Hichem Bennaceur

AI has compressed execution: first drafts, analyses, internal tools, hypothesis generation and large-scale data processing that once took days now take hours.

• That makes strategy more valuable.
• Deciding which problem matters, which metric misleads in the business context, and whether a winning test should be rolled out requires full context—the hardest thing to transfer to a model.
• The more context AI gets, the more useful it becomes as a thinking partner, but the person directing it must lead.
• The closing claim: the execution gap between good and great is closing, while the strategy gap is widening.

Both failure modes are specific: a test wins by 12% but should not be rolled out, or a landing page gets blamed for a problem that actually lies in the offer.

Read original

More conversions start with understanding how prospects become buyers: map their pain points, motivations, objections and decision-making process.

• Then refine the experience.
• The recommendation is explicit: step back and map the journey before tracking every available metric.

The map’s four dimensions—pain points, motivations, objections and decision-making process—form a concrete checklist. The post adds that customer experience is what almost every business owner forgets when measuring ecommerce and SaaS success.

Read this ordering alongside the tooling work: journey mapping comes first, and the analytics and review-mining outputs feed a picture the map has already framed.

Read original

The argument: automate this layer and join it to the quantitative stack rather than treating it as a standalone exercise. The output is structured data, not a slide deck, designed to sit alongside GA4, session recordings and funnel metrics.

A tool built on the open-source Firecrawl library scrapes Trustpilot and pulls close to 90% of a brand’s total reviews, then analyzes them through the Claude API at roughly $0.05 per run.

• It returns sentiment classification across the review set, NPS estimated from unstructured text, friction points ranked by how many customers mentioned them, and conversion opportunities that would not surface in GA4.
• The JSON output is described as the part used most in practice.

Read original

AI removes the friction between knowing what you want and building it, without replacing expertise.

• Someone who understands their routine well enough to describe it can build a tool that serves it and drop the recurring subscription.
• The same reasoning applies to internal research tools and a personal website.

A $15/month Framer subscription was canceled, and the site rebuilt in about one day using Claude Code and Astro, with no manual code written.

• The stated costs afterward were $0 for hosting on Fly.io’s free tier and roughly $10 per year for the domain.
• In the same post, Hichem Bennaceur describes a software engineering background; this is a self-description, not an independently verified credential.

Read original · Original 2

The method: collect category leaders, evaluate each against the same criteria and add a recommendation. That lets a brand see how peers present their offer and proof before deciding what to change.

More than 15 hours of research into garden and plants DTC brands in the U.S. produced a Figma file listing 18 homepages.

• Each entry covers unique value proposition, call-to-action effectiveness, product presentation, social proof and user engagement strategies, plus overall insights and recommendations.
• The file was distributed by asking readers to comment "Garden" and then sending it by direct message.

The argument is demonstrated in one category, garden and plants. Nothing in the material shows the same teardown applied elsewhere; a new category would require its own research pass.

Read original

Start here to see the method behind the pitch.

Hichem describes building a tool that scrapes Trustpilot reviews with the open-source Firecrawl library, pulls close to 90% of a brand's total reviews, and runs the analysis through the Claude API at roughly $0.05 per run.

• The output includes sentiment classification across the review set, NPS estimated from unstructured text, friction points ranked by how many customers mentioned them, and conversion opportunities that would not surface in GA4.
• They say the JSON output gets used most, feeding a wider CRO process alongside GA4 data, session recordings, and funnel metrics.
• Review mining is one input among several, not a replacement for quantitative data.
• It adds the qualitative layer many CRO processes skip because doing it by hand at scale is painful.

For brands planning a campaign

If conversions are weak and the cause is unclear, start by mining customer language, then decide which funnel element to test. The qualitative research is a separate step, with its own cost and output.

Useful for briefs in a specific DTC category, with comparable sites already analyzed.

Hichem spent more than 15 hours researching garden and plant DTC brands in the U.S. and produced a Figma file covering 18 homepages.

• Each site gets analysis of its unique value proposition, call-to-action effectiveness, product presentation, social proof, and engagement strategy, plus overall recommendations and notes on visual elements.
• The post argues these brands stand out through distinct value propositions and generous offers rather than a single tactic.
• Readers comment "Garden" to receive the file by direct message, making it a lead-capture step rather than a paid product.

For brands planning a campaign

For a garden, flower, or plant brand, an annotated teardown of 18 competitors can anchor a positioning or landing-page brief. It also shows Hichem's format for a category review: side-by-side annotated pages with a recommendation for each site, ready for an internal team to use.

Read this before a call to understand the offer: judgment and context, not tool access.

The argument has two steps.

• First, AI has compressed execution: first drafts, analyses, internal tools, hypothesis generation, and large-scale data processing that used to take days now take hours.
• Second, strategy becomes more valuable.
• Deciding which problem matters, which metric is misleading in a given business context, and why a 12% test win should not be rolled out all require full context—one of the hardest things to transfer to a model.
• More context makes AI a more useful thinking partner, but direction still has to come from the person using it.
• The execution gap between good and great is closing while the strategy gap widens.

For brands planning a campaign

The offer is strategy plus execution, not tooling. This is aimed at brands with analytics and AI tools already in place but no agreement on which conversion problem to tackle first.

Read this to see what the first phase of an engagement looks like.

Hichem argues that e-commerce and SaaS businesses forget customer experience when measuring success.

• More conversions start with understanding how prospects become buyers: mapping pain points, motivations, objections, and the decision-making process.
• Once the journey is mapped, the experience can be refined.
• The recommendation is explicit: before tracking every available metric, step back and map the journey—the best investment a brand can make.

For brands planning a campaign

Sets expectations for a first project: journey mapping and objection research before dashboard work. If you expect an immediate analytics build, ask how the qualitative first phase and subsequent measurement will be scoped.

Read this if you need someone who ships the implementation, not just the recommendation.

Hichem canceled a $15/month Framer subscription and rebuilt the personal site in about a day using Claude Code and Astro, without writing code by hand.

• The cost breakdown is direct: hosting on Fly.io free tier at $0, a domain at roughly $10 per year, and no monthly SaaS bill.
• The point is the pattern: AI reduces the friction between knowing what you want and building it, without replacing expertise.
• The post describes a software engineering background; ask for verified credentials if they matter to the brief.

For brands planning a campaign

Relevant when a brief calls for implementation during the engagement—a landing page, an internal tool, or a reporting script—rather than a slide deck of recommendations. The preference for avoiding ongoing platform fees can shape how a build is scoped and hosted.

Their approach starts by mapping how prospects become buyers: pain points, motivations, objections and the decision-making process. Teams should refine the experience and track metrics only after completing that map.

Journey mapping before analytics

For brands planning a campaign

For a CRO and analytics brief, hire for a journey-mapping phase that identifies buyer friction before dashboard work. Ask for a deliverable linking pain points and objections to specific funnel changes.

Examples: Source 1

Outputs include sentiment classification, NPS estimation from unstructured text, friction points ranked by customer mentions and CRO opportunities that may not surface in GA4. The JSON file is designed to feed a wider CRO workflow alongside GA4, session recordings and funnel metrics.

AI-assisted review scraping and classification

For brands planning a campaign

Commission a voice-of-customer sprint that ranks review themes and friction points, then maps them to test ideas. Require structured raw data files so findings can feed into existing analytics rather than live only in a slide deck.

Examples: Source 1

AI output depends on deep context; the strategist still has to lead.

For brands planning a campaign

Evaluate Hichem for engagements where an outside strategist frames the problem and uses AI to accelerate execution. Don’t treat them only as an AI tool operator; ask how they transfer business context and prioritize tests.

Examples: Source 1

It loads faster, is fully customizable and costs only about $10/year for the domain, with hosting on Fly.io’s free tier. Hichem says they wrote no code manually and completed the work in one focused day.

For brands planning a campaign

For a brand needing a fast, low-overhead web or landing-page build, test the claimed AI-assisted process on a small, scoped page. One personal-site rebuild doesn’t prove enterprise migration or complex integrations; if those are the need, ask for relevant examples.

Examples: Source 1

It lists 18 top home pages, with insights and recommendations on unique value propositions, call-to-action effectiveness, product presentation, social proof and user engagement. The post uses a comment-to-DM lead format.

Demonstrated here for garden and plants; applying the method to another category would need a new research pass.

For brands planning a campaign

Use Hichem for category-specific competitive UX audits when a DTC brand wants examples of how peers present offers and proof. Ask for the same method applied to the brand’s category and for a translation of teardown observations into tests.

Examples: Source 1

The posts identify it as their personal website and direct readers there.

View example

No sponsorship or partnership is disclosed.

View example

The posts present these as tools, not sponsorships.

View example

No sponsorship is disclosed.

View example

This is a data-source mention, not a disclosed partnership.

View example

Tony Ward describes putting NPS, CSAT and review data where decisions get made, including a Claude connector that lets a customer pull feedback data together with support tickets and product data in minutes, and agents that surface experience risks before anyone asks for a dashboard.

• Hichem Bennaceur’s review-mining tool tackles the same job from the other direction: it pulls review text, ranks the friction points customers mention, and exports structured data that joins GA4 and funnel metrics.
• Both argue that the value lies in acting on feedback, not collecting it.

Tony Ward writes from inside a customer-experience software company about product launches and named customers adopting its tools.

• His posts are vendor announcements with a client proof point.
• Hichem Bennaceur writes as an independent consultant about tools they built for their own CRO process and the strategy work around them.

Read original · Original 2

Both argue that AI compresses the time between idea and finished work, putting practitioners at risk if their own tooling slows them down. Richard Best’s Figma piece and their forecast of shifting design roles make the same case Hichem Bennaceur makes in their AI-and-strategy post: execution is getting cheaper, while judgment about what to build and why is becoming more valuable.

Richard Best focuses on design tools and design careers, making his case through a public critique of a named vendor and a list of alternatives. Hichem Bennaceur focuses on conversion and analytics for DTC and SaaS brands, building with the tools rather than reviewing them for a design audience.

Read original · Original 2

Joy Slabaugh argues that a technically skilled profession misses a human dimension it was never trained for, using Internal Family Systems to explain why clients ignore advice or sabotage their own plans.

• Hichem Bennaceur makes a structurally similar claim about CRO: the qualitative layer — journey mapping, objections and the language customers use — is what most processes skip, even though it is often the most useful.
• They make that case through their work.

The domains barely overlap.

• Joy Slabaugh works in financial advisory and wealth psychology, and her posts use personal narrative that often closes with a question to the reader.
• Hichem Bennaceur writes about conversion and analytics, focusing on costs, tool outputs and criteria rather than reflection.

Read original · Original 2

Tony Ward's posts describe AskNicely's Claude connector, which pulls NPS, CSAT, and review data into Claude alongside other business data, plus two agents: one that flags customer-experience risks, trends, and opportunities for the people who need to know, and another that drafts on-brand replies to customer feedback at scale. Teams that already collect survey and review data and want it triaged, summarised, and routed without a consultant fit this description better than a project-based strategist does.

The tradeoff is diagnosis.

• The posts describe product capabilities and one named customer's experience, but say nothing about pricing, implementation effort, or who decides which conversion problem to tackle first.
• Hichem's documented engagement is a retainer that runs review-mining analysis, ranks friction points, connects them to GA4 and funnel data, and can build the resulting page or tool.
• A platform layer keeps feedback flowing to decision-makers; it does not provide the project-level judgment to choose and own the next change.
• Choose the platform when coverage and consistency across the feedback stream matter most; choose the consultant when the bottleneck is deciding which single change to make next.

Examples: Source 1 · Source 2