The questions, answered in public.

Force Multiplier Digital (FM Digital) is a small growth marketing agency in Austin, Texas, working with high-consideration consumer businesses across home improvement, housing, warranty and surety, alongside direct-to-consumer ecommerce brands. It combines paid media, answer-engine optimization, lifecycle marketing, conversion design, creative and measurement into one accountable system.

FM Digital was founded in 2017 and works with consumer brands typically between $5M and $150M in annual revenue. Engagements start at $12,000 per month and run on a 90-day initial term, then month-to-month.

The name is literal. A force multiplier is a factor that makes an existing capability dramatically more effective without increasing its size. That is the brief we hold ourselves to: the same budget, the same team, materially more output.

FM Digital works with consumer businesses in two shapes: high-consideration lead generation across home improvement, housing, extended warranty, surety and self storage, and direct-to-consumer ecommerce. 13 clients are named on the site, and 4 categories hold two clients each.

Current clients: A1 Surety Bonds, TitleBonds.us, NoRepairCost, Empire Auto Protect, N FL Land & Home, Bathroom Design Center, The Window Upgrader, JT Builds Colorado, Columbia Falls Mini Storage, SafeMind Storage, SuperSenses, Festive Decor and Sojourn Supply Co.

Most of our book is the harder version of consumer marketing: a purchase someone researches for weeks, discusses with their partner, and makes once. The lead is worth nothing until a person picks up the phone, the close happens offline, and the feedback loop runs in weeks rather than hours. Ecommerce tactics do not port over unchanged, and we do not pretend otherwise.

We are a poor fit for pre-revenue businesses, anyone wanting a percentage-of-spend deal, and organisations where marketing decisions require four committees. We say so early, because a mismatched engagement wastes your quarter as well as ours.

FM Digital reports on incremental contribution margin rather than platform-attributed ROAS, runs structured incrementality testing as standard, and treats answer-engine visibility as a core discipline rather than an add-on. Every engagement is month-to-month after the first 90 days.

Three things follow from that in practice. We will tell you when a channel is not working, including a channel we recommended. We will not quote you a blended ROAS number we cannot defend in front of your CFO. And we publish our methodology, our pricing floor and our answers publicly, on this page, rather than holding them back for a sales call.

FM Digital engagements start at $12,000 per month. Single-discipline retainers typically run $12,000 to $25,000 per month; multi-discipline growth partnerships run $25,000 to $70,000 per month. Pricing is a flat retainer, never a percentage of ad spend.

We do not charge a percentage of media spend, because it pays us to recommend spending more regardless of whether spending more is right. Flat fees make our incentive neutral, which is the only arrangement under which our advice is worth taking.

Project work such as a measurement build, a conversion-path redesign or an AEO foundation is scoped as a fixed fee, generally between $35,000 and $120,000.

No. FM Digital engagements run on a 90-day initial term and then continue month-to-month with 30 days' notice. The initial term exists because rebuilding measurement, creative supply and account architecture genuinely takes a quarter to show its first honest read.

Most FM Digital clients see the first measurable movement between weeks six and ten, and a clear directional read by day 90. Conversion and creative work reports fastest; answer-engine visibility and media mix modelling take two to three quarters to reach full effect.

Anyone promising a transformation in thirty days is describing a coincidence, not a method. What we will commit to inside thirty days is clarity: you will know what is actually working, which is usually the more valuable of the two.

Every FM Digital engagement is staffed with a named growth lead plus three to five discipline specialists, all of whom are in your weekly call. The person who pitches the work also runs it. We do not operate a separate sales team.

Typical team: a growth lead who owns strategy and the relationship, a channel specialist per active discipline, a creative strategist, and an analytics engineer shared across a small portfolio. You get their calendars and their Slack, not a ticket queue.

Generative engine optimization (GEO) is the practice of structuring content, entities and third-party citations so that AI systems retrieve and cite a brand when generating answers. It differs from SEO in that the objective is inclusion inside a synthesised answer, not placement in a ranked list of links.

The tactics diverge from SEO in three ways. Content is written in extractable units, a self-contained answer in the opening sentences followed by supporting evidence, rather than as long narrative that only makes sense read in order. Facts are stated explicitly and consistently everywhere the brand appears, because contradictions make a model hedge. And third-party corroboration is weighted heavily, because retrieval systems trust what others say about you far more than what you say about yourself.

The terms GEO, AEO (answer engine optimization) and LLM SEO describe substantially the same discipline. We use AEO and GEO interchangeably.

No, but its job has changed. Classic SEO now functions as the substrate: crawlability, structured data, topical depth and authority are how a page becomes retrievable in the first place. What has died is the assumption that ranking produces a click, since AI Overviews and assistants increasingly answer without one.

The practical consequence is that organic success needs two scorecards. One measures traffic and rankings as before. The other measures citation share: how often your brand is named in generated answers, in what position, with what sentiment. Brands tracking only the first are watching a number decline while their actual influence may be growing, or the reverse, which is worse.

FM Digital builds a set of 150 to 400 real buyer questions per client, runs them on a fixed weekly schedule across ChatGPT, Perplexity, Gemini, Claude and Google AI Overviews, and records mention rate, citation rate, answer position, sentiment and competitive share of voice.

That prompt set becomes the baseline and the monthly scorecard. Because generated answers vary between runs, every prompt is sampled multiple times and reported as a rate rather than a single observation, a distinction that separates real measurement from a screenshot.

llms.txt is a plain-text file at a website's root that offers large language models a curated, machine-readable map of the site's most important content. It is cheap to publish and low-risk, but it is a supporting measure. Entity consistency and third-party citations move AI visibility far more.

Think of it as a courtesy to retrieval systems rather than a ranking lever. Publish one, keep it accurate, and spend the remaining effort on the things that actually change what a model says about you. Ours is at fm.digital/llms.txt.

FM Digital uses AI for research, structuring, variant generation and quality control, and never to publish unreviewed content. Every page that carries a client's name is written or substantially rewritten by a human strategist who understands the category.

The reason is commercial, not ideological. Undifferentiated generated content performs badly in classic search and, more importantly, gives answer engines nothing distinctive to cite. Original data, real customer language and genuine expertise are the things that get retrieved. AI helps us produce those faster; it cannot produce them alone.

Cost per lead is the wrong target. The number that matters is cost per sold job, which depends on your lead-to-appointment rate, appointment-to-sale rate and average contract value. A $40 lead that books 5% of the time is worse than a $120 lead that books 30%.

Work backwards instead. Take your average contract value, multiply by gross margin, decide what share of that margin you are willing to spend to acquire the job, then divide by your close rate to get an allowable cost per lead. That number is specific to your business and it is the only benchmark worth holding.

The practical consequence is that you cannot optimise media on cost per lead alone. You have to send close outcomes back to the ad platforms so they bid toward the leads that actually become customers.

Under five minutes during business hours. Contact speed is one of the few levers in lead generation with a large, repeatable effect, and most of the gap between a company's intended response time and its real one is routing and staffing rather than marketing.

Before spending anything on new traffic, measure the actual distribution of your response times, not the average, which hides the tail. The leads that wait until tomorrow morning are usually where the losses concentrate, and fixing that costs nothing in media.

Send the outcome back. Import closed deals from your CRM into Google Ads and Meta as offline conversions, so the platforms learn which leads became customers and bid toward those. Without it, the algorithm optimises for form fills, and form fills are not what you sell.

This requires stitching a click identifier to the lead record at capture, then returning the outcome with its value when the deal closes. It is a few days of plumbing and it is usually the single highest-leverage technical change available to a lead generation business.

Add call tracking at the same time, because in most of these categories a large share of conversions never touch a form at all.

Usually not. In high-consideration categories a large share of buyers purchase months after first enquiry, frequently from whichever company was still present when they decided. Leads marked closed-lost are often the cheapest revenue available, because acquisition has already been paid for.

Two fixes, in order. First, extend nurture to cover the real consideration window rather than the fortnight the sales team stays interested. Second, run a deliberate reactivation programme against the aged file. It typically returns more per dollar than new traffic, and it tells you how much demand you have been discarding.

Rising CAC is usually a creative supply problem or a lifetime-value problem, not a targeting problem. The two durable fixes are increasing the rate of genuinely new creative angles in market, and raising the second-order rate so the business can afford a higher acquisition cost.

Audience targeting has largely been automated away by the platforms; there is little left to optimise there. What still moves the number is what the ad says and what happens after the first purchase. Brands that fix the second one buy themselves permission to outbid competitors on the first.

There is no universal good ROAS, because the number depends entirely on gross margin. A brand at 70% margin can be profitable at 1.8x; a brand at 30% margin needs above 3.5x to break even on the same order. Measure against contribution margin, not a benchmark someone quoted on a podcast.

A more useful question is marginal ROAS: what return does the next $10,000 produce? Average ROAS describes history and flatters any account with strong branded search inside it. Marginal ROAS tells you whether to scale, which is the decision you were actually trying to make.

For a healthy ecommerce brand, owned channels typically produce 25% to 40% of total revenue. Below 20% usually indicates an underbuilt flow architecture. Above 45% often indicates an acquisition problem rather than a retention success, because the customer base is not growing.

Platforms count conversions they believe they influenced, using different lookback windows and attribution models, so several platforms routinely claim the same order. Shopify counts orders once. The gap is expected. The mistake is treating either number as the truth instead of triangulating with experiments.

The workable arrangement uses three tiers that check each other: clean server-side data capture for the day-to-day, a media mix model for quarterly budget allocation, and geo-holdout experiments to settle disputes neither can resolve. Reconcile all of it to bank-deposited revenue and real gross margin, or the argument never ends.

An incrementality test measures the revenue a channel produces that would not have happened without it, usually by withholding advertising from a matched control group of geographies and comparing outcomes. It matters because attributed revenue and incremental revenue frequently differ by 20% to 50%.

Branded search is the classic example. It reliably reports superb ROAS while a blackout test often shows that most of those customers would have arrived through organic results anyway. Until you have run the test, you are spending real money on the basis of an assumption.

Media mix modelling earns its cost above roughly $500,000 per month in media spend with at least two years of clean history. Below that threshold, a disciplined geo-holdout testing programme produces a sharper read for a small fraction of the investment.