Greg Spence writes about building and leading sales teams, improving revenue operations, and applying AI to sales work such as account research, forecasting, and CRM processes.
Greg Spence writes about sales leadership, revenue operations, and applying AI to sales work.
Best for
Fits a B2B SaaS revenue operations consultancy building sales enablement and AI governance.
Not for
Does not fit consumer packaged goods lifestyle content or paid creator placements for household grocery shoppers.
Key takeaways
Fix the sales system before adding headcount.
Revenue operations is the backbone of execution.
Inventory sales, service, and marketing AI agents for shared customer context.
Check first: Confirm LinkedIn audience analytics and engagement figures exist before setting campaign requirements.
Main ideas
Fix the sales system before adding headcount · Treat revenue operations as the backbone of execution · Use AI to improve a sound process, not speed up a bad one
Greg Spence writes about sales leadership, revenue operations, and the practical use of AI in sales.
Across their LinkedIn posts, they argue that predictable growth comes from building a usable sales system—clear go-to-market strategy, account standards, enablement, forecasting, and CRM processes—before adding more people or tools.
They also write about applying AI to reduce administrative work and coordinate sales activity, while warning that AI can make a broken process worse.
Their posts describe work across CPG, SaaS, and commercial signage, including claims about scaling sales teams and improving revenue and margins.
Spence connects operating advice to concrete examples and a clear sequence of action.
In one post, they say a national BDR team worked because playbooks, compensation, data analysis, and leadership alignment grew alongside headcount.
In another, they recommend inventorying sales, service, and marketing AI agents to find where they lack shared customer context.
That combination—sales-system design plus specific operational checks—makes their work useful to leaders deciding whether a growth problem calls for more activity, better infrastructure, or better-coordinated AI.
Their posts also range beyond AI: they argue that lessons from industries such as CPG and retail can transfer to SaaS and other sales environments.
Greg Spence’s posts return to a practical principle: sales growth depends on a clear, accountable operating system, not simply more activity or software.
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They apply that idea to hiring, revenue operations and AI, arguing that tools and teams work best when the process around them is joined up.
Fix the sales system before adding headcount
Spence argues that more salespeople cannot fix a weak go-to-market model.
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Without clear standards and usable tools, added headcount raises costs and activity without delivering predictable growth. Their recommended order: set strategy, account-management standards, enablement, forecasting and CRM practices before expanding the team.
Spence describes scaling the C-Suite Network BDR team from zero to 11, supported by playbooks, strategy-aligned compensation, sales tools, data analysis and stable leadership priorities.
Treat revenue operations as the backbone of execution
Spence presents RevOps as the connection between sales activity, CRM data, coaching, training and financial goals—not just a support function.
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Each part of the revenue operation should reinforce the others.
In one post, Spence says they brought CRM guidelines, KPI governance, lead capture, demand generation, compensation, sales training and AI-based analysis into a single system while leading an 11-person BDR team. They report revenue rising from $1.98 million to $4.32 million during the contract.
Use AI to improve a sound process, not speed up a bad one
Spence argues that AI amplifies the process it is given.
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Used to send more low-value messages, it adds noise; used to cut research and administrative work, it gives reps more time to sell. They also stress human review and governance rather than treating automation as a substitute for judgment.
Spence contrasts AI-generated email volume with using AI to prepare for a major account meeting. They describe Salesforce’s prospecting agent as review-then-send, not fully automatic.
For Spence, capable agents are not enough if sales, service and marketing systems cannot share information.
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Teams need to track which agents are running and whether they can see the same customer context, so one tool does not undermine another.
Spence imagines a sales agent trying to win back an account while a service agent handles that customer’s complaint. Their practical suggestion: list agents across CRM, sales engagement, support and marketing tools, then check which ones share customer context.
Spence says the most transferable lessons are frameworks and execution habits, not product knowledge specific to one sector.
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They emphasize understanding customers and their economics, defining shared measures, training teams consistently and improving through data and feedback.
Reflecting on work across CPG, retail, SaaS and commercial signage, Spence says a repeatable revenue model and objective-aligned KPIs helped them apply the same operating approach in different markets.
Spence says sales leaders should own commercial outcomes across revenue, forecasting, margin and coordination between departments—not just hold a title.
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In a separate hiring post, they challenge the assumption that an experienced candidate applying for a smaller role is automatically a risk.
Spence describes managing a $15.5 million FASTSIGNS national-accounts budget while aligning sales, marketing, operations, finance, legal and other teams around shared goals.
The clearest example of how Spence frames their operating work: a national accounts division run as a stand-alone P&L, with results and the model behind them in one place.
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Start here to see what they say they ran and how they explain the numbers.
Spence says they led a national accounts division at FASTSIGNS with a stand-alone P&L, exceeded quota and budget by 126%, generated $14.3M in top-line revenue, and raised gross margin from 39% to 42%.
They credit the lift to integrating go-to-market strategy, revenue forecasting, market expansion, CRM architecture, and sales enablement into one operating system rather than pushing harder on activity.
They sum it up in three lines: activity without structure creates noise, structure without accountability creates reports, structure with accountability creates revenue growth.
For brands planning a campaign
For a revenue operations consultancy, this is a reference case for pairing enablement with accountability and margin discipline. It supports an offer that starts with operating-model design, with tools as the supporting layer.
The most direct account of what Spence builds: a connected revenue operations system, not a single program.
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It explains what they were hired to do and what they say changed.
As VP of Sales Operations, Spence says they walked into an organization with clear growth goals but no scalable sales infrastructure.
They launched and led a national BDR team of 11 and connected CRM guidelines, AI-driven architecture, KPI governance, lead capture, demand generation, compensation design, sales training, and AI-based data analysis in one go-to-market system.
They report revenue doubling from $1.98M to $4.32M during their contract tenure, and argue that RevOps, built correctly, is the operating backbone of sales transformation—not a support function.
For brands planning a campaign
Useful credibility for an enablement-and-AI-governance offer, especially their specific claim that CRM data was connected to coaching and training to affect rep ramp time. That link sits at the centre of the offer a buyer is assessing.
Shows the earlier CPG and field-sales side of Spence's experience and the operating vocabulary they carry into later roles.
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It locates the start of their sales-scaling story.
Spence says they helped restructure and scale a CPG manufacturing sales division, reporting a 383% revenue increase and a projected $92M annual run rate.
They credit opportunity identification, focused discipline, sales and process improvements, a national salesforce of independent representatives and account executives, sharper strategic account management, retailer profitability, product placement, pricing, promotions, and a clearer go-to-market strategy.
Their summary: revenue growth is rarely accidental; sales transformation requires infrastructure, focus, and discipline.
For brands planning a campaign
Credibility for a consultancy selling to scaling sales teams: Spence says they rebuilt a field organisation, not just ran an inside sales function, and frames growth as a process problem.
A recent, specific, product-anchored statement of the governance problem Spence writes about.
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Spence describes a sales AI agent trying to win back a lapsed account while a service AI agent handles an open complaint from the same account, with neither aware of the other.
They point to HubSpot's Agent Hub launch and Zoom's real-time sales assist as signs that the problem is not whether agents can complete tasks, but that deployed agents cannot see each other.
Their practical inventory: open the CRM, sales engagement, support, and marketing tools, list every AI agent running, and ask which ones share customer context.
If none do, they say, you have not built an AI sales team; you have built a very polite chaos engine.
For brands planning a campaign
An AI-governance offer can adopt their audit framing almost unchanged: inventory the agents, map shared context, then manage configuration, activation, monitoring, and auditing in one console.
Sets out the judgment criterion Spence applies to every AI tool: does it remove wasted motion or just add volume?
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It answers how they distinguish useful AI from expensive noise.
Spence cites Salesforce's 2026 State of Sales finding that reps spend about 40% of the week selling, with Gen Z reps at 35%, then quotes Forbes calling AI an asymmetric amplifier: it compounds an excellent rep's process and speeds up a broken one.
Their test is not whether reps use AI, but what they use it for.
If AI helps them send more emails into the void, they say, you have bought expensive confetti.
If it removes the day and a half a senior rep loses preparing for one big meeting, you are buying back time in front of buyers.
The competitor is the team that worked out which 60% of the week to delete.
For brands planning a campaign
For an enablement offer, this supplies an outcome metric: time returned to selling and buyer-facing work, measured before and after deployment, rather than tool adoption counts.
A compact case for stack consolidation, with figures readers can use to sanity-check their setup and budget.
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Spence argues that B2B teams carry an average of 87 tools, only 23% of which directly affect revenue, and that reps lose 11 hours a week to context switching and broken integrations.
They contrast a 12-to-15-tool enterprise stack costing $200,000 to $500,000 a year with an AI-native stack of three to five tools costing $60,000 to $150,000, and cite the Salesforce and Anthropic Claudeforce announcement as the largest CRM confirming the problem.
They report pipeline generation per SDR improving 60% to 80% after consolidation and give reply-rate benchmarks: 8% to 15% for signal-triggered outbound against under 3% for volume blasts.
For brands planning a campaign
A cost-and-consolidation wedge for a sales enablement offer: fewer tools, defined revenue impact, and measurable pipeline output per rep.
A concise account of their recommended order of operations, echoed in their other posts.
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Read it to understand the sequence they sell.
Spence compares hiring more salespeople to fix a broken sales model with adding more hamsters to the wheel.
They say they have built and scaled national sales organizations four times and keep seeing the same assumption fail: more reps equals more revenue.
Their sequence puts go-to-market strategy before job descriptions, strategic account management standards before memory-driven account handling, sales enablement before ramp pressure, forecasting and KPI governance before pipeline claims, and usable CRM architecture before mandatory box-checking.
They cite C-Suite Network going from zero to 11 BDRs on clear playbooks, proprietary sales tools, compensation aligned to strategy, AI-based data analysis, and stable leadership alignment.
For brands planning a campaign
A consultancy can reuse the sequence as a diagnostic: before adding headcount, ask whether the operating system underneath can support it. It pairs naturally with the RevOps build post.
Build the revenue engine before adding sales headcount3
AI governance and coordinating multiple agents3
Bigger buying committees and the case for multi-threading1
Pricing pressure and data-driven negotiation1
Build the revenue engine before adding sales headcount
Spence argues that hiring more reps on a weak operating model adds cost and noise instead of revenue.
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They set out a sequence: clarify go-to-market strategy, define strategic account management standards, build enablement before ramp pressure hits, establish forecasting and KPI governance, and make the CRM genuinely usable. They cite scaling a BDR team from zero to eleven at C-Suite Network, where playbooks, compensation design and stable leadership alignment grew with the headcount.
Ties the claim to a first-person scaling example and a short, ordered checklist.
A separate post argues that experienced, so-called overqualified candidates are undervalued. The argument is against headcount without infrastructure, not against hiring.
For brands planning a campaign
Gives enablement vendors a ready frame for content on diagnosing the sales system before adding capacity, a fit for vendors whose buyers are scaling teams quickly.
Several posts detail revenue operations work with specific numbers.
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One covers launching and leading an eleven-person BDR team with CRM guidelines, KPI governance, lead capture, demand generation, compensation design, sales training and AI-based data analysis; revenue doubled from $1.98M to $4.32M during the contract. Others describe owning a $15.5M national accounts budget at FASTSIGNS, exceeding quota and budget by 126% on $14.3M revenue as gross margin rose from 39% to 42%, a 383% revenue increase at Sleep International with a projected $92M annual run rate, and roughly 30% higher operational cadence in commercial signage, driving revenue 26% past annual quota.
Presents results as a series of connected changes, then names the accountability that tied them together.
The figures are self-reported in the posts and cannot be checked from the posts here.
For brands planning a campaign
These credibility points are ready for revenue operations or enablement vendors to quote. The operating-model language also transfers directly to an AI-governance offer.
Spence argues that AI multiplies whatever process it is given.
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One post cites the NBER working paper "Generative AI at Work" (5,179 customer support agents, a 14% average productivity lift and 34% for novice workers) to argue that AI spreads good practice fastest rather than closing deals.
Another quotes Salesforce's 2026 State of Sales finding that reps spend about 40% of the week selling, with Gen Z reps at 35%, and Forbes' framing of AI as an asymmetric amplifier that compounds an excellent process and speeds up a broken one.
A third lays out a before-and-after view for a twelve-person SaaS team, ending with advice to automate just two decisions first: who to call next and what the next action is.
Cites named studies, vendors and publications, then distills each into a practical question or next move.
Some posts draw on vendor announcements, including Salesforce and Anthropic's Claudeforce and HubSpot's Agent Hub. Treat these as product news, not independent evidence of results.
For brands planning a campaign
Directly supports an enablement-plus-AI offer: the value of AI depends on the process underneath it, not just the tool.
A recurring theme is that the problem with sales AI is coordination, not capability.
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One post describes a sales agent and a service agent working the same account without shared context, and points to vendor consoles that configure, monitor and audit agents from one place.
Another reads Microsoft's 2026 Copilot for Sales plan as AI staged behind review gates and admin controls, with governance as the steering wheel rather than a brake.
A third describes committee-orchestration tools that map the buying group from CRM and conversation data and surface detractors early.
Opens with a specific failure scenario, then draws a general rule and offers a practical inventory step.
For brands planning a campaign
The closest match to a governance campaign: it offers a central argument and concrete language about agent inventories, shared customer context and human review gates.
Bigger buying committees and the case for multi-threading
One post draws on Forrester's 2026 State of Business Buying: purchases involving AI features double the size of the buying group, and the average committee has 14.4 stakeholders, up from 11 in 2018.
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It cites Gong's analysis of 1.8 million deals to contrast single-threaded deals, which close at 5%, with multi-threaded deals with five or more stakeholders engaged, which close at 30. It also notes that win rate peaks at four to five contacts before falling.
Builds an argument from third-party statistics, wrapped in an escalating, humorous account of a stalled deal.
For brands planning a campaign
Brands selling to enterprise sales teams can use this framing for content on committee mapping, deal risk and how AI features lengthen approval chains.
A separate post frames pricing as an infrastructure problem.
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It cites 51% of buyers using AI to compare total cost of ownership before a live conversation, finance involvement in deal approvals rising from 31% to 46%, and McKinsey's finding that a 1% improvement in price realisation translates to an 8.7% increase in operating profit. It says static discount schedules leak 200 to 400 basis points of margin a quarter, while AI pricing copilots cut deal desk approval cycles by 40%; teams using them close 24% more RFPs at the same or better margin.
Contrasts data-driven negotiation with gut feel, then closes with a one-line rule.
Pricing is a separate subject from the committee posts. Readers should not assume it describes the same accounts or buying situations.
For brands planning a campaign
Offers a distinct content angle on pricing discipline and negotiation, aimed at buyers who own margin as well as pipeline.
Spence draws on a career spanning CPG manufacturing, retail, SaaS, B2B, digital media, industrial services, automotive, HVAC and commercial signage to argue that logic, framework and consistency transfer better than product knowledge.
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A typical list runs: align on ICP, buying journey and decision makers; quantify customer economics, internal processes and org structure; design a repeatable revenue model; define objective-aligned KPIs; build behavioural compensation; train and mentor the team; then use data and feedback loops to scale what works.
Uses an arrow list to turn a career story into a repeatable sequence readers can apply elsewhere.
For brands planning a campaign
Gives a campaign a portable framework to build content around and a reason to position Spence as a cross-industry operator rather than a single-vertical specialist.
One is first-person career and leadership writing about scaling, accountability and ownership of national accounts, and why experienced candidates are undervalued.
The larger group is short, data-led commentary on AI in sales, including several posts labelled “AI tip of the week”.
These draw on named statistics and vendor news, then close with a question for readers.
Both groups share a conversational, mildly sardonic register, arrow lists, hashtags and reader questions.
Observation across the posts here rather than a claim about posting dates.
For brands planning a campaign
A brand should expect the weekly-tip format to deliver predictable recurring content and the career-story format to build credibility and leadership positioning. Each needs a different brief and measure of success.
Spence's posts describe sales leadership roles at three organizations and repeatedly cite AI sales software vendors whose products and research they analyze.
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Ask whether they have sponsorship, affiliate, or investment relationships with any of the organizations or vendors mentioned.
FASTSIGNS
Employment
Spence says they led a national accounts division at FASTSIGNS, owned a stand-alone P&L, and held a $15.5M budget covering all departments of that division.
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They report exceeding quota and budget by 126%, generating $14.3M in top-line revenue, and raising gross margins from 39% to 42%.
Spence says they helped restructure and scale a CPG manufacturing sales division at Sleep International into a national growth engine, building a national salesforce of independent representatives and account executives and tightening strategic account management, pricing, and promotions.
Read more
They cite a 383% revenue increase and a projected $92M annual run rate.
Spence cites C-Suite Network as an organization where they took a BDR team from zero to 11 people, crediting clear playbooks, proprietary sales tools, compensation tied to strategy, and AI-based data analysis.
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They describe scaling sales infrastructure alongside headcount.
Spence discusses Salesforce products and research across several AI-in-sales posts: the Agentforce Sales prospecting agent and its review-then-send design, the 2026 State of Sales finding on how little of a rep's week goes to selling, and Claudeforce, the Salesforce–Anthropic announcement.
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They use these as sources for their analysis. Ask whether they have any commercial relationships with the companies discussed.
Spence references Microsoft's 2026 Wave 1 Copilot for Sales plan as an example of AI being staged into sales admin work with explicit review gates and admin controls, calling governance the steering wheel rather than a brake.
Spence notes Zoom's launch of real-time sales assist and natural-language queries across conversation data, grouping it with other vendors whose common theme is unifying AI context or paying agents to undo each other's work.
Spence cites Anthropic as the partner in Claudeforce, the Salesforce–Anthropic announcement of 37 sales skills with live CRM data piped into Claude and an open beta in September.
Evolusn appears in the byline of one of Spence's posts, 'Tip of the AI Week – By Greg Spence - Evolusn.' The post does not explain their connection to Evolusn; ask them to clarify the relationship.
The AI tip posts are short, data-led commentaries on sales AI, agent coordination, pricing, buying committees and rep productivity; they usually cite a study or vendor announcement and close with a practical question.
The career and leadership posts are first-person accounts of scaling sales organisations, owning budgets, building revenue operations and applying cross-industry lessons.
Start with the AI governance and enablement pieces for arguments about how AI fits a sales process; start with the scaling stories for leadership and operating-model examples.
Similar creators include Vandan Jain on sales coaching and enablement systems, Omprakash Karuppanan on ABM and mid-funnel AI execution, and Gianni Newman on RevOps workflows and AI tool selection.
Writes about sales operations, coaching and enablement, with a similar focus on connecting deal data to rep behaviour.
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Jain argues forecast calls and coaching conversations should be one system rather than separate meetings, and describes building PitchSense AI around a single memory for pre-call briefs and practice drills.
Jain is more directly focused on product building and manager workflows, with posts promoting a specific tool and its features. Spence covers revenue operations, sales leadership, AI governance, pricing and cross-industry scaling, usually through first-person career examples or market commentary rather than product walkthroughs.
Works on ABM execution and AI in B2B sales, with attention to stalled deals, buying-committee gaps and where AI actually helps in the mid-funnel.
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Both question volume-led outbound and focus on decisions a sales team should make using data it already has.
Karuppanan is more tactical and account-program specific: HubSpot ABM, Claude skills, field marketing attribution and stalled-deal diagnosis, often from an agency execution perspective. Spence covers broader topics, including leadership, headcount, enablement, pricing and AI governance across several industries.
Focuses on revenue operations, AI tool selection and practical CRM and Slack workflows.
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Both stress measurable ROI, data quality and fitting AI into how teams already work rather than adding tools for their own sake.
Newman's posts are more hands-on, covering implementation through Slack prompts, canvases, scheduled reporting rituals and vendor-selection criteria. Spence writes more about sales system design, leadership accountability and coordinating multiple AI agents across an organisation.
Greg Spence's posts offer limited audience data: the only hard figure is the LinkedIn follower count — 1,936 as of 8 October 2026.
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They show who the writing is aimed at through recurring formats: a weekly AI-in-sales tip, long-form breakdowns of sales and revenue-operations rebuilds with named outcomes, and counterintuitive leadership takes (“hiring more salespeople won’t fix a broken sales model”; treating overqualified candidates as leverage rather than risk).
The AI tips target practitioners already running a stack and cite analyst and vendor figures — Forrester’s 2026 buying-committee research, Gong’s analysis of 1.8 million deals, Salesforce’s 2026 State of Sales, McKinsey pricing numbers, HubSpot and Zoom product launches — in a register suited to sales, RevOps and enablement leaders who will check the numbers.
One post carries a “Tip of the AI Week” banner that names Evolusn alongside Greg Spence.
For a revenue-operations consultancy building an enablement and AI-governance offer, the content alignment looks plausible, but the audience fit remains unverified.
Ask Greg Spence or the team for a LinkedIn analytics or audience-survey report covering seniority, function, industry, company size and geography, plus engagement figures for comparable formats — the weekly tip post versus the long-form revenue-operations breakdowns.
That will show whether the audience includes VP Sales, RevOps, sales enablement or BDR leadership, and how per-format reach matches your needs.
If no such data exists, treat audience fit as unmeasured when setting campaign requirements.
None of Greg Spence's posts documents a sponsorship or partnership offer.
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Greg Spence's LinkedIn posts are sales-leadership and AI-in-sales commentary — posts about revenue operations builds, hiring, quota results, and vendor tools such as HubSpot, Salesforce, and Microsoft Copilot — with no rate card, media kit, sponsored-content disclosure, affiliate note, or stated partnership terms in the posts here.
Several posts reference vendors and cite their product announcements, but a vendor mention in a post is not a partnership; nothing indicates paid placement, and no post carries a sponsorship label.
The series title "Tip of the AI Week – By Greg Spence - Evolusn" reads as newsletter branding rather than a sponsor arrangement, and the posts give no terms attached to it.
Because no format, deliverable, audience guarantee, pricing, or approval process is described, a brand cannot currently plan a paid engagement against stated terms.
Greg Spence's posts do not document commercial offers; this does not mean Greg Spence never accepts sponsorships.
Brands wanting to work with Greg Spence would need to confirm formats and terms directly, and a current official sponsorship or press page would be needed before this section could describe real options.
Need: a B2B SaaS revenue operations consultancy wants to design a sales enablement and AI-governance offer for scaling sales teams.
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Decision: strong fit, with two checks before committing budget.
Why it fits.
The consultancy's offer has two halves — enablement and AI governance — and Spence's posts cover both directly.
On governance, the posts are unusually specific about the failure mode: a sales AI agent works a lapsed account while a service agent handles an open complaint from the same customer, and neither knows about the other.
Spence then offers a concrete audit step: list every agent across CRM, sales engagement, support and marketing, then ask which share customer context.
That inventory framing can be lifted almost unchanged into a diagnostic-led consultancy pitch.
The corollary argument — that Microsoft's Copilot for Sales plan stages AI behind review gates and admin controls, and governance is the steering wheel rather than a brake — gives the offer a defensible position that does not read as vendor hype.
On enablement, they argue that AI multiplies whatever process it is given: the same tool compounds an excellent rep's process and speeds up a broken one.
The useful question is not "are reps using AI" but what they are using it for.
Their spending test is whether AI removes wasted motion — the day and a half a senior rep loses preparing for one large meeting — or just adds send volume.
That is a testable claim, which is what an enablement buyer needs.
Sequencing credibility.
Their order of operations is explicit: clarify go-to-market strategy, set strategic account management standards, build enablement before ramp pressure hits, establish forecasting and KPI governance, make the CRM usable — and only then add headcount.
They tie it to a rebuild where a national BDR team was launched at eleven people and CRM guidelines, AI-driven architecture, KPI governance, lead capture, demand generation, compensation design and sales training were joined into one system.
The link between CRM data, coaching, training and rep ramp time is precisely what the consultancy is selling.
Credibility points a buyer can quote: the stated RevOps rebuild with revenue doubling from $1.98M to $4.32M during the contract; a national accounts division run as a stand-alone P&L, with quota and budget exceeded by 126% on $14.3M revenue and gross margin moving from 39% to 42%; and an earlier CPG manufacturing division restructured and scaled with a stated 383% revenue increase.
The line "activity without structure creates noise, structure without accountability creates reports, structure with accountability creates revenue growth" is a ready-made framing device for the offer.
Two checks.
First, every performance figure is self-reported in their posts.
Attribute the numbers as their stated record and ask for a case reference before putting one in front of a client.
Second, audience composition is unmeasured: the only hard figure available is a LinkedIn follower count of 1,936 recorded on 8 October 2026, with no published breakdown by function, seniority, company size or region.
The content targets sales, RevOps and enablement leaders, but that does not establish who follows them.
Ask for an analytics report before promising reach.
The posts publish no sponsorship, rate card or partnership terms.
Confirm directly what paid arrangements are available before planning one.
When to choose a specialist
Need: a consumer packaged goods brand wants lifestyle-led content and paid creator placements to reach household grocery shoppers — recipe and product-use videos, in-feed lifestyle posts, and short-form vertical video.
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Decision: reject for a campaign aimed at household grocery shoppers.
The mismatch is not a matter of degree.
Everything in Spence's posts is B2B sales, revenue operations and sales-leadership commentary for practitioners: buying-committee dynamics, CRM architecture, KPI governance, discount floors, SDR pipeline benchmarks and agent orchestration.
Retail and consumer goods appear only as the industries they sold into — a CPG manufacturing sales division, national accounts, retailer profitability, product placement and promotions.
That is B2B selling to retail buyers, not consumer-facing product content.
Nothing in the posts addresses shoppers.
Three practical failures for a campaign aimed at household grocery shoppers.
Audience: the only measurement available is 1,936 LinkedIn followers as of 8 October 2026, with no demographic or geographic breakdown.
There is no basis for linking that audience to household shopping decisions at the scale a consumer campaign needs, and LinkedIn is the wrong primary surface for lifestyle video.
Format: the posts are written and text-led — posts, arrow lists and a recurring written tip series.
No video transcripts or short-form video are available, and there are no examples of visual formats, so a vertical-video deliverable cannot be quoted or planned from the available material.
Terms: the posts include no sponsorship disclosure, rate card, media kit, deliverable list or inquiry route.
Two adjacent needs would be a partial fit, with limits.
A B2B brand selling into grocery manufacturing or retail accounts could use their cross-industry operating framework — align on ICP and decision makers, quantify customer economics, design a repeatable revenue model, define KPIs and train the team — and their CPG field-sales rebuild as a reference case.
That is a B2B industrial or retail-supply need, not a shopper-marketing one.
A recruitment or talent-brand need is a weaker but real partial fit: they have argued publicly that experienced, so-called overqualified candidates are undervalued and are leverage rather than risk, and asked hiring teams what they are missing by screening them out.
That single post is a content angle, not a track record of recruiting content; treat it as one example, not a specialism.
Before revisiting the decision, ask for an official sponsorship or press page, any media kit, and per-post engagement or video performance data.
The rejection stands on format, audience and terms, not on an outright refusal to do commercial work.
A worked campaign example
Worked campaign angle for the revenue operations consultancy's sales enablement and AI-governance offer.
Positioning line: "Inventory your agents before you buy another one." The consultancy sells enablement and AI governance; the angle leads with the governance problem because it is the most specific subject in the posts and the least contested part of the market conversation.
Opening problem, drawn from the posts.
A sales AI agent reaches out to win back a lapsed account while a service agent manages an open complaint from that same customer, and neither knows the other is running.
The customer receives both messages.
The argument that follows: the limiting factor for agents in a revenue organisation is not whether they can complete a task, but that deployed agents cannot see each other.
Frame the consultancy's offer as fixing shared context between agents, not adding more of them.
First deliverable — a diagnostic checklist, given away.
Walk readers through opening their CRM, sales engagement platform, support platform and marketing automation, listing every AI agent currently running, and answering one question about each: does it share customer context with the others?
If the answer is none, the reader has not built an AI sales team.
Keep the checklist short enough to complete in one sitting and useful outside the article — that is the point of the asset.
Second deliverable — the flagship written piece, built on the amplifier argument.
Lead with the finding that reps spend roughly 40% of their week selling, with the youngest cohort marginally lower.
Then argue that AI compounds whatever process it is given: a broken process simply runs faster and costs more.
Land on the spending test: ask what reps actually use AI for.
Sending more email into the void buys expensive confetti; removing the day and a half a senior rep loses preparing for a single large meeting buys back time in front of buyers.
Close with the enablement sequence — strategy, account management standards, enablement before ramp pressure, forecasting and KPI governance, usable CRM, then headcount — and a worked before-and-after for a small SaaS team that automates exactly two decisions first: who to call next, and what the next action is on each open deal.
Third deliverable — a recurring tip cadence that mirrors the format already working for Spence.
Short, single-argument posts built from a named statistic plus one practical move, ending with a question to readers.
Measure this format differently from the flagship piece: by consistency and inbound conversation, not depth or SEO.
Credibility points to quote in the consultancy's own materials, attributed as Spence's stated record: the revenue operations rebuild that joined CRM guidelines, AI-driven architecture, KPI governance, lead capture, demand generation, compensation design, sales training and AI-based analysis into one system, with revenue stated as doubling from $1.98M to $4.32M during the contract; the national accounts division run as a stand-alone P&L, with quota and budget exceeded by 126% on $14.3M revenue and gross margin rising from 39% to 42%; and the sequencing argument that growth without a system is expensive while growth with a system compounds. Attribute each figure as self-reported, and ask for one referenceable client before using numbers in a pitch.
Next-step handoff.
End the flagship piece with a two-part template for readers considering the consultancy's offer: name the two decisions they want AI to automate, and list which agents currently share customer context.
That gives the consultancy an inbound signal to qualify and gives the reader an artefact to take to their own leadership.
What would invalidate this angle.
If the consultancy's actual buyers are marketing leaders rather than sales and RevOps leaders, the governance material still works but the enablement sequence carries less weight.
No published follower breakdown shows who follows Spence by function, seniority, company size or region, so test the campaign on a small budget against a control before scaling.
Before committing to a fixed deliverable count, ask for an analytics report, per-format engagement, and any sponsorship page. The posts do not provide engagement figures by format, per-post performance data, confirmed paid formats or terms, or video or event material.
Choose Om if the consultancy wants a campaign centered on practical account-based marketing workflows.
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Their posts describe five AI skills for deal reporting, audience targeting, account research, field marketing and stalled-deal diagnosis.
The account-research example distinguishes a signal worth investigating from a reason to pitch; the stalled-deal example identifies problems such as weak buying-committee coverage and an inactive champion.
Those specifics could support a hands-on campaign about where AI helps teams act on account signals.
The examples focus on ABM execution and enterprise deal movement, rather than Greg’s broader sales operating model, enablement sequence and AI-agent governance.
They don’t show experience designing sales training or governance across agents.
Because the comparison rests on two posts, ask for more representative work before choosing a larger campaign.
Choose Gianni if the consultancy wants a concrete demonstration of turning existing systems into repeatable reporting workflows.
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One post walks through summarizing sales activity in Slack, building a quarterly growth briefing, adding cited enablers and blockers, and scheduling the report.
Another offers a five-point screen for evaluating AI tools, including workflow fit, CRM integration, data boundaries, improvement over time and measurable return.
These examples suit a campaign that needs a practical workflow demonstration and a way to assess tools before purchase.
The posts emphasize Slack-based reporting and AI-tool selection, not the connected enablement and governance offer as a whole.
Greg’s posts address how AI should fit into sales processes and how disconnected agents can create customer-context problems; the two Gianni posts don’t show comparable depth on those subjects.
Ask for more examples if that depth is important to the campaign.
Choose Vandan if the consultancy wants the campaign to focus tightly on sales-manager coaching.
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One post argues that forecast risk and a rep’s skill gap should be considered together rather than in separate meetings; another describes bringing deal context, customer examples and practice into a pre-call brief. That makes a strong starting point for an enablement offer aimed at managers connecting pipeline problems to coaching actions.
The posts focus narrowly on coaching and sales enablement, with a product-building perspective.
They don’t demonstrate the broader sales-scaling sequence or the cross-agent governance scenarios in Greg’s posts.
Ask for examples of that work if it matters to the campaign.
Greg Spence has 14 public LinkedIn posts, alongside three comparison posts each from Vandan Jain, Omprakash Karuppanan and Gianni Newman, creators with similar content.
The posts fall into two streams: short, data-led AI-in-sales tips that cite a named study or vendor announcement and close with a question to readers, and first-person career and leadership posts about building sales organisations, owning budgets and applying cross-industry lessons.
These two streams show the tags, similar-creator choices and campaign-fit reasoning, and what Spence is known for.
Spence had 1,936 LinkedIn followers on 8 October 2026; the posts here carry no publication dates and no per-post engagement figures, so no date range is claimed for the writing.
Where Spence makes claims about results, the page identifies them as self-reported: the revenue, margin, quota and headcount figures are Spence’s own reported results, and study and vendor citations are reported as Spence presents them.
Brand and business entries distinguish roles Spence describes from vendors Spence only names as subjects of commentary; no sponsorship, affiliate or investment relationship appears in the posts here.
Before any campaign decision, marketers would need to collect information not published here: audience breakdown by function, seniority, company size or region; per-post or per-format engagement data; sponsorship formats, a rate card or inquiry route; and books, shows, talks, video transcripts or external articles that might explain name-search interest beyond these posts.
