The Findability Index: Which Local Trade Is Hardest to Break Into

Eight trades, four signals, one number

Over August 2026, Raveneye Global measured eight local service trades, chartered accountant, dentist, gym, interior designer, digital marketing agency, coaching institute, physiotherapist, and wedding photographer, across six ordinary Indian cities: Jaipur, Indore, Lucknow, Surat, Kochi, and Nagpur. The data came from live pulls against Google's search results and AI Overview data, Google Places, Google Ads search-volume tools, the Google Knowledge Graph, Google PageSpeed Insights, and DNS-over-HTTPS. Nine studies in this field study have already read that dataset one surface at a time: the AI answer, the aggregator's hold on the organic page, the star rating, the review count, near-me demand, the cost of a click, the site itself, the email record, and the knowledge graph. This is the tenth study, and the last. It adds no new measurement. It combines four of the ones already gathered into a single number, per trade, and asks which of the eight is structurally hardest to break into.

The scale behind the question is real, even where this study's own sample is deliberately narrow. India crossed 1.03 billion internet users by the end of 2025, a 70.0% penetration rate, and the Ministry of MSME's own registry counted a cumulative 7.83 crore, about 78.3 million, enterprises registered as of February 2026. Eight trades in six cities is a narrow slice of that population, and the index below describes the competitive terrain inside that slice, not a verdict on the whole of it.

Call it the Findability Index. It runs 0 to 100 across the eight trades measured, with a higher score meaning the trade is structurally harder for a new entrant to gain visibility in, not that any single business inside it is failing. On this measure, coaching institute scores hardest at 65.6, with gym close behind at 64.8. Dentist (57.8) and interior designer (50.7) sit in the upper middle. Wedding photographer (45.4) and digital marketing agency (44.0) sit just below the midpoint. Chartered accountant (37.9) and physiotherapist (35.4) score lowest, meaning easiest to break into, of the eight trades this study covers.

An index built from four normalized signals is a simplification by design, and the simplification is the point: eight trades on four different scales cannot otherwise be read against one another on a single number. What follows states the method in full before it restates the ranking, because a composite score published without its construction shown is exactly the kind of naked metric this field study has argued against in every study before this one. One trade's position needs a caveat stated plainly rather than left in a footnote, and it gets one, in a section of its own, before this piece is done.

How the index is built

The Findability Index draws on four signals, each already measured elsewhere in this field study and read here in combination rather than alone. The first is search demand: the combined monthly volume of near-me and head-term keywords for the trade, pulled from Google Ads' search-volume tool and log-scaled before it enters the index, because raw monthly search demand in this dataset runs from a keyword set that returned no matched volume at all to nearly three million a month for the highest trade, a gap wide enough that an unscaled average would let one signal swamp the other three entirely.

The second is aggregator load: how many of the organic top 10 results for the trade's representative query are occupied by a third-party directory or marketplace rather than an individual business's own site, averaged across the six cities. The third is the median review count of the businesses actually visible for that query, a proxy for how deep the reputation moat is that a new entrant has to climb. The fourth is local-pack absence: one minus the share of that trade's queries that returned a Google local pack, the block of local results shown with a map, which, when present, gives a business a second route into visibility that does not depend on ranking the organic page.

Each of the four signals is min-max normalized across the eight trades, meaning the trade with the highest raw value on a signal is rescaled to 100 on that signal and the lowest to 0, with the rest placed proportionally between. The four normalized scores are then averaged with equal weight, twenty-five percent each, to produce the final index. This is the standard construction for a composite indicator built from measures of different units and different scales, and it follows the general method described in the OECD and European Commission Joint Research Centre's handbook on constructing composite indicators. That handbook's central caution is worth repeating here: normalization method and weighting scheme are analytic choices, not neutral facts, and both should be stated rather than assumed.

Equal weighting is stated, not assumed, in this index. It was chosen because nothing in this dataset says a business owner should value review depth twenty percent more than aggregator crowding, or demand twice as much as pack absence. A different, defensible weighting exists for almost any specific business's actual situation, and a later section in this piece walks through what changing the weights would do to the ranking, so the published score is read as a starting point for that judgment rather than a final word.

The ranking, trade by trade

The list below is the index in full, hardest to easiest, with the component that does the most to explain each trade's position. All four raw components, monthly search demand, the average aggregator count in the organic top 10, the median review count, and the share of queries missing a local pack, are reported in full in the key findings further down this piece.

• Coaching institute, 65.6. The single highest median review count of the eight trades, 490, combined with zero aggregators in the organic top 10 and a local pack that did not appear in any of the six cities searched.
• Gym, 64.8. The highest raw search demand measured in this study, 2,913,000 near-me and head-term searches a month, paired with the same zero aggregator presence and full local-pack absence as coaching institute.
• Dentist, 57.8. A local pack appeared in every one of the six cities searched for this trade, the strongest map-pack presence of the eight, which should ease its score down; a median review count of 469, second-highest of the eight, and demand of 540,500 a month outweigh it and keep dentist third-hardest.
• Interior designer, 50.7. The heaviest aggregator crowding measured in this study, an average of 3.5 directory or marketplace domains inside the organic top 10, is the single largest driver of its position, even with a local pack present in five of the six cities searched.
• Wedding photographer, 45.4. Low raw demand, 13,500 a month, but full local-pack absence and a median review count of 167, ahead of both digital marketing agency and chartered accountant, keep it above the midpoint.
• Digital marketing agency, 44.0. Demand of 61,600 a month, more than four times wedding photographer's, is compressed by the index's log scale and does not overcome a thinner review count, 105, the second-lowest of the eight.
• Chartered accountant, 37.9. A local pack appeared in every city searched, the same full presence as dentist, and the thinnest review moat measured, a median of 97, leave it second-easiest despite demand of 148,000 a month, the third-highest raw figure in the set.
• Physiotherapist, 35.4. The lowest score in the set, and the one that carries this piece's central caveat: its demand component reads zero because the physiotherapist keyword set returned no volume match in this study's Google Ads pull, not because real demand is zero. Its true position is addressed on its own, below.

Reading the mechanism

Three comparisons make the mechanism behind the index concrete: how a smaller trade can outrank a much larger one, how a local pack acts as a shortcut when it appears, and how a single lopsided signal can carry a trade's whole position.

The physiotherapist floor: a data gap, not a finding

One result in this index needs to be read differently from the other seven, and stating why is not optional. Physiotherapist scores lowest of the eight trades, 35.4, and on the surface that reads as the easiest trade to break into of the eight measured. It is not a reliable reading, and the reason is specific: the physiotherapist keyword set returned no matched search-volume figure in this study's Google Ads pull, so its demand component entered the index as zero. Zero is not a description of the underlying reality. It is what an empty match returns.

The other three signals do not support a bottom-of-the-table position. Physiotherapist's median review count, 260, is the fourth-highest of the eight trades, ahead of chartered accountant (97), digital marketing agency (105), interior designer (128), and wedding photographer (167). Its aggregator count is zero, tied for the lowest alongside four other trades, and its local-pack absence is 100%, the same as coaching institute, gym, wedding photographer, and digital marketing agency, none of which sit anywhere near the bottom of the index. On three of the four signals, physiotherapist looks structurally closer to the middle of the set than to the floor.

What is actually being measured when a keyword-volume pull returns no match is worth stating plainly: either the specific near-me and head-term keyword combinations used for this trade fall below the tool's reporting threshold, or the keyword set itself needs rebuilding for a trade whose buyers may search in different terms than the other seven. Either explanation points to a gap in this study's method for this one trade, not to a real absence of demand for physiotherapy across six Indian cities. This piece publishes the number as measured, 35.4, and says clearly that it is a floor: physiotherapist's true position on this index, once its demand is captured correctly, is very likely higher, plausibly in the same band as chartered accountant or digital marketing agency, not below both of them.

What a different weighting would change

Equal weighting, twenty-five percent to each of the four signals, is a choice this study makes and states, not a law the data obeys. The OECD and European Commission's own handbook on constructing composite indicators is explicit that the weighting scheme is one of the most consequential and least examined decisions in building any index of this kind, and that publishing an index without naming the weighting choice invites a reader to mistake a choice for a fact.

Reweighting this index toward review depth, the signal this field study's companion piece on the review moat measures directly, would pull coaching institute (490 median reviews) and dentist (469) further toward the hard end and pull chartered accountant (97) and digital marketing agency (105) further toward the easy end, widening the gaps at both extremes rather than closing them. Reweighting toward local-pack absence, the signal closest to what this field study's piece on who answers a local query measures, would compress the gap between the five trades that already show full absence, coaching institute, gym, wedding photographer, digital marketing agency, and physiotherapist, into something closer to a tie, since all five would then share the same top score on the signal carrying the most weight.

Neither reweighting is presented here as more correct than equal weighting. The point of naming them is that a business owner reading this index for their own trade should treat the published number as one legitimate reading among several, built on a stated method, rather than as the only possible score. A chartered accountant competing mainly on review depth against an unusually active competitor is operating a harder version of this trade than the index's 37.9 suggests; a coaching institute in a city where a local pack does regularly appear, unlike the pattern in the six cities measured here, is operating an easier one than 65.6 suggests.

How to read a difficulty score without misusing it

A high score on this index is not a warning to avoid a trade, and a low score is not a promise that it is easy money. Coaching institute and gym sit at the top because the visible competitive bar, in review count and in the absence of a map-pack shortcut, is already high across six ordinary Indian cities as of August 2026, not because any specific coaching institute or gym is destined to struggle. The index describes the terrain a new entrant walks into, not the outcome for any one business on it.

The more useful reading runs the other direction. A trade's index score sets the baseline competitive intensity a specific business should expect before it starts measuring its own position, and it names which of the four underlying mechanics is doing the work. A physiotherapist reading this study should not conclude the trade is uncontested; the caveat above says plainly that its true position is unresolved. A chartered accountant should read the low index alongside the reason for it, a reliable local pack and the shallowest review moat measured, rather than as a signal that visibility work is unnecessary. The review count behind that moat is not only a classic ranking signal either: peer-reviewed research on generative engine optimization found that adding corroborating evidence, citations, statistics, and authoritative sourcing measurably raises a source's odds of being named inside an AI-written answer, which means a review moat that looks shallow on a classic ranking can matter again on the newer surface this field study's companion piece on who answers a query measured directly.

What the index cannot do, by construction, is say where one specific business inside a trade stands. A gym in Kochi and a gym in Nagpur both inherit the same 64.8 trade-level score, and their actual positions, measured against their own local competitive set on the same four signals, can differ by more than the gap between any two trades in this ranking. The trade-level number is a starting frame. The business-level read is a separate measurement.

What this closes out in the field study

This is the tenth study in Raveneye Global's field study on how India's local businesses get found, and it is built entirely from data the other nine already gathered. The demand figures behind this index come from the same Google Ads pull behind the study on near-me search demand. The aggregator and local-pack figures come from the same 48 live search-results pulls, one per trade per city, behind the studies on the aggregator's hold on the organic page and on who actually answers a local query. The review figures come from the same Google Places pull, across the same 960 businesses, behind the study on the review-count moat. Nothing here is a new measurement. It is four of the existing ones, combined and shown together.

That also means this index inherits every limitation the nine studies before it already disclosed, six cities, eight trades, one representative query per city per trade, a single August 2026 snapshot, on top of the two limitations this piece adds: an equal-weighting choice, stated rather than hidden, and a specific gap in the physiotherapist demand figure that floors its score below what the other three signals support. A different month, a different set of six cities, or a wider keyword set behind the demand signal would move the exact numbers. What is less likely to move is the broad shape: trades with a deep review moat and no map-pack shortcut, coaching and fitness among them in this dataset, sit harder to break into than trades where a reliable local pack gives a new entrant a second way in.

The four signals combined here do not exhaust what decides whether a specific business gets found. The review moat a market leader holds, and what it costs to buy visibility while an organic position is still being earned, are two of the surfaces this index folds in only partially. Read alongside the other nine studies in this field study, the index is a place to start reading a trade's terrain, not the last word on any single business standing inside it.