Tony Ward writes about AskNicely’s customer-feedback and reputation products, including ways AI can help businesses turn customer feedback into action and improve their online reputation.
Tony Ward is most useful for linking customer feedback, AI and reputation to service-business product messaging.
Best for
A B2B SaaS campaign on customer experience, reputation, or AI products.
Not for
Consumer fashion promotion, paid posts, guaranteed reach, or direct sales attribution.
Key takeaways
AI assistants, not search rankings, now decide whether customers find a service business.
Customer feedback only counts once it reaches the place decisions are made.
The next wave of AI is about acting on data you already have.
Check first: Before planning, confirm availability, format, fees, audience data, inquiry route, and reuse rights.
Main ideas
AI assistants, not search rankings, now decide whether customers find a service business · Customer feedback only counts once it reaches the place decisions are made · The next wave of AI is about acting on data you already have, not generating more of it
Tony Ward writes about AskNicely’s customer-feedback and reputation products, and about how AI is changing the way people find and choose service businesses.
The posts show a consistent focus: customer feedback matters when teams can use it to make decisions, not when it stays in dashboards.
Follow them for examples of how that idea connects AI tools, customer experience and online reputation.
Ward connects a broad shift in customer discovery to practical product examples.
They argue that reviews can shape what AI tools say about a business, then show different ways teams might act on customer data: asking questions of NPS, CSAT and review information inside Claude; surfacing risks and recommendations for frontline teams; or routing review requests to platforms that need them.
A named customer account gives a concrete picture of faster access to insight, while a shoe retailer’s reported rating and review-volume changes illustrate the potential commercial stakes.
For evaluation, ask for supporting data behind those performance figures.
The distinctive value is this bridge from an AI-and-reputation argument to examples of how service businesses could put feedback to work.
Ward’s recurring argument is that customers now discover service businesses differently: they ask AI assistants rather than scan search results, and those assistants judge businesses by their public reviews.
Read more
From that premise, they conclude that reputation work has become a growth and cost lever, not just a marketing metric, and that customer feedback matters only when someone acts on it. The products they write about are framed around that test, with claims supported by named customers and before-and-after numbers rather than feature lists.
AI assistants, not search rankings, now decide whether customers find a service business
Ward argues that buyers ask ChatGPT, Claude, Gemini and similar tools to recommend local businesses, and that those tools form opinions from a company’s whole digital footprint, with reviews carrying the most weight.
Read more
Their practical conclusion: reputation, not SEO, is becoming the main growth lever for service brands, because recommendations happen before anyone sees a search results page.
Ward cites a rise from 6% to 45% in the share of people using AI for local recommendations within a year, then challenges businesses to shape what AI says about them or leave that story to someone else. In a later post, they add that buyers no longer rely on one review site, so a weak presence on Google, Yelp or Facebook costs visibility in AI searches, not just stars.
Customer feedback only counts once it reaches the place decisions are made
Ward’s recurring complaint is that survey, review and feedback data piles up in dashboards until someone asks for a report.
Read more
Their test for any feedback tool: does the data reach decision-makers, and does it eliminate the manual work of exporting and chasing it?
Announcing the AskNicely Claude Connector, Ward says feedback used to sit in a dashboard until someone requested a report.
They describe the change as feedback living where decisions get made.
The post also notes that customers can combine NPS data with support tickets, product data and social listening, then ask questions as they arise—even mid-meeting.
The next wave of AI is about acting on data you already have, not generating more of it
Ward frames the AI features as a shift from reactive reporting to proactive intelligence.
Read more
The problem, they say, is not a shortage of information but the challenge of acting on it. Their aim is to free teams from hunting for insight, not to replace people.
Ward opens the launch of Insights Agent and Response Agent with a familiar frontline complaint: managers have more customer data than ever but struggle to turn it into action, as surveys, reviews and feedback arrive faster than teams can absorb dashboards and competing priorities. Insights Agent surfaces risks, trends and opportunities for the people who need them; Response Agent handles on-brand replies at scale.
The agent posts sharpen the argument in Ward’s earlier product posts. Reputation Manager is pitched mainly as a way to consolidate surveys, reviews and listings in one view; the agent posts shift the focus to software pushing recommendations to people rather than waiting to be read.
Reputation is an operating and cost problem, not a marketing metric
Ward links reputation to how customers discover and experience a business, how it ranks in search, and what it costs to acquire and retain customers.
Read more
Their target audience is consistent: multi-location service brands that want surveys, reviews and listings in one view so they can spot issues early, respond faster and generate more positive reviews.
Ward uses a driving analogy: look ahead, understand what is behind and watch a dashboard that signals the right direction. They say customers asked for an aggregated view because reputation now affects visibility and revenue, rather than sitting with marketing alone.
Ask for the review at the moment of feedback instead of running follow-up campaigns
Ward argues that businesses should ask for a review immediately after a customer gives feedback and route it to the platform that needs it most.
Read more
They say this replaces email campaigns and chasing team members, which can stall review volume.
Ward describes Review Routing Agent prompting for a public review immediately after an AskNicely survey and directing it to the platform that needs it most. They report that one large North American shoe retailer moved its Trustpilot rating from 2.7 to 4.6 in 30 days while increasing Google review volume by 161%.
Claims are carried by named customers and before-and-after numbers
Ward’s launch posts make their case through customer accounts and specific results rather than feature descriptions.
Read more
They credit customers by name and role, describe the old manual process and quantify the improvement in elapsed time or measured outcomes.
Ward relays Shane McCarthy, chief digital officer at [solidcore], who previously needed to request help from the support team, exchange a CSV file and wait days through back-and-forth.
Now McCarthy pulls NPS data together with support tickets, product data and social listening, and can ask questions in minutes.
Ward also notes that the connector surfaced things McCarthy was not looking for.
Answers why reputation work matters now and supplies the framing the other posts build on.
Read more
Start here for the argument before the product detail.
Ward argues that discovery has shifted from search results to AI assistants: customers ask ChatGPT, Claude or Gemini to recommend a business, and those tools form a view from the company's entire digital footprint, especially its reviews.
They cite a shift from 6% to 45% of people using AI for local recommendations within a year, and conclude that reputation, not SEO, is becoming the main growth lever for service brands.
The post ends with a clear warning: businesses that do not shape what AI says about them leave that story to someone else.
For brands planning a campaign
Works as the opening frame for a talk or advisory session on AI-era reputation, establishing the problem and stakes before any product or vendor enters the conversation.
Shows what “turning feedback into action” looks like in practice, with a named customer rather than a vendor claim.
Read more
Ward announces the AskNicely Claude Connector, which puts NPS, CSAT and review data into Claude alongside other business data.
Shane McCarthy, chief digital officer at [solidcore], says they previously needed a request to the support team, a CSV file and days of back and forth.
Now McCarthy combines NPS data with support tickets, product data and social listening, and can ask questions as they arise, including mid-meeting.
Ward says feedback now lives where decisions get made and notes that the connector surfaces things the customer was not looking for.
For brands planning a campaign
The session's before-and-after contrast—days of manual exports versus questions answered in minutes—is more persuasive to a product or CX audience than a feature list.
Shows how review generation is changing as buyers start searches inside AI tools, with the set's most concrete performance figures.
Read more
Ward describes Review Routing Agent, part of AskNicely's NiceAI suite.
Buyers no longer rely on one review site; they start searches inside AI tools that pull from any public review source, so a weak presence on Google, Yelp or Facebook costs visibility as well as stars.
The agent prompts for a review immediately after an AskNicely survey and routes it to the platform that needs it most, eliminating email campaigns and follow-ups.
They report that one large North American shoe retailer moved its Trustpilot rating from 2.7 to 4.6 in 30 days while increasing Google review volume by 161%, and connect the approach to service businesses that run on public trust.
For brands planning a campaign
A strong anchor for a data-backed pitch or case-study slide: the 2.7-to-4.6 rating move and 161% volume increase are quotable. Before repeating them as verified results, ask for the retailer's identity and the measurement window.
Explains the problem these AI features are meant to solve, using what frontline managers actually say rather than a product description.
Read more
Ward opens with a recurring complaint from frontline managers: they have more customer data than ever but struggle to turn it into action, while surveys, reviews and feedback arrive faster than teams can absorb dashboards and reports.
The two agents split the work: Insights Agent surfaces customer-experience risks, trends and opportunities, with recommendations sent to the people who need them; Response Agent helps businesses answer feedback at scale in an on-brand way.
They frame the shift as moving from reactive reporting to proactive intelligence and state plainly that the intent is not to replace people.
For brands planning a campaign
Useful for a messaging workshop on problem-first framing: the post leads with the customer's stated frustration and positions AI as a way to spend less time hunting for insight and more time acting on it.
Explains what multi-location service brands asked for and how Ward connects reputation to revenue—the commercial argument a brand audience needs.
Read more
Ward announces Reputation Manager, built for multi-location service brands that wanted surveys, reviews and listings consolidated in one place so they could respond faster, generate more positive reviews and link sentiment to visibility and revenue.
The post uses a driving analogy: look ahead, understand what is behind, and watch a dashboard that signals the right direction.
They argue that reputation is no longer only a marketing metric: it shapes how customers discover and experience a business, how it ranks in search, and what it costs to acquire and retain customers.
For brands planning a campaign
Best for operations or multi-location leadership rather than marketing: the aggregated-view argument and acquisition-cost framing speak to people accountable for consistency across sites.
AI search is displacing the old page-one reputation playbook2
Customer feedback only counts when someone acts on it2
Named customer proof with specific before-and-after numbers2
Reputation as a discovery, ranking and retention problem2
Agentic AI framed as proactive intelligence, not headcount replacement1
AI search is displacing the old page-one reputation playbook
Ward's central argument is that buyers now ask ChatGPT, Claude and Gemini for local recommendations instead of scanning search results, and those tools form opinions from a business's whole digital footprint, especially reviews.
Read more
They frame this as a shift from SEO to reputation as the growth lever, and say the change is happening fast, citing a move from 6% to 45% of people using AI for local recommendations within a year.
Opens with the reframe, backs it with a single directional statistic, then closes with a challenge to the reader: if you are not shaping what AI says about your business, someone else already is.
For brands planning a campaign
A CX or reputation vendor can borrow this framing: position reviews and sentiment as an input to AI-driven discovery rather than a marketing metric. Ward can carry that argument as a talk opener, and a brand should re-source the 6%-to-45% figure before repeating it.
Customer feedback only counts when someone acts on it
A recurring complaint across Ward's posts is that survey, review and feedback data piles up in dashboards until someone remembers to request a report.
Read more
They contrast that with feedback that lives where decisions get made, letting teams spend less time hunting for insights and more time acting on them.
States the operational failure first, then presents the product as the fix, supported by a named customer outcome.
For brands planning a campaign
For a SaaS vendor whose messaging sounds like 'more data', Ward offers the counter-position: the value is action, not volume. This is the strongest angle for a proposed advisory session, because it is a message problem before it is a product problem.
Named customer proof with specific before-and-after numbers
Ward's launch posts lean on concrete customer results rather than feature lists.
Read more
A chief digital officer at [solidcore] reportedly went from a support request, a CSV file and days of back-and-forth to pulling NPS data alongside support tickets, product data and social listening in minutes. A large North American shoe retailer is described as moving its Trustpilot rating from 2.7 to 4.6 in 30 days while increasing Google review volume by 161%.
Credits the customer by name and role, describes the old manual process, then the new one, and lets the customer's own feedback carry the claim.
For brands planning a campaign
Useful as a template for case-study structure in the vendor's own content, and as a talking point for a session on proof-led messaging. Before reusing the retailer figures publicly, verify them with the customer.
Reputation as a discovery, ranking and retention problem
Ward argues reputation is no longer just a marketing metric but something that shapes how customers discover a business, how it ranks in search, and how much it costs to acquire and retain customers.
Read more
The stated customer segment is consistent: multi-location service brands that want surveys, reviews and listings consolidated in one view so they can spot issues early and respond faster.
Frames reputation as an operating problem for distributed businesses, using a dashboard analogy about looking ahead and behind while checking direction.
For brands planning a campaign
Gives a vendor a segment-specific hook: multi-location service brands, not all B2B. Ward can speak to that audience's consolidation problem, and brands should test the reputation-to-revenue link with customers before putting it into a campaign.
Agentic AI framed as proactive intelligence, not headcount replacement
When Ward introduces agents, the pitch is that the next wave of AI is not about generating more information but about enabling people to act on what they already have.
Read more
Insights Agent is described as surfacing risks, trends and opportunities to the people who need them; Response Agent as handling on-brand replies to feedback at scale. They are explicit that this is not about replacing people.
Contrasts reactive reporting with proactive intelligence, then pre-empts the displacement objection in the same paragraph.
For brands planning a campaign
For a vendor selling AI features into frontline teams, this is a ready-made way to defuse buyer anxiety about automation. It is worth testing as the narrative spine of a talk, since it pairs a product claim with a staffing objection answered up front.
Across the posts here, the structure repeats: announce the product, explain what customers asked for and why the old way fell short, add a customer or team proof point, then close with a link and a fixed hashtag set spanning CRM, CX, AI, reputation and SaaS.
Read more
The voice is first-person plural for the AskNicely team, with credit passed to customers and colleagues.
Short paragraphs, arrows or dash-prefixed feature bullets, a congratulatory line to the team, and a short link plus hashtags.
For brands planning a campaign
A brand can evaluate the format as a reusable pattern for its own launch comms and expect the same structure from Ward in sponsored or advisory content. It is product-announcement shaped, so campaigns needing opinion or critique rather than launch support should plan for a different framing.
Tony Ward's posts focus on AskNicely, a customer-experience and reputation software company.
Read more
They write in the company's own voice, saying "our AskNicely team" and "at AskNicely we get asked all the time," which points to an insider role. The connection is described as a disclosed partnership, but its exact nature is unstated; ask them to clarify their job title, employment relationship and any ownership stake.
AskNicely
Disclosed partnership
Five posts announce and explain AskNicely products: Reputation Manager, the NiceAI Insights Agent and Response Agent, the Review Routing Agent, and the AskNicely Claude Connector.
Read more
Tony Ward writes as part of the company ("our AskNicely team," "we launched") and links to AskNicely product pages. Ask them to confirm their title, employment status and any ownership interest.
Tony Ward writes about how customers now find service businesses through AI assistants rather than search results, and what that shift means for reviews, feedback and reputation.
Read more
The argument unfolds in two stages: posts make the case that AI tools judge a business from its whole public footprint, especially reviews; product posts show how they apply that idea—pushing feedback into the tools where decisions get made, sending review requests when a customer gives feedback, and using agents to surface risks and trends instead of waiting for someone to request a report.
The recurring test is whether customer feedback reaches the people who can act on it, and they point to named customers and before-and-after numbers rather than feature lists.
For hands-on search and data-hygiene tactics, a practitioner's view from inside a job search, or the HubSpot partner ecosystem, look to the creators below.
Sawyer covers the same customer-experience territory from the practitioner side: how support teams move from transactional metrics such as handle time to experience measures including NPS, CSAT and customer effort, and how to treat feedback as a continuous loop rather than a checkbox.
Read more
Readers who follow Ward's argument that feedback only counts once it is acted on will recognise the same problem stated in operational terms.
Sawyer writes as an individual practitioner between roles, mixing CX operating advice with job-search and mental-health guidance and a stated availability for a director or VP position. Ward writes in the first-person plural for a vendor, building each post around a product launch.
Hirbod uses the same format as Ward: take a platform or AI product release and translate it into what partners and clients can actually do with it.
Read more
The HubSpot Deep Research Connector post follows the structure of their connector post—a launch announcement followed by a concrete list of applications, from AI-powered audits to persona and journey work using real CRM data.
Hirbod focuses on the HubSpot partner ecosystem and getting CRM data into AI tools, for an audience of solutions partners and clients. Ward focuses on reputation, reviews and customer feedback for service brands, centring the customer's public footprint rather than internal CRM data.
Soloff shares Ward's starting premise and teaches it directly: people ask ChatGPT instead of Googling, businesses now compete to be recommended rather than clicked, and trust signals outside their own site decide whether they are picked.
Read more
That is the same shift Ward describes in the 6%-to-45% post, argued from the search side rather than the reputation side.
Soloff offers hands-on tactics operators can apply—product titles, variant URLs, category structure and data cleanliness—for local businesses and e-commerce stores. Ward stays at the level of strategy and product for multi-location service brands, making the case through customer results rather than a how-to list.
Tony Ward covers customer feedback, reputation management and AI for service businesses.
Read more
The topics may suit a B2B SaaS campaign, but audience fit is unverified. Before booking, request recent audience data for relevant professional roles and sectors, plus results from comparable topics and formats.
All five posts are AskNicely product announcements — Reputation Manager, Review Routing Agent, the Insights and Response Agents, and the Claude Connector — written for customers and prospects rather than brands seeking to sponsor Ward’s work.
Read more
The posts include no partnership or sponsorship offer, published rate card, media kit, audience package, editorial calendar, sponsorship policy, or stated inquiry route.
The only public channel mentioned is Ward’s LinkedIn profile.
A buyer cannot currently plan a paid placement from the information available; brands interested in a campaign aimed at customer experience and reputation should contact them through LinkedIn to confirm availability, formats, pricing, and terms.
Strong subject-matter fit, but commercial fit is unconfirmed. Ward’s posts make a consistent case: customer feedback is wasted when it stays in dashboards; AI can help teams act on existing feedback; and public reviews may affect how service businesses are found. Their examples make the discussion practical, including a [solidcore] account of moving from days of CSV requests to answering questions in minutes, and a post about reputation management for multi-location service brands. The posts are product announcements and do not establish paid speaking or advisory experience. Ask about availability, format, fees, audience reach, inquiry route and the company’s right to reuse any material before planning a booking.
Read more
Brief
A B2B SaaS company wants to sharpen its customer-experience and reputation product messaging with a talk or advisory session on turning feedback into action and growing reputation in the AI era.
Suggested use
A moderated talk or working session for CX, product and marketing leaders at a service-focused SaaS company. Focus on the customer problem and the information needed to support claims, rather than assuming an endorsement.
When to choose a specialist
Poor fit for a fashion campaign aimed at consumers. The five posts concern AskNicely products, customer feedback, reputation and AI; none shows fashion coverage, consumer product promotion, sponsorship, sales performance or an audience profile that would support this campaign. The posts also do not describe a paid-post format, reach guarantee, rate card or partnership contact. Ask for evidence of a relevant audience and the commercial terms; otherwise, a fashion creator with demonstrated consumer-fashion work would be a more relevant alternative.
Read more
Brief
A consumer fashion brand wants a paid LinkedIn post promoting a new clothing line to a measured fashion-shopping audience, with guaranteed reach and direct sales attribution.
A worked campaign example
Buyer checks
Ask for a proposed format, availability, fee and inquiry route.
Request recent audience data relevant to the intended professional roles and sectors; the posts establish subject matter, not audience composition.
Agree whether the session is editorial, advisory or promotional. Do not assume an endorsement or sponsored content.
Verify customer permission and underlying evidence before reusing the [solidcore] account or any performance figures. Treat the retailer results and other product claims as Ward’s reporting unless independently substantiated.
Set a clear deliverable and success measure, such as a reviewed messaging map or an agreed list of customer-evidence gaps—not an assumed reach or sales outcome.
Campaign angle
From feedback overload to action: what changes when customer evidence reaches the people making decisions?
Execution
Open with the frontline-manager problem Ward describes: more surveys, reviews and customer data, but too little time to turn them into action. Ask them to lead a session mapping one real feedback path—from customer comment, through the team that receives it, to a decision or response. Use the [solidcore] example as a discussion prompt: what made the move from support requests and CSV exchanges to questions answered in minutes useful, and what conditions would another company need to reproduce that approach? Close with a short exercise: identify one feedback signal the company currently misses, who should act on it, and how the team would know the response helped.
Choose Sawyer for a CX operating-model workshop rather than a reputation-product story.
Read more
Their January 2025 post lays out how to convert a traditional support centre into a CX function: shift from transactional KPIs to experience measures such as NPS, CES and CSAT; move from reactive triage to proactive frontline teams; and turn feedback from a checkbox into a continuous loop that closes with the customer. For a session aimed at changing processes at a support organisation, that framework is more practical than Ward's product-announcement posts, which explain what the tools do rather than how a team reorganises around feedback.
The argument stops well before AI-era reputation: it does not discuss how reviews shape discovery, listings or review visibility, or vendor and product context.
Another post addresses job-search mental health, and the post signature says the author is open to a Director, VP or Head-of role in customer support or CX.
Ask Ward to frame how AI search and public review presence connect to their work, and confirm their availability for paid speaking or advisory work.
Choose Hirbod when the audience is a CRM or platform-partner ecosystem rather than a CX or reputation buyer.
Read more
Their June 2025 post explains what the HubSpot Deep Research Connector means for solutions partners and clients, naming uses such as AI-powered audits, persona and journey mapping, enriched QBRs and automation workshops.
Their September 2024 post recaps INBOUND releases.
For a campaign that needs partner-enablement content, platform-adoption examples or an ecosystem-facing voice, that is closer to the need than Ward's service-brand reputation argument.
The material follows one vendor's roadmap, so a vendor-neutral company risks built-in competitor framing. It does not cover feedback-to-action operations, review-site presence or how AI assistants form reputational opinions—the core of Ward's position.
Choose Soloff when the problem is AI-era discovery for a product or ecommerce brand.
Read more
The April 2026 post makes the argument directly: shoppers ask ChatGPT instead of searching, the buyer path shortens from search-scroll-compare to ask-three-recommendations, and product-data hygiene determines who gets picked.
The March 2026 post separates local from national SEO and ties each to a business model.
For a paid-acquisition or search brief, that discussion covers the part of Ward's thesis a marketing team would actually implement.
The focus is discovery and content, not customer feedback or reputation operations.
Soloff does not address NPS or CSAT loops, review routing or multi-location service reputation, leaving a CX team running a feedback or reputation programme with thinner material.
Both posts invite readers to get in touch about SEO work, but neither shows a review-management engagement.
