Diego Lorenzo Carvalho

In the old model, the manager owns quota attainment, reads the pipeline, checks deals one by one, finds fixes for sales problems, coaches reps, studies the most, and joins field visits to show sellers how to sell.

• In the emerging model, sellers own quota, arrive with their own CRM insights and proposed solutions, and analyze their numbers themselves.
• The manager no longer treats a joint visit as a test of who sells better.
• Their job narrows to two things: leading in service of the team with a smaller ego, and using data to develop specific skills tied to a conversion metric chosen jointly with the seller.
• Pipeline conversations and deal checks become less frequent.
• A companion post turns this into a meeting rule: when a team misses quota, a seller in the recovery zone must arrive at the pipeline review with a detailed funnel analysis and at least one consistent root cause.
• The seller's analysis starts the recovery plan, with the manager's joint support.

Before a pipeline review, sellers bring their analysis of funnel data and at least one credible root cause.

• The manager analyzes, supports, and follows through with a joint action plan.
• Diego also notes that sellers differ, rather than casting them all as shirkers.

A later post adds a practical carve-out: sellers should take responsibility, but can also press managers for tools that free up selling time. Self-reliance has limits.

Read original · Original 2 · Original 3

The post says the era of padding CRM qualification to make funnel numbers look better to a manager is over.

• If a company's CRM lacks usable qualification fields, sellers should qualify opportunities alongside it.
• Diego matches three frameworks to deal types: BANT for average deals, covering budget, decision maker, need, and purchase timing; MEDDIC for big, complex deals, covering metrics, economic buyer, decision criteria, the customer's buying process, pain, and champion; and SPICED for average deals, especially first meetings, covering situation, pain, impact, critical event, and decision.
• Within MEDDIC, the customer's buying process is singled out as absurdly important.

A seller working a large, complex deal would use MEDDIC fields; one in a first meeting on an average deal would use SPICED. Diego asks readers to select the fields that fit their business and say which model suits them best.

Read original

A separate post links CRM data quality to qualification.

• It opens with a familiar excuse: the CRM is out of date because there's no time to update it.
• Outside unusual cases, Diego reads this as weak prioritization and poor opportunity qualification.
• Sellers should know their own numbers and act on them, rather than leave reporting to the manager.

The checklist calls on sellers to know their conversion rate, generate performance reports from the CRM's BI layer, and identify which funnel stage needs work. A CRM post frames outdated data as a time-management and qualification problem first, not just a tooling problem.

Read original · Original 2 · Original 3

The capability checklist calls heavy AI use a differentiator, both for that purpose and for researching prospects before meetings.

• The CRM post names Salesbud as a tool that analyzes meetings, provides insights, and fills in the CRM.
• Diego presents two paths: take full responsibility for your results and learn to make AI work for you, or ask your manager to bring in tools that free up your time—a legitimate demand today, not a future request.

Salesbud is named as a tool that reviews meetings, produces insights, and fills in the CRM, giving the abstract AI claim a specific product and use case.

Read original · Original 2

A niche expert studies market trends, challenges, and competitors' strengths and weaknesses.

• A social media content generator draws on their sales journey to build authority in what they sell.
• A consistent reader of sales books rounds out the list: be studious.
• The manager-transformation post echoes the point, saying sellers should arrive at meetings with their own insights and proposed solutions instead of outsourcing their thinking.

In the six-part checklist, knowing competitors' strengths and weaknesses sits alongside data analysis and AI use, not in a separate marketing category. The content advice is to publish from your own sales journey rather than chase volume.

Read original · Original 2

Start here: it lays out the frame the other posts build on—the manager's role is shifting, and the seller is taking on analysis that used to sit with the boss.

Diego Lorenzo Carvalho compares two versions of a commercial manager's job.

• In the older setup, the manager owns quota attainment, reads the pipeline, checks deals one by one, finds fixes for sales problems, coaches reps, studies the most, and joins field visits to show reps how to sell.
• In the emerging setup, quota responsibility moves to the seller, who arrives at meetings with their own CRM insights and proposed solutions and analyzes their own numbers.
• The manager stops treating a joint visit as a test of who sells better.
• The role is narrower but harder: a leader who serves the team, checks their ego, and focuses relentlessly on building specific skills tied to a conversion metric chosen jointly by manager and seller, with fewer pipeline conversations and deal checks.
• Responsibility moves down the org chart as the manager's attention shifts to skill development.

For brands planning a campaign

For a sales training company, this is a ready-made thesis for a management-track content collaboration: a session on how to run a team where sellers own funnel analysis and the manager coaches against a specific conversion metric. The post also shows Diego's argument format—a before-and-after role comparison—that translates well into a webinar script, carousel, or newsletter section.

Read it second: it turns the previous post's argument into a concrete meeting routine that a training buyer can adopt.

Diego Lorenzo Carvalho describes the usual sequence when a team stops hitting quota: the manager pulls the seller into a pipeline review, checks their CRM and BI numbers, cross-references funnel conversion rates by stage, identifies a probable root cause, builds an action plan, and sends the seller back to the field.

• They suggest making a change before that meeting.
• A seller who drops into the recovery zone has to arrive with a detailed analysis of their funnel data and at least one consistent root cause already identified.
• The seller's own read of the problem then becomes the starting point for the recovery plan, with the manager analyzing, supporting, and following through jointly.
• The post also names the incentive problem plainly: for some sellers, less functional responsibility plus more pay means problems get thrown at the manager.
• They note that not every seller behaves that way.

For brands planning a campaign

This is the most directly usable piece for a sales-management training proposal. It offers a clear, testable meeting rule—share responsibility for success and failure—that a collaborator could turn into a workshop exercise, a one-page review template, or a checklist the audience can trial the same week.

Start here for the quickest snapshot of how Diego defines a capable seller: the list covers data, psychology, niche depth, AI use, content, and reading.

Diego Lorenzo Carvalho lists six capabilities they see changing the game for salespeople.

• A data analyst uses AI to research prospects and personas, prepares before a first meeting, knows their own conversion rate, generates personal performance reports from the CRM's BI layer, and knows which funnel stage needs work.
• A deep student of people and mental triggers asks strategic questions and deliberately tests rapport and closing techniques.
• A niche expert studies their market's trends, challenges, and competitors' strengths and weaknesses.
• A heavy AI user hands administrative work to AI to free up time with decision makers and influencers.
• A social media content generator turns their own sales journey into authority positioning.
• A consistent reader of sales books rounds out the list.
• The post asks readers which of the six they have and whether anything should be added.

For brands planning a campaign

A clean six-part skills framework can become a training series, with one lesson per item, or a self-assessment for the audience. It also shows the premise behind the rest of the content: AI literacy and personal data fluency are now seller expectations, not add-ons.

Read it for a compact example of how Diego turns a familiar complaint into a practical choice for the reader.

Diego Lorenzo Carvalho opens with a common exchange: a seller says the CRM is out of date because there's no time.

• Outside unusual cases, they see this as a time-management problem as much as a qualification problem.
• In their framing, time management means knowing what to prioritize.
• AI can absorb some operational and administrative work today, not in a few years, leaving more room to work on and close deals.
• Salesbud is named as one example of a tool that reviews meetings, produces insights, and fills in the CRM.
• They then split the reader into two paths: take full responsibility for your own results and learn the AI tools yourself, or push your manager to supply tools that free up your time—a legitimate demand, the post says.

For brands planning a campaign

This is a natural hook for a CRM-hygiene module in a collaboration: it names the objection sellers raise, a tool category, and a fork in responsibility that a trainer can use to open a session on which tasks to automate and which to keep.

Start here if you need to decide how to qualify deals rather than why qualification matters.

Diego Lorenzo Carvalho argues that qualifying opportunities matters more than before because time is scarce, and time spent on someone who cannot close is time a competitor will use.

• The post says that stuffing qualification into the CRM to protect how your funnel numbers look to a manager is over.
• If a company's CRM lacks usable qualification fields, a seller should run qualification alongside it.
• They match three frameworks to deal types: BANT for average deals, checking budget, decision maker, need, and purchase timing; MEDDIC for big and complex deals, covering metrics, economic buyer, decision criteria, the customer's buying process, pain, and champion; and SPICED for average deals and especially first meetings, covering situation, pain, impact, critical event, and decision.
• Readers are told to take whichever fields fit their business and asked which model works best for them.

For brands planning a campaign

A three-framework comparison with deal-size guidance is a strong co-marketing asset for a sales training company, whether as a written guide, decision aid, or session where attendees pick fields for their own pipeline. It gives the training provider a neutral, practical entry point into the qualification conversation rather than a pitch.

The manager’s value shifts toward two roles: a “genuine leader”—servant, problem solver, facilitator, hardcore supporter—and a fanatical, data-driven operator who develops the specific skills a seller needs to move a jointly chosen conversion metric.

• That leaves less time for pipeline conversations and deal-by-deal checks.
• A companion post makes the same point from the seller’s side: when a team misses quota, the seller should arrive at the pipeline review with their own funnel analysis and at least one credible root cause, making their diagnosis the starting point rather than the manager’s verdict.

Contrasts a labelled "old scenario" against an emerging one and then names the new roles ("genuine leader," "data-driven fanatic"). The companion post turns the idea into a meeting rule: before a pipeline review, the seller must bring a detailed funnel analysis and at least one consistent root cause.

For brands planning a campaign

Frame a sales training collaboration around coaching and skill development tied to a measurable conversion metric—not dashboards, quota policing or managers riding along on sales calls. The shared-responsibility rule—seller diagnoses, manager supports—is a concrete, teachable practice with an obvious exercise, making it a better fit for a training or enablement partner than general sales-leadership commentary.

Examples: Source 1 · Source 2

One describes a familiar pattern: a team misses quota, and every question lands on the sales manager, who runs the pipeline review, reads CRM reports, checks stage conversion rates, identifies the likely root cause and writes the action plan.

• The seller takes less responsibility and passes problems up to the boss.
• Diego Lorenzo Carvalho calls for shared responsibility for success and failure, with the seller owning the diagnosis and the manager supporting the plan.
• The other post is blunter: sellers blame an outdated CRM on lack of time, which the author reads as weak prioritization and poor opportunity qualification.
• The fix is for sellers to take full responsibility for their results rather than expect the manager to supply tools.

Frames the issue as a rule to institute, not a sentiment: sellers in a recovery zone must arrive at the review with a detailed funnel analysis and an identified root cause. The second post offers a two-option choice: take responsibility, or ask the manager to bring solutions that free up selling time.

For brands planning a campaign

The editorial stance is clear, giving a partner room to amplify or productively challenge it: sellers should carry the analysis load, and managers should stop absorbing it.

• For a training company, this supports content on pipeline-review mechanics, diagnostic skills and time prioritization.
• It also makes messaging that presents a tool as a way to remove the seller’s responsibility a poor fit.

Examples: Source 1 · Source 2

First is “data analyst”: using AI to study a prospect and persona before a first meeting, knowing personal conversion rates, generating performance reports from the CRM’s BI layer and identifying which funnel stage needs work.

• The author presents these as skills changing the game now, and adds that sellers should know their niche deeply, including market trends, challenges and competitors’ strengths and weaknesses.
• A separate post links CRM data quality to qualification: sellers who keep the CRM current spend time on people who can actually close.

Uses a short numbered checklist with a one-line justification for each attribute, making it easy for readers to assess themselves. The CRM post recasts a common excuse (“lack of time”) as a prioritization and qualification problem.

For brands planning a campaign

This is useful material for partners selling sales analytics, CRM or enablement tools: the creator treats self-serve performance reporting as a baseline, not a nice-to-have. A collaboration should show a seller or manager working with specific numbers, rather than pitch data literacy as a new idea—the audience is already being told it is the entry ticket.

Examples: Source 1 · Source 2 · Source 3

The skill checklist lists “heavy user of AI” as a differentiator, specifically for handing off administrative tasks so sellers can spend time with decision makers and influencers; it also recommends using AI to research prospects before meetings.

• The CRM post names Salesbud as a tool that analyzes meetings, surfaces insights and fills in the CRM on the seller’s behalf.
• It tells sellers there is no excuse: either learn to make AI work for them or ask their manager to bring in tools that free up their time.

Anchors the abstract claim in a named product, described by what it does (analyzes meetings, gives insights, fills the CRM), then offers readers two paths: adopt AI or press their manager to provide it.

For brands planning a campaign

A vendor collaboration around AI meeting notes, CRM auto-fill or sales productivity tools fits the creator’s existing framing and the audience’s stated pain points. The Salesbud mention shows they name specific products, so a partner should expect a concrete example rather than a category-level endorsement—and verify that any product claim in the content is accurate.

Examples: Source 1 · Source 2

The reasoning is about scarcity: time is limited, and if sellers are not investing in prospects who can close, a competitor is.

• The author recommends selecting fields from established frameworks to match the business: BANT for average deals (budget, decision maker, need, buying moment); MEDDIC for big and complex deals (metrics, economic buyer, decision criteria, buying process, pain, champion—with the client’s buying process called out as absurdly important); and SPICED for average deals, especially in a first meeting (situation, pain, impact, critical event, decision).
• If the employer’s CRM lacks satisfactory qualification fields, sellers should qualify in parallel.

Lists three named frameworks and matches each to a deal profile, then asks the audience which model suits their business—a direct prompt for comments rather than a single prescribed answer.

Separates average deals from big, complex deals rather than recommending one framework for everything. It also explicitly lets sellers keep qualification outside the company CRM, setting a lower bar than a system-of-record argument would.

For brands planning a campaign

This is the corpus’s most directly teachable item and strongest hook for a training partnership: framework selection, field design and qualification outside the CRM when the system cannot support it.

• Content built around one framework per format—a MEDDIC walkthrough for complex deals, a SPICED first-meeting exercise—would match how the creator presents the material.
• The advice to qualify in parallel is a distinctive angle a partner could develop.

Examples: Source 1

The list also calls for consistently reading sales books, describing the seller as “studious.” A separate post on manager transformation reinforces the point: sellers are expected to bring their own insights and suggested solutions to meetings rather than outsource the thinking.

Puts niche knowledge, social content and reading alongside data and AI skills in the same self-assessment checklist, framing positioning as part of operational discipline rather than a separate marketing topic.

For brands planning a campaign

For partners in personal branding, social selling or content enablement, this establishes an on-record position: sellers should publish from their own experience and know their market better than the competition. The emphasis is on sellers’ own authority and research, not follower growth or posting cadence, so volume-driven tactics may not land with this audience.

Examples: Source 1 · Source 2

Most posts open with a direct challenge or question, present a scenario or checklist, and close by asking readers what they think. Several end with the same short sign-off, identifying Diego Lorenzo Carvalho as founder of SalesX and describing the topics they write about: prospecting trends, tools and techniques, and how to generate more demand and close more deals.

Most posts close with a call for comments, a consistent engagement device. The SalesX sign-off recurs across the corpus and is one of the few stable self-descriptions.

For brands planning a campaign

Plan collaborations around this house style: a text-first LinkedIn post with a named audience, a scannable list, one idea developed into a practical rule or checklist, and a closing question. For video or long-form, ask for a separate test; the corpus does not establish those formats.

Examples: Source 1 · Source 2 · Source 3 · Source 4 · Source 5

They use the posts to describe their sales operations work there and invite readers to contact them about reviewing their commercial operation.

View example

They present it as an illustration of what AI can already do, not as a partner, sponsor, or client. Ask whether they have any other relationship with the company.

View example

Amorim’s WMS/TMS/DMS and cross-docking posts follow the same teaching pattern as Diego’s posts on sales management and qualification: break a complex choice into parts, explain what each part does, and point to indicators or implementation cautions.

Amorim focuses on logistics and supply-chain operations, especially warehouse, transport and distribution systems. Diego focuses on sales behaviour, sales management, CRM discipline and opportunity qualification.

Read original · Original 2

Soares’s posts on data quality for AI and on CIOs communicating AI adoption share Diego’s habit of translating broad change into concrete organisational requirements.

Soares focuses on data quality, governance and the cultural work of introducing AI into companies. Diego focuses on seller and manager routines: funnel analysis, CRM updates, qualification fields and meeting rules.

Read original · Original 2

D’Elia’s posts on AI as an operational layer and GTM engineering stress that CRM data, documented processes and integrated teams must come before automation. This overlaps with Diego’s argument that sellers need clean CRM habits before AI can help.

D’Elia focuses on GTM engineering, RevOps and AI as a cross-functional operating layer across marketing, sales and customer success, including technical stack examples. Diego focuses more on frontline seller and manager behaviour, especially qualification, coaching and personal accountability.

Read original · Original 2

The two posts use the same B2B format as Diego Lorenzo Carvalho's work—Portuguese LinkedIn text, a stated audience, bulleted lists and a closing question—and teach the same way about a different decision: how a company should choose between WMS, TMS and DMS based on its operational pain, and what cross-docking changes about storage cost, delivery speed and inventory risk.

• The cross-docking post also names the numbers to watch, including on-time-in-full, logistics cost per order, returns rate and vehicle occupancy—the kind of measurable targets a logistics client would want taught.
• A buyer seeking process-before-technology in warehouses and distribution will find the argument already made here, including the warning that no system fixes a broken process on its own.

The tradeoff is a clean topic swap.

• Diego Lorenzo Carvalho's posts do not cover sales management, pipeline review, CRM hygiene or opportunity qualification, so a sales-training buyer would be buying a different subject in a similar language and format.
• The audience is logistics and operations professionals, not sellers.
• The posts show no engagement, reach or commercial terms, so ask for those details before an engagement.

Examples: Source 1 · Source 2

The posts address leadership and technology audiences: one argues that clean, organised data is the foundation for AI and that scattered files, servers, clouds and printed documents block the work; the other shares a trade article on the chief information officer's new job of selling AI internally to colleagues, stressing explanation, education and training instead of replacement. That framing suits a buyer selling data or AI adoption to CIOs and executives who need the cultural and organisational argument, and matches the language and LinkedIn text format a Brazilian B2B audience expects.

The tradeoff is depth of method.

• One post ends with a direct offer to discuss the sender's own team taking over the complexity; the other largely paraphrases a linked article.
• Compared with the frameworks in Diego Lorenzo Carvalho's posts, there is little here that a training buyer could turn into an exercise or template.
• There is no sales-management, CRM or qualification content, or coaching method for frontline managers.
• Ask for engagement and commercial details before proceeding; the fit rests on topic and audience, not a repeatable teaching format.

Examples: Source 1 · Source 2

The posts describe AI as an operational layer that follows the work instead of a tool opened on demand, with concrete routines: checking whether CRM fields make sense for the funnel stage, testing whether the expansion pipeline has enough volume, timing and forecasting, watching which squads are slipping, reading financial data for cash risk, converting daily commitments into follow-ups, and flagging client ad accounts that lose performance without matching optimisation.

• The second post adds a structured argument that data architecture, a shared methodology, documented processes, a standardised stack and a named GTM engineering owner must exist before any AI agent.
• It lays out a roadmap, sprints, deploys and monthly reviews, with first results in 60 to 90 days.
• That is a ready-made thesis and set of examples for a buyer selling operations change to senior revenue leaders, and it is the strongest of the three alternatives on the AI-and-process theme closest to Diego Lorenzo Carvalho's tooling posts.

The tradeoff is audience and register.

• These posts address operations and engineering leaders and lean on a named personal stack of tools and models, making them read as technical rather than as seller training.
• There is no qualification framework, pipeline-review rule for the individual seller, manager-as-coach role shift or teaching exercise a frontline sales team could use as delivered.
• A sales-training buyer looking for the practical manager and rep material in Diego Lorenzo Carvalho's posts would be swapping it for a denser, leadership-level argument.
• Ask for engagement and commercial terms before proceeding.

Examples: Source 1 · Source 2