John Gaspar is useful for practical customer success retention and AI workflow ideas for B2B SaaS teams.
Best for
B2B SaaS retention and AI adoption campaigns needing customer journey mapping and CS rollout planning.
Not for
High-reach consumer awareness pushes measured by impressions, follower growth, or top-of-funnel leads.
Key takeaways
Retention is won after go-live, not at onboarding.
AI agents should augment customer success work, not replace it.
AI is becoming an assumed workplace baseline, and unease is part of transition.
Check first: Confirm follower job titles, seniority, geography, engagement, and comparable post results.
Main ideas
Retention is won after go-live, not at onboarding · AI agents should augment customer success work, not replace it · AI is becoming an assumed workplace baseline, and unease about it is part of the transition
John Gaspar shares practical ideas about customer success, SaaS retention, and AI at work.
Their posts frame retention as a continuing journey, not just an onboarding task: teams can map value milestones, tailor support to different customer roles, and clarify who owns each step.
They also present AI as support for everyday work, from finding customer information to preparing success plans, with guardrails and integration into existing processes.
Gaspar turns broad themes into practical prompts for customer-success teams.
In a retention post, they connect customer milestones with role-specific journeys, ownership, success measures, and early risk signals.
In posts about AI, they pair concrete use cases—such as meeting follow-up and QBR preparation—with a clear principle: augment existing work, set guardrails, and integrate AI tools into the process.
John Gaspar writes about customer success in SaaS, and two priorities run through the posts: retention is decided after a customer goes live, not when onboarding ends, and AI should be adopted as a practical teammate inside customer success rather than a replacement for it.
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The posts are instructional rather than personal — numbered checkpoints, named metrics, cited benchmarks, and closing questions that invite other practitioners to compare notes.
Their recurring move is to take a topic usually discussed at the level of slogans ("journey mapping," "AI in CS") and break it into a small set of decisions a team can actually make: which milestones matter, who owns each touchpoint, when AI is allowed to talk to a customer, and what to pilot first.
A short post about a team visit adds a quieter priority — automation is adopted alongside human coordination, not instead of it.
Retention is won after go-live, not at onboarding
Gaspar's core argument is that strong customer success teams have moved past onboarding checklists to what they call value expansion arcs.
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The riskiest part of a customer relationship is the long stretch after implementation, when the customer can quietly stop seeing new value even though the product is technically live and working.
Gaspar turns that into five decisions a team can make: treat post-go-live as its own stage; segment the journey by persona, since admins, executives, and end users move differently and expansion signals differ for each; define lifecycle metrics rather than one-time health scores, such as adoption stickiness by month six and business value realization by month nine; document who internally owns each milestone touchpoint; and build in proactive risk detection rather than reacting at renewal.
Journey maps are living frameworks that change with customer needs, product complexity, and revenue strategy — not documents completed once.
In the journey-mapping post, Gaspar cites a figure that 71% of churn occurs post-go-live when customers do not realize expanding value, and that losing an executive sponsor raises churn risk by 2.3 times. The post closes by asking which milestone customer success teams most often miss, a question aimed at practitioners rather than buyers.
AI agents should augment customer success work, not replace it
Gaspar frames AI agents in customer success as having crossed from novelty to necessity in about a year, but bounds the argument: the future of CS is "augmented, not automated."
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Agents are already good at defined, high-volume tasks that sit on top of existing systems, while the risk lies in letting them loose without rules.
Gaspar recommends setting guardrails that specify when AI speaks to customers directly versus staying internal, piloting a few high-leverage use cases rather than everything at once, and integrating AI into existing processes so tools do not become isolated from the team's work.
The post reads as a rollout plan in miniature.
Gaspar ends by asking others what is working so they can compare notes, offering guidance in the spirit of shared practice.
The concrete AI agents Gaspar names are email and chat agents that draft personalized replies from CRM history, meeting agents that summarize calls, tag action items, and update Salesforce, and health bots that surface risk from product signals. Suggested first pilots are QBR preparation, success-plan drafts, and onboarding documents; the post cites Gartner on adoption expectations for 2026.
AI is becoming an assumed workplace baseline, and unease about it is part of the transition
Gaspar's second thread about AI is cultural rather than operational.
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It runs through a historical analogy: interview questions about email, Word, and Google Slides once mattered and now sound absurd, so saying "I use ChatGPT" will soon be unremarkable as AI becomes an invisible assistant threaded through ordinary work.
The case for adopting it early is capacity — Gaspar points to routine work being handed to AI so people can invest more in the roles they already have.
The post does not stop at enthusiasm: it names job shifts and a general "is this too much?" feeling before landing on staying curious and deliberately doing something uncomfortable.
That willingness to hold both the practical upside and the unease at once is a recurring tone.
It is the main tension across the posts: the same writer who describes AI agents as a necessity also treats discomfort as legitimate rather than something to argue away.
Everyday illustrations include untangling a water bill, digging up an old customer question, and juggling school pick-up, two meetings, and a pitch deck. For evidence, Gaspar cites IBM shifting hundreds of routine HR tasks to AI to free capacity for existing roles, and a Gainsight CS Index finding that over 50% of companies are already embedding AI in day-to-day customer success workflows.
Automation does not remove the need for teams to stay connected
A short post from a visit to the Frontify head office states a simpler priority that sits underneath the longer pieces: AI and automation are changing how people work every day, but they have not changed the need for connection.
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It matches the posture of the other posts — tools are added to a team's work, not swapped in for the coordination that holds the team together.
Gaspar describes a visit to the St. Gallen office and an active stretch ahead for the customer success team, framing the moment around teamwork and design thinking rather than a tool.
See how Gaspar frames retention after onboarding and what a customer journey mapping workshop should cover.
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Gaspar argues that customer journey maps should extend beyond onboarding checklists to cover the arc of value expansion.
The post outlines five priorities: post-go-live value expansion; journey segments for admins, executives, and users; lifecycle metrics such as adoption stickiness by month six and business value realization by month nine; ownership across teams; and proactive risk detection.
It cites figures including 71% of churn occurring post-go-live and a 2.3x increase in churn risk after losing an executive sponsor, then asks which milestone customer success teams most often miss.
For brands planning a campaign
Use this as a workshop agenda for a retention brief: map post-go-live value milestones, assign an owner to each touchpoint, define adoption and value metrics, and flag executive-sponsor loss as a risk trigger.
See where Gaspar thinks AI agents fit in customer success workflows and what to pilot first.
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Gaspar describes AI agents moving from novelty to necessity: agents that draft personalized replies from CRM history, meeting agents that summarize and update Salesforce, and health bots that surface risk from product signals.
They cite Gartner’s projection that 45% of customer success teams will use AI agents in daily workflows by 2026.
The post recommends setting clear guardrails for when AI speaks to customers versus serving internal users, piloting QBR prep, success plan drafts, and onboarding documents, and integrating AI into existing processes rather than isolating it.
For brands planning a campaign
Use this to shape an AI rollout plan: set customer-facing and internal guardrails, choose high-leverage pilot use cases, and connect tools to the existing CRM and customer success process.
See how Gaspar makes AI adoption feel ordinary and addresses unease about change.
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Gaspar compares AI adoption to email, Word, and Google Slides: tools that once came up in interview questions are now assumed parts of work.
They predict AI will become an invisible assistant for tasks such as untangling a water bill, finding an old customer question, juggling school pickup with meetings and a pitch deck, and getting three teams to agree on a process.
The post cites IBM shifting routine HR tasks to AI to free capacity for existing roles and a Gainsight CS Index showing over 50% of companies embedding AI in customer success workflows, while acknowledging job shifts and unease.
For brands planning a campaign
Use this for change management in a customer success team: normalize everyday AI use, lead with capacity gains, and acknowledge anxiety without making it the whole message.
Customer journey mapping aimed at retention, not onboarding completion
In a post about mapping a customer journey that drives retention, John Gaspar argues that top customer success teams have moved past onboarding checklists to what they call value expansion arcs.
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They lay out five checkpoints: the period after go-live, when most churn happens once customers fail to see expanding value, according to a figure they cite; journeys segmented by persona, since admins, executives, and end users move through different stages, with customer-success-qualified-lead tracking used to spot expansion; lifecycle metrics such as adoption stickiness by month six, business value by month nine, and risk alerts near the renewal window; documented ownership of each milestone touchpoint across internal teams; and proactive risk detection, with the loss of an executive sponsor described as sharply raising churn risk. They close by treating journey maps as living frameworks that change with customer needs, product complexity, and revenue strategy.
Gaspar writes in numbered best-practice checkpoints, each tied to a metric, an owner, or a cited benchmark, and ends with a question inviting practitioners to name the milestone teams most often miss.
Several benchmarks are attributed to third-party reports (Gainsight, TSIA, Totango). Ask Gaspar for the underlying reports if the figures will inform a campaign.
For brands planning a campaign
For a campaign aimed at improving retention, this is the closest match in the posts here to a workshop outline: a brand could structure a journey-mapping session around the five checkpoints and ask Gaspar to pressure-test where the company's current map stops at go-live.
AI agents in customer success: augmented work, guardrails, and pilot scope
Gaspar describes AI agents in customer success as having moved from novelty to necessity within a year, pointing to agents that draft personalized replies from CRM history, meeting agents that summarize and tag action items and update Salesforce, and health bots that surface risk from product signals.
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They cite Gartner on adoption expectations and recommend three practices: set clear guardrails about when AI speaks to customers versus staying internal, pilot high-leverage use cases such as QBR preparation, success-plan drafts, and onboarding documents, and integrate AI tools into the existing process rather than letting them sit in a silo. Their framing is that the future of customer success is augmented rather than automated, and they invite others to compare notes on what is working.
The post moves from a short trend claim to concrete agent use cases, a cited adoption statistic, and three named operational practices.
For brands planning a campaign
This maps directly to an AI-agent rollout plan: the guardrail, pilot, and integration steps read as a sequence a brand could adopt, and Gaspar's stated interest in comparing notes makes a rollout-plus-review engagement plausible. Ask for evidence of hands-on delivery experience; the post offers guidance rather than a documented implementation.
AI as an assumed workplace baseline, and adapting to it
Using a comparison to job interviews that once asked whether candidates could use email or Word, Gaspar argues that within a couple of years saying you use ChatGPT will sound unremarkable because AI will be an invisible assistant woven through daily work.
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They give everyday examples, from untangling a water bill to recovering an old customer question to coordinating school pick-up around meetings and a pitch deck, and point to IBM shifting routine HR tasks to AI to free capacity for existing roles and to Gainsight's CS Index reporting that over half of companies already embed AI in customer success workflows. They acknowledge the unease around job shifts and uncertainty about where this lands, and end by urging readers to stay curious and deliberately do something uncomfortable.
The argument moves from a familiar historical analogy to current examples, names the discomfort rather than dismissing it, and closes with encouragement to act.
For brands planning a campaign
Useful as an opening frame for change management: a brand rolling out AI in customer success could use this tone to normalize adoption without overpromising. Keep the engagement anchored in the specific agents and guardrails described in Gaspar's other posts.
In-person connection and teamwork inside a customer success team
In a short post about visiting a head office in St.
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Gallen, Gaspar notes that AI and automation are changing daily work but have not changed the need to stay connected, and points to an active stretch ahead for the customer success team. The post is social rather than instructional, but reinforces a theme in the longer pieces: automation is adopted alongside, not instead of, human coordination.
A short event post offers supporting context rather than a topic in its own right.
For brands planning a campaign
Signals a preference for team-level, in-person enablement moments, which could shape how a workshop or rollout is delivered.
John Gaspar's posts show one explicit brand relationship: they associate John Gaspar with Frontify, referring to “our Frontify head office” and “our Customer Success team.”
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IBM, Gainsight, Gartner, TSIA, and Totango appear as research or industry examples, not as disclosed commercial relationships.
Frontify
Employment
Describes a visit to “our Frontify head office” in St.
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Gallen and refers to “our Customer Success team,” indicating an employment connection to Frontify.
John Gaspar's posts sit where customer success practice meets AI adoption in SaaS.
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Two commitments organize the posts: retention is decided after go-live rather than at onboarding completion, and AI agents are worth adopting only when they have clear guardrails and a narrow pilot scope.
For the workshop-shaped side, start with the customer journey mapping post, which breaks retention into five decisions — treat post-go-live as its own stage, segment journeys by persona with customer-success-qualified-lead tracking, define lifecycle metrics such as adoption stickiness by month six, document which internal team owns each milestone touchpoint, and build proactive risk detection instead of waiting for the renewal window.
For the rollout side, the AI agents post names concrete uses (email and chat drafting from CRM history, meeting summarizers that update Salesforce, health bots reading product signals) and gives a three-step sequence: set guardrails about when AI speaks to customers, pilot high-leverage use cases such as QBR preparation and success-plan drafts, then integrate rather than isolate.
A third post treats AI as an ordinary workplace baseline while leaving room for the unease that comes with it, and a short post about a team visit adds the quieter point that automation is adopted alongside human coordination, not in place of it.
They write in numbered checkpoints and close with questions inviting other practitioners to compare notes, so the work reads as shared practice rather than personal narrative.
They cite benchmarks attributed to Gainsight, TSIA, Totango, Gartner, and IBM.
Blum writes short LinkedIn posts on customer success practice, in the same register as Gaspar: both argue a position, then lay out checkpoints a team can act on.
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Blum's scaled-success post lists what a scaled program actually contains — onboarding that drives adoption, content matched to customer maturity, in-app guidance, signals on who is thriving or ready to expand, targeted plays, community, and education — which follows the same instructional shape as Gaspar's five retention checkpoints and three AI practices.
Blum's center of gravity is where customer success sits in the organization and how it is delivered at scale.
One post argues that customer success needs a place in the executive room to inform decisions on stickiness, expansion, adoption gaps, and renewals; the other separates scaled customer success from neglect and defines what a scaled program should include.
Gaspar stays closer to the mechanics of a single journey and AI tooling, with cited benchmarks and named lifecycle metrics.
Read Blum for questions about positioning and coverage model, and Gaspar for journey stages and agent rollout.
Foster works the same seam as Gaspar — the period after a customer success platform goes live and the reality of what teams actually use.
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Foster describes platform work at Medallia and Gainsight, dozens of implementations, and clients who return to spreadsheets and Outlook after eighteen months.
They then set out what the work should involve: configuring around how a team really works, picking the twenty things that matter for the next two quarters, building journeys that run without a human, and telling the client honestly whether they have the hours to run it.
Gaspar's journey-mapping post makes a compatible argument from the other direction: the risk sits after go-live.
Foster writes as a founder offering platform implementation and administration, so the posts carry a first-person origin story, client anecdotes, and an explicit offer.
A second post argues that departmental metrics at the top, not collaboration habits, explain why customer success is hard inside large organizations.
Gaspar offers practitioner guidance and cites third-party benchmarks rather than client work, while pushing further into AI agents and guardrails.
Foster touches on AI only by noting that agents are arriving whether the underlying process is defined or not.
Patin publishes serialized customer success playbooks on the retention-adjacent moments Gaspar also covers.
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The onboarding post argues that clients must see value in the first thirty days, describes how to pick quick wins and celebrate them loudly with dashboards, shoutouts, and executive summaries, and gives a client example where one reporting feature saved ten hours a week and adoption rose.
The renewal post treats renewal as a proof point rather than a transaction and says conversations should start months early.
Both writers turn customer success topics into step lists a working CSM can follow.
Patin writes shorter, clearly numbered series posts built on direct client examples and framed as advice to fellow CSMs, without cited benchmarks or engagement with tooling. Gaspar's posts are longer, anchor claims in figures attributed to named reports, and carry a distinct thread on AI adoption and agent guardrails that Patin's posts do not address.
The available audience information points to a small, topic-focused LinkedIn following rather than broad reach.
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John Gaspar's LinkedIn profile recorded 3,124 followers on October 8, 2026.
The four posts, dated May 6, 2025 to April 24, 2026, focus on customer success, retention, customer journey mapping, and AI in customer success workflows.
That makes them relevant to a B2B SaaS retention and AI rollout campaign: they address practitioners and leaders who would take part in a customer journey mapping workshop or own an AI agent rollout.
The posts do not identify follower job titles, seniority, company size, geography, or engagement rates, so buyers cannot tell whether the following is mostly CS leaders with budget authority or junior practitioners.
Before committing, request results for comparable posts—one on retention or journey mapping and one on AI in CS—including impressions, reactions, comments, and any platform-provided follower demographics by job title, seniority, and geography.
The four posts read in full target customer success practitioners, offering advice on retention and journey mapping and explaining where AI fits into CS work.
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IBM, Gainsight, Gartner, TSIA and Totango appear as cited research and news, not disclosed commercial arrangements.
A brand mention or campaign-style hashtag does not establish a paid deal.
The Frontify reference in the April 2026 post reads as an employer or workplace connection—a visit to a company head office—not a sponsored placement.
John Gaspar's posts include no media kit, rate card, sponsorship policy, advertiser audience specification or disclosure of past brand collaborations.
No official inquiry route for partnership or sponsorship requests is published.
Treat a paid partnership as unconfirmed; ask through a direct professional contact if pursuing one.
Brief A, taken from the reader brief: a B2B SaaS company wants to improve retention and adopt AI in customer success workflows, and wants a customer journey mapping workshop plus an AI agent rollout plan.
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This is the strongest match the posts support.
The four posts read in full cover the same subjects as the brief — post-go-live retention and journey mapping, AI agents inside customer success, and how to get a CS team comfortable with AI — and they are written for the practitioners who would sit in the workshop: CS leaders, CS ops, and the people who own onboarding, QBRs, and renewal risk.
John Gaspar's material is structured, not just opinionated.
The retention post lays out five checkpoints for a workshop: post-go-live value expansion, persona-based journey segments, lifecycle metrics tied to months six, nine, and renewal, ownership of each milestone touchpoint, and proactive risk detection.
It cites concrete triggers, such as executive-sponsor loss raising churn risk.
The AI agents post gives a brand an adoptable rollout plan: define guardrails for when AI talks to customers versus stays internal, pilot high-leverage use cases such as QBR prep and success-plan drafts, and integrate tools into the existing process instead of running them alongside it.
The AI-as-baseline post offers change-management framing for an internal rollout without overselling it.
The audience is small — 3,124 LinkedIn followers recorded on October 8, 2026 — so this is a practitioner-credibility engagement, not a reach play, and it should be bought and measured that way.
Before committing, ask for engagement results on the two closest posts (one retention or journey-mapping post, one AI-in-CS post) and a short walkthrough of how they have run a workshop or rollout in practice; the posts show the frameworks but not delivery.
When to choose a specialist
Brief B, deliberately different in shape: a consumer fintech or marketplace brand wants a high-reach awareness push, measured on impressions, follower growth, and volume of top-of-funnel leads, with a paid sponsorship package priced per thousand impressions.
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This is a poor fit based on the posts.
A 3,124-follower LinkedIn audience cannot carry an impressions-based awareness target.
The posts do not document reach beyond LinkedIn, a second channel, a media kit, a rate card, or any past paid brand collaboration, leaving no basis for pricing or scoping a sponsorship.
The topics compound the mismatch: customer journey mapping, CS-qualified-lead tracking, adoption stickiness by month six, Salesforce updates, and AI guardrails speak to a B2B software audience already running a customer success function.
A consumer audience deciding whether to open an account would get little from that material, and asking John Gaspar to produce consumer lifestyle content would discard the specific value the posts demonstrate.
The posts also do not document a track record of delivering workshops, rollouts, or campaign deliverables to an external brand.
Brief B would therefore buy an untested format at scale; revisit only if direct engagement data, a second channel, and a documented commercial route are provided.
A worked campaign example
A worked angle for Brief A, written as a sequence the brand can brief against.
The frame comes from the AI-as-baseline post: treat AI in customer success as ordinary infrastructure rather than a transformation project, and say so explicitly to the team.
The post argues that tools become assumed once they are part of daily work and that change is easier when people stay curious.
Deliver it in two coupled phases.
Phase one, a half-day customer journey mapping workshop.
Open by shifting the map's endpoint from go-live to expansion value, using the retention post's five checkpoints as the working agenda: (1) map what happens after go-live and mark where customers currently stop seeing expanding value; (2) separate journey stages for admins, executives, and end users, since the post argues these move differently; (3) set lifecycle metrics at named points — adoption stickiness by month six, business value realization by month nine, risk alerts inside the renewal window; (4) assign one named internal owner per milestone touchpoint; (5) define proactive risk triggers, with loss of an executive sponsor as the first flagged.
Each checkpoint produces an artifact, not a discussion: a revised journey map, a persona-by-stage grid, a metric table with owners, and a risk-trigger list.
Close on the post's own prompt — which milestone does the team most often miss — and record the answer as the workshop's first repair item.
Phase two, an AI agent rollout plan built on the three practices from the AI agents post.
First, write the guardrails: a one-page rule set stating which customer-facing interactions AI may handle, which stay internal, and who approves an exception.
Second, pick three pilot use cases from the post's own examples — QBR preparation, success-plan drafts, and onboarding documentation — and scope each to a single team for one quarter, with a before-and-after measure taken from the workshop's metric table rather than a separate reporting system.
Third, integrate rather than isolate: connect the chosen agents to the existing CRM and CS process so drafted replies draw on CRM history and meeting notes update Salesforce, matching the agent patterns the post describes.
Add a short internal review at the end of the pilot that asks what worked, not whether AI worked, mirroring the post's invitation to compare notes.
For the brand, the deliverables are the revised journey map, the metric-and-owner table, the guardrail page, and a three-use-case pilot plan with measures.
Give John Gaspar the current onboarding flow, churn data by cohort, the CRM's data model, and access to the CS leads who own renewals; without those inputs, the workshop produces a generic map.
Before signing, ask for recent engagement results on the retention and AI-in-CS posts and a walkthrough of how they have run a comparable workshop or rollout.
The posts do not state a sponsorship policy, rate card, or inquiry route, so raise commercial terms through direct professional contact and agree them in writing.
Choose Craig Blum when customer success has no standing in executive decisions, or when the account base is too large for named CSM coverage and the job is to design a scaled programme.
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Blum's two posts focus on position and operating model, not the mechanics of a customer success journey.
One argues that CS leaders need a place in decisions about NRR, churn, feature stickiness, expansion readiness, adoption gaps, and renewal and advocacy blockers, bringing data, story, and commercial impact rather than unread Slack updates.
The other defines scaled success as a designed mix, not neglect: onboarding that drives adoption, content matched to customer maturity, in-app guidance that solves problems fast, signals separating accounts that are thriving, drifting, or ready to expand, targeted plays that feel personal, a community users want to join, and education that turns casual users into power users.
This is the stronger starting point for a workshop with the executive team on the scaled-coverage model, rather than one with the CS team on journey mechanics.
Blum's posts argue a case; they do not provide a sequence.
Neither gives a workshop agenda, persona-by-stage journey segments, metrics pinned to month six, month nine, and the renewal window, named owners per touchpoint, or AI agent guardrails and pilot use cases.
Gaspar's posts supply those parts of the customer success journey workshop and rollout plan.
If you're buying a revised journey map, a metric-and-owner table, and a three-use-case agent rollout plan, Blum's posts leave all three to be built elsewhere.
Ask for evidence of delivering a workshop or rollout to an external brand.
Choose Auste Foster when the platform is already bought and underused.
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Her founder post describes that situation exactly: a company spends five or six figures a year on a customer success platform and eighteen months later the CSMs are back in Outlook.
She writes from inside that stack, having worked in Medallia and then Gainsight as the first person on the EMEA professional services team and later a senior solutions architect, across sixty implementations.
She says the projects she valued most came after go-live, once the implementation team had left.
For a customer success platform configuration and automation engagement, the work she describes happens inside the platform you already own: pick the twenty things that matter for the next two quarters, build journeys that run without a human in them so a stretched team can focus on accounts that need attention, and leave the team something they can run, with an honest answer about whether they have the hours.
Her second post adds a diagnosis Gaspar's posts do not make.
When a CRO is measured on growth and a CCO on retention, each leader can be doing exactly what they were hired for while the company struggles to move in one direction.
The friction is a measurement problem wearing a collaboration costume, and technology will not solve a disagreement about what success means.
The tradeoff is scope and starting point.
Foster's posts focus on platform implementation.
They do not cover a customer journey mapping workshop with persona-segmented stages or lifecycle metrics at named months, which the workshop described here requires.
Her AI reference is to journeys running without a human in them, not guardrails for when an agent may speak to a customer, so the two posts do not cover the governance side of an agent rollout.
Her writing is also explicitly tied to the business she is building, so engaging based on these posts would mean a services arrangement to configure an existing instance, rather than the workshop-and-rollout format Gaspar's posts sketch.
Where Gaspar gives a CS team a workshop frame, Foster offers a narrower, more technical view: what to fix in the instance you already pay for, and what to leave for later.
Choose Chris Patin when the immediate problem is the first 30 days and the renewal date rather than the whole journey.
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The onboarding post works through quick wins as the mechanism for early momentum: look for use cases that solve an immediate pain point, prioritise features that are easy to adopt but deliver visible results, and celebrate those wins loudly through dashboards, shoutouts, and executive summaries.
The example is concrete — a client with low engagement after onboarding pivoted to a single reporting feature that saved the team ten hours a week, and adoption rose because value arrived fast.
The renewal post extends the same logic: start the conversation months in advance rather than weeks, treat renewal as a milestone inside the success journey rather than the end of it, and make the case from data, stories, and outcomes rather than from the relationship.
Clients renew because they have seen results.
For a brief centred on onboarding adoption and renewal preparation, that is a tighter operational match than a journey-wide map.
Patin's two posts are CSM-level playbooks, focused on the practitioner doing the work.
They offer no persona-segmented journey across admins, executives, and users, no metric set pinned to month six, month nine, and the renewal window, no ownership map across internal teams, and no proactive risk-trigger list.
They contain no AI agent material, so an AI agent rollout would need another source.
Gaspar's posts also make the change-management argument for introducing AI to a CS team as ordinary infrastructure; Patin's do not.
Ask for evidence of running a workshop or rollout for an external brand.
This profile draws on four LinkedIn posts by John Gaspar covering customer journeys and retention, AI agents in customer success, AI in everyday work, and a Frontify team visit.
It describes the ideas in those posts, not a timeline.
Statistics cited in the posts are attributed to their named sources; those sources were not independently reviewed here.
