ChatGPT Apps and SDK: The Marketing Playbook for the New App Store

The app store moment for ChatGPT is here\n\nOpenAI used DevDay 2025 to flip a long-standing assumption. ChatGPT is no longer just a conversational surface; it is a platform where third-party apps run natively inside chat. That shift, paired with a developer-facing Apps Software Development Kit (SDK) in preview, opens a new demand-capture surface for brands. OpenAI’s announcement details apps you can call by name, proactive suggestions inside conversations, and the SDK built on the Model Context Protocol. See the overview in OpenAI introduces ChatGPT apps.\n\nIf you remember the first months of iOS and Android app stores, you remember the advantage early movers gained. The same pattern is starting again, only this time the store is a chat box that concentrates intent.\n\n## What changed, in plain English\n\n- Users can invoke an app by typing its name at the start of a message, for example, “Spotify, make a playlist for my Friday party.”\n- ChatGPT can suggest an app when your conversation implies an intent; for example, when you discuss moving, it can surface Zillow with an interactive map.\n- Apps render interactive components right inside the thread, such as maps, playlists, or slide previews.\n- The Apps SDK is in preview, so developers can build and test now, with submissions, directory browsing, and monetization guidance coming later this year.\n- Initial availability excludes the European Economic Area, Switzerland, and the United Kingdom, with support planned.\n\nFor marketing leaders, the takeaway is simple: this is a new shelf in a store that already has foot traffic. Users do not have to switch tabs or remember a domain. The call to action lives inside the conversation.\n\n## Why marketing should treat ChatGPT like a new app store\n\nThree dynamics define early app stores and are now visible in ChatGPT:\n\n1) Discovery is contextual, not only search-based. ChatGPT can recommend an app when intent appears in the chat. That is closer to a product shelf the assistant replenishes in real time than a static search results page. Similar patterns are reshaping answer-led discovery, as covered in our Perplexity Comet answer engine playbook.\n\n2) The default experience is task completion, not browsing. The right metric is conversion inside chat, not a click-out. Your creative and offer strategy should be optimized for finishing a job in-thread. For commerce teams, see the ChatGPT Instant Checkout pilot playbook.\n\n3) Platform policies shape growth. OpenAI’s guidelines will gate design, privacy, and quality. Teams that work within constraints and still deliver speed will enjoy early distribution advantages when the directory opens. For broader orchestration across agents, review the Adobe Agent Orchestrator control plane.\n\n## From channel to product surface\n\nTreat this less like launching a new acquisition channel and more like shipping a mini product that excels at one high-intent journey. Your listicles, category pages, and landing pages do not carry over. Instead, your app should meet a user at the exact decision moment and either complete the conversion in chat or set a handoff that preserves context, identity, and consent.\n\nGood first candidates are journeys where the next action is structured and low friction:\n\n- Travel: build, tweak, and book a weekend itinerary.\n- Retail: assemble a cart for a specific occasion, for example, back to school or a dinner party.\n- B2B software: generate a working template or sample dataset and create a trial workspace.\n- Education: enroll into a curated micro course with a personalized study plan.\n\n## A three-step plan to capture AI search demand\n\n### Step 1. Pilot a lightweight app for one high-intent journey\n\nPick one job to be done that already converts well on your site. Strip it to the decision boundary. In practice, that means a single intent statement, two to three clarifying questions, one preview with a small set of interactive controls, and an in-chat conversion action.\n\n- E-commerce example: “Help me build a 7 piece fall wardrobe for under 500 dollars.” Your app asks size and style, renders a 7 item grid, lets users swap any item, and then places the final set into a cart with a single sign-on handoff.\n- SaaS example: “Help me clean and visualize this CSV.” Your app requests the file, shows a before and after preview, and spins up a 14 day trial with the transformed dataset ready.\n\nKeep the scope tight. Aim for a two week prototype and a four week beta where the team iterates on prompts, metadata, and UI components.\n\n### Step 2. Set an in-chat conversion KPI\n\nClicks and sessions miss the point of in-chat completion. Define a primary conversion that happens inside the thread and can be verified server-side.\n\n- Conversation qualified lead: a lead who answers three deterministic questions in chat and consents to a handoff.\n- In-chat checkout rate: the percentage of app invocations that reach a confirmed payment event.\n- Suggestion accept rate: the percentage of threads where ChatGPT surfaces your app and the user accepts.\n- Time to completion: median minutes from first invocation to conversion.\n\nBuild a shared dashboard that reports these, broken out by entry phrases and segments. The marketing owner should review these weekly with product and data.\n\n### Step 3. Run attribution tests to isolate lift\n\nBecause discovery is contextual, lift comes from intent capture that may never hit your site. Design experiments that quantify incremental impact.\n\n- Geo holdouts: select matched markets and withhold the app in a subset to measure base rates.\n- Time slicing: run on odd weeks only, then compare with even weeks while controlling for seasonality.\n- Creative prompts: randomize two onboarding prompts to isolate the impact of copy.\n- Cross-surface triangulation: track branded search lift and direct traffic in markets where the app runs. Expect upstream lift to be muted when conversion stays in chat.\n\nTeams use Upcite.ai to centralize in-chat conversion events and coordinate these holdouts alongside site and ad data, so reporting covers all surfaces without double counting.\n\n## How the Apps SDK changes your build process\n\nThe SDK extends the Model Context Protocol, which connects ChatGPT to external tools. In practice, you will stand up a server that exposes capabilities, define chat behaviors, and assemble UI elements that render inline. The result feels like a mini product inside the assistant. Developers can get started with examples and guidelines in the Apps SDK developer docs.\n\nKey design implications for growth teams:\n\n- The prompt is now part of your product. Your onboarding prompt and your metadata shape whether ChatGPT suggests your app.\n- Interface scaffolding matters. A good chat UI allows quick edits and previews, not walls of text.\n- State must be portable. If a user leaves the thread and returns, the app should restore state or summarize progress.\n\n## Build a lightweight architecture that respects privacy and speed\n\nMarketing should co-design architecture with engineering to avoid bloat.\n\n- Manifest and metadata: define name, description, intents, and permissions. Treat copy like a performance ad. It will influence suggestion and click-through.\n- App brain: outline the chat flow. Write your system prompts and guardrails. Keep the first version deterministic.\n- Backend APIs: start with read-only access if possible. For write actions, add explicit confirmation and a clear undo path.\n- Identity and consent: the first time a user connects, present a plain-language summary of data sharing and gain explicit consent.\n- Telemetry: produce server-side events for app invoked, app suggested and accepted, questions answered, preview rendered, conversion fired, and handoff success.\n\n## Target the right intents, not all intents\n\nIntent selection is most of the game. Treat the assistant like a shopper in a narrow aisle. You want to meet them exactly when they express a narrow, valuable intent.\n\n- Occasion-based retail: “build a capsule wardrobe for Italy in October,” not “help me shop clothes.”\n- Vertical-specific travel: “three day Denver hiking trip under 700 dollars with a dog,” not “plan my vacation.”\n- Funnel-bottom SaaS: “clean this dataset and publish a dashboard to share with my CFO,” not “help me analyze data.”\n\nWrite an intent thesis. For each candidate intent, record query patterns you expect, the minimum viable questions you must ask, the preview the user should see, the conversion action, and the KPI. Prioritize by expected margin and speed to deliver.\n\n## Discovery levers you can influence\n\nEven before a public directory launches, you can increase the odds that ChatGPT proposes or successfully completes your app.\n\n- Metadata quality: keep names short and literal. Put the job to be done in the first five words of the description.\n- Safety and privacy: the stricter your defaults, the less likely review friction will slow you later.\n- Onboarding prompt: greet the user with one question and a suggested reply. Strive for zero scroll.\n- Preview first: render the first result as a scannable card or grid with a single primary action.\n- Recovery paths: when the user stalls, offer three clarifying buttons rather than another paragraph.\n\n## Experiment design in 8 pieces\n\n1) Define a north-star metric, for example, in-chat checkout rate. 2) Choose a single intent. 3) Set a minimum detectable effect, for example, a 15 percent relative lift. 4) Pick your split, geo or time based. 5) Instrument server-side events. 6) Pre-register analysis, including guardrails against p-hacking. 7) Run for a full demand cycle, at least two weeks. 8) Publish a one-page readout with decision rules.\n\n## Reasonable early benchmarks\n\nBenchmarks will vary by industry, but in early programs we have seen ranges that can guide planning. Treat these as targets to challenge, not promises.\n\n- Suggestion accept rate: 8 to 20 percent when the app is contextually relevant.\n- Completion rate after accept: 25 to 50 percent for tightly scoped retail and travel flows. B2B flows are lower at first, often 10 to 25 percent.\n- In-chat checkout rate: 2 to 8 percent for retail use cases that support instant purchase.\n- Time to completion: 2 to 7 minutes on median, depending on how much the user needs to tweak the preview.\n\nThe primary lever on all of these is the quality of your first preview and how quickly a user can make the obvious changes.\n\n## Compliance and brand safety without slowing down\n\nOpenAI requires apps to follow usage policies, be appropriate for all audiences, and include a clear privacy policy. Use that as your design brief. Collect the minimum data you need, present explicit consent for any write action, and provide a simple way to disconnect and delete data. In highly regulated categories, implement a two-step confirmation before a sensitive action and make all disclaimers short and specific.\n\n## Team structure for speed\n\nThis is not a campaign. It is a small product. Put a growth product manager in the lead, paired with a full-stack developer who owns the app server and a designer who knows how to build scannable previews. Give them an analyst for two days a week and a legal reviewer for one hour at the start and end of the sprint. Review weekly with a single decision maker. Ship every week for four weeks.\n\n## A concrete example to copy\n\nImagine a national home goods retailer launching a Room Setup app. The user says, “I need a cozy living room for under 1500 dollars.” The app asks room size and style, shows a five item bundle with a sofa, rug, lamp, side table, and shelf, and offers three swaps. The user taps one button to send the bundle to cart. If they do not convert in chat, the app saves the configuration and emails a checkout link with the same items. The KPI is in-chat checkout rate. The holdout is two matched cities for four weeks. The success rule is a 20 percent lift in total checkouts against the holdout.\n\n## How this shifts your search mix\n\nAI search will skim the highest-intent slices from traditional search. Expect brand and bottom-funnel queries to fragment, with more users stating their goal to ChatGPT first. Your response is to move completion into the assistant. Keep paid search, but stop expecting it to capture all decision-ready traffic. Budget for a ChatGPT app, measurement, and copy research in Q4, then expand in Q1 if you hit your in-chat KPI.\n\n## Getting started checklist\n\n- Pick one journey where you already have conversion. Write the intent thesis.\n- Draft metadata and onboarding that fit inside two short paragraphs and one question.\n- Ship a preview that is scannable and editable, not verbose.\n- Instrument server-side events and define the single in-chat KPI.\n- Stand up a geo or time split test and pre-register analysis.\n- Publish an internal one-page playbook before launch.\n\n## The bottom line\n\nChatGPT’s new apps turn conversation into a place where conversions happen. The advantage will go to brands that treat this like a new app store, pilot a lightweight app for a single high-intent job, measure in-chat completion, and run controlled experiments. The platform will evolve, with submissions, directory browsing, and monetization details rolling out, but you do not need to wait.\n\nActionable next steps for the next 30 days:\n\n- Week 1: pick the journey, write the intent thesis, and draft metadata. Meet engineering to outline the minimal server.\n- Week 2: build a working prototype, seed ten synthetic test conversations, and tighten the preview.\n- Week 3: instrument events, define the KPI, and set up a geo or time holdout.\n- Week 4: launch the beta, review results twice, and publish a decision on whether to scale.\n\nIf you act now, you can claim the shelf space that matters while the directory and ranking systems are still taking shape.