Most people think about the B2B growth mindset based on unachievable things.
Most people believe that the CRM is perfect; the attribution tool captures every interaction someone made with the product; and that the sales and marketing teams work perfectly together.
Unfortunately, reality is much darker than the theory.
For example, the sales rep forgets to log their call, the automation platform sent an email to the wrong list, and the buyer spent months researching on different channels but did not use any of that research during the purchase process.
When you have large revenue targets (e.g., doubling revenue) and you're attempting to grow using only a vendor's "best practices," you're on your way to losing pipeline.
B2B businesses need a method that is built for the "messy middle" of execution.
A method that will work, even if your data is small and fragmented, when your customer's buying cycle is very long, and when you don't have all the information you need.
To help B2B companies be more successful, The Operational Model provides the framework to help navigate the challenges of:
Aligning RevOps with Demand Generation
Knowing When to Push or Pull Back on Your Budgets
Knowing What to Measure and How to Act on Those Measurements
The Operational Model is designed to:
Create Local Signals for Measurement and Action
Provide a Clear Decision Matrix for Measurement and Action
Place Importance on First-Party Data vs. Renting Audiences
Ensure Alignment Between Marketing and Closed-Won Revenue
The most critical component of the process is defining friction-based thresholds, which will clarify both when your data is good enough to act on and when simply scaling will increase your inefficiencies.
When you hit a wall in your pipeline velocity, you need a clear structure to support you.
The biggest problem with standard data-driven marketing frameworks
Searching for frameworks for B2B growth will show you a large number of results that come from software-as-a-service (SaaS) providers and high-level thought leaders.
However, most of these solutions do not translate well into the day-to-day lives of marketers who are trying to manage their customer acquisition process.
The common mistake of these frameworks is that they put too much emphasis on the technology stack and do not address the operational realities of human decision-making.
They talk about using a single source of truth for all of your data, but they provide no tools to help marketing operations teams deal with the chaos of duplicate accounts and broken APIs.
Many sources will provide you with heavily fictionalized examples, such as a fictitious software company that doubled its lead volume through the use of AI personalization.
This example does a disservice to the actual process of defining and agreeing upon lead scoring thresholds with a wonky sales director over months of arduous, back-and-forth discussions.
When it comes to building data-driven marketing strategies, many marketing leaders rely on building dashboards instead of developing methodologies to interpret dashboard data.
Unfortunately, dashboards do not make decisions; they merely display data.
Without an established methodology to interpret the data from dashboards, when the organisation is under pressure, marketing leaders often rely on their own gut feelings.
Step 1: Establish baseline CRM hygiene and data governance
You cannot scale a business that does not have a solid foundation.
Before spending more money on online advertising or growing your sales development representative (SDR) team, you must first understand the current state of your customer data system.
Many of the larger B2B companies in the middle-market and enterprise space use a technology stack that has been built up over time through a number of different acquisitions rather than by a single design.
The marketing automation system will integrate with the CRM, but the way that the information is captured in the two systems does not match up accurately because there are too many custom fields.
The sales engagement platform will change the status of a lead to another state when that lead has been marked as an MQL.
The webinar tool may dump a pile of unformatted data into the system which will result in thousands of duplicate lead records being created.
When there is fragmentation in the data, the trust in that data goes away.
In the example of marketing saying that the marketing campaign generated $2 million in pipeline and sales leadership looking at Salesforce and seeing that it was only $1 million in pipeline, a fight will ensue over the discrepancy and result in no one being able to ship anything.
The process of conducting a detailed audit requires that your sales and marketing teams know where the lead object has travelled from when it was first created to when it has been converted into a won deal.
You must identify where the data dropped off the tracking path, where the UTM parameters have been lost, and where the tracking of leads has been broken due to the manual entry of lead status by the SDR.
Setting a minimum viable truth for your data
Achieving 100% pure data is not a realistic goal.
The objective of this step is to establish a Minimum Viable Truth (MVT).
The MVT should represent a common agreement between marketing and sales regarding what is considered to be valid data.
What defines an active pipeline opportunity? At what precise point does an MQL become an SQL?
According to the MVT definition, a lead that sits in the status of "contact attempted" for 90 days must be recycled (i.e., the lead should be treated as a new lead).
By defining the limits of the MVT, the sales and marketing teams are able to diagnose the effectiveness of their go-to-market approach and no longer be concerned about the accuracy of a dashboard used for measuring the effectiveness of the go-to-market efforts.
Addressing attribution that is incomplete
The buying journey for a B2B company is often a very complex process.
A potential customer may first listen to a podcast, see a LinkedIn post, and search for this brand three weeks later, finally purchasing a product through an advertisement they saw.
Most of the time the software will identify this as the branded search.
Recognizing these limitations and challenges can greatly benefit an organization's Data Governance.
By combining the multi-touch attribution method of attribution through the use of software with the self-reported method of attribution (using questions like "How did you hear about us?" on forms used to express intent to purchase), a marketing organization can identify exactly what drives a company’s Pipeline.
Step 2: KPIs should be connected to pipeline velocity and revenue
Vanity and the idea of "volume" do not help scale an organization.
Click-through rates, impressions, and form submits mean nothing if they don’t produce revenue.
However, when faced with pressure, Marketing organizations will likely revert back to measuring Volume at Top of Funnel.
Release the emotional attachment to value engagement
If you want to be extremely successful with scaling, stop putting so much emphasis on achieving cheap conversions.
For example, a campaign that results in 500 downloads of an eBook for $15 cost per acquisition is a failure if none of those 500 downloads generated a qualified sales conversation.
Conversely, a targeted account-based marketing (ABM) effort that generates five meetings at $2,000 CPA should be valued greatly as three of those meetings end up being in the late-stage pipeline.
The framework overall should focus on Deep Funnel (Metrics).
Track your conversion rate from SQL to Sales Qualified Opportunity. Track win rates by Lead Source. Calculate Average Contract Value against Customer Acquisition Cost (CAC) on each marketing channel.
Assessing the cost of inaction
When transitioning to KPIs based on revenue, teams typically face backlash from executives who expect to see graphs where volume/time increases (e.g. "website traffic") on a y-axis & "up" on x-axis.
Therefore, education is necessary.
One way to help them understand what is happening is to demonstrate the cost of inaction.
By educating leadership about how optimizing for "cheap leads" is clogging the SDR calendars with too many unqualified opportunities (calls), reducing morale and wasting substantial resources on F/T employees.
When KPIs are tied directly to Pipeline Velocity (how fast qualified sales opportunities close), this means marketing owns quality of the pipeline in addition to quantity.
The importance of win/loss analysis
When scaling with data, it is important to understand why it is occurring (“how” it is happening) by gaining access to qualitative context.
Win/Loss interviews with prospective buyers should be conducted regularly.
In the event that a significant amount of deals from the same paid social channel are lost due to "budget constraints", it is obvious that marketing is targeting the wrong audience and sending messages to the wrong people.
These are often the end-users rather than the economic buyers who will ultimately determine whether or not a deal closes.
Step 3: First-party data collection & intent mapping
By the year 2026, businesses that depend on third-party data/keyword volume are going to be losing propositions.
Privacy issues and poor browser tracking are causing third-party tracking capabilities to diminish.
The most valuable asset to a B2B organization will be the company's owned first-party data.
How AI-assisted behavioral scoring works
New-age intent mapping provides insights (in much more detail than just tracking page views).
It is done by taking a closer look at all of the behavioral signals across the company's own ecosystem.
How much time did the prospect view the video of a specific product? Do not their internal colleagues, or did they attend the highly technical web seminar and ask a question about integration with API.
Machine learning and artificial intelligence tools provide a means of bringing together these disparate signals and converting them into a single intent score.
Rather than waiting for a potential customer to reach a static score of 100 points, AI models are capable of identifying large sudden increases in velocity.
For instance, if an entire buying committee from a targeted account started to consume implementation documentation aggressively within a 48-hour time frame, the system should alert the enterprise sales team as soon as possible with a high-priority flag.
Closing the gap between marketing and sales
Intent data will not be effective unless it is used operationally.
Many marketing departments will collect intent signals and only send them over the fence to sales when they meet the strict criteria of being a marketing qualified lead (MQL).
Top scaling teams integrate intent directly into their Sales Development Representative (SDR) playbooks and processes.
When a rep accesses an account page on their CRM, they should see the exact behavioral signals that caused the account to be flagged.
Sales plays should be designed around intent signals.
If there is a spike in activity around an account on topics related to "legacy system migration", the SDR should not send a generic check-in email.
Instead, the SDR should follow a targeted outreach sequence that will provide an architectural teardown of typical failure points during migration.
The move to de-anonymized sales
Much of the traffic generated by B2B websites remains anonymous.
Advanced scaling frameworks can use IP resolution and identity graphs to de-anonymize the traffic at an account level.
Although you might not have a complete idea of who is visiting your SOC2 compliance page at Acme Corp, the fact that Acme Corp is performing extensive due diligence on security solutions allows your marketing team to identify and target the members of Acme's buying group with specific ads through platforms like LinkedIn and connected TV.
Step 4: Building-out the scaling decision matrix
The Scaling Decision Matrix is the operational backbone of the system.
For most organisations, failure to adopt a structured approach to allocating resources will lead to them scaling output based on existing budget availability instead of the proven unit economics.
The Scaling Decision Matrix describes the rules by which campaigns/channels should be scaled (i.e., when a campaign/channel deserves additional budget); optimised (i.e., when scaling must occur); and removed (i.e., ruthlessly remove those campaigns/channels that do not generate revenue).
When to scale budgets
Scalable budgets should only be established once a campaign/channel has proven through direct revenue attribution and has met certain, pre-established, measurable conditions.
Simply producing a low CPA for top-of-funnel campaigns does not necessarily make that campaign/channel a candidate to be scaled.
Budgets can be scaled once the campaign/channel meets the pre-established ratio of Lifetime Value vs. Customer Acquisition Cost (CAC).
For a B2B SaaS to scale, it typically requires that its ratio be between one-to-three (1:3) or greater, and that the channel have an acceptable level of pipeline velocity.
Leads converting to opportunities within an acceptable timeframe for the sales cycle show the campaign/channel is functioning and getting the desired result.
The second most important metric for scaling is the "Opp Creation Rate."
If you increase spend by 20%, will your opportunity creation rate increase in proportion to your spending? Or will there be severe diminishing returns?
Scale-up slowly, and measure the elasticity of each stage of the campaign's performance.
When to pause and reassess
When a product’s top-of-funnel metrics look good but the bottom-of-funnel revenue is not coming in, the product is in “pause” mode.
This is where marketers get into trouble.
Marketers assume success based on the high volume and low-cost leads they generated, even though the lead count stalls once they reach the Discovery Call stage.
This gives the impression they are being productive while actually impairing the efficiency of sales.
Immediately stop all budget growth for any channel that reaches this level. The next step is to diagnose.
Are the SDRs failing to follow up properly? Does the ad copy lead to the opportunity but the product cannot deliver? Are the lead routing systems functioning incorrectly?
Do not renew your growth efforts until the conversion rates from SQL to Closed-Won return to the norm.
When to terminate a channel
Kill thresholds should be established before launching any campaign.
If you are repeatedly unsuccessful in creating pipeline from a given channel for an acceptable CAC and have exhausted all possible ways to make that channel work, then terminate the channel.
The sunk cost fallacy is a common problem within marketing teams.
Teams devote months to developing a complex outbound sequence or invest heavily in a content syndication program, and they cannot bring themselves to terminate that investment because they have already invested the time.
If your analysis shows that it is not economically feasible for the channel to operate within your specific B2B sales cycle, terminate it immediately. Use your budget for push channels instead.
Step 5: Data-driven optimization and feedback loops
Scaling with data is a continuous iterative cycle, not a quarterly process.
There will be changes in the market, new features released by competitors, and shifts in what buyers want.
A static model will decay.
AI for personalizing conversations
When AI optimizes content in real time, it goes from a term you hear often (buzzword) to something you can strategically leverage (tactical advantage).
If you have captured enough quality first-party data (i.e., demographic information about website visitors through sign-ups or registrations), you can use this data along with AI to create a customized digital experience.
This is based on what company a visitor works for, their job title (firmographic profile), and how they've interacted with your business (behavioural history).
For example, the homepage of a company will be tailored on different levels based on who is visiting that page.
A company's Chief Technology Officer (CTO) shouldn't see the same messaging or call-to-action as a mid-level manager of a small business when they visit your website.
Using dynamic personalization, you can create customized experiences for individual website visitors in real-time by creating different headlines, using different case studies, and providing different calls-to-action based on visitors' behaviours in real-time.
Dynamic personalization helps eliminate any roadblocks in the buying journey for the customer and dramatically increases conversion rates for accounts that represent the greatest value to your business.
Looking at closed-won business deals
The best source of feedback for B2B marketers comes from the end-of-funnel stage.
In the closing phase, it's critical for both marketing and RevOps to examine all closed-won business deals systematically.
Start by looking backward through the complete buying journey to find the first point of contact with your business, and work down the funnel to determine which content helped in the middle of the buying process.
Even more important is to find out exactly what was said during the final sales presentation to understand what message resonated with the customer to help close the deal.
The information you gather from examining the first touchpoint, the content that helped accelerate the deal, and the messaging used in the final sales presentation can all be fed back into both Step 1 and Step 2 of the above process to help marketers improve their strategies in reaching prospective customers.
If, for example, you see a pattern that indicates that your largest accounts by annual contract value (ACV) all consumed highly technical whitepapers prior to requesting a demo of your product, you should ensure that this whitepaper is a primary focus of your top-of-funnel paid strategy.
Avoiding algorithm fatigue
Paid advertising channels are susceptible to algorithm fatigue over time.
This occurs when the ads creatives become worn out or stale, and you have saturated your audience with too many ads for too long.
Real-time optimization will require constant creative testing to provide the algorithms with fresh data to use for optimization.
Advertisers should implement a formalized testing plan for each of the components of an ad to continuously test different versions of ad copy, designs for landing pages, and subject lines of email communication.
You cannot rely on the same collection of assets (hero asset) to keep your scaling process going.
Create an operational system of new, well-researched creative content that will replenish assets as soon as they fail to deliver on their expected results.
5 Steps to building a data-driven business to business scaling within 30 days
If a plan does not include a timeline to achieve success through implementation, then the plan fails.
To achieve this level of operational integrity will require strict adherance to a phased change management approach, and cannot be done in one week without overwhelming or destroying the organization.
Weeks one & two: Audit and align
For the first fourteen days, all focus must be placed on identifying the present state of the system.
Put a halt to any new budget increases.
Create a meeting of the marketing operations team, demand generation team, and sales leadership to define and create a detailed map of the lead lifecycle.
Identify every broken integration, unfilled CRM fields, and all disconnected attribution parameters.
Create the Minimum Viable Truth document.
Have a brutally honest discussion about the real definition of a qualified opportunity.
If, for example, the sales team has the opinion that the marketing team provides unqualified leads, the sales team must be able to provide definitive data that supports their claim.
Conversely, if the marketing team believes the sales team is ignoring good leads, the marketing team should be able to verify this by reviewing the call logs.
The focus of this exercise must be to remove emotions from the conversation and replace with operational evidence.
Weeks three and four: Construct the matrix and execute
Once the data is stable and definitions have been established, it is time to create the Scaling Decision Matrix.
Audit historical channel performance on all active channels and identify current average cost of acquisition (CAC) and lifetime value (LTV) of customers and pipeline velocity metrics.
Establish accurate thresholds for the movement, pausing or discontinuance of all marketing campaigns.
Once the rules of engagement have been documented, testing can commence.
Apply this framework to the highest priority channel (typically a paid search or LinkedIn) for the first testing phase.
To effectively monitor the flow of leads in your new, strictly-governed lead generation process, you should monitor how well the aligned SDR team are managing the signals from intent-based selling.
Once you have had success in this initial pilot, then only can you scale the full go-to-market motion across your entire organization.
Final recommendation: No more gut feeling
B2B Enterprise Sales is, by nature, a complex and risky business, due to the amount of capital invested into producing revenue and the lengthy sales cycle between producing revenue and receiving payment.
Therefore, the impact of a single misstep can have a significant impact on your revenue targets.
Many companies will fail simply because they fail to predict what will happen after the sale.
The time for relying on generic marketing templates and executive gut feelings is over.
Scaling your business depends on a well-defined operational framework to deal with the complexity and uncertainty of enterprise market data.
By leveraging data governance, connecting actions to closed-won revenue, operationalizing intent signals, and implementing a disciplined decision matrix, your revenue teams will go from guessing to knowing.
This eliminates the emotional aspects of determining where to allocate your marketing budget.
When you let the data dictate your sales strategy, you convert your hope for growth into a mathematical certainty of growth through scalable success.
Build your infrastructure suitably, understand the limitations in your sales pipeline, and allow your matrix to guide your future decision-making.
Frequently Asked Questions (FAQs)
What do you do for B2B companies that have long sales cycle?
Long Sales Cycles break the immediate feedback loop.
For instance, when you run a Google Ads Campaign you cannot afford to wait nine months to determine if you have achieved success.
For this reason, the framework has been built on optimizing for Leading Indicators (Pipeline Velocity/SQL-to-SQO Conversion).
By documenting historical benchmarks for the average speed at which high-quality leads flow through early stage progression, you can identify potential revenue impacts over long term without having to wait until the final closed-won designation is assigned.
What happens if your attribution software doesn't provide complete visibility?
Attribution software never provides full visibility.
There will always be gaps due to dark social activity, word of mouth, and the multi-device journey that all consumers face.
The framework consists of a combination of the attribution software and self-reported sources (mandatory "where did you hear about us?" fields on high value forms).
The framework also moves away from focusing exclusively on "first touch" measurements to measure total pipeline generation across all channels and how much each channel cost the organization.
It acknowledges that some high intent channels hide the actual source of awareness for the customer.
How much of my marketing budget will I need in order to test this method?
There are no budget constraints with this framework; it's based on operational logic.
Your monthly budget could be as low as $10k or as high as $1M; all of the principles/guidelines outlined in the Decision Matrix apply equally to both sizes of budgets.
Smaller budgets will just require longer time frames to reach statistically significant results before deciding whether to "push" or "kill" a program.
Smaller budgets will gain the most from using this discipline since there is no margin for error with a small budget.
Can this method replace traditional demand gen and SEO models?
This framework does not replace either of the two; it provides a governance structure for both of them.
Traditional Demand Gen provides the tactics—creating content, optimizing websites, running paid media, and hosting events—while the framework outlines the operational operations system that powers those tactics.
It is the tool that allows you to ensure that your SEO strategy focuses on the terms that drive the pipeline and not just website traffic and to determine, based on actual revenue data level, when to increase the funding for Demand Gen Programs.