# Md Tahidul Islam

Md Tahidul Islam shares practical advice on paid advertising and digital marketing, including audience targeting, reducing wasted ad spend, and converting traffic from AI search.

- Profile: https://b2binfluencer.marketing/creators/md-tahidul-islam/
- LinkedIn: https://www.linkedin.com/in/md-tahidul-islam
- LinkedIn followers: 1,902
- Topics: artificial-intelligence, paid-ads, meta-ads, paid-media, conversion-rate-optimization, generative-engine-optimization
- Based on 10 dated posts, latest 2026-10-09

## Summary

They are most useful for practical paid media optimization and AI-referred traffic conversion advice.

- Best for: A B2B SaaS marketing agency improving paid media and AI-referred traffic conversion.
- Not for: A consumer or lifestyle brand seeking sponsored placement with audience data, rates, and deliverables.
- Check first: Confirm accounts, measurement windows, audience data, and whether sponsorships are available.

### Takeaways

- Excluding weakest-converting segments cut wasted impressions 19% and lowered CPA in one ecommerce account.
- AI-referred visitors converted at 3.2 times regular organic traffic across SaaS and ecommerce dashboards.
- Recommended separate landing path with proof, visible pricing, and one clear next step.

## Main ideas

- **Wasted ad spend is an exclusion problem, not only a targeting problem**: Md Tahidul Islam argues that most teams obsess over building lookalikes and stacking interest layers but leave money on the table by ignoring who to exclude. The practical move is to pull conversion data, identify people who converted but show weak signals—lowest average order value, highest return rates, one-time buyers who never returned—and remove those segments from prospecting and retargeting. They recommend reviewing this monthly as an active optimization lever, not setting it up once and forgetting it. The reasoning: fewer low-quality impressions mean more budget flows to higher-propensity users, and the algorithm learns from cleaner data.
- **AI-referred visitors are pre-qualified and need a different landing path**: Md Tahidul Islam argues that falling organic traffic is not the whole story. Visitors arriving from AI citations such as ChatGPT, Perplexity, and AI Overviews convert at much higher rates because the AI has already answered their questions and compared options before sending the click. The failure is routing those visitors into the same generic homepage or blog post built for cold, uninformed traffic. The proposed fix is a separate path for AI-referred visitors: proof and specifics above the fold, pricing visible quickly, and one obvious next step instead of several navigation options. The metric to watch becomes revenue per visit rather than sessions.
- **Ad platforms reward consistency and follow-up, not one-off targeting**: Across the Google and Facebook explainers, a recurring priority is that advertisers must keep showing up. The Google post describes tracking every click and conversion and using remarketing to bring back people who visited but did not convert. The Facebook post describes the algorithm as analyzing user behavior to find likely buyers and optimize delivery, while the advertiser's job is to craft memorable ads and re-serve them consistently. The lesson: platform targeting does not remove the need for message discipline and follow-up.

## Work to read

- [Negative audience pruning cut wasted impressions by 19% in one ecommerce account](https://www.linkedin.com/posts/md-tahidul-islam_facebookads-metaads-paidmedia-activity-7470610786554675201-2vMD)
- [AI-referred visitors convert at 3.2x regular organic traffic](https://www.linkedin.com/posts/md-tahidul-islam_generativeengineoptimization-aisearch-conversionrateoptimization-activity-7496218263228461056-YdTe)
- [Google Search Ads explained through a fictional small business](https://www.linkedin.com/posts/md-tahidul-islam_googlesearchads-advertisingstrategies-googleads-activity-7145109368777146368-6BDD)
- [How the Facebook ad algorithm works, told as a story](https://www.linkedin.com/posts/md-tahidul-islam_facebookabrads-activity-7110545440260964352-pmEd)

## Partnerships

The posts contain no sponsorship offer, rate card, media kit, or published partnership terms. Md Tahidul Islam shares paid-media and search-marketing advice rather than promoting brand collaborations; none of the four posts discloses a paid partnership, sponsored format, or business inquiry route for partnerships. Buyers would need to scope any collaboration directly with them and agree on terms, deliverables, and disclosure practices from scratch.

## Alternatives

- Bivas Kumar Roy: Choose Bivas Kumar Roy when the agency's paid-media problem is that it does not trust its own numbers. Both posts work through the same buyer decision that stalls a SaaS agency's reporting: Meta Ads shows 103 purchases while Shopify or GA4 shows 82, and the first instinct — the platform is overcounting — is usually wrong. The discrepancy can come from attribution windows and click-through versus view-through credits; a Purchase event sent through both the browser Pixel and the Conversions API without working deduplication; Purchase firing more than once on a refresh or repeated checkout; test, staff or $0 orders; wrong event values and currencies; external payment flows such as PayPal and Klarna; or consent state affecting what is measurable. The useful part is the method: separate tracking errors from attribution errors first, then audit in a fixed order — tracking, deduplication, data quality, consent, attribution. Fixing duplicate events will not resolve a difference caused by attribution windows, and changing attribution settings will not fix a broken Purchase event. The second post adds practical checks a Shopify agency can run the same week: confirm Event IDs match across browser and server events, verify deduplication in Events Manager, test unique transaction IDs, walk the full journey from landing page through external payment redirect and cross-domain configuration to the confirmation page, and review consent-granted, consent-denied and consent-changed scenarios. For a SaaS agency whose client disputes the dashboard before any optimisation decision, this is a better starting point than a media-buying method: the measurement cannot be judged until it is settled.
- Josh Gregory: Choose Josh Gregory when the agency's client is spending enough that scale itself has become the problem. The posts name the threshold and symptoms: once budgets cross roughly £25–30K a month on Meta or Google, return on ad spend starts to decline, acquisition costs climb and results swing unpredictably. The default responses — changing bids, testing random creatives, duplicating campaigns — are treated as noise because they do not fix the system behind the ads. At that stage, money is lost through funnel gaps for cold audiences as warm buyers are exhausted by scaling, Google and Meta working in isolation so the channels overlap and pay for the same clicks, and the lack of a lifetime-value or offer strategy to support paying more to acquire a customer. The proposed structure is operational rather than a single lever: weekly creative testing cycles, margin and customer-acquisition-cost checks before budgets are raised, offers built for first-time buyers, a funnel progression mapped from awareness to purchase, and landing pages designed for cold clicks. The second post narrows to Google Ads and the common complaint that more spend is not moving sales. Its diagnosis: increasing the budget too quickly broadens targeting, dilutes the best audiences and drops conversion rate, so campaign structure, audience signals and exclusions, search terms, product feeds and creative have to evolve alongside the spend. For an agency asked to scale a client account rather than diagnose a dashboard, this answers the same buyer decision at the level of account structure and cross-channel planning.
- Nicolò Pauletto: Choose Nicolò Pauletto when the agency needs a view of where Meta's ad delivery is heading rather than how to clean up last quarter's reporting. The post on Meta's Muse Spark covers three changes with direct consequences for the agency's paid-media needs. First, the algorithm is natively multimodal and processes images and video the way it processes text, so it reads creative before serving it: weak hooks, cluttered frames and slow first three seconds are now distribution inputs, not just performance variables. Second, a conversational shopping layer inside Instagram, Facebook and WhatsApp pulls from creator content, community posts and brand storytelling already on the platform, so a brand absent from those conversations is absent from a discovery surface. Third, the post concludes that creative is the new targeting — bad creative has become a distribution problem rather than a performance problem — a shift that supports an agency's case for creative production budget instead of more media spend. That maps to the agency's paid-media needs and the broader question of how buyers now find products. It is the only one of the three alternatives that treats platform-level AI changes as the main subject.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/