The principles behind that connection are deeply specific to your domain:

• How to frame a proposal around the outcomes that matter to that specific client?

• Which offering to lead with depending on what a particular prospect is going through?

• Which accounts would genuinely benefit from your positioning and unique selling proposition that your competitors don’t have?

All the companies I work with already have a ton of this knowledge.

The problem is it lives with a few experienced people and doesn’t scale.

It works on selective accounts, but it means growth is tied to their capacity and doesn’t survive turnover. Every account that gets their attention benefits from that depth. Every account that doesn’t – get a generic treatment, or doesn’t get reached at all.

That “depth on a few accounts vs generic treatment” trade-off has held because the processes available couldn’t scale what’s specific to your company.

Playbooks still rely on people who understand how to execute them.

Off-the-shelf AI tools can personalise based on LinkedIn posts, but determining why one of your offerings matters for a specific prospect’s situation and building a bespoke narrative around it requires something different.

What’s changed is that you can now capture how your people think — their judgment, their pattern recognition, the reasoning behind their decisions — into datasets and task-specific context specific enough for AI systems to apply that logic across your full market.

Over the last two years I've been focusing on building systems that capture and scale the expertise that makes companies unique — for my own consultancy and with clients.

What follows in this post is what I’ve learned about what this process actually looks like, where most companies should start, and what it takes to get to a system that delivers the same depth as your best people would bring if they had the time.

NB: Here we don’t talk about AI replacing your ideation or creativity, nor do I try to teach you about your domain — it's about scaling what you already know and giving the same depth to accounts you can't currently reach.

The process of making domain expertise operational has three layers.

1. The first is the knowledge your people have about your market.

2. The second is capturing and structuring that knowledge into structured databases with your strategy and task-specific rules and context that are specific enough to be actionable.

3. The third is the workflows that apply those rules to produce actual outputs — account research, qualification, narrative — across your full market.

Most of the work — and most of what determines whether this succeeds or fails — sits in the second layer. The workflows are the visible output, but they’re only as good as the knowledge system underneath them.

Strategy Alignment and Capturing Your Knowledge

The first step is always about identifying which process to build first.

The right first workflow is the process that sits at the intersection of volume, added value, and required domain expertise — where you need a high volume of outputs and where domain judgment is most important to your sales funnel and GTM motion.

For example, if your offerings can target different teams within an organisation, your people might spend hours reading through LinkedIn profiles, drawing account maps with proper decision-making units, and segmenting which messaging applies to which stakeholder. That’s a high scale, high value-add process.

Or if the starting point of your sales funnel is thought leadership events and your team crafts bespoke narratives about why this specific person from this specific company is a great fit for a particular panel — so the invitation doesn’t read like you’re contacting everyone — that’s another one.

Selecting the right first workflow — and the right sequence after it — is one of the most important factors in whether this succeeds.

The procedure and the actual questions to capture strategic datasets and task-specific process are highly dependent on the process you’re building, but the single framework we always apply at Contextual is reverse-engineering reasoning patterns — either from completing tasks manually with your team or from analysing historical data.

In practical terms, if your first workflow is building a compelling case for why a company should attend your thought leadership event, sit down with your team and go through researching 5-10 great fit companies and what makes them a “great fit” and how you would convey that fit in your outreach.
What you want to achieve is a step-by-step overview of the process where for each stage you have a breakdown of signals, their weight, and their relations to others.

What you need to capture isn’t a linear flowchart — it’s the implications behind each signal and their relationship to others.

One thing that consistently surprises our clients is how valuable this process is on its own — purely from an alignment perspective. We often see that people across the organisation think about the same things from different lenses, and one of the few ways to spot where you have drift at the strategy level is by breaking down processes at the operational level — where the strategy actually shows up in practice.

The second source of reasoning patterns is your historical data — CRM records, past proposals, won and lost accounts. But data is only as useful as the segmentation behind it.

A common example I hear from clients: “Let’s do a download of our CRM data to understand what job titles we should be focusing on in our outreach. We have 10k actual client contacts in the CRM, so the data should allow for rich analysis.” This is the kind of oversimplification that may seem more efficient since the data already exists, but it actually risks wasting months before you arrive at the right definition.

The problem is that unsegmented data compares apples with pears. Each of these axes, if not aligned, makes the comparison progressively worse:

When your data is segmented around each axis that matters to your process, it’s gold for analysis — without that segmentation, it produces more noise than insight.

I often think about this in the same way as incrementality tests in digital marketing — but no incrementality test works without volume of experimentation, and in high-value B2B relationships you rarely have that volume available for testing. You need to augment it with individual contextual analysis of your historical data.

A rich deep dive into 10 account relationships — with a detailed trail of “what were the causes of this” for each attribute of that relationship — is far more valuable than an unsegmented comparison of 1,000 accounts.

Operational Outputs

Aligning on the company strategy and capturing how you approach each step is the critical foundation — but if that data ends up in a document that someone still needs to read, interpret, and apply per account, it’s just a more detailed playbook.

The shift is when that structured knowledge stops being a reference and starts powering workflows that produce operational outputs.

The easiest way to think about workflows is a set of automations, data pipelines, and agent-optimised rule files that enable production of actual outputs.

There’s a number of automation, data-orchestration, and agentic tools out there, and when we work with our clients on selecting the right infrastructure, we always start with the existing process. Some questions you can ask your team:

• What’s the existing tech stack for this process — especially, where does the data that enables it live?

• What parts of the process carry a lot of subjective rationale or are hard to quantify without extensive experiments — and require an additional QA layer or even human in the loop?

• What’s the desired output format and end-user experience? End-to-end research and narrative generation is a fundamentally different build from enriching contact data and uploading it to your CRM for your team to use in building touchpoints.

• What does the roadmap look like beyond this first workflow?

By the time you ask whether to use Clay, n8n, or Claude Code (or any other tool), you’ll have a much better understanding of what your current process looks like — what data sources and stages are involved — which will give you a better idea of the decision-making criteria for your case. A complete set of recommendations on when to select which tool is outside the scope of this post (you’ll find a ton of information online). What I’m trying to give you is frameworks and examples to help you navigate the process and streamline your approach.

To give you a sense of what this looks like in practice — one of my own workflows assesses company propositions and determines if and where domain expertise is structurally required for proactive motions. This is one workflow (one stage of the process), not the full pipeline.

Building it took rounds of distilling how I think about my market, sharpening the rules and creating task-specific context, and testing outputs against my own judgment. The goal was to reach a point where the workflow consistently produces the same strategic analysis I would produce if I sat down and did it manually for each company.

Here’s what that means in practice. Three companies in a similar space — creative and experience agencies — go through the same workflow.

One is an experience marketing agency with a single integrated service across 12+ unrelated sectors. The workflow determines that in their case domain expertise is required to construct why their specific expertise matters for each prospect’s situation, because every company thinks about this offering in their own ways.

Supported by the case studies, we know that a logistics company activating a sports sponsorship had different entry pain points from a tech company transforming their annual conference, even though both need the same core capability.

Another is a purpose-driven agency with five distinct service areas, from brand building to digital transformation, serving charities and commercial brands. Here the workflow identifies a different bottleneck: which of those five services to lead with depends entirely on what each prospect is going through. The domain expertise required to make that call is the capacity constraint.

A third is a full-service creative agency with a generalist offering. The workflow concludes that domain expertise isn’t structurally required for their growth. Any brand with advertising needs is their potential client, the pitch works uniformly, and the friction in their growth mainly sits in market awareness. That’s not a negative conclusion, it’s a useful one. Around 60% of the prospects I assess land here.

Here’s a short video walking through the full analysis from this stage and the output the workflow produces:

The point is that each answer reflects the same reasoning I would apply — which signals matter, what they suggest about this company’s situation, where the bottleneck actually sits, and what that means for how I’d approach them. This analysis isn’t based on firmographic data. It’s deeply specific to my offerings, my market positioning, and the criteria I’ve developed through years of working in this space.

And because the reasoning is captured, what flows downstream is strategic context that shapes everything after it.

This only becomes possible when you’ve done the work in the previous section — distilling how you think about a specific task, mapping the signals and their weights, building task-specific rules detailed enough that the workflow can make the same judgment calls they would. Without that foundation, you get inconsistent or unusable outputs. With it, you get analysis that your best people would recognise as their own thinking.

What Changes When This Is Working

When your market knowledge and domain expertise is encoded into workflows, every account in your addressable market gets the same depth of analysis and strategic framing that today only happens for the accounts your best people have time for. The accounts that currently get generic treatment — or don’t get reached at all — can now receive the same depth of thinking.

And because the reasoning is captured, not just the outputs, what your team learns feeds back into the system. A new insight about what signals matter, a shift in positioning, a new offering — these update the knowledge system rather than living in someone’s head until they remember to apply them.

But approach this with caution — none of the stages I’ve shared with you today is a weekend project, nor is it set-and-forget.

Building the foundational datasets and task-specific rules requires genuine strategic alignment so that you can capture decisions and reasoning precisely enough for workflows to replicate it. The work for capturing knowledge alone surfaces questions about your positioning and your market that many teams haven’t had to articulate precisely before.

What I’ve covered in this post is the process for a single workflow. What moves this further is when your strategic datasets and workflows become connected — so that an insight at one stage propagates across every related stage. That’s where the knowledge system starts functioning as a living reflection of how your company thinks. That’s what I’ll cover next.

If your team’s domain expertise stays locked in experienced heads, you’re choosing between depth that doesn’t scale and volume that doesn’t reflect what makes you effective.

Our work focuses on capturing domain expertise and building the systems that scale it. If you have clarity on your offerings and want to explore what a first workflow could look like for your specific situation — get in touch.

But preserving that knowledge and clarity in systematic proactive engagement is where companies with multiple strategic offerings hit a wall.

This post walks through what it takes to make this diagnostic messaging operational — how companies with multiple complex offerings build the logic that determines which one to lead with into bespoke AI workflows.

My own consultancy faces this exact challenge. I have three distinct service lines, each addressing a different problem for a different type of strategic service and solution provider.

I’ll demonstrate how this works in practice — showing how one account moves from raw research through diagnosis to a message that leads with the relevant offering.

Where and Why Generic Approaches Fail for Complex Offerings

For companies with multiple complex and distinct offerings, sending a template listing all service lines is basically asking clients to do your work while also losing control of the perception they make of you.

They used to click through to your website and self-diagnose, but with automated outreach that behaviour has declined.

From my work, outreach that asks prospects to connect the dots for you normally results in 5x lower engagement rate.

But this requires diagnosis — understanding enough about the prospect to determine which offering to lead with and why. Your senior people do this intuitively. But that judgment takes time and doesn’t transfer easily.

For companies with complex offerings where the right service or product depends on the prospect’s situation:

• Automated generic templates ask prospects to do the diagnosis for you — which dramatically increases the friction, while also increasing the chance of wrong perception.

• Handover to execution — junior team, VA, outsourced BD — increases volume but loses diagnostic depth. The issue is that without domain expertise, the person executing can’t recognise which patterns matter or which offering fits, resulting in the same results as automated static templates about the value prop.

• AI personalisation tools pull LinkedIn posts, company news, and podcast appearances into openers. This earns a moment of attention, but if prospect’s recent post or campaign doesn’t connect to their needs it doesn’t demonstrate you understand why your offering matters to their situation.

These approaches fail for the same reason: they can’t encode the diagnostic logic that determines account challenges specific to your services and which offering fits which situation.

That logic is domain-specific. It lives in experienced heads. And without it, any execution — human or AI — defaults to generic.

Demonstrating Diagnostic Messaging in Practice

Even for complex client analysis, LLMs are capable of delivering strategically aligned results — but they require extensive context about you as a company, and more importantly, guidance on the specific task you’re applying them for.

To be clear — this isn’t adding company information to ChatGPT memories or just writing longer prompts. For complex, knowledge-based tasks, context injection involves building purpose-built datasets and decision-making frameworks and rules for each stage of a process.

The best way of getting there based on what I’m seeing is splitting the work in two layers:

First, sharpening and capturing your strategy: what makes an account a genuine fit for each offering, what signals indicate which problem they’re likely facing, what determines which angle to lead with. This is strategy work — company-wide clarity on the offerings, the market, the patterns experienced BD people recognise intuitively, captured into structured context that guides the workflow.

Second, translating that logic into task-specific rules and context datasets or files to build workflows that preserve complex decision-making.

Before I reach out to any company, I need to answer a diagnostic question: which of my service lines — if any — addresses a genuine challenge they’re facing?

The companies I work with typically fall into one of three situations:

• They have a qualification problem — they know their ideal client profile but can’t identify those accounts systematically

• They have a messaging problem — they have multiple offerings and can’t determine the right angle for each account

• They have a timing problem — their services become relevant around specific-to-the-offering events, but they’re not catching those triggers

Each requires a different service line and different narrative. Generic outreach would list all three. Relevant outreach diagnoses which one matters — or recognises when none do, which is the case in around 85% of companies from a top-line “advertising services” Sales Navigator filter.

What Makes the Diagnosis Work

Achieving accurate AI-led diagnosis requires clear rules and priorities at each stage — what to look for, what to trust, what to validate before passing downstream, and what to explicitly avoid.

For my research stage, the rules ensure the relevant-to-my-offerings data is captured in a way that prevents downstream errors. In my case for determining GTM challenges it’s separating what the company can do from what their clients need, flagging gaps in the data and noting ambiguities. For your company and growth priorities, these rules and context will be very different.

For the qualification stage, the context encodes validation logic. It distinguishes between signals that have direct evidence and signals that are inferred. It also assigns confidence levels that constrain what downstream (i.e. narrative creation) stages can claim.

These rules and context came from my own work — learnings about what leads to accurate diagnosis and what leads to errors.

The two videos that follow show this process end-to-end for a single account. The first shows how research data becomes validated diagnosis — what the system looks for, what it trusts, what it flags. The second shows how that diagnosis becomes messaging inputs — the transformation from operational signals to strategic framing, including what the messaging is explicitly told not to say.

What This Enables

When the diagnosis is validated upstream, downstream narrative creation stages can produce accurate output without hallucination.

The next stage receives a diagnosis with explicit guidance: which signals to trust, which to treat with caution, what confidence level applies. It transforms this into messaging inputs — not by making strategic judgments, but by applying rules that have already been validated.

Following the example from the videos, it’s framing what prospects ARE rather than what they NEED — avoiding the circular logic or generic claims that make outreach sound like it was written by someone who doesn’t understand the space and their problem. The messaging reads as peer-to-peer, not sales pitch.

According to the defined rules, the messaging template doesn’t need to figure out which offering matters or what to emphasise.

The hard work — organising the process, building rules for strategic diagnosis, validation, transformation, constraints — happened before the message was written.

This precision — in diagnosis, in what to emphasise, in what to avoid — is what closes the engagement gap between generic outreach and messaging that demonstrates genuine understanding.

What Changes When Diagnostic Logic Becomes Operational

The shift isn’t about sending more emails or automating more touchpoints. It’s about where your senior BD people spend their time.

When diagnostic logic is encoded into workflows, the research and synthesis that used to consume senior hours happens systematically. The output isn’t generic templates with variables swapped — it’s messaging that reflects genuine understanding of why your offering matters to each account’s situation.

Senior time moves to what actually drives pipeline: applying insights, building relationships, converting conversations. The goal isn’t to replacing people. It’s to empower them to do more of the work that creates actual impact.

For complex and strategic offerings, this isn’t a straightforward implementation project. It’s not set-it-and-forget-it.

Building the context and frameworks that enable accurate diagnosis requires consultative work upfront — understanding the offerings deeply, identifying what signals actually indicate fit, encoding the validation logic that catches errors before they cascade. Establishing the foundation plus learnings from patterns on average takes at least a month.

And the system requires ongoing refinement as offerings evolve, markets shift, and new patterns emerge from outreach results.

But this investment unlocks significant value because it delivers results:

• Diagnostic depth applied at scale — 5x increase in engagement rate

• Messaging that’s accurate to the prospect’s situation, not hallucinated from generic patterns

• Senior capacity freed for relationship building that closes deals

If your team’s diagnostic logic stays locked in senior heads and manual processes, you’re choosing between strategic depth that doesn’t scale and execution that doesn’t convert.

My work focuses on translating domain expertise into systematic workflows. If you have clarity on which offerings fit which client situations and want to explore what a diagnostic workflow could look like for your specific positioning — get in touch.

Get in touch

But translating that strategic clarity into systematic business development is where most agencies hit a wall in their proactive growth initiatives.

When I start working with new BD teams, leaders are typically forced to pick: spray-and-pray outreach based on surface-level filters, or manual qualification for criteria that actually matter to their service offerings—spending hours to identify real targets.

For strategic partners, spray-and-pray outreach now generates 0.5%-1% positive reply rates. This doesn’t work when only 500 accounts across the globe would find your positioning as a competitive advantage and can become a key account.

At the same time, contacting the right stakeholders, at the right account, with the right message at the right time requires:

1. Domain expertise—meaning it can’t be outsourced

2. Time for strategic qualification

3. Continuous updates to ensure relevancy and the best way in

The bottleneck to generating engagement from your target accounts with proactive tactics isn’t that your team lacks strategic knowledge about who needs your services.

The bottleneck is translating this clarity into operational workflows and applying them systematically across hundreds of accounts.

This post walks through how to translate domain expertise into systematic qualification.

Using a creative effectiveness testing agency as the example, I’ll show how their qualification principles identified 14 targets with genuine need—out of a sample of 50 companies matching Sales Navigator filters—with specific angles to approach, increasing response rates to 20%.

The Operational Bottleneck

Let’s take an agency specialising in predictive creative testing using emotional response measurement. Their BD leaders know exactly what makes a company a good fit:

Strategic marketing behaviour: Balancing brand building with performance. Investing in emotional creative beyond pure direct response. Making campaign-based decisions where testing delivers ROI.

Organisational readiness: Analytics maturity, testing infrastructure, and marketing sophistication to adopt predictive methodology.

Market context for positioning: Recent campaigns to reference. Marketing strategy on brand building and creative effectiveness. Strategic priorities that create timing and entry points.

These qualification criteria are specific to this agency and don’t live in Sales Navigator. They require cross-referencing multiple data sources—ad creative execution, media buying patterns, organisational structure, campaign activity.

Their experienced BD people spend 2-4 hours per qualified account synthesising these sources to make confident qualification decisions.

The challenge: domain knowledge that lives in senior BD people’s heads needs to become operational and be applied across hundreds of accounts while maintaining decision making quality.

Based on 50 initial accounts, a systematic qualification workflow identified 14 priority brands with specific evidence and complex decision-making applied to each stage—in less time than manually qualifying 5 accounts.

Translating Domain Expertise into Systematic Qualification

The qualification layers that follow are specific to creative testing, but the methodology transfers to any agency domain where you know who genuinely needs your expertise. If your agency has strategic clarity about client fit—you can articulate what organisational readiness, behavioural signals, and market context indicate genuine need—this systematic approach can be applied.

The prerequisite isn’t special data access or AI tools, but confidence in what patterns matter in your domain.

Layer 1: Assessing Prerequisite Capabilities

The first judgment: does the company have baseline capabilities to adopt your methodology?

For this agency, that means organisational infrastructure—analytics roles, data science teams, and testing infrastructure. They review org charts and individual profiles to evaluate marketing maturity.

Making this assessment manually takes 20-30 minutes per qualified account.

This video shows how the entire layer—including scoring across analytics maturity, marketing infrastructure and nuanced decision-making specific to this agency—translates into a qualification workflow that evaluates accounts systematically.

Out of 50 accounts matching Sales Navigator filters [Industry: Retail; Size: 1,000-10,000 employees; HQ: UK/US], only 22 had baseline infrastructure for these services to be adoptable.

Layer 2: Detecting Behavioural Signals of Strategic Need

The second judgment: what behavioural signals reveal genuine strategic need for your services?

For this agency, that means understanding marketing sophistication through multiple data sources—Meta Ad Library for creative approach, media buying data for channel investment patterns, top-performing ads for production quality and messaging strategy.

Each source reveals different aspects of strategy. But based on their experience, checking one source alone creates misleading signals—companies that look like poor fits can be strong prospects, while high advertising spend doesn’t always indicate strategic sophistication.

Their BD people cross-reference these sources to answer strategic questions: Is low performance paired with sophistication elsewhere? Is high spend in brand building or promotions? Does creative execution match investment?

Cross-referencing data takes 1-2 hours per account manually. Synthesising to detect patterns and make qualification decisions takes another 30-45 minutes.

This video shows how multi-source analysis and nuanced judgement—checking each source independently, then synthesising to detect strategic patterns and contradictions—translates into a structured system that classifies companies based on their actual marketing behaviour.

Out of 22 organisationally-capable accounts, systematic behavioural synthesis identified 14 brands whose marketing behaviour indicated genuine need for creative testing services. Each classification came with specific evidence and reasoning.

Layer 3: Gathering Context for Personalised Positioning

The third judgment: what context enables personalised positioning for qualified accounts?

For this agency, that means company-specific research—recent campaigns to reference in outreach, leadership interviews revealing testing philosophy, press releases showing strategic priorities and timing signals.

Browsing AdWeek, Cannes Lions and LinkedIn and synthesising research manually takes 1-2 hours per qualified account. Let’s see how this bespoke intelligence can be operationalised.

Systematic research gathered this context for all 14 qualified brands—recent campaign details, leadership perspective on creative effectiveness, current strategic priorities. Qualification decisions became outreach-ready positioning.

The complete systematic workflow assessed 50 accounts in the time their team previously spent manually qualifying 5. Each of the 14 priority brands came with specific reasoning explaining strategic fit and context about their marketing approach.

Senior BD time shifted from cross-referencing data sources or sending generic templates to everyone to applying insights: crafting positioning for qualified accounts informed by evidence of why each company needs their expertise and what makes outreach timely.

From Spray-and-Pray to Strategic Engagement

The creative testing agency example demonstrates a fundamental shift in how BD operations can work.

When qualification becomes systematic, BD teams stop spending the majority of their time gathering and cross-referencing data. That time allocated to what actually drives pipeline: crafting positioning for qualified accounts that demonstrates understanding of their specific situation and building relationships.

The agency’s 20% response rates came from contacting fewer companies with better understanding of why those companies needed their specific expertise.

For your agency, the qualification layers will differ. The data sources you cross-reference will be specific to your domain. But if you can articulate what organisational readiness, behavioural signals, market context, and other criteria specific to your ICP indicate genuine need—those patterns can be systematised.

For some agencies, services become relevant around specific events—like M&A branding expertise becoming critical when organisations announce integration initiatives—so on top of strategic qualification you need to implement trigger monitoring workflows. If your domain works this way, read my other post about systematically catching strategic triggers that signal timing for engagement.

Does your BD team’s domain expertise stay locked in senior’s heads and manual processes, or does it get embedded into workflows that can be applied across your entire target market?

My work focuses on translating agency domain expertise into systematic qualification workflows. If you have clarity on companies that find your expertise as a competitive advantage and want to explore what a qualification workflow could look like for your specific domain—get in touch. I’d be happy to share thoughts on how your strategic knowledge can translated into operational BD process.

In the previous post, we established the foundation: defining bespoke signals that indicate genuine need for your expertise. That strategic clarity is the vital first step. But what ultimately matters is making this strategy operational—translating intent into systematic workflows that determine who your team targets and when.

This shift is increasingly important. Channel saturation and changing buyer behaviour make generic spray-and-pray outreach ineffective for complex services. Across the agencies I work with, strategic targeting and messaging moves reply rates from 1-2% (across all accounts) to 10-15% (for target accounts).

The bottleneck has always been that firmographic criteria (size, location, sector) are baselines, not indicators of genuine need. But with AI-powered qualification workflows built on criteria specific to your positioning, you can systematically move from 120 accounts matching generic filters to 15 high-priority accounts worth your resources—and transform what you can say to prove expertise at first touchpoint.

Today I’ll show you how this works through two agency examples: how strategic qualification determines who you target, and how upstream research enables messaging that demonstrates understanding of their specific challenges and proves your expertise before the call.

Catching and Analysing Strategic Signals → Triggers

Let me show you how this works in practice with an M&A and transition branding agency—comparing two approaches to the same market.

The agency serves organisations with complex multi-brand portfolios navigating corporate transitions—M&A, IPOs, carve-outs, spin-offs—in highly regulated industries like financial services, healthcare, and insurance.

Their challenge: Large companies exist continuously, but only need transition branding during specific corporate events. And only a fraction operate with the portfolio complexity and regulatory constraints that make brand architecture a board-level decision—not just a marketing exercise.

Approach 1: Spray-and-Pray

Starting point: Sales Navigator filters

• Companies with $1B+ revenue / 5,000+ employees

• Sector filters: Financial services, healthcare, insurance

These criteria are generic—they don’t reflect who genuinely needs sophisticated brand architecture.

The problem: Manual qualification for criteria that actually matter (multi-brand portfolio structure, M&A frequency, corp dev team sophistication) consumes senior BD time and can’t scale. High-volume lists include companies that might do M&A someday—but most aren’t in active transition, and some will never be in-market because they lack portfolio complexity. Messaging gets watered down to avoid sounding irrelevant.

The blind spot: Static filters can’t distinguish between a company with 248 acquisitions and a sophisticated 9-person corporate development team versus one with a single historical acquisition and no M&A function. Both pass the same criteria, but their transition branding needs—and purchase timing—are completely different.

Approach 2: Strategy-Led Qualification

After aligning internally on what makes transitions strategic versus operational for their positioning, this agency built a systematic qualification process.

Stage 1: Strategic Complexity Qualification

Starting with the same 120 companies from Sales Navigator, AI workflows analyse information from their org websites and third-party sources for criteria that actually indicate need for sophisticated brand architecture:

• Multi-brand portfolio structure (subsidiaries, not just service lines)

• International operations (cross-border regulatory complexity)

• Regulated industry presence (where brand integration faces compliance scrutiny)

Result: 46 priority accounts where corporate transitions would be strategic, board-level decisions—not simple rebrands.

Below is a demonstration workflow built in Clay (clay.com) showing how this qualification process works in practice. For clarity, I’ve focused on the core logic—production versions would include additional validation steps and data sources, but this captures the essential qualification framework.

Set the playback speed to 1.25x.

Stage 2: M&A Propensity Assessment

46 strategically complex accounts is better targeting. But the agency wants accounts that systematically execute complex transitions.

Enrichment from Crunchbase and LinkedIn assesses:

• Acquisition history (count and pattern)

• Corporate development organisation (team size, seniority, specialisation)

The differentiation:

One major insurer: 248 acquisitions + sophisticated 9-person corp dev team led by SVP = systematic acquirer (6/6 M&A propensity)

Another major insurer: 1 acquisition + no visible corp dev team = selective/opportunistic (1/6 M&A propensity)

Same industry. Similar scale. Massively different transition branding needs and purchase timing.

Result: 15 high-propensity accounts with proven M&A programs worth monitoring.

Set the playback speed to 1.25x.

Stage 3: Trigger Monitoring

For those 15 accounts, automated monitoring tracks M&A announcements—acquisitions, mergers, carve-outs, IPO filings. When a qualified trigger fires, the BD team receives company name, transaction details, timeline, and identified corporate development stakeholders (name, title, LinkedIn profile).

Set the playback speed to 1.25x.

The strategic shift: From “5,000 financial services companies over $1B revenue” to “15 companies with proven M&A programs and sophisticated brand architecture needs—here’s the corp dev leader to contact when they announce their next acquisition.”

The qualification doesn’t just improve targeting quality—it transforms what’s operationally possible. Systematically monitoring 46 strategically complex accounts for transaction triggers is realistic; manually tracking 5,000 companies for M&A news is impossible.

This is where strategic clarity becomes operational infrastructure. No research workflow adds value if built on principles misaligned with how the market perceives your expertise. But once that strategic layer is defined, systematic qualification stops being a bandwidth constraint and becomes a competitive advantage.

From Generic Templates to Strategic Messaging

Section 1 showed how strategic qualification identifies which accounts to target and when. Now let’s explore how that upstream qualification transforms messaging.

Think of a brand and design consultancy specialising in strategic brand transformation. Their ideal clients are legacy organisations repositioning for premium markets AND brands undergoing transformation moments across hospitality, automotive, healthcare, and retail.

Their challenge: Making their expertise relevant to each specific vertical and pain point. A hospitality group acquiring premium properties faces different brand architecture challenges than an automotive manufacturer transitioning to electric—even though both need strategic brand transformation.

Approach 1: The Generic Template

When uncertain about relevance, agencies hedge:

Hi [First name],
Hope you’re well.
We’re a strategic brand consultancy working with organisations across hospitality, automotive, and retail on brand transformation and visual identity systems.
Our work spans portfolio brand architecture to positioning strategies for organisations entering new markets or repositioning for different customer segments.
We’ve delivered projects for [Client A], [Client B], and [Client C], and I think there could be interesting opportunities to collaborate.
Would you be open to a 30-minute conversation to explore how we might support your team?
Best regards,

Vague service descriptions, multi-industry mentions without specificity, unrelated client name-drops. The “could be”, “might support”, “explore opportunities” language signals uncertainty about relevance.

\This isn’t a semantic or tone-of-voice issue—the need to hedge comes from genuine uncertainty about what’s relevant to the given account.

Approach 2: Qualification-Enabled Messaging

After implementing trigger monitoring and qualification, they target specific moments where their positioning creates advantage:

• Cities selected to host major international events

• Hospitality groups acquiring premium properties

• Automotive manufacturers transitioning product lines

• Heritage retailers repositioning from mass-market to premium

When a trigger fires, qualification data becomes the building blocks for specific messaging.

1. Specific diagnosis instead of generic claims

Generic: “Our work spans brand architecture to positioning strategies”

Qualified: “Saw the announcement about hosting [Event] in 18 months. Having worked with [similar host city], their main challenge was managing brand activation across franchise vs. direct-owned venues—the Games brand requirements were the same, but their franchise operators needed local flexibility that their corporate venues didn’t.

In your recent [annual report/acquisition announcement], you’ve got a similar mix with [X franchise properties] and [Y corporate venues] across [regions], and I thought how [similar city] approached this would be relevant.

I’ve attached how they structured the decision framework for what stays rigid vs. what flexes. If this resonates with where you’re heading, I’d be happy to share more details.”

The diagnosis combines trigger data (event announcement, timeline) with qualification insights (portfolio complexity, ownership structure, geographic scope). This proves understanding of why THIS organisation specifically needs sophisticated brand architecture—not just that an event is happening.

2. Vertical-specific proof instead of generic name-drops

Generic: “We’ve delivered projects for [Client A], [Client B], and [Client C]”

Qualified: “We worked with [similar host city] through [previous event]. Given your franchise/corporate venue mix, the approach they took on managing brand consistency across different ownership models might be directly relevant—particularly how they structured what stayed rigid versus what could flex locally.

The franchise operators got clear brand guardrails but enough flexibility to activate without constant approval loops. Your [Y corporate venues] would work differently—tighter control, direct rollout.

I’ve attached a breakdown of their decision framework for what elements were centrally controlled versus locally adapted. If the franchise/corporate balance is something you’re working through for [Event], happy to discuss how those principles would apply to your setup.”

Why this specific case study detail: The upstream qualification revealed their portfolio combines franchise properties with corporate venues. A different organisation—say, all corporate-owned venues across regulated markets—would receive different collateral: how [similar city] navigated regulatory approval variations while maintaining brand consistency. Same case study project, different attachment, based on what account qualification surfaced about their operational structure.

3. Value-add collateral instead of meeting requests

Generic: “Would you be open to a conversation to explore how we might support your team?”

Qualified: “I’ve attached how [similar host city] approached brand system design for [specific challenge relevant to your context]. Would it be useful to discuss how those principles apply to your timeline?”

The strategic shift: When qualification confirms which transformation moment and vertical-specific challenges apply, messaging proves understanding at first touchpoint. For agencies where project context varies dramatically, upstream qualification solves the portfolio problem: you know which expertise and proof point to lead with before outreach, not after a discovery call.

The qualification work—identifying triggers, researching context, selecting case studies—becomes systematic. BDs add their voice, but the research-to-message translation that normally consumes hours per account is now enabled by upstream qualification data.

The mechanics of translating qualification data into message elements—including where automation ends and BD’s craft begins—will be covered in the next post.

Making Strategic Qualification Operational

The examples in this post demonstrate what becomes possible when strategic intent translates into systematic workflows. But this shift from spray-and-pray to strategic, systematic prospecting requires real investment.

What This Requires

1. Strategic clarity and internal alignment

Who benefits most from our expertise? What signals indicate they need us? Which qualification criteria separate genuine fit from noise?

2. Learning to translate strategy into operational workflows

What databases contain the signals that matter for your services? How do you systematically monitor for triggers? How do you structure enrichment logic that reflects your strategic criteria? This requires building new operational capabilities.

3. Understanding data limitations

Some signals can’t be systematically caught. Some qualification criteria require human judgment. Testing reveals what can be automated versus what remains manual.

4. Tool evaluation and technical capability

Different tools fit different strategies. More advanced workflows require automation experience. Teams either build this capability internally or work with partners who understand agency positioning AND workflow design.

5. Maintaining strategic foundation

A workflow not grounded in clarity about who values your expertise will produce marginally better results than basic firmographic criteria. The sophistication is in the strategic logic, not the automation itself.

Why This Investment Matters

Generic outreach is not cutting through the noise. Strategic qualification at moments when accounts are in-market is becoming table stakes for proactive growth. The shift from 1-2% reply rates to 10-15% engagement—prospecting as resource drain versus growth engine—justifies the investment.

What’s Next in This Series

How do you integrate these workflows into your existing team structure and capabilities?

The next post explores this through agencies with different team sizes, technical capabilities, and GTM strategies. We’ll cover where existing BD skills apply, where new capabilities need building, and how to phase implementation based on your bandwidth.

This bespoke approach—building workflows around your strategy, team structure, and value network rather than adapting to generic sales tech—is what separates systematic strategic qualification from just adopting another tool.

For growth leaders ready to move beyond spray-and-pray: the foundation is strategic clarity on what signals prove your value. Everything else serves that strategic layer.

They light up when a brand’s shift to Gen-Z audiences aligns with your social/gaming expertise. Or they spot when a Shopify POS partnership creates integration opportunities for retail experience work.

But they can’t manually monitor for these signals across 200 target accounts while also managing pipeline, writing proposals, and running presentations.

This is the bandwidth constraint that caps BD effectiveness—not lack of domain expertise, not poor positioning, but the operational challenge of scaling what your best people already do.

When solving this, agencies typically hit two dead ends:

• Outsourcing to BD agencies* who lack your domain expertise. They can’t tell you why a “strong IP brand entering a gaming franchise” matters differently to your studio than to an e-com CGI competitor, often defaulting to volume-based, spray-and-pray outreach.

• Adopting one-size-fits-all tools built for SaaS GTM, not agency positioning. They track job changes and funding rounds—isolated generic signals that, when used alone, put you in a crowd of 20+ vendors competing on awareness, not relevance.

The old mantra “we just need to get them on a call and we can tailor our pitch” is breaking because buyers expect credible, relevant messaging already in the first touchpoint—they won’t research your website to figure out if you’re relevant (like people used to).

So the question is: how do you make signal-based prospecting systematic and scalable without losing the strategic nuance that makes it effective?

This post kicks off a series on how agency leaders can scale good prospecting whilst making it more effective and aligned with their in-house strategy and positioning.

Today we break down the foundation: what signals actually matter for agency GTM, how to qualify them against your specific positioning, and why this layer can’t be separated from your agency strategy.

*Note: When I refer to BD agencies here, I mean outsourcing cold outreach and top-of-funnel prospecting whilst treating these relationships as just “adding scale.” In other circumstances—like outreach following PR coverage—these partnerships can add significant value.

Why Signal Relevance Starts With Strategy Clarity

Signal-based workflows are a great place to start when you want breadth on discovery and improved targeting quality without necessarily changing your drafting process.

Most BD challenges for agencies are strategic translation problems.

Your agency has positioning. You know your differentiation, your proof points, the verticals where you win. But translating “we specialise in data-driven retail activations” into which accounts to target, when to reach out, and what angle to lead with requires a layer of strategic context that generic BD approaches can’t access.

Here’s where it breaks:

Isolated generic signals (new CMO, funding round, job posting) put you in awareness plays. Personalisation based on these signals may make prospects spend more time reading your email—even reply with a polite “no thank you”—but actual conversion rates stay low because the signal doesn’t indicate need for your specific services.

Relevant buying signals (e.g., a retailer launches a flagship refit with dwell-time and omni-channel share KPIs) let you enter as a domain expert responding to expressed need. You’re not pitching—you’re demonstrating you already understand their challenge and can help.

Here are some stats from my recent activations:

• Reply rates: 2-4% with generic signals vs. 10-15% with domain-specific signals

• Qualification: 15-20% of generic replies are worth pursuing vs. 60%+ with relevant signals

Your best BD people can identify these signals because they understand your positioning and differentiation deeply.

This strategic layer—knowing which signals prove your specific value—can’t be outsourced or automated away. But the manual monitoring, research, and qualification work that follows can be systematised.

That’s the unlock: clarity on what signals matter (strategy) + infrastructure to catch and act on them systematically (workflow).

Signal Spotting: The Foundation Layer

Buying signals surface constantly across industries. The question is which ones actually indicate relevance to your agency’s positioning.

In the agency context, I think of signals in two categories:

• Buying signals: The account is in-market for your services or your specialisation is newly relevant

• Personalisation signals: Allow 1-to-1 customisation (e.g., an agency wins an award and you mention it)

Personalisation improves open rates, but buying signals drive conversion. If you mention an award but your expertise isn’t relevant to the account’s current needs, you’re stuck at awareness.

Relevant buying signals let you prioritise accounts where your positioning creates advantage—and reach them when need is emerging, not after they’ve engaged other vendors.

Here’s what this looks like for different agency profiles:

1) Production Studios (VFX / CGI / Post)

Tier 1 — Differentiated

Signal: Brand announces a character-led campaign with explicit CG mentions

Where: Brand newsroom; agency PR; Adweek/shots

Why it fits: Direct CG/VFX need; continuity + scope control is your moat

Tier 2 — Contextual

Signal: Creative agency wins a new account in a vertical you’ve delivered heavily for

Where: Trades; agency LinkedIn; client PR

Why it fits: New agency-of-record needs reliable executional partners in that vertical; you have relevant, named proof

Tier 3 — Generic (requires qualification & stacking)

Signal: Spike in CGI artist job posts (no specific tech)

Where: LinkedIn Jobs; Mandy; Staff Me Up

Why it fits: Indicates upcoming CG volume, but broad on its own

2) Brand Experience Agency (Events / Retail)

Tier 1 — Differentiated

Signal: Flagship store refit announcement with explicit KPIs (app sign-ups, membership, dwell-time) that match your specialism

Where: Brand newsroom; earnings deck

Why it fits: KPI-driven brief aligns with your “big moment → measurable outcome” expertise

Tier 2 — Contextual

Signal: Partnership PR — Brand × Snap AR / Shopify POS / Niantic

Where: Partner press rooms; dev blogs

Why it fits: Integration challenge you’ve solved before (AR ↔ POS, tracking, analytics)

Tier 3 — Generic (requires qualification & stacking)

Signal: Participation in a major exhibition/trade-fair (e.g., Salone del Mobile)

Where: Event site; sponsor lists; brand PR

Why it fits: Implies content + experience needs

3) Brand / Strategy Agencies

Tier 1 — Differentiated

Signal: Market expansion or brand shift to Gen-Z where you have social/gaming expertise

Where: Brand newsroom; investor deck; product line updates

Why it fits: Clear service–market match; you bring relevant playbooks/case proof

Tier 2 — Contextual

Signal: End of current tenure of a competitor agency that’s different in USP & positioning

Where: Trades; procurement timelines; pitch gossip

Why it fits: Window for new partners; you can position as de-risked continuity + sharper POV

Tier 3 — Generic (requires qualification & stacking)

Signal: New CMO/CSO appointment

Where: LinkedIn; brand newsroom

Why it fits: Often signals narrative refresh; broad alone

Signal effectiveness is always tied to your agency strategy and how differentiated these signals are.

• Tier 1 signals are tightly connected to your core service differentiation—spotting them proves domain expertise

• Tier 3 signals are broad; the stakeholder has likely received 10 pitches this morning from various vendors

The combination of strategic clarity (what signals indicate need for our services) and workflow infrastructure (systematically catching and acting on them) is what moves you from competing on awareness to competing on relevance.

This strategic layer is why off-the-shelf BD tools built for SaaS don’t translate to agency GTM—they can’t encode what makes your positioning different.

From Signals to Qualified Opportunities: The Framework

A signal—even a strong Tier 1 signal—tells you there’s potential relevance. But BD decisions require more: Is this the right account? Is timing aligned? Should we prioritise internal resources for winning this one over other opportunities?

This is where qualification layers become critical. Multiple signals and qualification criteria compound confidence and help you separate high-priority targets from noise.

The Qualification Framework

Different inputs serve different purposes:

• Buying signals (Tier 1-3): Indicate potential need for your services

• Account qualification: Confirms fit with your ICP (budget/authority, sector, scale, value network)

• Timing indicators: Windows of opportunity (contract cycles, initiative launches, leadership changes)

• Receptiveness signals: Additional opportunities to prove expertise (e.g., someone you’ve worked with before)

Strong opportunities have multiple layers aligned.

Simplified Example: Brand Experience Agency

You specialise in retail activations with analytics and measurement capability.

Scenario A: Single Tier 3 Signal

• Signal: New VP of Retail Experience hired

• Decision: Monitor, possibly nurture long-term

• Priority: Low

Scenario B: Qualified Opportunity

• Buying signal (Tier 2): Brand announces Shopify POS partnership

• Account qualification: 50+ retail locations; matches ICP

• Timing: Tech investment suggests active initiative; current agency contract ending Q4

• Receptiveness: New VP from brand where you’ve worked; LinkedIn activity shows exploration

Decision: Immediate outreach with stakeholder-specific angle referencing POS integration, analytics-driven activations, and relevant proof from their previous brand.

Priority: Top tier

This is where strategic clarity becomes operational: Your agency’s positioning defines what “qualified” means. A qualified account for a VFX studio with Unreal Engine specialisation looks different from one focused on character design.

This strategic context—what signals matter, how to weight them, which accounts are genuinely in-market—can’t be outsourced. It requires your domain expertise: what’s worked historically, where your case studies create advantage, how your positioning differentiates you.

But once that strategic layer is defined, the systematic application of these criteria across hundreds of accounts can be workflow-enabled.

Building the System: Where Strategy Leads Tech

Signal-based prospecting breaks when agencies try to scale it manually or adopt tools that don’t account for their strategic context and established processes / channels.

Here’s the fundamental challenge: The tool can’t tell you what signals matter. You can automate monitoring for “new CMO appointments,” but if that signal doesn’t indicate need for your services, you’ve just systematised noise and you’re stuck competing on awareness with dozens of other vendors.

The Strategic Layer Comes First

Before investing in monitoring infrastructure or workflow tools, you need clarity on:

1. What does a Tier 1 signal look like for each of our service lines?

2. Which qualification criteria separate good-fit accounts from distractions?

3. How do we connect each signal type to our proven capabilities in messaging?

This strategic work requires your domain expertise—what’s worked historically, where your case studies create advantage, how your positioning differentiates against competitors, how your target clients prefer to buy.

Only when this strategic context is clear should you look into the tech that can make the process systematic and scalable. Without it, tools just add another drop in the sea of noise and erode your credibility.

Where Tools Fit

Tier 1 (Differentiated) signals often require custom setup, monitoring, and qualification because they’re unique to your positioning:

• Brand newsrooms, agency press pages, trade publications (RSS/alerts)

• Industry communities where deals get discussed

• Platform-specific sources (e.g., Shopify partner directory if you do integration work)

Tier 2 (Contextual) signals appear in more structured places and require manual / AI powered transcription:

• LinkedIn Sales Navigator for job changes, company updates

• Trade press aggregators (Campaign, Marketing Week, Adweek)

• CRM alerts for news on target accounts

Tier 3 (Generic) signals are easiest to automate with standard tools:

• LinkedIn Sales Navigator, ZoomInfo, Apollo for company changes

• Job boards for hiring signals

The principle: Tools aggregate signals. Your strategy determines which ones matter and how to qualify them.

Implementation Considerations

Most agencies should start simple:

1. Define Tier 1/2 signals for your service offerings

2. Set up basic monitoring (RSS, LinkedIn, trade press) — next post in this series (link below)

3. Build a qualification checklist / AI workflow — link to the post about this below

4. Test with 20-30 target accounts before scaling

The goal isn’t end-to-end automation from day one. It’s moving from reactive/opportunistic prospecting to systematic signal intelligence that reflects your domain expertise—even if it means manual qualification at first.

This is also why bespoke workflow design matters—your team structure, ICP, and strategic priorities are unique. One-size-fits-all campaign builders don’t account for the nuance that makes agency BD effective.

What’s Next in This Series

This post focused on the foundation: what signals matter, how to qualify them against your strategy, and why this layer requires strategic clarity that can’t be outsourced.

But operationalising this at scale involves additional layers:

Coming up:

• Signal monitoring infrastructure: Practical playbooks for tools, workflows, and aggregation systems that make signal tracking systematic without requiring manual daily monitoring — link below.

• Signal-to-message translation: How to take qualified signals and systematically build stakeholder-specific messaging that connects your positioning to their context — link below.

• Implementation and resourcing: How to build this capability within existing BD teams, timelines for different complexity levels, and when custom workflows make sense vs. off-the-shelf tools

UPDATE: In my new post, I talk about how you can bring this thinking to life with setting up account & signal qualification, and signal to message-translation:

The principle stays consistent: Strategy and domain expertise stay in-house. Workflow infrastructure makes it scalable.

If you’re a growth leader at a B2B agency or strategic partner trying to move from reactive BD or high-volume/low-conversion outbound to systematic, high-quality prospecting—this is the starting point. Clarity on what signals prove your value. Everything else builds from there.

Thought leadership and value-led content are powerful—when the right people see them.

Getting most of your engagement from your own team lately?
The issue usually isn’t the content; it’s reach. Even educational content now needs a deliberate distribution system that puts it in front of a small, named buying group within your ICP—the niche buyers it was written for.

Thought leadership and value-led content are the expert’s GTM. They de-risk complex buys, make your how legible, prove your expertise, and broadcast a point of view competitors can’t copy.

The problem today

But today, in order to generate results, agency leaders not only need to create content that adds value to a well-defined ICP and reflects their expertise and perspectives;
they also need to make the right people at about 300 target accounts notice it and engage with it.

What this post covers

• What to consider and how peer agencies are tackling distribution (options, trade-offs, when to use what).

• How to run it: the emerging trends and the process I’m using to distribute this article—which you can apply to your next post.

What’s changed recently?

The common trap is assuming that if you share expertise for free, the right audience will eventually find it. Even when a piece is rich with first-hand insight, the reality is:

• Feeds are saturated; organic reach to non-followers is getting weaker—especially for long-form.

• Search is increasingly summarised; fewer clicks make it to your site—historically a key discovery path for thought leadership.

• Buyers apply a harsher “what’s in it for me?” filter and bounce faster from anything that isn’t clearly relevant to them.

A simple way to see the system:

Relevance × Delivery × Proof

• Relevance — a visible why me, why now.

• Delivery — the piece actually reaches those people (not just followers).

• Proof — evidence their time pays off (data, method, outcomes).

The “obvious” solution: LinkedIn targeted ads (and where it cracks)

Why teams pick it

• You can point ads at exactly the accounts/titles you care about.

• Your ICP already lives on LinkedIn.

• It’s operationally simple if you already run paid.

Hidden trade-offs for niche, high-ACV plays

1. List size + ops cost — Matched Audiences need ≥300 matched contacts (emails). In practice, you’ll research ~500+ to be safe.

2. Budget gravity — For LinkedIn ads to work, you need multi-touch over ~12 weeks; tight enterprise CPMs add up quickly (the last time I launched LinkedIn Targeted Ads, the CPM was £80-£140).

3. Message flatness — Your uploaded list is for targeting only; the creative/message is the same for everyone. There’s no “you’re receiving this because…” at an individual level.

The absence of a personal reason-why is the most reliable way to be ignored—especially by senior buyers drowning in content and paid outreach.

The rising alternative: Content Promotion with a Clear ‘Why’

Micro-framework

• Relevance — Prove the match (topic ↔ role/company signals).

• Reason-Why — “You’re receiving this because…” (transparent qualification).

• Strategy-first — Be clear about who this is for.

What it is: promoting your piece to qualified ICPs with a clear reason-why it’s relevant to them, tailored to their role/company signals—and showing your work.

What this is not

• Not a cold pitch / cold outreach.

• Not spray-and-pray outreach. The message must emphasise why this is relevant to this person.

• Not pseudo-personalisation (surface-level touches that only sound tailored).

Examples

• “Since {{BRAND}} signed {{ATHLETE NAME}}), this benchmark shows which channels move merch and sponsorship KPIs;

• “With {{BRAND}} entering Benelux via {{RETAIL PLATFORM}}, this study distils what worked (and didn’t) for recent US FMCG entries—and how they navigated early hurdles.”

You might be thinking

“Sounds interesting in theory—but how do we find companies that fit a narrow definition for this piece? And if we have to write each note manually, won’t it take weeks?”

That’s exactly what comes next - I will show how to build a campaign like that on the example of pushing this article.

Overview of the process I’ll use to research and build messages to put this article in front of the right people

Below is the summary process I use with high-ACV agencies: small, verified cohorts; one URL per company; a one-line reason-why; respectful routes; decisions based on qualified engagement.

(As always) It starts with clarity on who this is for. For this article, my qualification criteria are:

• Company type: Agency or service provider with strategic offerings and an expert position (not a commodity vendor).

• TL presence & freshness: Recent thought leadership or value-led publications that demonstrate expertise (not internal/stakeholder reports only).

• Engagement signal: Ideally, recent publications show limited engagement beyond their own team (room to improve distribution).

• In-house roles: Active Marketing, New Business, and Business Development functions—signal that growth isn’t purely referral-driven.

• Location: US, UK, Canada, and Western Europe.

• Size: 10–200 employees.

• Note: Traditional firmographics are the least important criteria; the key qualification happens first.

Tech stack I’m using

• LinkedIn Sales Navigator — build the base dataset (UK core licences ~£80/mo).

• PhantomBuster — export Sales Navigator lists for processing (from £60/mo).

• Clay — enrichment, qualification, message builder (from £149/mo).

• OpenAI API — extraction/classification/reason-why drafting.

• Smartlead — light sequencing and sends (£40/mo).

Step 1 — Export a tight base list

I start by building a list in Sales Navigator, pulling Advertising/Marketing/Media Production/Design Services accounts in US, UK, Canada, Western Europe, 10–200 people. I export with PhantomBuster and stage it in Clay.

Outcome: an account list with valid company LinkedIn URLs and basic firmographics.

Step 2 — Keep only expert/partner firms (drop vendors)

I only keep agencies that sell strategic services, not just output. To apply AI-powered enrichment and qualification I use Clay (you can also use freckle.io—gentler learning curve, but less powerful).

In Clay, I scrape the homepage/services/about/insights and structure the information so I can run a follow-up qualification and label each company “Strategic Partner” or “Production Vendor”.

Signals I look for: strategy/advisory language, named methods, decision support, outcome-led cases, active thought leadership. I drop menu-only “tactics shop” sites.

Outcome: a qualified list plus structured data I can reference later (“we selected you because…”).

Step 3 — One GTM-serving article per company (date-gated)

I first check how often each company posts TL/educational content.
From there, I select one piece that serves a buyer outcome (positioning, demand/ABM, CRO/SEO, launch, measurement, distribution). I only accept pieces published on or after 2024-09-01. If nothing clears that bar, I skip the company—it’s unlikely this activation will be relevant to them right now.

Outcome: best_gtm_url per company—singular message, no thread-splitting.

Step 4 — Shortlist the people (fewer, better)

I keep roles that can use or sponsor the idea now: Marketing, Growth/New Business, and Strategy/GTM. To do that, I scrape organisational charts for the pre-qualified companies to exclude team that don’t have any of the above roles so that we don’t contact agencies that still heavily rely on referral-based growth.

I apply a simple scoring pass per person and select the contacts from each company for whom this topic will be most relevant.

Outcome: clean recipient_LI_links per company.

Step 5 — Enrich their article

From their chosen piece I pull: author, any client types named, services implied by the content, a one- to two-line value to the reader, and a ≤30-word “why this helps your GTM.”
This step clarifies the role the article plays in their GTM and grounds the reason-why line in evidence.

Outcome: a tiny evidence pack I can reference in one sentence.

Step 6 — Additional qualification and message build

Now that we have a highly curated list of individuals for whom this topic is relevant, here comes the most interesting part:

• I use the qualification data from Steps 3–5 to build the messages that explain why this piece is relevant to the selected people.

An example of the final message using this process:
—

Hi Richard,

Thought leadership and value-led content can be highly effective in positioning and selling premium large-format DOOH programmes to media planners and advertisers.

But the problem now is that even if your content provides insights fostering higher attention and long-term profitability, getting the right buyers to notice and engage is the hard part.

I’ve published a short, practical piece on getting thought leadership in front of the exact buying group it was written for: [link]

This should help make pieces like your ‘From streets to strategy: how large format digital OOH outshines online video to redefine media planning’ reach media planners and advertisers

Best regards,
Max
—

Conclusion

The growth challenges in front of agencies have changed; the answers need to change, too. Sending more emails or posting more content alone won’t fix saturated mailboxes / feeds and more skeptical decision makers. But—as with everything—it all rests on two fundamentals: sharp clarity on your agency strategy and an honest map of your client’s preferred buying journey.

If this resonates, subscribe to my Substack. I’m sharing how other agency leaders are navigating the same constraints—plus practical frameworks, prompts and teardown examples you can implement today.

Key point: Automation and AI won’t deliver incremental, durable gains for strategic services unless your agency’s strategy is clear.

But clarity alone is not enough. It must show up where it matters — in client inboxes and meeting rooms.

Top-of-funnel for high-ACV relationships is where many new tech tools focus. Sequencers, AI personalisation and sales-intel databases make it easy to create more touchpoints for less time and budget. But without alignment on your USPs, ICPs and positioning, these tools mainly amplify noise, not impact.

What “noise” looks like

• Prospect lists where only 50% could realistically use or afford your services

• Thought leadership that sounds generic and makes your positioning less clear

• Outbound that could have come from any competitor, with no credible proof of why your agency is different

A big shift in the B2B customer journey is that buyers no longer click around your site to decode what you do or who your services are for. It is easier than ever to ignore. Many stakeholders receive 100+ emails a day and see countless B2B posts. To cut through, your GTM must reflect your strategy, speak to a specific pain, and make your difference obvious.

Examples of what strategy clarity means here

• Real-world triggers: the few signals that mean “why now” (e.g. brand refresh, key hires, channel shifts)

• ICP with rules, not generic firmographics: inclusion and exclusion criteria, buying journeys per service line

• Disqualifiers you stick to: which accounts you will not pursue and why

• Message + proof tied to pains: core promises per service line, each backed by a case or metric

Check before you try to scale

Before you invest in more top-of-funnel activity — a lead-gen vendor, new hires, or another automation tool — run an internal audit of how clear your strategy is.

The good news: you don’t need months with external consultants to validate this. There are many ways to test and improve your team’s understanding and alignment — and to tighten your focus.

In my earlier article, I included some of the questions I normally use at the start of a growth engagement:
Read: Lack of ICP clarity is breaking your growth → https://www.insights.contextual.partners/p/lack-of-icp-clarity-is-breaking-your

They’re a quick, agency-specific litmus test of whether your strategy, or your team’s grasp of it, needs tightening.

P.S. I am Max. I write about proactive growth for agencies. Subscribe for insights, tips and practical frameworks on scaling strategic service lines and winning target accounts.

There’s been plenty said about how hard it is to stand out in a sea of noise. But for independent agencies selling strategic services across multiple offerings, there’s another layer to this: outbound often needs to generate demand and create discovery moments — not just catch and convert people already in market.

The old approach — “Let’s just get them on a call and we’ll tailor it from there” — doesn’t work like it used to.
Today, clients form an opinion long before they’re willing to commit to a meeting, so generic touchpoints and misaligned execution fall flat.

And that’s what today’s post is about:

How misaligned ICPs quietly derail growth — and how to bring your team back into focus.

Let’s dive in!

Your Outbound Isn’t Failing Because of Bad Emails

It’s failing because execution drifts away from strategy.

You want your BD team to spend time on companies that actually benefit from your agency’s USPs, appreciate your positioning, and can be engaged with trust-building messaging.

Instead, hours are wasted on accounts that were never going to be able to afford your expertise — while true target accounts receive generic messages.

Not because your team is incapable — far from it.
The issue I see in most agencies I work with is poor alignment between strategy and execution — especially around ICP and positioning. Strategy never makes it into daily workflows, particularly in multi-service-line agencies.

This problem isn’t new — but its consequences have gotten worse recently.

Two reasons why:

• Clients now ignore generic outreach that doesn’t map to their pain. They don’t go to your site to figure out what you do — they just delete the email.

• It’s easier than ever to scale noise. Tools like Apollo, sequencers, and AI SDRs don’t solve the problem — they amplify it, enabling you to reach more irrelevant people, faster.

The old mantra: “It’s a numbers game.”
The new mindset: Deliver relevant, trust-building touchpoints at scale — and that starts with strategic clarity.

Why ICP Clarity Drives Everything

Agencies sell complex services.
Trust is earned through specificity — service-line relevance, category nuance, credible proof.

When your ICP is fuzzy, everything downstream suffers:

• Targeting

• Research

• Prioritisation

• Messaging

Why Drift Happens — and How to Fix It

1. Strategy lives in heads, not workflows

A slide might say: “consumer brands with content needs.”

But what does that mean in real life?

• Do they outsource creative or have an internal team?

• What are their core GTM channels?

• What triggers suggest they’re about to need your services?

Without a clear company-level definition, it’s unrealistic to expect individual BDs to draw the same conclusions — or stay aligned with leadership’s vision.

2. Qualification happens too late

You only discover an account isn’t a fit when they push back on price — because they were never properly qualified to begin with.

3. Messaging becomes generic

And that’s not enough to spark interest — especially when cold outreach is also your demand gen channel.

Let’s say your strategy is to target consumer brands for content production.
If every BD is interpreting that differently, you’re guaranteed to dilute the opportunity.

So What Can You Actually Do?

Start by solving two critical bottlenecks:

Clarity and alignment on company strategy

Begin with ICP and positioning before layering in more advanced elements like:

• Ideal buyer journeys (how clients want to be sold to)

• Buying triggers

• Where clients see your agency in the value chain

Build a bridge from strategy to execution

If BDs don’t understand — or trust — the strategy, you’ll only ever capture a fraction of its value.

Quick Wins This Week

Here are simple moves I use at the start of a growth consultancy engagement to test and improve alignment:

Narrative Check

Ask 3 BDs to summarise your pitch for one service line in 3 bullets.
→ Are you telling one story or five? Standardise the best version.

Trigger Shortlist

Define 5 real-world signals that indicate buyer readiness.
→ E.g., brand refresh, content/VFX hires, shift from AOR to project model.

No-Go Rules

Write 3 exclusion filters (e.g. too small, no budget, wrong stage).
→ Removing bad-fit accounts is the fastest way to raise conversion rates.

20-Touch Audit

Review 20 recent outbound touches from across your BD team (for the same service line).
→ How many clearly map to your ICP?
If it’s under 70%, you have drift.

Longer-Term: Bridging Strategy to Execution

There’s no plug-and-play fix for this. But one approach that consistently works for agencies I partner with is building bespoke growth engines.

Some call it GTM engineering. Others say it’s combining AI with commercial ops. I, personally, stick to growth engines.

Whatever the label, here’s the idea:

A growth engine is a centralised, tech-enabled system that turns your strategy — positioning and ICP — into daily top-of-funnel execution.

The spine of a growth engine (not the full blueprint):

• Operationalised ICP: Inclusion/exclusion rules — with service-line variants — used as filters, not just preferences.

• One source of truth: Positioning, proof points, and case snippets your BD team actually uses.

• Context-tied outputs: Emails, micro-decks, ABM assets — all generated from the same strategic spine (human-edited).

• Learning loop: Wins, objections, and no-go reasons feed back into the system to improve targeting.

I’ll unpack the full workflow — from signals to research to messaging — in a future post.

Examples of This Approach in the Wild

• Growthx.AI (Series A, $12M raised): Human + AI outbound for organic content

• Workflows.io: Builds modular growth engines — content ops, AI RevOps, ABM workflows — tailored to B2B

What Are the Benefits?

• Strategic growth: Target accounts receive messaging that highlights your expertise and why they’re a fit

• Consistency: Everyone aligns on ICP and story

• Efficiency: Less time wasted on poor-fit accounts

• Relevance: Messages reflect real service-line pains and triggers

• Less noise: Avoid the spammer perception

• Execution-ready strategy: Not just a PDF once a year — strategy shows up in daily BD activity

Recap

Proactive growth fails when strategy doesn’t survive contact with the calendar — and modern tools only amplify the drift.

Reflection Question

If a new BD joined tomorrow, would they target and speak to the same accounts your strategy calls “ideal”?
→ If not, where’s the gap? ICP? Triggers? Narrative?

Then ask:

What can you do to ensure your strategy shows up where it actually matters — in the workflows and decisions of your BD team?

A growth engine fixes this by translating your strategy into how your team targets, researches, messages, and learns.

Start with clarity. Scale with discipline.

Next Up

📌 I’ll share real-world examples of growth engines by service line — and how they helped agencies close the gap between positioning and pipeline.

With this piece, I’m kicking off my Substack — a place where I’ll share practical insights and my observations on agency growth: what works, what fails, and why.

Who This Is For

Referrals and repeat work get most agencies started. But for smaller independents (15–50 FTEs), there comes a point where they’re not enough to take you to the next growth phase. If that ceiling feels familiar, this series is for you.

At this stage, pipelines are still heavily reliant on word of mouth. That’s fine for survival, but breaking into larger accounts requires a more proactive and strategic approach.

Agencies can grow in many ways — building brand and thought leadership, forming partnerships, orchestrating referrals. But when leaders want faster, more predictable, and more controllable growth, outbound, targeted outreach, and ABM usually rise to the top.

And that’s when the critical decision appears: do you outsource new business, build it in-house, or find another path?

TL;DR

• Referrals only take smaller agencies so far — breaking the growth ceiling requires proactive new business.

• Outsourcing works only if you already have clarity on ICP, positioning, and messaging. Without that, it leads to misalignment and wasted budget.

• Building in-house works only if you avoid the common traps: wrong talent archetype, unrealistic timelines, underestimating process complexity, and misusing tech.

• Neither path is “wrong.” Both can work, but only with the right foundations.

• A hybrid approach is emerging for agencies without in-house expertise who want to keep control of strategy while generating results fast.

Why This Decision Matters

Agencies often underestimate how different proactive growth is from client service. Account managers and client leads are not growth operators. Farming existing relationships is not the same as hunting new ones.

That’s why the outsource vs. in-house decision is such a critical inflection point. Both paths can work — but both are littered with pitfalls.

From my experience working with lead-gen partners — from founder-led boutiques like Cherry Business Consulting to more established groups like Ingenuity — success always came down to one factor:

Did the agency have its new business strategy and positioning foundations in place?

In other words: was there clarity on ICP, differentiation, USPs, how agency clients prefer to buy, and what messaging hooks resonate? Without that alignment, neither outsourcing nor in-house hiring delivers much beyond frustration.

Outsourcing: When It Works, and When It Doesn’t

The Extension Model

Outsourced lead-gen can add real value — but only when it’s treated as an extension of a system you already own.

Agencies that succeed with outsourcing already have:

• A clear ICP and qualification criteria.

• Service positioning and value propositions that resonate.

• Messaging hooks aligned to the buyer journey.

• Internal oversight to guide outreach and campaigns.
  

Here, the partner’s role is capacity, not strategy.

The Common Trap

Most agencies approach it the other way round. They assume proactive new business = sending templated emails, and expect partners to “get” their positioning, ICP, and messaging.

That’s where misalignment creeps in. I’ve seen relationships collapse not because the partner was poor, but because the growth strategy wasn’t established or owned internally.

Best case: the agency realises early what’s not working and pivots.
Worst case: they spend months nurturing the wrong buyers — only to later discover they were chasing poor-fit leads with the wrong budgets and needs.

It’s the classic case of treating the lead-gen partner like a neglected child and then expecting them to build a rocket ship.

The Commoditised Work Caveat

Outsourcing can also “work” when you’re selling tactical, commoditised services with a broad ICP and low differentiation. At that point, blasting outreach at scale will generate volume.

But let’s be honest: competing on price is a race to the bottom. If you’re aiming to break through the ceiling and win larger accounts, this path rarely works.

The Iterative Reality Check

You don’t need every detail perfectly defined before you start. But you do need to own the discovery and strategy process.

Outsourced partners can help with execution.
They cannot invent your ICP, positioning, or narrative for you.

When agencies treat proactive growth as “sending more emails” and leave everything to lead-gen partners, it always ends in wasted budget and frustration.

Building In-House: The Hidden Challenges

On the other side is building an internal growth function. On paper, it feels safer: more control, more ownership. In practice, it brings its own traps.

1. Hiring the Wrong Talent Archetype

The most common mistake? Promoting account directors into new biz roles.

The issue isn’t bad hires — it’s role mismatch.

• Account management = farming.

• New business = hunting.

Hunting means researching at scale, mapping accounts, testing ICPs and messaging, building campaigns, understanding buyer journeys, and converting cold outreach into conversations.

Without systems to enable this, even your best account director will struggle.

2. Misunderstanding the Time Horizon

Even with the right hire, many founders expect results in weeks. In reality, breaking into larger accounts through outbound often takes 6–12 months.

And before deals close, you’ll need to test and refine ICPs, positioning, and messaging. That learning process can’t be skipped.

Without setting expectations upfront, leaders get frustrated, pull the plug too early, and drift back to referrals and passive new biz.

3. Underestimating Process Complexity

Business development isn’t just sending generic templates to companies you “could potentially work with.”

It’s a system that spans:

• Research and targeting.

• Signal spotting and qualification.

• Messaging and narrative development.

• Sequenced outreach and nurturing.

• Pipeline management and discipline.
  

Without clear expectations and upfront commitment, initiatives fizzle fast.

4. Misusing Technology

Modern biz dev depends on CRMs, sequencing tools, enrichment, and increasingly AI.

The trap? Agencies adopt them in fragments — a half-set-up CRM here, a few campaigns in Lemlist there.

Tools amplify clarity. Without clarity, they just scale noise.

The Trade-Offs: Capacity vs. Control

• Outsourcing gives capacity — but fails if you don’t already have positioning clarity and strategy foundations.
  

• Building in-house gives control — but fails if you underestimate role-fit, timelines, and process complexity.
  

Neither option is “wrong.” Both can work. The key is being strategic and clear-eyed about what’s required — and whether your agency is ready.

What’s Emerging Next

In recent years, a hybrid approach has been gaining traction. Instead of outsourcing or building full new biz teams from scratch, some agencies are starting to design growth engines — systems that combine external guidance with hands-on execution, while keeping ownership of strategy internal.

This makes particular sense for agencies that lack in-house growth expertise but want to stay in control of their strategy and learnings, while still generating results without years of trial and error.

I’ll explore how this model works — and why more agencies are starting to explore it now — in the next post.