact PPC performance and how you should be using this data to improve your results.
The Set Up
Hopefully it goes without saying, but it’s a bit tough to use CRM data if you don’t have a CRM. That’s the prerequisite for this approach.
There are lots of tools available out there to track how leads move from lead to customer. The most common setups we work with are Marketo + Salesforce, HubSpot + Salesforce, or a complete HubSpot solution, but there are plenty of tools available to help you achieve these goals.
The needs are relatively simple: be able to track where leads are coming from and following their progress through a sales cycle to its completion (lost or close). So let’s break each of these down a bit.
Where a Lead Comes From
In easier terms, we would call this lead source, but ideally we have so much more data than just “source”.
The common type of campaign tagging is UTM tracking, where you apply values to five major UTM categories: source, medium, campaign, term, and content.
Although lead source is certainly impactful, when rolling out a sophisticated, full-funnel, multi-channel PPC strategy, those other UTM parameters become a necessity. (And with this sentence, I think I’ve hit my buzzword bingo for this post.)
It’s one thing to know a lead came from Facebook, it’s another thing entirely to know that the lead came from your 2% Lookalike model of a customer list on a carousel ad where you highlighted how your product is made.
There’s a lot that can be gleaned from that. Just think of all the different target audiences you’ve run across all different channels, how many different ad messaging approaches you’ve tested over the years, and it becomes apparent that this detailed tracking can be a window into your best performing marketing strategies.
So with that, I leave you with this: track everything.
Use all of the tracking options you can and get that information into your CRM. From a PPC perspective, make those tracked values clues to where exactly in your account you saw that performance. It needs to lead you directly back to the ad unit, within the ad group, within the campaign, that saw that success.
Now that we have data in the CRM, let’s tackle the second part.
Following Through a Sales Cycle
Ideally, all of the tracking we just set up will follow users through a number of different stages. A typical cycle looks something like this:
Your individual sales cycle milestones may look different depending on your sales or business model, but this is a solid generic outline to work from for the sake of this post.
Each time someone moves from one stage to another, we want to still be able to track back to that specific creative in that ad set in that campaign. If we’re successful, we can then know which strategies not only generate leads, but which ones generate leads more likely to become MQLs, SQLs, Opps, and Customers (aka, the revenue drivers).
With data in the CRM tracking users through a sales cycle, we have a whole new world of optimization strategies to lean into.
The Optimizations
Typically, optimizations without CRM data accompaniment are focused on driving leads on. Efforts and budgets are focused on the portion of an account driving the most leads at the lowest cost. But not all low-cost leads are good leads. As your CRM gets more sophisticated, the adjustments you make should be shifting down the funnel.
Focusing on Good Quality Leads
Anyone who has been around lead generation before knows that sometimes leads come in that are full of completely fake or incorrect information. Based on in-channel conversion tracking, this lead is just as valuable as one that eventually generates thousands of dollars worth of revenue.
Hopefully, you or your clients’ marketing department is using some form of lead scoring model. In this strategy, each lead generated is categorized based on the potential the lead has. In some versions, leads are literally awarded point totals based on the information they provide (and it’s accuracy) to help evaluate which are higher quality than others. In other versions, lead status designations are provided: rejected, good, nurture, etc..
No matter which model you’re using, these early lead score indicators can greatly help influence PPC strategy by showing you which areas are driving higher quality leads and which are simply padding your cost per lead stats to look amazing, but are really driving lower quality leads.
Beyond lead quality at initial assessment, all other stages of the buying cycle can be optimized for. As long as the tracking follows a user through the buying cycle, you can optimize for any of the subsequent buyer stages in the same manner as lead quality.
With this data at your disposal, here are just a few of the optimizations you can make in your accounts:
- Budget allocations
- Keyword theme strategies
- Keyword bid adjustments
- Ad copy message strategies
- Ad scheduling
- Target audience usage
- Network/Channel targeting
And, quite frankly, so many more.
Additionally, you can utilize a direct integration with your CRM to get this information passed directly into the ad platforms for even more optimization abilities. Check out our post here on ideas for linking Google Ads to Salesforce.
Optimization Caveats:
While it may be enticing to run off and start optimizing on CRM data, there are two caveats I want to call out first.
Attribution Models
Depending on the set up of your CRM, there are two ways leads can be tracked: status change date and lead creation date.
This concept can be a bit tough to write out, so I’m going to show you in 2 charts how these models differ. The key thing to focus on is WHEN the success of a lead is attributed to.
In the chart above, you can see a status change model following a Lead through to Customer. This happened over a period of six months, which is normal, but also means that if you were optimizing your PPC campaigns for Customers, you would think that your strategies in July were successful when it was really the lead generated in January that drove success.
What PPC marketers need is the data from the lead creation date model, which attributes all lead stages to the date the lead initially entered the CRM.
In many CRMs, there’s an option to have both models run and see how each compares. Most sales teams rely on the lead status change model to show progress, but we marketers need the lead creation model. Ideally, you can find a way to have both so both teams can be successful.
Conversion Lag Time
Piggybacking a bit from the example above, there are many times when it takes weeks, months, even years to see a Customer generated from a single Lead.
With this in mind, it’s important to make optimizations for key metrics that are realistic based on average conversion tag times.
For example, let’s say your business has a typical lag time between stages seen below:
This means it takes about 60 days on average for you to close a Customer. If you were going to make optimizations in your account for Customers, it’s not realistic to do that only on the last 2 weeks’ worth of data. You’re likely better off making changes using at least the last 60 days worth of data, but more likely 90 days to the last 6 months is more realistic.
The same logic goes for all other stages of the funnel. Always have enough data to optimize from by looking back during a realistic time frame. Don’t make decisions about strategy before your data has had a chance to fully mature and show you how successful you have been.
The KPIs
With this more advanced strategy for account optimization, it’s only natural to see a shift in what is deemed a success and what’s not.
Typical KPIs shift from leads created and cost per lead to any combination of those metrics below:
- Lead volume & cost per lead
- Cost per MQL
- MQLs per month
- Opps created
- Revenue
One of these might become your key focus with PPC campaigns, but it’s important to have a number of benchmarks as these don’t always work together for a few reasons. Here are some real world examples from accounts I’ve worked on in the past:
- Cost per lead is 2x goal, but Opportunities are 50% below target and ROAS is slightly up.
- ROAS is down compared to last quarter, but SQLs are up 25%.
- Lead volume is down, but the Rejected lead rate is also down.
Each of these scenarios we were trailing in one metric, but succeeding in another. There’s no perfect formula for how to fill a sales pipeline while simultaneously hitting every single metric’s goal.
Additionally, some of the metrics down the pipeline are explicitly out of the marketing team’s hands. Once a lead makes its way to SQL status, it’s up to the sales team to close them into a customer, and that process can go awry for all kinds of reasons.
More often than not, it’s best to use the marketing impacted metrics as KPIs of focus, then use the metrics down the remaining portion of the buyer cycle as directional markers rather than explicit KPIs. They can help you focus where to put budget and what strategies to focus on, but it shouldn’t be the primary measure of success given the number of variables included.
Conclusion
Lead generation accounts have always been a passion of mine and there’s a world of opportunity to grow and optimize accounts if the data is available. Hopefully, you can walk away from this post with more ideas on how to use data to optimize for metrics beyond the lead and make your lead generation account a revenue-driving machine.
