How to Optimize App Store SEO and Rank Apps: A Practical ASO Guide
App Store Optimization works best as an ongoing product-marketing discipline: match store metadata to real search intent, improve the listing for qualified visitors, and test changes instead of treating launch metadata as permanent.
What is How to Optimize App Store SEO and Rank Apps?
App Store SEO (ASO) in 2026 should be managed as a store-specific discovery and conversion process. Use the metadata fields each platform provides, choose search language that accurately matches the app, make screenshots and descriptions explain the product clearly, and use native experiments where available.
Treat retention, ratings, crashes, web traffic, and other behavioral or external signals as product, trust, and acquisition diagnostics unless the store documents a direct ranking relationship. Measure changes against a dated baseline, avoid changing too many variables at once, localize only where the product experience supports the market, and use observed results to choose the next iteration.
Key Takeaways
- Build ASO around platform-specific metadata, accurate positioning, listing conversion, and iteration rather than keyword placement alone
- Use the app title for clear brand and product relevance while staying within each store's current metadata rules and avoiding awkward keyword stuffing
- Use competitor research to understand category language, then choose terms that accurately describe your own app instead of copying another listing
- Treat review generation as a trust and feedback process: ask eligible users consistently for honest ratings without incentives, review gating, or selecting only happy users
- Use conversion-focused creative testing to compare screenshots, icons, and other eligible store assets with evidence from your own traffic
- Localization can widen discovery when metadata and the in-app experience genuinely support the market; start with 3-5 additional locales only when each can be researched and maintained properly
- Prefer search terms that match the app's real job, audience, and use case over broad terms that may attract poorly matched visitors
- Treat web content, press, communities, and social distribution as acquisition and brand-demand channels; do not assume they are documented direct app-store ranking factors
- Use the testing tools available in each store to separate creative assumptions from changes that actually improve listing performance
- Use the first 30-day operating period after launch or a major listing refresh as a disciplined measurement cycle, not as a claimed algorithmic privilege
Introduction
App Store Optimization is easiest to manage when you separate what each store documents from what your own data can reveal. In 2026, founders and growth teams still need strong search metadata, but keyword placement is only one part of the job.
A useful ASO process asks four practical questions: Can the store understand what the app is and which searches it is relevant to? Does the listing explain the product clearly enough for the right visitor to consider installing it?
Does the app experience support the promise made on the listing? And can you measure whether a metadata or creative change actually helped? Apple App Store and Google Play expose different metadata fields and testing capabilities, so a single copy-and-paste optimization plan is usually too crude.
Start with the rules and analytics inside the relevant store console, then use competitor listings, search suggestions, support conversations, paid acquisition data, and your own query research as inputs rather than as proof of a ranking mechanism.
This guide shows how to build a practical workflow for keyword research, metadata, screenshots, ratings, external acquisition, localization, and post-launch iteration without treating undocumented signals as guaranteed ranking factors.
The goal is not to manufacture activity for an algorithm. It is to make the listing easier to discover for relevant searches, easier to understand, and easier to improve through controlled observation.
What Most Guides Get Wrong
A common ASO shortcut is to reduce the task to finding popular keywords with manageable competition. That can produce a useful research list, but it does not tell you whether the terms fit the product, whether the listing converts the people those terms attract, or whether the app experience matches the promise.
Broad category phrases can be tempting because they appear to offer reach, yet a term is only useful when the app legitimately satisfies the intent behind it. The better decision rule is relevance first, evidence second, and scale third.
Map keywords to actual product capabilities and audience language, place them only in fields where the store permits and values them, and then watch store impressions, product-page views, installs, conversion measures, retention diagnostics, ratings, and support feedback in the tools you already use.
If a term produces visibility but poor downstream quality, treat that as a hypothesis to investigate rather than proof that a store is penalizing the app. ASO improves when keyword decisions, listing clarity, and product reality stay aligned.
Start With What the App Stores Actually Expose
App-store search is not a single public formula that marketers can reverse engineer from the outside. Apple and Google publish metadata requirements, policy guidance, store-console reporting, and product tools, but they do not provide a complete weighting model for every search result.
That distinction matters because it prevents an ASO plan from turning observations into invented ranking factors. Begin with relevance: make the app name, subtitle or short description, keyword field where available, category, and long description accurately explain the product in the fields each platform uses.
Then evaluate listing performance with the platform's own reporting. Store impressions, product-page views, acquisition sources, installs, and conversion measures help you see whether the listing is reaching and persuading appropriate users.
Retention, crashes, engagement, and support feedback remain important product-health diagnostics, but do not describe them as confirmed direct search-ranking weights unless the platform documents that relationship.
Apple and Google Play also differ in how metadata is supplied and indexed, so optimize them separately instead of forcing identical text into both stores. On Apple, visible metadata and the dedicated keyword field should be planned together without unnecessary repetition.
On Google Play, the title, short description, and long description should use natural language that accurately reflects the app and complies with current store policies. The practical objective is a coherent listing: the search language, creative assets, product promise, and actual in-app experience should describe the same thing.
Key Points
- Separate documented store guidance from third-party observations about ranking behavior
- Use store-console data to evaluate discovery and conversion instead of inferring performance from rank positions alone
- Optimize Apple App Store and Google Play metadata independently because their fields and rules differ
- Treat retention, engagement, and crash data as product-quality diagnostics unless a store explicitly documents a direct ranking relationship
- Keep keyword choices tightly connected to the app's real capabilities, audience, and use cases
- Make the promise in screenshots and copy consistent with what users encounter after installation
💡 Pro Tip
Before changing metadata, record a baseline from App Store Connect or Play Console for the metrics you already have. Note the date, traffic source mix, current listing version, and any concurrent product or campaign changes. This makes later comparisons more useful and reduces the risk of crediting ASO for movement caused by something else.
⚠️ Common Mistake
Treating a third-party correlation as an official store ranking rule. Use outside research to form hypotheses, but use documented platform guidance and your own controlled observations to decide what to change.
Find Search Terms From Category Language, Not Just Category Leaders
Competitor research is useful when it helps you discover how real products in the category describe features, outcomes, audiences, and use cases. It is less useful when it becomes a copying exercise.
A practical approach is to look beyond the most visible category leaders and inspect apps that appear across a wider part of the results. Compare apps around positions eight through twenty as well as positions one through five, because mid-field listings often expose more varied wording and narrower use cases.
For each relevant competitor, capture the language used in the app name, subtitle or short description, long description, screenshots, release notes, and category positioning. Then compare that language with search suggestions, paid-search terms where available, support tickets, onboarding responses, and the phrases users use in reviews.
An ASO intelligence tool can help organize this research, but its search-volume or difficulty estimates are vendor measurements rather than store-provided facts. Turn the research into a keyword map organized by what the app does, the outcome the user wants, and the context in which the app is used.
The categories are descriptive, not an official store taxonomy. Keep only terms that truthfully match the product. If several competitors use a phrase but your app does not deliver that capability, the phrase is not an opportunity.
Prioritize the terms you can support with clear metadata and a matching product experience, then test whether they improve relevant discovery and conversion rather than assuming a particular ranking position proves demand.
Key Points
- Review apps around positions 8-20 as well as 1-5 to broaden the language and use cases included in your research
- Treat positions 3-10 in third-party ASO tools as observations to investigate, not proof that a keyword will be easier for your app
- Organize candidate terms by product function, user outcome, and usage context so each phrase has a clear reason to exist
- Reject competitor phrases that imply features, audiences, or outcomes your app cannot honestly support
- Use third-party keyword estimates as directional inputs and reconcile them with store-console and customer data
- Record why each target term is relevant before deciding which metadata field, if any, should contain it
💡 Pro Tip
When a narrower audience or use-case phrase repeatedly appears in search suggestions, competitor copy, and customer language, investigate it before a broad category phrase. Multiple independent inputs can make it a stronger research candidate, but you still need your own store data to judge performance.
⚠️ Common Mistake
Building the keyword list entirely from the most prominent apps. Category leaders often have brand demand and positioning advantages that do not transfer to a newer app, so use them as references rather than templates.
Build Metadata Around Relevance, Clarity, and Store Rules
Metadata is where keyword research becomes an actual store listing, so every term has to fit both user expectations and platform rules. On the App Store, the app name allows up to 30 characters. Do not automatically spend most of the 30 on generic keywords or force a formula into the name; use the available space to identify the product clearly and include a relevant search phrase only when it reads naturally and complies with Apple's requirements.
The subtitle also allows 30 characters and should add useful context rather than repeat the app name. Google Play's app title has a 30 character limit, so the same principle applies: clarity first, supported relevance second.
Google Play also provides a short description with up to 80 characters. Treat the 80-character space as concise store copy, not as a place to stack disconnected phrases. For Apple's dedicated keyword field, you have 100 characters to work with, so avoid unnecessary duplication and use the field for relevant terms that are not already represented efficiently elsewhere.
For Google Play's long description, write for users first while including important concepts naturally. Across both stores, keep terminology consistent with screenshots and the in-app experience because that reduces ambiguity for users, not because there is a special hidden markup or cross-asset keyword mechanism you can rely on.
Before publishing, read every field as a prospective user would: can they tell what the app does, who it is for, and why the wording is accurate?
Key Points
- Use the app name or title to identify the product clearly and include relevant search language only when it reads naturally
- Use the Apple subtitle and Google Play short description to add new context rather than repeating the same claim
- Reserve Apple's keyword field for relevant additive terms and avoid wasting space on needless duplication
- Keep store metadata consistent with screenshots and the product experience so users receive one coherent message
- Write Google Play's short and long descriptions as readable product copy that also reflects validated search language
- Remove filler, unsupported superlatives, and keyword combinations that make the listing less clear
💡 Pro Tip
Audit the app name, subtitle or short description, keyword field, and long description side by side. Mark repeated words, unsupported claims, and terms with no clear user intent. Keep repetition only when it improves comprehension or is required by the platform's field structure.
⚠️ Common Mistake
Filling Apple's 100-character keyword field with repeated or loosely related phrases. The constraint is a reason to prioritize relevant terms, not to squeeze in every keyword discovered during research.
Design Screenshots to Explain the App Before They Decorate It
Store creatives have a practical job: help a qualified visitor understand the app quickly enough to decide whether to keep evaluating it. A polished screenshot can still fail if the visitor cannot tell what the product does.
Start by deciding what each screenshot must communicate, then design the visual around that message. The opening asset should establish the core value or use case without making an unsupported promise.
The next assets can show the key workflow, a meaningful result inside the product, an important differentiator, and credible evidence such as an accurate feature demonstration or properly sourced recognition already permitted for use.
Do not invent ratings, press logos, user quotes, or outcomes to create social proof. If you use a testimonial or rating reference, make sure it is genuine, current enough for the context, and presented in a way that complies with store policies.
Keep caption text readable on the devices and placements where the store actually displays the creative. A visitor may decide from the first visible assets without reading the long description, so front-load comprehension rather than design flourish.
For a quick comprehension check, show the creative to someone unfamiliar with the product for 10 seconds and ask what the app does, who it appears to help, and what they would expect after installing.
That test does not predict store conversion, but it can reveal confusing messaging before you spend traffic on a formal experiment.
Key Points
- Give every screenshot a communication job before choosing its visual treatment
- Use the first visible creative to explain the core value or use case without exaggeration
- Show real product workflows and outcomes instead of decorative UI that lacks context
- Use differentiators only when they are accurate and meaningful to the intended audience
- Include ratings, testimonials, or recognition only when they are genuine and permitted for use
- Use store-native experiments where available to test whether a creative change improves listing performance
💡 Pro Tip
Write the caption sequence before the final visual design. If the captions alone do not tell a coherent story about the app, stronger gradients, device frames, and illustration will not solve the underlying message problem.
⚠️ Common Mistake
Judging a screenshot set only by visual consistency. A cohesive gallery can still underperform if the first visible assets do not explain the product or if the promise does not match the installed experience.
Use Ratings and Reviews as Trust and Product Feedback Signals
Ratings and reviews influence how prospective users perceive an app, and they also give the team direct language about satisfaction, confusion, bugs, and unmet expectations. Avoid turning that into an undocumented claim that recent review velocity has a specific hidden ranking weight.
Instead, manage reviews as a durable trust and feedback program. Ask eligible users for honest feedback at a reasonable moment after they have had a fair chance to experience the product. Do not offer incentives for positive ratings, discourage negative feedback, or show the store prompt only to users who first indicate that they are satisfied.
Use the platform's supported review-prompt mechanism and respect its frequency limits and policies. A useful trigger is a meaningful completed action, provided the timing is not manipulative and the user has enough context to form an opinion.
Track incoming ratings and review themes over time so you can see whether releases, bugs, pricing changes, or onboarding revisions coincide with shifts in sentiment. Respond to reviews when a helpful response is possible, especially where you can clarify a fix or support path.
Do that for customer service and public trust, not because a review-response rate should be presented as an official ranking factor. If you want more feedback, improve the number of legitimate opportunities to ask all eligible users consistently rather than selecting only users likely to leave favorable comments.
Key Points
- Treat ratings and reviews as user-trust and product-feedback inputs without assigning them an undocumented ranking weight
- Ask eligible users consistently for honest feedback after they have enough experience to form an opinion
- Do not use incentives, review gating, or satisfaction screening to manufacture positive ratings
- Use supported in-app review mechanisms and follow each platform's prompt policies
- Track review themes alongside release and product changes to identify issues worth investigating
- Respond to feedback to support users and clarify issues, without describing review-response activity as a confirmed app-store search factor
💡 Pro Tip
Create a simple review-theme log for recurring praise, complaints, bugs, feature requests, and expectation gaps. The most valuable ASO insight may be wording users repeatedly use to describe the app, while the most valuable product insight may be the promise the listing is failing to deliver.
⚠️ Common Mistake
Optimizing for a higher count of favorable reviews by filtering who receives a prompt. That can become review gating and distorts the feedback you need. Use consistent eligibility rules and ask for honest feedback.
Use External Channels to Create Qualified Demand, Not Mythical Ranking Signals
App discovery does not begin and end inside a store. People can arrive from a product website, organic web search, paid campaigns, editorial coverage, newsletters, communities, creators, support content, or direct referrals.
Those channels matter because they can create qualified store visits, branded demand, and installs that you can attribute and evaluate. What they should not become is a list of undocumented claims that backlinks, social mentions, press coverage, or web click-through rates directly increase an app's search ranking.
Build a useful app landing page that accurately explains the product and links to the correct store destinations. Publish supporting content when it genuinely helps prospective users understand a problem the app solves.
Pursue editorial coverage or directory inclusion when the placement is relevant and earned, not simply because a third-party page has authority metrics. Use campaign parameters, store attribution tools, referral reporting, and analytics where available to see which channels send visitors who install and continue using the product.
If external demand rises at the same time as store visibility, record the relationship as an observation and investigate further rather than assuming causation. This keeps web SEO, PR, social, and ASO connected operationally while maintaining an evidence boundary around what the stores have actually documented.
Key Points
- Treat web, PR, social, communities, and paid media as acquisition channels that can send qualified visitors to the store
- Do not describe backlinks or social mentions as confirmed direct app-store ranking factors without platform documentation
- Use an accurate app landing page to explain the product and route visitors to the correct store
- Seek editorial mentions because they can reach relevant audiences and build brand awareness, not because a ranking lift is guaranteed
- Measure external acquisition with attribution and analytics tools where available
- Record correlations between external demand and store performance as observations until you have stronger evidence
💡 Pro Tip
Align the main promise on your app website with the promise on the store listing, but do not force identical copy. The website can answer deeper questions while the store page stays concise. Use attribution data to learn which external messages bring visitors who actually want the product.
⚠️ Common Mistake
Treating web SEO and ASO as either completely independent or mechanically linked by a hidden authority score. They can support the same acquisition journey without requiring an undocumented direct ranking connection.
Localize Metadata Only Where the Product Experience Supports It
Localization can improve relevance and comprehension when users in different markets search with different language, spelling, terminology, or expectations. Treat it as market-specific research rather than a direct translation task.
Start with your existing acquisition data to identify markets already showing legitimate demand, then confirm that the app, onboarding, support, pricing, legal information, and store assets can serve those users accurately.
For each supported locale, research the terms people actually use for the product category in that language or market. Competitor metadata and third-party ASO tools can suggest vocabulary, but local customer language, search suggestions, paid query data, and native review are useful checks against awkward translation.
English-speaking markets can still differ in terminology, yet separate metadata should not be created merely to occupy more fields. It should reflect a real localization decision and stay within the stores' current locale behavior and policies.
When the product itself is not translated, be explicit about the experience a user will receive so localized marketing does not imply functionality or support that does not exist. Prioritize markets using practical criteria such as current demand, commercial value, language capability, support burden, compliance needs, and the effort required to maintain accurate assets.
After publishing, compare discovery and conversion by territory where reporting allows, and revise wording based on local evidence rather than assuming a translated keyword will perform like its source-language equivalent.
Key Points
- Use localization to improve market-specific relevance and comprehension, not simply to multiply keyword fields
- Validate that the product, support, pricing, and legal experience can serve a market before localizing its listing
- Research search language natively for each supported locale instead of translating a source keyword list word for word
- Prioritize markets by observed demand, commercial value, language capability, support needs, and maintenance effort
- Be transparent when the app interface or support is not available in the language used by the store listing
- Measure discovery and conversion by territory where possible and revise the localized listing from local evidence
💡 Pro Tip
Check existing territory-level acquisition before choosing the next locale. Organic demand from a market you already serve can justify deeper research, but it does not by itself prove that a localized listing will increase installs.
⚠️ Common Mistake
Publishing direct translations without native review or product support. A linguistically correct phrase can still be unnatural for the category, and a localized listing can create the wrong expectation if the installed app remains unsupported in that language.
Use the First 30 Days as a Structured Optimization Cycle
A launch or major store-listing revision creates a useful 30-day operating period for concentrated measurement, but treat that as a management window rather than a documented promise of special ranking treatment.
Before launch, make sure analytics, attribution, crash reporting, onboarding measurement, support routes, and store-console access are ready. If you already have an audience, communicate the release to people who have a legitimate reason to use the app; do not manufacture installs or ratings to create artificial activity.
During the period, monitor store impressions, product-page views, installs, conversion measures, acquisition sources, early product engagement, crashes, retention diagnostics, review themes, and support issues.
Use those signals to find mismatches between acquisition promise and product experience. If a search term attracts visitors who do not convert, examine the term, creative, audience, and product fit before changing metadata.
If installs convert but onboarding loses users, the store listing may be doing its job while the product journey needs attention. For an update, document which metadata, creative, pricing, onboarding, or product changes shipped together so you can avoid crediting one variable for movement caused by another.
When possible, stagger experiments and use the stores' native testing tools rather than changing several major assets at once. At the end of the cycle, keep what has evidence behind it, revert or revise weak hypotheses, and queue the next focused test. That operating discipline is more defensible than treating launch as an algorithm-conditioning event.
Key Points
- Treat launch as a measurement and learning period rather than claiming an undocumented visibility boost
- Prepare analytics, attribution, crash reporting, and store-console access before traffic arrives
- Acquire users through legitimate channels that match the app's intended audience
- During the 30-day cycle, separate listing-conversion problems from onboarding and product-retention problems
- Record all concurrent product and marketing changes so you can interpret movement more carefully
- Use focused tests and repeatable reporting to turn launch observations into the next optimization decision
💡 Pro Tip
Create one launch scorecard before the 30-day cycle begins. Include store discovery, listing conversion, acquisition source, onboarding completion, crash diagnostics, retention measures, and review themes. The point is not to optimize every metric at once; it is to know where the acquisition journey is breaking.
⚠️ Common Mistake
Treating launch traffic as something to maximize at any cost. Broad, poorly matched acquisition can make the data harder to interpret and waste budget. Prioritize legitimate audience fit and measurement quality.
Your 30-Day App Store SEO Action Plan
Create the baseline. Export the store metrics available to you, document the current app name, subtitle or short description, keyword field where applicable, long description, categories, creatives, territories, and recent product changes. Mark duplicated terms, unsupported claims, and places where the listing does not match the installed experience.
Expected Outcome
A dated ASO baseline that separates current metadata, creative, product, and acquisition conditions before you start changing them.
Research category language across apps appearing around positions 8-20 as well as prominent leaders. Compare competitor wording with store suggestions, customer reviews, support language, and any search-query data you already have. Keep only terms that truthfully match the product.
Expected Outcome
A prioritized keyword and message map grounded in relevant category language rather than copied competitor metadata.
Rewrite the store metadata platform by platform. Improve the app name or title, subtitle or short description, Apple keyword field, and Google Play long description as applicable. Remove unnecessary repetition and make every claim consistent with the product experience.
Expected Outcome
A cleaner metadata set with explicit reasons for each target term and no reliance on unsupported ranking claims.
Audit the creative sequence. Write the message each screenshot should communicate before redesigning it, verify that every demonstrated feature exists, remove unsupported social proof, and choose the first creative hypothesis you want to test in the store's native experiment tooling.
Expected Outcome
A store creative plan that explains the product clearly and identifies a focused, measurable testing hypothesis.
Review your rating and feedback process. Confirm that prompts use supported platform mechanisms, apply consistent eligibility rules, ask for honest feedback, and do not gate reviews. Group recent review themes by praise, confusion, bugs, feature requests, and expectation gaps.
Expected Outcome
A compliant review process and a feedback summary that can inform both store messaging and product priorities.
Connect external acquisition to the store journey. Audit the app website for message consistency, confirm store links and attribution, identify relevant editorial or community channels, and measure which sources send visitors who actually want the product.
Expected Outcome
A documented acquisition path from external discovery to the correct store listing, with measurable sources instead of assumed authority effects.
Review territory demand and choose localization priorities only where the product and support experience can serve the market. Publish or prepare one supported locale, then schedule the next ASO review around a single metadata or creative hypothesis with a defined baseline.
Expected Outcome
A defensible localization decision and a recurring optimization process that can be repeated after this initial cycle.
Frequently Asked Questions
How long does it take to see results after changing App Store SEO?
There is no universal ASO timeline because indexing, traffic volume, category competition, product demand, campaign activity, seasonality, and concurrent app changes can all affect what you observe. After a metadata update is approved, confirm that the new listing is live, then watch the store-console metrics and search visibility available to you.
Judge a change only after you have enough relevant traffic to compare it with the prior baseline. If several variables changed together, treat any movement cautiously and plan a narrower follow-up test.
Does the App Store description affect keyword ranking?
On Apple's App Store, plan search metadata around the fields Apple provides for the app name, subtitle, and keyword entry, while using the long description primarily to explain the product accurately and support conversion.
On Google Play, the long description is part of the store listing and should include relevant product language naturally. Do not force repetition for density. The first 167 characters can be written for clarity before the user expands the text, but do not claim that this specific cutoff has a special ranking weight unless current platform documentation says so.
What is the best way to find keywords for App Store SEO?
Combine category research with first-party language. Review relevant apps around positions 8-20 as well as category leaders, then compare their wording with store suggestions, customer reviews, support conversations, paid-query data, and your own product terminology.
Group candidates by function, outcome, and usage context, and remove any phrase that implies a feature or audience your app does not support. Third-party ASO tools can help estimate demand and competition, but treat those figures as vendor data rather than official store search-volume measurements.
How important are app ratings for App Store SEO?
Ratings and reviews are important to prospective-user trust and provide useful product feedback, but avoid assigning them an undocumented hidden weighting formula. Build a compliant review process instead: ask eligible users consistently for honest feedback after they have enough experience to form an opinion, use supported in-app prompts, and never offer incentives, discourage negative feedback, or route only satisfied users to the public store. Track themes over time and use them to improve both the listing and the product.
Is App Store SEO different from Google Play Store optimization?
Yes. The stores expose different metadata fields, policies, reporting, and testing tools, so each listing needs its own implementation. On Apple, the app name and subtitle allow 30 characters each. On Google Play, the app title allows 30 characters and the short description is a distinct field.
Apple also provides a dedicated keyword field with 100 characters. Use the current store documentation as the source of truth for limits and eligibility, because policies can change. Keep the positioning consistent across platforms while adapting the copy to each store's structure.
Can I run A/B tests on my App Store listing?
Yes, where the store and your listing are eligible for the platform's native experimentation features. Google Play provides store-listing experiments for supported assets and text, while Apple provides Product Page Optimization for eligible App Store product-page assets.
On Apple, the feature applies to supported iOS 15 experiences and later. Start with a meaningful hypothesis, change the smallest practical set of variables, and interpret the result only after the experiment has sufficient traffic. A test is useful because it replaces preference with observed behavior for your own audience.
Do app updates affect App Store SEO rankings?
Updates give you an opportunity to improve the product and, when appropriate, revise store metadata or creatives, but do not assume a release automatically receives a ranking reward. After a major update, use a 30-day review cycle to compare store discovery, conversion, acquisition, crashes, onboarding, retention diagnostics, ratings, and support themes against the prior baseline.
Record which product, listing, pricing, and campaign changes happened together so you do not mistake correlation for causation.
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