In my experience, navigating long sales cycles is like orchestrating a complex symphony, with people, timing, and operations all playing vital roles. I’ve learned that when I value leads appropriately, I can give paid media platforms the clarity they need to perform better.
In these extended sales journeys, much of the action post-lead submission revolves around the human element. If I focus my campaign optimization efforts solely on sales outcomes, I’m essentially allowing ad platforms to react based on the sales team’s monthly performance, which often overlooks lead quality—a dilemma no amount of tweaking can resolve.
The advice to “optimize the full funnel” suggests monitoring media expenditure through to revenue generation. However, beyond capturing leads, the factors that drive sales often exist outside the realm of paid media—it’s tied to the sales team composition, their workload, and other myriad factors beyond your control with targeting or creative updates.
When My Sales Team Becomes the Signal
With over 15 years in financial services marketing under my belt, I’ve seen this phenomenon extend beyond industries like mortgages or insurance. If human interactions are a key part of your sales process, this will resonate with you.
Picture someone like Dave in your organization. For example, in my case, Dave is a talented mortgage advisor, but in your world, he might be your leading enterprise sales rep, an outstanding business development manager, or the star project estimator.
Dave isn’t just successful because he gets better leads. His natural gift for establishing connections, asking insightful questions, and reassuring clients enables him to close deals at a rate far exceeding his peers.
But Dave isn’t omnipresent. He deserves vacations, he might pursue new career opportunities, or your company may recruit more like him. Consequently, the composition of your sales team is in constant flux. A surge of seasoned closers one month might juxtapose a shortfall the next, influenced by recruitment drives or personnel departures like Dave moving on with two coworkers.
This variability can lead to targeting conundrums. When conversion rates plummet as a junior rep fills in during Dave’s absence, algorithms may misinterpret it as a targeting issue rather than a staffing concern.
If my campaigns are programmed to optimize towards sales, the algorithm might surmise, “Targeting malfunctioning—these clicks now yield lower quality conversions; time to redirect spending.”
Such assumptions can lead to previously effective keywords being disabled, active audience engagement dwindling, and overall account performance declining, despite leads remaining unchanged.
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Operational Influences on Conversion Data
There’s more at play than merely the sales team’s structure. Imagine this scenario:
During Q4, workloads often intensify as everyone races to finalize deals by year-end. Response times may surge from two days to over a week, prompting impatient clients to look elsewhere.
Market dynamics could shift abruptly, leading to the withdrawal of your most competitive product. Or, summer vacations reduce staffing, resulting in some leads growing cold long before follow-up. Then, in September, everything stabilizes again.
These are just typical examples of everyday operational hiccups. Be it budget sanctions being stalled, fluctuating product ranges, or project delays, each can uniformly distort your conversion metrics.
The algorithm may misinterpret targeting effectiveness when, in reality, your team is simply juggling leads from other originations.
When Dave Becomes Unstoppable: The Santa Claus Rally
The Santa Claus Rally, often referred to as the December Effect, is a fascinating instance I’ve witnessed where human actions can throw algorithmic targeting for a loop.
Every December around the third week, something peculiar unfolds in the financial services arena: lead-to-sale conversion rates soar, with uplifts skyrocketing up to 150% compared to usual weeks.
Optimizing for sales might lead the algorithm to deduce, “This week’s strategy is phenomenal!” Yet, reality hits during the holiday week, plummeting conversion rates to fractions of their regular levels.
None of this is attributable to paid media strategies. By week three, individuals like Dave enter ‘goal-accomplishment’ overdrive. They’re motivated by year-end bonuses, pushing through one last campaign before the break—swiftly reaching out to leads, following up assertively, and converting deals they might usually spend longer nurturing. Dave’s productivity hits a new high.
With the advent of the holiday week, everyone checks out mentally. Customers stop answering calls, and Dave finally uses his PTO. Meanwhile, those still working spend more time planning family events than business goals.
The lead attributes, targeting, and ad placements remain consistent. The program simply adjusts bids and valuations based on the seasons, reflecting when Dave and team take their much-deserved vacations.
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Knowing When to Cease Optimization
So, if I find that sales-focused optimization skews due to uncontrollable factors, I wonder where this optimization boundary should be drawn. How can I curb this distortion while ensuring the right leads?
The answer lies in finalizing control at lead submission—but evaluating leads isn’t about counting them. It requires ascertaining their probability of conversion and the financial worth of the final sale.
An issue with high-value industries is their frequently low sales numbers, making it nearly impossible for automated systems to gather meaningful insights. Lead valuation counters this by providing a greater volume of conversion events as opposed to sparse sales data.
Consequently, automated bidding performs efficiently, facilitating campaign testing and audience analysis, while maintaining data accuracy. Optimizations draw from lead quality before Dave—or the sales crew—steer the wheel.
Importantly, while downstream conversions or revenue may be imported into platforms powerfully, it only succeeds if volume is ample, conversion delays are short, and sales processes are stable.
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Creating Lead Valuation Systems
I begin with a robust analysis of historical data, preferably spanning a year, although six months can suffice. My goal is to discern which leads converted and assess their value, identifying any shared characteristics evident at inquiry.
For financial endeavors, relevant metrics might include loan value or terms. In a B2B context, relevant dimensions might involve business size or industry. Construction projects often boil down to scope and immediacy.
Afterward, I categorize leads by their conversion probability and typical deal size, then assign an estimated revenue value.
The checkpoint for accuracy is straightforward: ensure that your leads’ cumulative projected value closely mirrors actual generated revenue over a timeline. If discrepancies exist, the model needs adjusting. It’s prudent to revisit these models routinely, ideally quarterly, in response to dynamic campaign and operational changes.
For instance, I might qualify a high-probability lead at $850, a median lead at $420, and lesser-chance leads at $120.
Upon formulating this, conversion tracking is configured to relay anticipated values back to platform conversion actions, thereby deploying value-based bidding (like Google Ads’ target return on ad spend) to guide the algorithm towards valuable leads.
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Focusing on Controllable Aspects
The advice to “optimize the full funnel” resonates as common sense till we grasp how much we can’t control. For instance, I can shape targeting, craft compelling creatives, enhance landing pages, and streamline initial form engagements. Thereafter, it’s primarily on Dave or the sales team and extraneous factors far removed from my campaigns.
Expecting an algorithm to optimize for invisibles misleads it into chasing erroneous audiences from flawed assumptions.
Instead of ceasing post-lead tracking, I recommend sustained monitoring, as it sheds light on areas of triumph and those needing rectification. Consider these pointers:
- With steady lead quality and declining sales, it’s an operational challenge, not a paid media dilemma.
- Simultaneous drops in both lead quality and sales might prompt campaign evaluations.
- Sudden sales surges with stagnant lead quality often indicate Dave excelling, not improved targeting.
Such detailed insights are invaluable but shouldn’t dictate optimization strategy.
Develop robust lead value assessments, convey expected valuations back to your systems, and allow algorithms to excel at identifying optimal leads. Leave other aspects to Dave’s capable hands.
It’s essential to delineate where your control ceases, marking where optimization should logically end.
Inspired by this post on Search Engine Land.
