What is IGG Marketing and what services do they provide?

IGG Marketing is a full service digital marketing agency based in Florida. We handle SEO, paid search and social, email and SMS, website builds and rebuilds, creative, social content, and video for clients across numerous verticals including e-commerce, publishing, medical, manufacturing, hospitality, and home services. Our team consists of senior specialists, so clients get experienced people on their account without paying for big agency overhead.

I also run IGG Audit, which is exactly what it sounds like. We do completely independent digital marketing audits to help businesses identify areas of improvement, agency/team oversight, or simply a different pov to help improve results.

How has the way your clients think about AI on their site shifted over the past year?

A year ago, AI on the site meant a support chatbot. Deflect some tickets, save the team a few hours. It was filed under cost savings, and honestly, most of them were bad enough that clients were nervous to turn them on.

That flipped. Now the questions I get are about revenue. How do shoppers who start their search in ChatGPT find us? Can our site answer a real question, or does it just display products? Where does all that conversation data go? The conversation moved from "should we do this" to "how do we turn this into an additional revenue channel."

The Adaptive Storefront runs the same intelligence across a brand’s entire site, not just a chat window. After learning about it, what stood out to you most?

The data loop. Most on-site AI is a dead end. The shopper asks, the tool answers, and the exchange evaporates. The Adaptive Storefront treats every question as first-party data the brand keeps, then feeds it back into email, ads, content, and the catalog itself. That's a very different product than a chat bubble in the corner.

The second thing was the brand safety work. Each brand gets its own model, and it has to pass evaluations on brand voice, product accuracy, and compliance before it goes live. As an agency, that's the part that lets me put it on a client's site without losing sleep.

Of all the places a brand can put the Adaptive Storefront, which one would you turn on first for a client, and why?

Paid ad landing pages. That's traffic the client is already paying for, and if the page can't answer the visitor's question, the ad spend was wasted. Turning the storefront on there gives you a clean before-and-after on money that's already going out the door. It's the easiest ROI conversation I can have with a client, and the friction it surfaces tells you how to fix the landing page itself. You improve the paid program twice with one install.

Which placement do you think would surprise brands, in terms of where shoppers actually end up engaging?

Product pages. Brands assume the homepage is where people want help, but the PDP is where shoppers open up. They're one click from buying and stuck on one specific thing. Will this shrink in the wash, can I take this with my blood pressure medication, will this couch fit through a 30-inch doorway, does this work with a Mac. Those are the questions people won't dig through a reviews tab or a spec sheet to answer, but they'll type them into a box on the page. The specificity of PDP questions surprises every brand that sees their own transcript.

Which placement do you think most brands would overlook or underrate?

Email and SMS. Nobody thinks of an on-site AI as an email product, and that's a miss. If a shopper asked about sizing and then abandoned, the follow-up shouldn't be a generic 10% off. It should address sizing, because that's the actual hangup. Abandonment flows built from the exact question a shopper asked before leaving beat template flows, and most brands don't even know that's an option.

A lot of businesses chase AI visibility without a clear framework. When an AI-referred shopper lands on a client’s site today, what usually goes wrong?

They arrive mid-conversation. That shopper already asked an AI something specific and got sent to the site as the answer. Then they land on a generic homepage or a product page written for a casual browser, and nothing on the page picks up where their question left off. So they bounce back to the AI, and it recommends the next brand on the list. High intent, wrong page.

The other problem is that nobody's watching. Most clients can't even segment AI-referred traffic in their analytics, so they have no idea how much of it they're getting or losing.

Why is capturing the questions those shoppers ask valuable to a brand beyond the single sale?

Because it's voice-of-customer research collected continuously and tied to revenue. Brands pay for surveys and focus groups to get worse versions of this data.

One question might save one sale. The pattern of questions runs your roadmap. If "does this come in a wide width" shows up 200 times a month, that's a merchandising memo. The objections people raise before buying become your ad copy. The words they use become your subject lines. And the questions nothing on your site answers become your next 20 pieces of content.

Give us your top five Adaptive Storefront placements. For each, what is the client problem, and what would the storefront do about it?

1. Paid ad landing pages. The problem: expensive Meta and Google traffic hits a page built for everyone and converts for almost no one, so CPMs creep up while ROAS slides. The storefront answers each visitor's actual question on the page, and the question log shows you exactly what friction to fix in the page itself.

2. Homepage, top of funnel. The problem: new visitors don't know where to start, especially with a big catalog, and most leave without giving you a single signal. The storefront gives them a guided way in. Envive's data shows top-of-funnel questions run about 50% longer than anywhere else, which means this placement produces your richest intent data.

3. Product and collection pages. The problem: one unanswered question about fit, specs, or compatibility kills the sale, and the shopper leaves to go find the answer on Google or in an AI. Most don't come back. The storefront answers it right there on the page, no redirect, no friction.

4. Email and SMS flows. The problem: abandonment emails are templates and blanket discounts, so they get ignored. The storefront lets you build flows from the exact pre-purchase question each shopper asked, so the follow-up speaks to the real hangup instead of guessing at one.

5. AI-referred traffic. The problem is twofold: shoppers arriving from ChatGPT and other LLMs land on cold pages that drop the thread, and the catalog itself isn't structured for AI discovery in the first place. The storefront continues the conversation for the shoppers who arrive, and the catalog enrichment side makes sure the brand keeps showing up in AI answers at all.

There is a lot of noise around AI SEO and geo tactics versus proven SEO. Where does first-party question data from the storefront fit into a legitimate SEO strategy?

Most of what gets sold as AI SEO or GEO right now is guesswork with a new name on it. Question data is different because it isn't a tactic, it's research. It's keyword research pulled from your own buyers instead of a third-party tool estimating search volume.

You feed it into the proven work: page copy, FAQs, schema, new content targeting real long-tail queries. The GEO benefit comes along for free, because a page that answers a specific question in plain language is exactly the kind of page LLMs cite. You don't need a separate AI SEO strategy. You need better inputs to the strategy that already works.

Walk us through how you would turn a month of shopper questions into a content and on-page plan.

First, pull the full month of questions and cluster them by theme. Sizing, compatibility, shipping, comparisons, use cases. Then rank the clusters two ways: how often they come up, and how close they sit to a purchase decision.

Then work the list in order of speed. Product page and FAQ updates first, since those fix live revenue leaks this week. Category page copy second. Then new content for the question clusters nothing on the site currently answers, which is usually the comparison and use-case stuff. Add FAQ schema as you go so the answers are structured for both Google and LLMs.

Then repeat it next month. The questions change as the catalog and seasons change, and the pages you updated will start pulling long-tail and AI-referred traffic you can measure against the question log. It becomes a monthly operating rhythm, not a one-time project.

How does writing in the customer’s actual language change how a page performs, in both classic search and AI-generated answers?

Customers don't search the way brands write. A brand writes "moisture-wicking performance fabric." A customer types "will this keep me from sweating through my shirt." If your page only contains the brand version, you lose the query in classic search and you give an LLM nothing to quote.

When the page uses the customer's own words, three things happen. You rank for the long-tail queries people run, because the language matches. LLMs pull your page into answers, because you gave them a plain, direct response to a real question. And the page converts better, because the copy addresses the objection in the same words the reader was thinking. No brand would invent a phrase like "can I take this on an empty stomach" on their own. Customers hand it to you. You just have to use it.

What would you say to an agency peer who has not started building around shopper conversations yet?

The conversations are happening whether you collect them or not. Your client's shoppers are asking questions somewhere. On the site, in ChatGPT, in a competitor's chat box. The only decision is whether your client owns that data or someone else does.

Start small. One client, one placement, thirty days of questions. Let the data make the case, because it will. And here's the selfish reason: this makes everything else you sell better. Your ads improve, your email improves, your SEO improves, because for once you know exactly what customers want to know. Waiting doesn't protect you from anything. It just means the client's competitor gets the data first.