If you are trying to decide whether AEO work is worth the time and budget, the question that usually decides it is: how much do I get back for each point I improve?
The naive answer is linear. One point of score gives you one unit of payoff. Four points give you four units. The investment scales the same way the score scales. That is how most marketing investments are modeled, and it is wrong here.
Our two-stage model translates a one-point lift in AEO score to a 4 percent lift in expected AI bot crawl rate. That is the per-point coefficient from the unconditional Stage B regression. The 4 percent number is not where the story ends. It is where it begins.
AI bot attention is multiplicative. Each point of score is applied on top of the previous gains, not next to them. That is the structural feature most teams miss. Here is what that compounding looks like in practice.
A 1-point lift produces a +4.0 percent expected crawl lift. A 2-point lift produces +8.2 percent. A 4-point lift produces +17.0 percent. A 6-point lift produces +26.5 percent. A full 8-point lift, the difference between an average small business (score 2) and a fully optimized site (score 10), produces a +36.9 percent expected lift.
Note the shape: doubling the number of points improved adds more than doubled the crawl lift. That is the multiplicative compounding doing its work. The math is just (1.04)^N - 1, where N is the number of points improved.
AI bots run on attention budgets. Every operator (OpenAI, Anthropic, Perplexity, Google, Apple) has a finite crawl budget and allocates that budget toward sites it has previously found worth citing. A site that climbs from score 4 to score 8 is not just "more crawlable." It is now in a higher-frequency rotation. Each subsequent visit confirms it as a high-quality source, which in turn raises its priority for the next round.
This is the same dynamic that produces compounding in traditional SEO authority, but it operates on a different signal set. Traditional SEO compounds via backlinks. AEO compounds via structured-data trust. A site that has been crawled, cited, re-crawled, and re-cited builds AI-side momentum the same way backlinks build search-side momentum.
If you assume a linear payoff (1 point = X dollars of value), AEO optimization looks like a marginal investment. The numbers seem small. Four percent here, four percent there.
If you model the actual compounding correctly, the math flips. Moving from a typical score of 4 to a high-citation score of 8 is not a 16 percent improvement. It is a 17 percent improvement. Add the cliff effect from Section 3.3 (mean crawls 3x at score 8 in descriptive cuts), and the full picture is even more favorable.
The descriptive cuts and the model coefficient agree on the direction and disagree on the size: descriptive cuts suggest the gain is larger than what the smooth Stage B model implies. The conservative interpretation is to use the compound table as the floor and treat the descriptive cliff as the ceiling. Both are real. Both are larger than a linear model would predict.
The compound table is an average across the crawled population. It does not predict the absolute crawl count for any single brand. A 6-point lift will not turn a brand with zero current crawls into a brand with 6 percent of some target volume. The compound multiplier acts on the brand's existing baseline.
If your baseline is low because of factors outside your control (small audience, niche category, low domain popularity), the compounding still applies but on a smaller base. If your baseline is already strong, the compounding produces meaningful absolute gains. The model is a multiplier, not a generator.
This is also why the published Stage B R squared is 0.123. AEO score is one input. Business category, domain popularity, brand recognition, and other structural factors collectively explain most of the variance in crawl counts. AEO is the largest single input a brand can control directly. It is not the only input.
The free AEO score at engagemii.com/aeo returns your current number and a prioritized fix list. Knowing your starting baseline is the only way to estimate what the compound table actually means for your specific brand.
If you are scoring 3 or 4, the compound math says that moving to 7 (a 4-point lift) produces a 17 percent expected crawl lift. Moving to 8 (a 5-point lift, plus crossing the cliff) produces materially more. Most teams reach 7 in about a week of work. The path from 7 to 8 is harder and usually requires more careful attention to the weakest of the six AEO categories.
The per-point coefficient and compound table come from Section 3.4 of Engagemii's research brief. The Stage B model is an unconditional LightGBM regression of log(crawl_count + 1) on AEO score, fit on the 395,022 crawled brands in our directory snapshot. Full methodology at engagemii.com/research/aeo-crawl-drivers.
If you want to cite this article, the URL is engagemii.com/blog/every-point-aeo-score-compounds.
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