Marco Di Mizio

In a course recap, they say a trigger event alone does not make a strong prospecting message; preparation and context matter. They also relay instructors’ views that qualification models should suit the product and that marketing-to-sales scoring needs governance and clear exit criteria.

Di Mizio classifies a trial signup as a Lead whose interest still needs qualifying, and a booked demo as a Deal because the person has already chosen to speak with an agent. The distinction turns funnel stages into operating rules, not labels.

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Logging is useful when it reveals a deal slowing down, though it asks experienced sellers to change how they work.

In the CRM build, trials enter as Leads and demos as Deals.

• To stay within Airtable’s free-plan row limit, Di Mizio uses a weighted interaction counter on each contact instead of creating a separate record for every click.
• That makes engagement readable without an ever-growing event list.

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They describe putting the go-to-market plan into operation before adding CRM measurement, then using the CRM to track engagement and sales activity. The posts show the tradeoff: delaying measurement lets the team start but leaves early signals to interpret later.

For a seven-agent team selling two SaaS products, Di Mizio lists mostly free or low-cost tools and plans 32 weeks of AI-assisted content segmented by product, version and active modules. The early update reports low adoption, two meetings, two trial requests and articles read mostly by non-customers.

The first project update says the CRM will follow initial results; a later post describes it nearing completion and explains its design. In a course recap, Di Mizio frames the CRM more broadly as the commercial source of truth.

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A course recap also presents the view that AI should remove complexity and free time for higher-value work, while leaders’ energy remains important. Di Mizio draws that conclusion in a recap that credits instructors for several sales-process ideas.

Di Mizio says the agents had been working through calls and scattered notes, without a CRM requiring each step to be recorded. They set aside two hours a week for the project and report that four of seven agents are working almost autonomously, while the others are still expected to learn the process.

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Marco Di Mizio describes a project called “Evolvere,” three weeks in: guiding seven sales agents to sell two SaaS products with a small go-to-market plan and no formal authority over the team.

• The post lists the tool stack and prices: Brevo and Calendly are free, the website costs €70 a year, Make costs €16 a year per agent, and Airtable is free.
• Everything else, they say, is a method already tested.
• The plan covers 32 weeks of AI-planned content, with targets segmented by product, version and active modules.
• The CRM is deliberately postponed on the reasoning that results come first and numbers second.
• After roughly a month, reported outcomes are low adoption, two meetings booked, two trial requests, and articles read mostly by people who are not customers.
• The two named challenges are teaching people to use new tools and changing habits built over years of working the same way, with a budget of two hours a week.

For brands planning a campaign

Directly relevant to a sales-enablement brief: it shows how Marco Di Mizio scopes a go-to-market system when budget and time are tight, and what adoption and pipeline data they expect to report. The modest first-month numbers are worth discussing as a realistic baseline, not a failure.

This post details the build behind the project.

• Four of the seven agents are described as working almost fully autonomously, with the remaining three expected to follow.
• Inbound is routed automatically: a trial signup enters as a Lead, with interest still to be qualified, while a booked demo enters as a Deal, on the reasoning that booking a demo is already a choice to talk to an agent.
• The hardest design question was how to track interest without exceeding Airtable's free-plan row limit.
• One record per click would have pushed the plan into paid territory within weeks, so each contact instead carries a weighted counter that rises as they interact with content.
• This lets a lead's temperature be read as a single number rather than an endless list of events.
• Marco says the CRM was designed piece by piece and translated into practice with Claude, with work done in the evenings because the project has no budget.
• The agents have worked by hand until now, and the data they record can reveal a deal slowing down before it is too late.

For brands planning a campaign

The strongest example in Marco Di Mizio's posts of hands-on CRM design: lead-versus-deal definitions, a scoring approach and a design shaped by a hard cost constraint. It also highlights an adoption risk clients should plan for: experienced agents who have never logged every step are the bottleneck, not the software.

A recap of the AI & Account Management master at Talent Garden, written a few days after it ended.

• The central argument: selling now needs less intuition and more system; the traditional playbook is no longer enough.
• The goal is a process that holds up under complexity and growth, with AI making work predictable rather than serving as an accessory.
• The CRM is framed as the commercial source of truth, not a filing cabinet.
• Prospecting depends on context: a trigger event marks the moment, but without preparation the message stays weak.
• Qualification should rest on explicit models, not opinion.
• Marketing and sales efficiency turns on scoring, governance and discipline in exit criteria.
• Retention is treated as growth, measured through weak signals, churn reduction and structured referrals.
• Weekly and quarterly business reviews are named as the moments when the system corrects itself and forecasting becomes reliable.
• The post argues that AI in sales works when it removes complexity and frees time for high-value work.
• It closes by thanking named instructors and classmates and crediting leaders' energy as the deciding factor.

For brands planning a campaign

Useful as a map of the topics Marco Di Mizio speaks to and the vocabulary they bring to client conversations. Since this is a course recap rather than original research, ask how they have applied these ideas in client work.

By the third week, four agents were working almost fully autonomously, with the remaining three expected to follow.

• Funnel events have explicit definitions: a trial signup enters automatically as a lead whose initial interest still needs qualifying, while a booked demo enters as a deal because booking signals a choice to speak with an agent.
• An inbound meeting with a seven-person law firm arrived for the flagship product, but the agent didn’t know why.
• Di Mizio says the agent doesn’t yet know the inbound rules, but will learn them.

Designs entry criteria for each funnel step, using Claude to translate intent into practice.

For brands planning a campaign

A client buying agent-assisted pipeline work should expect staged autonomy, funnel-specific entry rules, and a plan to teach agents what they don’t know yet. Ask upfront how leads differ from deals and what milestones mark greater autonomy.

Examples: Source 1 · Source 2

The toughest design question was how to track interest without exceeding Airtable’s free-plan row limit.

• Rather than log every click as a separate record—a route that would have forced paid tiers within weeks—Di Mizio used a weighted counter for each interaction on the same contact.
• As a lead engages, the counter rises, showing interest as a number rather than an endless event list.

For brands planning a campaign

Di Mizio can specify cheap or free stack alternatives and knows where free-tier limits shape design choices, a fit for small teams that need a working system before they have a software budget. Expect the tradeoffs to be explicit.

Examples: Source 1 · Source 2

In the build itself, the CRM comes after results: first outcomes, then numbers, then tracking of engagement, interest and sales metrics per agent. Di Mizio designed the CRM piece by piece, with Claude as the implementation partner, and is finishing it in evening hours.

For brands planning a campaign

For a CRM implementation, expect a phased approach: run the process first, instrument it second, then define what counts as engagement and interest. Agree what the CRM will be the source of truth for before the build starts.

Examples: Source 1 · Source 2 · Source 3

It’s one part of a small, method-driven GTM strategy for selling two SaaS products, alongside the tool stack and agent setup.

Early results were modest: low adoption, two meetings booked, two trial requests, and articles read mostly by non-customers.

For brands planning a campaign

Di Mizio can produce a segmented content calendar and estimate the team time it requires. For a content-led go-to-market campaign, ask how segmentation maps to products and modules, and what two hours a week from the team can realistically deliver.

Examples: Source 1

Scoring can make the marketing-to-sales handoff more objective, but governance and disciplined exit criteria matter most.

• Weekly and quarterly business reviews help the system correct itself and make forecasting more reliable.
• In prospecting, a trigger event marks the moment, but without preparation the message stays weak and conversation quality depends on the information brought into the process.

For brands planning a campaign

A sales enablement client can expect concrete process artefacts: qualification models matched to the product, exit criteria, governed scoring, and weekly and quarterly reviews. Request these deliverables, not slide decks.

Examples: Source 1

Growth means making more of the existing customer base, not just adding new logos. This connects to Di Mizio’s argument for marketing-sales alignment: the grey zone between departments is an operational alignment problem, not a turf dispute.

For brands planning a campaign

For post-sale or customer-success work, Di Mizio treats churn reduction and structured referrals as growth levers that require the same process discipline as acquisition. Ask how the team will define weak signals and who will own them.

Examples: Source 1

The agents previously worked ad hoc, across phone calls and scattered notes, and had never used a CRM that requires every step to be logged.

• Those records can reveal a deal slowing down before it’s too late.
• Di Mizio budgets two hours a week and reports early signals plainly, including articles read mostly by non-customers, while noting that the team still needs to learn how to interpret the signals.

For brands planning a campaign

Treat adoption, not tooling, as the main risk and expect a change-management plan with a time budget. Ask what evidence will show new habits are sticking and how the team will separate measurement noise from genuine traction.

Examples: Source 1 · Source 2

They argue that even if AI accelerates processes, the passion of leaders guiding the system remains the real differentiator, and thank peers and the programme coordinator.

The credited instructors and peers are named for specific contributions, not as endorsements of the project.

For brands planning a campaign

Di Mizio attributes methods to named people, helping clients trace where a technique comes from. Their conclusion puts leader energy above tooling, so expect recommendations that pair process design with how a team is led.

Examples: Source 1

Brevo is part of a low-cost stack, not a partner.

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The post names it as a working tool, with no commercial relationship described.

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The post names it as a paid tool the project uses, not as a sponsor or client.

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Instead of creating one record per click, they use a weighted counter on a contact. The constraint drives a concrete design decision, so the mention is worth keeping.

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Claude is a build tool for a system they describe as their own design, not a sponsor.

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They write about what they took from the course; no commercial or partnership relationship with Talent Garden is stated.

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Gnoli describes rebuilding an automotive CRM around real customer records, standard objects, explicit lead and deal rules, and a data model that makes marketing and sales visible.

• Di Mizio describes defining funnel entry rules and designing a CRM piece by piece.
• Both make clear rules and usable data central to the system.

Gnoli's example is a consultancy-scale CRM rebuild involving more than 100,000 records, reconciliation logic, and Salesforce certification advice.

• Di Mizio's is a no-budget build for a small agent team selling two SaaS products, with free-tool limits shaping the design.
• Gnoli also writes about outbound offers and AI agents for professional categories, while Di Mizio's posts focus on internal team adoption and measurement.

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Canovese writes from a senior consultancy role about cost structures, supplier contracts, and supply chains under economic uncertainty.

• Di Mizio covers qualification, handoffs, retention, and the operating rules that hold a sales process together.
• Both favor preparedness and clear models over reacting to the market.

Canovese writes about procurement and cost optimization for larger companies facing international risk, with reflective posts rather than build logs. Di Mizio details a go-to-market and CRM build for a seven-agent team, including named tools, weekly time budgets, and early adoption numbers.

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Salimbene criticizes optical businesses that run campaigns, CRM, social, and AI as separate containers, calling for continuity between marketing, sales, data, and technology.

• Di Mizio makes a similar case for CRM as the commercial source of truth, with content and agents as parts of one method.
• Both write about AI changing the work rather than replacing judgment.

Salimbene writes mainly about optical retail and brand coherence, drawing on frames, collections, and business ethics.

• Di Mizio writes for B2B SaaS sales teams, using funnel-stage definitions, agent autonomy, and CRM logging as concrete examples.
• Salimbene examines the customer experience over time; Di Mizio focuses on the sales process and its operating rules.

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Choose Gnoli when the engagement’s hardest problem is the data underneath the sales process, not the process itself.

• One post describes rebuilding a customer view for an automotive dealer: records pulled from several closed systems, 105,169 records reconciled to 98,146 unique clients, and a merge rule applied only when a tax code or VAT number matches, or when a name matches alongside an email or phone.
• The same post sets funnel rules a team cannot fudge, including creating a customer record only when a quote exists, and closing deals or marking them lost at 60 days.
• A second post describes building outbound pipeline for an agency at roughly 15 euros of setup: scraping tools, an email script, an A/B tracker and a CRM, with 13 positive replies in the first week.
• For a small team whose real problem is untrustworthy customer data or no pipeline, these posts are closer to the job than a content-led GTM build.

The reviewed work is narrower in two ways.

• The CRM rebuild is presented as a single build in one vertical, automotive dealer and aftersales, and reads as a case study rather than a repeatable handover method.
• The outbound system generates pipeline with an engagement hook, asking readers to comment for the script, rather than teaching the client to run it.
• Neither post shows a content calendar, a change-management or adoption plan, or work through a team the practitioner has no authority over.

Examples: Source 1 · Source 2

The two posts cover supplier negotiation, energy procurement and supply-chain exposure.

• One argues that economic and shipping uncertainty is already changing purchasing decisions, supplier contracts and cost structures, and that the right response is to analyse costs more rigorously, renegotiate before the market moves, and secure energy at better prices.
• The other marks a Senior Partner appointment at ERA Group EMEA, reached after passing thresholds of managed projects and generated revenue, and sets out an approach of listening before proposing, asking why something has always been done a certain way, and building tailored paths with the client.
• If the same buyer wants a commercial rebuild justified by cost reduction rather than sales-system design, this is the relevant experience.

This is adjacency, not substitution.

• Marco Di Mizio's posts do not cover the core needs of defining leads and deals, CRM architecture, AI agent workflows, or a content-led go-to-market calendar.
• For a small SaaS team seeking an AI-augmented sales system handover, hiring based on these posts would mean buying a cost and procurement adviser, then sourcing the sales-system build elsewhere.
• Ask for evidence of client outcomes before deciding whether their work can deliver the sales-system build.

Examples: Source 1 · Source 2

One post names the pattern in optical businesses: campaigns, promotions, digital tools, a CRM, social and now AI, each correct on its own, fail when they do not speak to each other, so each cycle restarts from scratch.

• The argument is to connect, simplify and give direction before adding anything: continuity lets customers trust, keeps the team from exhausting itself, and makes data usable.
• A second post sets a readiness test for AI: a model that operates a computer amplifies whatever it finds, so a CRM with fields filled three different ways and templates that exist only in people's heads gets disorder at double speed.
• Salimbene counted the Monday-morning realignment work across two of their own brands and client brands and found fewer than half the tasks structured enough to hand to a model that runs on its own.
• For a buyer whose real problem is incoherence and overstated AI readiness, that diagnostic is sharper than a build plan.

The work targets optical and eyewear businesses and brand and marketing management, not sales-team process design.

• Marco Di Mizio's posts contain no funnel definitions, CRM schema work, agent workflow build, or adoption plan.
• Their AI post analyses a model launch and audits one person's own week, showing judgement about readiness but not delivery of the system a small SaaS team wants.

Examples: Source 1 · Source 2