Google Ads Conversion Value Rules: How to Tell Smart Bidding Which Conversions Actually Matter

A software company runs one conversion action called “Demo Request.” A student researching a class project fills out the form, gets a call, and never buys anything. Two weeks later, a director at a 400-seat company fills out the same form, on the same page, and turns into a six-figure contract. As far as Google Ads is concerned, those are identical events. Same conversion action, same default value, same weight in every bidding decision the account’s Smart Bidding strategy makes overnight. The algorithm has no idea one of those leads was worth roughly two thousand times more than the other, because nobody ever told it.

That gap between “a conversion happened” and “this conversion was worth something specific to us” is exactly what conversion value rules exist to close. They’ve been available in Google Ads since 2021, expanded steadily since, and picked up a genuinely useful new reporting column in the past year — yet a lot of accounts that would benefit from them still aren’t using them, mostly because nobody explained clearly what problem they solve or how to set them up without breaking something else. This post covers what value rules actually do, the conditions and math behind them, how to set them up without falling into the account-wide constraint that trips people up, and how to check afterward whether they’re actually helping.

The problem value rules were built to solve

Every bidding strategy that isn’t purely about clicks or impressions — Target CPA, Target ROAS, Maximize Conversions, Maximize Conversion Value — makes its decisions based on the conversion data you feed it. If you’re running a fixed-value conversion action (a lead form worth “1” every time, or an ecommerce transaction where value is just the cart total with no other context), Smart Bidding treats every conversion of that type as equally desirable. It will happily chase more of the cheap, low-value ones if that’s what the auction rewards, because from its perspective there’s no difference between them.

In reality, almost no business generates uniformly valuable conversions. An ecommerce store selling the same product often finds that customers on desktop have a meaningfully higher average order value than customers on mobile, simply because desktop sessions skew toward people doing more deliberate research rather than impulse-browsing on a commute. A local service business — a plumber, an HVAC company, a law firm — usually sees call and form value vary a lot by service area, because some zip codes correlate with bigger jobs or higher-margin services than others. A B2B company selling into multiple verticals might close enterprise accounts at ten or twenty times the rate of self-serve signups, but if both come through the same “trial signup” conversion action, that difference is invisible to the bidding algorithm unless someone encodes it.

Conversion value rules let you encode it, without needing a developer to change how conversions fire. Instead of touching your tracking setup, you set conditions in the Google Ads interface — based on the device someone’s using, where they’re located, or which audience segment they belong to — and tell Google Ads to adjust the reported value of a conversion whenever those conditions are true. The adjustment happens in real time, at auction time, which means Smart Bidding sees the adjusted value immediately and can use it the same day, not after some batch process catches up. This is also where value rules connect directly to the broader conversion tracking picture: a rule is only as good as the conversion data underneath it, which is exactly why auditing what your conversion numbers actually represent is worth doing before you start reweighting them.

The three conditions, and the constraint that catches people off guard

Google Ads currently supports three condition types for value rules: device, location (called “geographic” in some of the documentation), and audience. You can use them individually or combine two of them on a single rule — for example, “mobile users in a specific set of regions” as one combined condition. What you can’t do is stack all three at once; a rule supports a primary condition and, optionally, one secondary condition, which caps you at two dimensions per rule.

The part that actually trips people up isn’t the two-condition cap, though — it’s a constraint at the account level rather than the rule level. Every value rule in an account has to use the same primary condition type, and if a secondary condition is used, it has to match across all rules too. So if your first rule uses “Location” as the primary condition and “Device” as the secondary, every other rule you create in that account also has to have “Location” as primary, and if it has a secondary condition, that has to be “Device” as well. You can’t mix a Device-primary rule with a Location-primary rule in the same account. This isn’t obvious from the setup screen until you try to add a second rule that doesn’t match, and by then some people have already built their first rule around whichever condition happened to occur to them first, without thinking about which one they’d want to prioritize across the whole rule set. It’s worth deciding upfront, before creating rule one, which condition matters most for your business — because that decision effectively locks in the shape of every rule you add afterward.

Device conditions split into the categories you’d expect: mobile, desktop, and tablet, matched against the device the person was using at the moment of conversion. Location conditions work off the same geographic targeting building blocks used elsewhere in the account — countries, regions, or more granular areas — matched against the location associated with the click or conversion. Audience conditions are the most flexible and, for a lot of B2B and service businesses, the most useful: you can build a rule around remarketing lists, in-market segments, custom segments, or customer match lists, meaning you can value a conversion differently depending on whether the person converting was already a past customer, a member of an audience built from your CRM data, or someone google itself has classified as being in-market for related services.

Add versus multiply — the two ways a rule can change a value

Once you’ve picked your conditions, each rule needs an adjustment, and Google Ads gives you exactly two ways to apply one: add or multiply.

An “add” adjustment adds a flat amount on top of whatever value the conversion is already reporting. If your base conversion value is $50 and you add a rule that adds $20 for a specific audience segment, that conversion now reports as $70 for anyone matching the condition. This works well when you have a genuinely fixed dollar amount you want layered on top regardless of what the base value happens to be — for example, if returning customers reliably generate an extra fixed amount of downstream revenue that isn’t captured in the initial transaction value.

A “multiply” adjustment scales the existing value by a factor instead of adding a flat number. A rule that multiplies by 1.4 turns a $50 conversion into $70, but it also turns a $200 conversion into $280 — the adjustment scales proportionally rather than staying fixed. This is the more common choice for most use cases, because it preserves the relative differences between higher- and lower-value conversions that were already baked into your base tracking, rather than flattening them with a uniform dollar bump. If mobile conversions in your account are reliably worth about 70% of what desktop conversions are worth, a 0.7 multiplier applied to mobile captures that relationship regardless of whether a given conversion’s base value is $30 or $300.

You’ll generally get more defensible results from multiply rules unless you have a specific, well-evidenced reason to add a flat amount — a known fixed upsell rate, a fixed offline revenue component that doesn’t scale with the online transaction, something like that. If you’re not sure which one applies, multiply is the safer default because it doesn’t distort the shape of your existing value distribution, it just reweights it.

A worked example, to make the math concrete

It helps to walk through a single hypothetical case rather than leave the add-versus-multiply distinction abstract. Say a B2B software company sends a fixed value of $100 into Google Ads every time someone books a demo, regardless of who they are. Looking at closed-won data in their CRM, they notice that demos booked by people matching a customer-match audience built from their “enterprise” account list close at a rate several times higher than demos from everyone else. They decide the enterprise segment is worth roughly three times as much as an average demo booking, so they build an audience-based rule targeting that customer match list with a multiply adjustment of 3.0.

From that point forward, a demo booking from someone on the enterprise list reports as $300 instead of $100, while a demo from anyone not on that list keeps reporting its original $100. Smart Bidding, if the campaign is running Target ROAS or Maximize Conversion Value, now sees a real difference between these two kinds of demo bookings and can bid more aggressively to win auctions where an enterprise-list match is likely, and pull back where it isn’t. Nothing about the actual demo booking form, the thank-you page, or the underlying tracking tag changed — the only thing that changed is the number Google Ads associates with that specific kind of conversion once it recognizes the audience condition is met. That’s the entire mechanism: a condition check at auction time, followed by a simple add or multiply against whatever value would otherwise have been recorded.

Value rules versus bid adjustments — they’re not the same lever

It’s worth being precise about a distinction that gets blurred a lot, because Google Ads has had device and location bid adjustments for years, long before value rules existed, and the two features sound similar enough to get confused. A bid adjustment tells Google Ads to raise or lower your bid by a percentage for a given device, location, or audience, applied uniformly regardless of how a Smart Bidding strategy is actually valuing the conversion. Value rules don’t touch your bid directly at all — they change the reported value of the conversion itself, and then let Smart Bidding decide how to bid based on that adjusted value, alongside everything else it already knows about the auction.

For manual or semi-automated bidding, bid adjustments are still the right tool, since there’s no value-based algorithm interpreting a value signal for you. But if you’re running Target ROAS or Maximize Conversion Value, stacking manual bid adjustments on top of the campaign is often redundant at best and counterproductive at worst — you’d effectively be telling the algorithm to bid a certain way through the bid adjustment, while also feeding it value data it’s designed to interpret on its own. In most cases, once an account moves to value-based Smart Bidding, value rules become the more precise and more maintainable way to express “this segment is worth more,” and legacy bid adjustments left over from a manual bidding era are worth reviewing and, in many cases, removing rather than leaving stacked on top of a strategy that’s already reacting to value.

Which campaign types actually use this, and how it plugs into Smart Bidding

Conversion value rules apply to Search, Shopping, Display, Performance Max, and Travel and Hotel campaigns. They’re built specifically to work with value-based Smart Bidding strategies — Target ROAS and Maximize Conversion Value — since those are the strategies that make real-time decisions based on how much each conversion is worth, not just whether one happened. If you’re running Maximize Clicks, Target Impression Share, or a bidding strategy that doesn’t factor conversion value into its optimization target, value rules won’t have anything to act on, because the strategy isn’t using value as an input in the first place.

This is a good moment to sanity-check which bidding strategy you’re actually running, since it’s a common source of confusion. If you’ve been going back and forth on whether Target CPA, Target ROAS, or Maximize Conversions is the right fit for a given campaign, that decision has a direct bearing on whether conversion value rules will do anything useful for you at all — building out a careful set of value rules on a Target CPA campaign is wasted effort, because Target CPA optimizes toward a cost-per-conversion goal without weighting conversions by value in the first place.

Because the adjustment happens at auction time, in real time, the effect on bidding is immediate rather than delayed. If a rule multiplies mobile conversions in a particular region by 1.3, and the algorithm has been slightly underbidding on mobile traffic from that region because the recorded values looked lower than they actually are, that correction shows up in the very next auction, not after a multi-day recalibration. This is one of the more underrated aspects of the feature: it doesn’t just change your reporting, it changes what the bidding system is chasing while it’s still in the process of chasing it.

Setting up a rule, step by step

Value rules live under the Goals section of Google Ads rather than tucked inside an individual campaign, because they apply at the account level and then get referenced by whichever campaigns are eligible. In the left-hand navigation, go to Goals, then Conversions, and look for the Value rules tab. From there, creating a rule involves a handful of decisions in sequence.

First, pick your primary condition: device, location, or audience. Remember that whichever one you pick for your very first rule sets the primary condition type for every rule you’ll ever add to this account, so it’s worth pausing here rather than defaulting to whichever option is listed first.

Second, decide whether you want a secondary condition. This is optional — plenty of useful rules use just one condition type — but if you add one, it has to be a type that’s consistent across your other rules too, for the same account-wide constraint reasons covered above.

Third, define the specific values within your chosen condition that this rule applies to. For a device rule, that means picking mobile, desktop, or tablet. For a location rule, it means selecting the specific countries, regions, or areas the rule should fire in. For an audience rule, it means choosing which remarketing lists, in-market segments, or customer match lists trigger the adjustment.

Fourth, set the adjustment itself: add or multiply, and the specific amount or factor. This is where it’s worth having actual numbers to work from rather than a guess — pull your own conversion data segmented by the condition you’re targeting, and base the adjustment on a real, observed difference rather than an assumption about how the segments probably perform.

Finally, save the rule and give it a little time to start affecting bidding decisions before you draw conclusions. Because the adjustment applies going forward from the moment you activate it, you won’t see historical conversions retroactively re-valued — only new conversions that happen after the rule is live will reflect the adjustment. That matters for how you think about the “before and after” comparison later.

One thing worth doing before you build out a full set of rules: start with one or two, not five or six at once. If you launch a wide set of rules simultaneously and performance shifts, you have no clean way to tell which rule caused which effect. Roll out cautiously, watch the reporting for a couple of weeks, and add more rules once you understand how the first ones are behaving.

How to check whether your rules are actually doing anything

This used to be a genuinely weak spot. For years after value rules launched, the reporting around them was thin enough that it was hard to tell, with any confidence, whether a rule was meaningfully changing bidding behavior or just quietly sitting there. That’s improved meaningfully with a newer column that separates raw and adjusted values cleanly: Original Conversion Value. This column reports the unadjusted, raw monetary value of a conversion before any value rule, lifecycle goal, or other adjustment touches it — giving you a clean baseline to compare against whatever your adjusted, reported value ends up being.

To actually see the effect of your rules, segment your reporting. From the Campaigns menu, open the segment options and look for “Value rule adjustment” as a segmenting dimension. This breaks your existing rows out by the condition each rule used — Audience, Location, Device, or “No condition” for conversions that didn’t match any rule — and shows you the adjustment applied to each segment. Paired with the Original Conversion Value column, you can directly compare what a conversion was worth before your rule touched it against what it reported afterward, campaign by campaign.

There are also two more specific columns worth knowing: “Original conv. value (rule applied),” which totals the original value of conversions that did get a rule applied, and “Original conv. value (no rule applied),” which totals the original value of conversions that didn’t match any rule’s conditions. Comparing these two totals tells you something useful on its own — if the “no rule applied” bucket is unexpectedly large, it usually means your conditions are narrower than you intended, and a meaningful share of your conversions are passing through with their original, unadjusted value because they don’t match any rule you’ve set up.

Beyond the reporting screens, the most practical gut-check is a before-and-after comparison of the metric your bidding strategy is actually chasing. If you’re on Target ROAS, look at how your reported ROAS and your actual, real-world revenue-per-dollar-spent align in the weeks after activating a rule versus the weeks before. If a rule is correctly upweighting your genuinely higher-value segments, you should see the bidding system shift a bit more spend toward those segments over time — more impressions and clicks flowing to the conditions you’ve upweighted, relative to where they were before. If nothing shifts at all after a couple of weeks, either the adjustment you set was too small to matter, or the segment you targeted wasn’t different enough from the rest of your traffic for Smart Bidding to meaningfully reallocate around it.

Where value rules quietly go wrong

The most common mistake isn’t a technical one — it’s applying value rules to fix a problem that’s actually a conversion tracking problem. If your base conversion values are inconsistent for reasons unrelated to device, location, or audience — duplicate conversions firing, a tracking tag misfiring on certain page types, values being passed inconsistently between your backend and Google Ads — layering a value rule on top doesn’t fix any of that. It just applies a multiplier to numbers that were already unreliable, which can make a messy signal look falsely more precise without actually improving what Smart Bidding is optimizing against. It’s worth confirming your underlying values are trustworthy before you start adjusting them, and if you’re using Enhanced Conversions, it’s worth double-checking that match rates are healthy first, since a rule built on top of poorly matched conversion data inherits that same unreliability.

A second mistake is picking condition thresholds that are too narrow to generate a meaningful sample. An audience-based rule that only fires for a remarketing list with a few hundred people in it isn’t going to meaningfully move a Smart Bidding strategy that’s already working with a modest volume of conversions overall — the segment is too small a fraction of total traffic for the algorithm to act on with any confidence. Value rules work best on conditions that represent a real, sizeable share of your traffic, not an edge case.

A third, more subtle mistake is setting up rules and then never revisiting them. The relative value differences that justified a rule six months ago might not hold today — a service area that used to generate bigger jobs might have shifted, an audience segment that used to convert at a premium might have normalized, a device gap that used to be wide might have narrowed as your mobile experience improved. Value rules aren’t something you configure once; they’re closer to a bid adjustment than a permanent fact about your business, and they deserve the same periodic review.

A fourth mistake worth naming explicitly: stacking too many rules with overlapping conditions and losing track of which one is actually firing in a given auction. If you build out a large rule set — several device rules, several location rules, layered secondary conditions — it gets harder to reason about which combination applied to any specific conversion, and debugging an unexpected shift in performance becomes a lot more time-consuming. Keep the rule set as small as it can be while still capturing the value differences that actually matter to your business. A handful of well-evidenced rules beats a dozen speculative ones.

What this actually looks like across different kinds of businesses

The shape of a useful value rule setup depends a lot on what kind of business is behind the account, so it’s worth walking through a few concrete patterns rather than treating this as one-size-fits-all advice.

For an ecommerce business, the most common starting point is a device rule, because desktop-versus-mobile average order value gaps are common enough to be close to a default assumption worth testing. A store that already has good, itemized transaction values passed into Google Ads can often get real signal from a simple multiply rule on mobile, calibrated to the actual AOV gap it observes in its own reporting rather than an industry rule of thumb. A location rule can layer on top for stores with significant regional variation in basket size — a national retailer might find that certain regions consistently order larger baskets, independent of the device split.

For a B2B or SaaS business, audience conditions tend to matter more than device or location, because the biggest value gaps usually come from who’s converting rather than what device they’re on. A company that’s built a customer match list from its CRM, segmented by deal size or company tier, can use that list as the audience condition for a rule and meaningfully upweight conversions from accounts that resemble its best existing customers. This is also where the connection to conversion tracking accuracy is tightest — a value rule built on a customer match list is only as good as how well that list is maintained and how cleanly it’s synced, so it’s worth treating the CRM-to-Google-Ads pipeline as part of the value rule setup, not a separate concern.

For a local service business — the kind covered on Growera’s local services page — location rules tend to be the highest-leverage option, since job value by service area is one of the more consistent patterns in this category. A plumbing or HVAC company that tracks call value by zip code, even roughly, often finds a real and durable gap between areas, and a location-based value rule lets that gap directly influence where the bidding system leans in, rather than treating every call in the service area as interchangeable.

None of these patterns are mutually exclusive within a single account over time — you might start with the condition that matches your biggest known value gap, get comfortable reading the reporting, and add a second rule using the same primary condition type later. The point isn’t to implement every possible rule on day one; it’s to start with whichever condition captures the value difference you’re most confident actually exists in your data.

Where continuous account monitoring fits into this

Value rules aren’t a set-and-forget feature, and that’s exactly the kind of thing that tends to slip through the cracks between scheduled account reviews. A rule that was calibrated correctly when it was built can drift out of sync with reality over months without anyone noticing — the underlying value gap it was based on narrows, a new audience segment emerges that isn’t captured by any existing rule, or a tracking change elsewhere in the account quietly shifts the base values the rules are adjusting. This is part of what Growera’s continuous daily account monitoring is built to catch: instead of waiting for a monthly or quarterly review to notice that a value rule’s assumptions no longer match the account’s actual performance, the account gets checked every day, so a rule that’s started producing an odd or unexpected effect on bidding gets flagged while it’s a small, cheap adjustment rather than months of budget quietly being steered by an out-of-date assumption.

Summary

Conversion value rules exist because most businesses don’t generate uniformly valuable conversions, and fixed-value conversion actions hide that reality from Smart Bidding by default. Rules let you adjust reported values in real time based on device, location, or audience conditions, using either a flat add or a proportional multiply, and they plug directly into value-based bidding strategies like Target ROAS and Maximize Conversion Value.

The setup itself is straightforward once you know about the account-wide constraint that locks every rule to the same primary (and, if used, secondary) condition type — decide which condition matters most to your business before building your first rule, not after. Base your adjustments on real, observed differences in your own conversion data rather than assumptions, start with one or two rules before expanding, and use the Original Conversion Value column alongside the Value rule adjustment segment to actually verify the rules are doing what you built them to do. And treat the whole setup as something to revisit periodically, not a box to check once — the value gaps that justify a rule today are exactly the kind of thing that shifts quietly over time if nobody’s checking back in on them.