Fixing top-funnel (TOFU) marketing issues (and automating TOFU processes) is now a major initiative among many demand marketers. Considering CRM’s effect on the bottom of the funnel (BOFU) and marketing automation’s results with the middle, focusing on centralizing, standardizing and automating top-funnel demand gen efforts is a natural progression.
While several articles have made strong arguments for the need and value of top-funnel MarTech, I’ve yet to see any support the case with hard numbers. (And hard numbers are critical when you’re building a business case to evaluate potential investment value.)
So I thought I’d do just that, focusing on an important element of automating top-funnel efforts: filtering and correcting unmarketable leads before they’re injected into your prospect database.
What’s an unmarketable lead?
Pretty simple – it’s a lead that doesn’t meet marketing’s defined standards. While such standards vary from organization to organization, a fairly common list of marketable lead characteristics include:
- all targeting criteria is met (e.g., persona, account type, etc.)
- verifiable email address
- complete information (no missing fields)
- a new contact (non-duplicate)
- formatted correctly (so you don’t have to spend hours normalizing lead files)
Such criteria (as well as specific disqualifiers) are obviously unique to certain channels. For instance, with inbound leads you typically don’t need to worry as much about incorrect formatting since your website-hosted forms are usually standardized. However, invalid email addresses remain a major problem. (As does incomplete info, because requiring your website visitors to fill in a ton of fields to access content undermines the user experience and conversion rates. I’ll discuss this at greater length at the end of the post).
Performance-based lead programs (i.e., CPL campaigns), on the other hand, often require an even higher level of governance due to inconsistencies in partner-hosted forms and unstandardized lead files. From our own research, we’ve found that non-filtered third-party lead gen campaigns tend to produce a 40% rate of unmarketable leads.
Fixing these issues with automation presents a major opportunity for quick, significant pipeline scale. This blog post will first focus on the value of correcting the leads generated from these kinds of third-party CPL programs (since they’re the biggest quick win). We’ll then look at the ROI benefits of correcting event- and inbound-generated lead integrity.
CPL campaign lead filtering and the “Pipeline Effect”
Whether and at what point you filter and/or correct your leads has an enormous impact on demand marketing program ROI – especially for third-party campaigns. For our current purposes, we’ll narrow the filtering possibilities to three options:
- Automated lead filtering at time of lead acquisition from media partners/lead data providers
- Manual lead filtering after lead acquisition
- No lead filtering – that is, marketable and unmarketable leads are both directly injected into your prospect database (typically your marketing automation platform or CRM system)
For each of these three cases, let’s assume a $100,000 budget (quarterly or annually…doesn’t matter). For simplicity’s sake, let’s also assume an average lead cost (CPL) of $50 and an average contract value (ACV) of $30,000.
Now let’s see the return on these investments in each of the three scenarios.
BEST: Automated top-funnel CPL lead filtering
In this case, all unmarketable leads are automatically filtered and returned to the lead provider for correction or replacement (due to invalid email address, missing fields, duplicate data, etc.) before it’s sent to you. Thus every lead is marketable (2,000 leads), and you maintain a $50 cost per lead.
Now if we assume a 5% marketable lead-to-opportunity conversion rate, that investment translates to: $3,000,000 sales pipeline contribution.
BETTER THAN NOTHING: Manual top-funnel lead filtering
This is a very common point of lead data filtering. Demand marketers (or sometimes marketing ops teams) receive leads in batch files about once a week from their lead gen partners. They then spend upwards of 5-8 hours per week scrubbing leads that don’t match campaign parameters, have obvious invalid email addresses (e.g., Donald Duck or just misspelled entries), have missing fields or are duplicates.
The staff time alone saps ROI, but sticking to hard numbers, we find:
- 6% of leads contain invalid emails that can’t be manually identified, and an additional
- 2% of leads slip through manual checks that should’ve been filtered as unmarketable
Moreover, due to the 7-day average time it takes to processes leads, conversion rates also suffer as prospects either lose interest or are engaged by a competitor. This affects pipeline impact significantly:
BAD: No lead filtering at all
Most marketing teams filter incoming leads to at least some extent, but over the years I’ve found that many organizations – in the interest of time and limited human resources – dismiss lead filtering altogether. They simply upload all their purchased prospect data directly into their database without cleaning it first.
So how does this translate to marketable lead volume and pipeline value? Our research of more than 778k leads shows that on average 40% of CPL campaign leads are unmarketable due to: duplicates, missing fields, incorrect formatting, non-targeted audiences or invalid email.
This obviously decreases pipeline contribution drastically. Moreover, the conversion rates are again reduced due to skewed performance analytics affecting program optimization.
This $100k in demand generation investment can result in a range of $1.44 million to $3 million in sales pipeline depending on when and to what extent you filter out unmarketable CPL campaign leads.
In other words, automating top-funnel lead gen campaign filtration can create up to 108% greater pipeline from the same demand gen investment – and this percentage only increases as investment grows.
If you run these types of third-party lead gen programs (content syndication being the most common), automating top-funnel initiatives is a no-brainer. And if you’re looking to continually scale and or run specific account-based marketing programs, you’ll likely need to start investing in these types of outbound marketing programs to fuel your pipeline.
Top-funnel automation for event-generated leads
What about event-generated leads? These too suffer from integrity issues. Let’s assume you invest $50k in an event that generated 1,000 leads. Let’s also assume that those leads, when marketable, convert to opportunities at a 7% rate (event leads do typically convert higher). And your ACV is still $30k.
Events don’t ensure completed fields, conform to formatting requirements, or provide all the fields you need to ensure target matches, which can easily result in a 20%-unmarketable lead rate.
Now since events aren’t performance-based, top-funnel automation can’t simply filter leads and send back the unmarketable ones to the event hosts for correction or replacement. So it’s not about filtration, but rather all about lead correction, standardization and data enhancement. All of this can be done in close to real time with TOFU automation, saving team resources, increasing lead velocity and conversion rates, and stoking pipeline value and event ROI.
The variance in sales pipeline contribution between top-funnel auto-corrected leads and unfiltered leads can look something like this:
That’s a potential gain of $420k in pipeline opportunity from just a single event. If you depend heavily on events for new leads, this too makes TOFU automation a clear win.
Inbound marketing leads need data integrity checks too
Marketing automation platforms have worked wonders for inbound marketing initiatives. But major problems still exist, specifically with regard to unstandardized data, invalid email addresses and incomplete leads.
Unstandardized lead data
This is typically the most significant drain on inbound marketing’s contribution to pipeline. It’s not unusual for 25%-30% of inbound leads to have field data that must be normalized; for example, “Chase,” “JP Morgan Chase” and “JP Morgan Chase Bank” are all the same, but marketing automation systems don’t see it that way.
Top-funnel automation translates varying company names, job titles, etc. into a standardized naming convention recognizable to your marketing automation and or/CRM system. This is especially significant for account-based marketing programs.
Of course, as I mentioned with regard to CPL campaigns, most teams will catch some of these inconsistencies and fix them (with great resource expenditures), but not without letting a large portion slip through. A 10% drop in marketable leads is expected.
Invalid email addresses
Invalid email addresses often account for more than 6% of form-generated leads. This can be a result of a fake email address being given, but it’s more commonly a consequence of misspelling. Through data enhancement integrations, some top-funnel automation systems have the ability to match leads by contact name and company to correct email addresses. This won’t cover the full 6%, but on average it can account for 2%.
Incomplete leads
In addition to correcting email addresses, these data enhancement capabilities also allow you to gain more insights on the prospect immediately after they submit the lead form. This allows you to quickly score leads and engage them with content and/or offers more specific to their needs. In effect, you’re lead velocity and conversion rate goes way up because you’re bypassing progressive profiling, which increases pipeline contribution.
Perhaps more importantly, top-funnel lead data enhancement enables you to cut down inbound lead form fields to the bare essentials, which increases user experience and boosts lead volume. Without this enhanced data, you can easily be dropping 3% of marketable leads.
Aside from boosting inbound lead volume, correcting invalid email addresses, providing a fuller lead profile and standardizing data, top-funnel automation simply filters out leads that you’re not targeting. This saves data usage fees and as well as the resources spent cleaning out your database. The costs on pipeline here are hard to calculate, so I didn’t add this into the equation, but it should at least be mentioned.
What’s a safe estimate on pipeline loss due to top-funnel auto-filtered/corrected inbound leads? Between unstandardized lead data, invalid email addresses, and incomplete prospect info, a 15% loss rate is typical. So for every 1,000 inbound inquires (among targeted personas and/or accounts), only 850 of those would be marketable. This is what it looks like for your pipeline.
That’s a difference in $570k per month. Of course, pipeline gains will depend greatly on a number of factors: from program investment size to lead nurturing and scoring processes to average contract value.
But it’s safe to say that for most marketing organizations, no matter your mix of outbound CPL campaigns, events and inbound initiatives, the numbers support the case for adding a top-funnel automation system to your MarTech stack.
