The Customer Journey Beyond Lead Scoring

Lead scoring is an outdated method that sorely needs an overhaul.

We’re not saying you shouldn’t perform lead scoring, but teams that are overly dependent on it miss out on crucial benchmarks and opportunities.

The average lead scoring model is stagnant and limited, regardless of company size.

AEs, BDRs, and SDRs are overly dependent on their marketing team for lead scoring. Sales reps often complain that the qualified leads marketing send over are irrelevant, even if they have been adequately scored and qualified.

Don’t get us wrong, the last thing we want is to take away from your attribution report. We’re not blaming your marketing team.

Someone might be feeding your teams the wrong definition of “qualified.”

Whoever is responsible for setting your qualification parameters needs to consider the fundamental changes in modern selling.

The current sales process causes this disconnect, the new focus on product-led growth (PLG), and the modern buyers’ complex journey.

You simply can’t use classic lead scoring parameters like form submissions and email engagement to measure the total SaaS customer lifecycle, regardless of your marketing effectiveness.

New selling developments have brought many traditional techniques and strategies up to sailing speed, but lead scoring has sadly been left behind.

Hooking that individual customer is a fantastic moment, but what about account scoring?

You need an appreciation of your users’ entire journeys with you and your product.

What are the limits of lead scoring?

A recent Forrester study found that just 40% of sales reps source value from lead scoring, despite 68% of B2B companies’ reliance on predictive scoring.

We promised not to hate on marketing, but a perceived source for this failure is the flaws in many marketing departments’ methods.

Your typical marketing software employs a lead scoring system construed by set metrics that supposedly paint a clear picture of potential customers.

You've allegedly identified a lead if a prospect’s behavior aligns with your marketing automation tool’s definitions of intent.

These inbound marketing identifications are largely based on assumptions, even when you’ve codified and optimized your automated marketing tools.

Predictive lead scoring’s place as an oracle is limited to your marketing campaigns and B2B businesses’ internal definitions.

It’s almost impossible to prove that your tactics and tools effectively convert unqualified leads. Here’s why:

These useless qualified leads, and undetected, potentially hot leads will often be mutually ignored by your sales teams.

Who are you supposed to convert, then, and how?

Any high-value prospects interacting with your services and sites have particular needs, producing a baffling plethora of buying signals.

We’re not sorry that the days of blanketing thousands of leads with stringent expectations are over. It’s simply detrimental to test your lead scoring precision continually.

It’s a lot of unsustainable work

Any content marketing veteran will tell you how laborious lead scoring is.

Even with a well-integrated CRM system at hand, there are many nuances to accurately spotting high-quality leads.

Account-based marketing, a process that relies heavily on lead scoring, requires the engagement of various decision-makers in a business. You need to win over every organizational shot-caller through exhaustive potential customer interviews.

If you use CRMs for lead scoring, you probably rely on automation to do the heavy lifting to calculate engagement scores.

Automation tools only score leads rewardingly if you’re feeding them the correct CRM data on a regular basis.

Modern selling relies on enriched and real-time data, so these automated tools often become redundant or plain misleading. It’s also difficult to set accurate predictive score algorithms if you’re limited to email addresses or company size.

These fundamental details don’t establish buying intent.

You need a selective application of machine learning focusing on enriched and usage data. Algorithms only yield accurate results when their parameters and sales metrics are 100% correct.

Worse, every time you think you’ve perfected your static lead scoring formulae, a new scoring rule comes along months later, making all your hard work redundant.

Scoring models also need to adapt to individual customer journeys and needs.

You need a dynamic system that can be quickly edited to turn the tide. One that focuses on the metrics impacting the customer journey.

Your lead scoring creation must evolve alongside your buyer personas’ needs.

How do you even score a non-sequential buying journey?

Anyone of a certain age will fondly remember how simple sales cycles were.

Vendors would be approached by buyers through classic email marketing tactics, discuss their needs with sales departments, and finalize the sales tasks with decision-makers.

Marketing teams ushered potential prospects toward sales leaders after a few email interactions. Sales reps engaged them and closed a mutually pleasing deal.

As sales productivity increasingly intertwined with the digital world, the buyer journey (and general online behavior) became convoluted.

According to Revenue Operations Consultant Sebastien van Heyningen, there are many conversations about your product you aren’t aware of. Professionals could be recommending you in private LinkedIn messages or Slack channels.

These positive communications are difficult to track even with a powerful PLG tech stack. Heyningen emphasizes the importance of a developed scoring system.

Products are rarely purchased today without adequate due diligence.

A prospect rarely has their credit card handy when they visit your website and browse through your services, even if they might try your free trial.

Sure, you can score such events, but you’ll lose points from the many potential intent indicators that are harder to track.

This person of interest might spend even more time poring over review sites. Established buyers reach out to thought leaders through their social networks and other communication channels for advice.

What you might be tempted to write off as low-quality leads conduct rigorous research and consider the latest sales trends long before reading your latest blog post.

When users can self-serve a product for the entire company, your outreach efforts need revamping, especially your automated scoring tools.

Your benchmarks need to focus on product usage at a granular level. Behavioral lead scoring highlights popular features and their use.

When a user is committed to your product to the point that they’re prepared for an upgrade, they become a viable land and expand strategy candidate, as well as a product-qualified lead (PQL).

The shift from MQLs to PQLs

PQLs can generally be found in your freemium or demo services, eager to weigh anchor. Unlike MQLs, which we’ve established are a more abstract type of lead, you can readily show a PQL’s intentions.

As many SaaS startups focus on product-led growth (PLG) models, PQLs are naturally far more valued than MQLs. PQLs are often on the brink of converting from users into customers.

When an SDR reaches out to a PQL, there’s a good chance they’re pretty far down the sales funnel. That is provided that you’ve defined what a PQL is in the first place.

Identifying your PQL criteria

PQLs might be high-value individuals with a satisfying conversion rate, but defining them according to your benchmarks can be tricky.

If you aren’t identifying PQLs according to actionable metrics, they could become as abstract as MQLs. You must define the behaviors tied to conversions or upgrades.

We know your product will continue developing, as will its structure and usage. Another aspect of your business that should stay flexible are your value metrics.

A few examples of key value metrics include:

Once you’ve identified your value metrics, it’s easier to nail the significant user events within your product’s key offerings. This understanding brings your sales and marketing efforts closer together.

A professional synchronization makes converting free users much more straightforward and simplifies your experience of user pain points.

Several product behaviors demonstrate buying intent, such as:

This product data can be adequately managed and analyzed using essential tools like Amplitude or Mixpanel.

Building customer reports and measuring user engagement and retention are essential to creating PQLs and tracking the customer journey.

Remember that many of your free or demo users will often sign up with minimal firmographic attributes beyond a work email address.

We’d then strongly recommend that you use Clearbit to ensure your accounts are as enriched as possible, regardless of how cagey your customers are with your contact forms.

The customer journey needs more guidance

Traditional customer journey scoring methods are geared toward tracking predefined steps. The sales funnel or flywheel are readily appreciated and tracked, but they don’t reveal the actual customer journey.

We know many buyers don’t follow a predictable path through the buying cycle.

You may have identified your key value metrics and PQLs, but remember that you also need a contextual appreciation of the total customer journey.

This loose cannon approach to scoring reveals a poor familiarity with the buyer journey and, worse, “a lack of empathy.”

Focus on the right accounts

We decided at Breyta to remove the limits and blindspots of lead scoring and focus more on account scoring. The user data you work hard to collect and optimize should empower your selling and customer retention rather than confuse it.

Breyta lets you put as much weight on your determined benchmarks as needed. Now you control your lead scoring and can determine your personalized PQLs.

We wanted to create a system that evaluates the entire customer journey, well beyond the top of the funnel. Once you’ve integrated your tech stack and the proper user data with Breyta, we can score and rank your ICP (ideal customer profile) list members using our customer fit scores.

You decide which fields, metrics, and values you wish to prioritize and combine, all fed by enriched data from an integrative tool like Clearbit.

We want you to weigh your leads appropriately.

Once you’re happy with your metrics, Breyta automatically creates your ideal PQL list, specifically focusing on those with high buying intent. Our PQLs also consider user engagement based on your selected and most valued events.

We’re facilitating the natural evolution of a PQL. Breyta considers your most engaged and best-fitted accounts and adds particular attributes and events like signups to create a signal list.

The sustainable scoring model

We’re not too prescriptive about how you qualify your leads, but we are sticklers for focusing on potentially valuable opportunities.

According to Sebastien van Heyningen, SDRs should be wary of curious prospects. Anyone that simply wants to “look at your product” could easily do that via G2 or YouTube.

You should only qualify a lead when you know where they stand. This positioning becomes clearer when you have your industry benchmarks at hand.

Even if a lead turns cold, you should be proud that you made a difference in their professional life.

You must take control of the entire customer journey, lead scoring included. You are, after all, the one that will be using this model, and need to know it inside and out.

We’re tired of seeing teams wasting their time and resources using outdated models to chase complicated customers. Breyta provides a real-time tracking platform where every one of your tools works in tandem.

You’re now ready to score your potential and existing customers accurately. This precision lets you rank accounts according to your most valuable opportunities.

You’ll have an actionable oversight of the customer journey. A synchronized and targeted approach qualifies your leads properly and enables evergreen customer satisfaction.