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Choose when help articles appear
The two main patterns solve different problems. A search card on messenger home gives customers a way to browse before they explain their issue. Question-based suggestions respond to a specific request and can match intent rather than requiring the customer to know the right search terms. Some messaging workflows combine these patterns; others place suggestions inside an automated conversation before a human handoff.
| Pattern | What the customer sees | Useful when | Key consideration |
|---|---|---|---|
| Messenger-home search card | A visible help search surface, sometimes with suggested articles, when the messenger opens. | Customers may want to browse or solve a known issue before starting a conversation. | Decide who can see the card, which articles are eligible, and whether search is optional. |
| Suggestions after a question | Articles selected in response to the customer’s message. | The customer has described a problem and relevant content may resolve it. | Check language, content eligibility, and what happens if the suggestions do not help. |
| Suggestions within an automated workflow | Articles appear as one step in a messaging or bot flow, potentially before a human handoff. | Support wants self-service to be part of a defined conversation path. | Set automation to fit the support workflow and preserve a clear escalation route. |
These are implementation patterns, not universal labels or capabilities. For example, Intercom documents both a public Article search card on Messenger Home and Smart Suggestions after a customer asks a question. Zendesk describes article deflection as one possible part of a messaging workflow. See Intercom’s public article suggestions documentation, Intercom’s Smart Suggestions documentation, and Zendesk’s conversational messaging workflow guidance.
Plan a useful, safe suggestion experience
Match the placement to the customer’s intent
A home card is a prompt to search or browse; it should not be presented as if it already understands the customer’s problem. After a customer asks a question, relevant suggestions can reduce the effort of finding an answer. Intercom’s Smart Suggestions documentation illustrates intent matching with the question “How do I invite a teammate?” and an article about adding a teammate. That is more useful than requiring an exact wording match.
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Keep access and audience rules intact
Only show customers articles they are allowed to view. Intercom says its public Article search card supports audience rules and that customers see only Help Center articles they have permission to access. Its documentation also describes excluding articles from the public card. These controls matter when a Help Center includes content for particular customer groups or information that should not be exposed to everyone.
Check content and language eligibility feature by feature
Suggestions depend on the product feature, not a universal messenger standard. Intercom says Smart Suggestions require at least ten live articles and that the articles need to be in collections; its documentation describes English-language conditions and situations in which suggestions will not appear. Those conditions apply to the described Intercom feature, not every platform. Intercom documents different language behavior for the public card: localized help may match the browser language, while unsupported languages fall back to the Help Center default. Zendesk also documents platform-specific article suggestions across Web Widget and SDK surfaces. Consult the relevant product and client documentation for the surface you use rather than applying one feature’s requirements everywhere.
Make it easy to continue to support
An article suggestion should help, not trap the customer in self-service. Explain that the result is a suggested article, let the customer open it easily, and offer a clear next step if it does not solve the problem. Before requiring customers to search before they can start a conversation, consider whether the available articles adequately cover their likely needs and whether the support route remains understandable. Intercom documents an option to require search before starting a conversation; it also notes that audience eligibility for the article card affects who can search or start a conversation. Zendesk recommends setting automation to match support goals and workflow needs.
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Configure the pattern in your messaging product
Exact controls vary by vendor, account cohort, plan, and client or SDK version. The available documentation establishes the following behaviors, but does not support a single set of universal setup clicks or labels across products.
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- Choose the audience. Define who should see the search card and confirm the rule does not inadvertently block customers who need to contact support.
- Choose the placement. Put the search surface where customers can find it when the messenger opens, without obscuring the conversation or handoff controls.
- Select eligible content. Include current, published articles that match common support needs. Apply exclusions and article permissions where available.
- Review language behavior. Check how the card handles localized articles and unsupported browser languages for the specific product feature.
- Decide whether search is optional. If the system can require search before a conversation starts, enable that only when it fits the support experience and customers retain a clear route forward.
Intercom’s public Article search card can display up to four suggested articles. That is a limit for the documented Intercom card, not a general cap for in-app messengers. Its card suggestions may be personalized using machine learning, custom data attributes, and visited URLs. Details are in Enable public article suggestions in the Messenger.
For suggestions after a question
- Prepare the Help Center. Keep articles published, current, and organized in the way the feature requires. For Intercom Smart Suggestions, the documentation specifies at least ten live articles and says they need to be in collections.
- Check language and exclusions. Confirm the customer’s question language is supported and identify workflow conditions that suppress suggestions. Intercom’s Smart Suggestions documentation specifies English-language conditions and says suggestions may not appear in some cases, including when a Custom Answer has participated.
- Place the suggestions at a useful point. Decide whether they appear immediately after the question, inside an automated conversation, or before a human handoff. Avoid delaying human help without a clear reason.
- Provide a follow-up route. If the article does not answer the question, let the customer continue or indicate that the issue remains unresolved. Zendesk’s Answer Bot documentation describes query-based article suggestions of up to three at a time and a follow-up prompt asking whether the article solved the issue.
- Test representative questions. Try common customer wording, including questions that differ from article titles, and check the result on each client surface you support.
Intercom Smart Suggestions show up to three articles for the documented feature; Zendesk’s Answer Bot documentation also describes up to three at a time. These are product-specific display limits, not a shared standard. Read Intercom’s Smart Suggestions guidance and Zendesk’s Answer Bot engine documentation.
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Compare the details that affect implementation
| Decision area | Questions to answer | Documented examples |
|---|---|---|
| Timing | Do articles appear on messenger open, after a question, or inside a workflow? | Intercom documents a public home card and question-based Smart Suggestions; Zendesk documents article deflection as one messaging-workflow option. |
| Matching | Is the list curated, query-based, or personalized using available context? | Intercom says public-card suggestions can use machine learning, custom data attributes, and visited URLs. Intercom Smart Suggestions respond to a question; Zendesk Answer Bot documentation describes query-based suggestions. |
| Eligibility | What permissions, audience rules, content minimums, or exclusions apply? | Intercom documents audience and article access controls for the public card, plus feature-specific content requirements and exclusions for Smart Suggestions. |
| Language | Which question languages and localized article behaviors are supported? | Intercom documents English-language conditions for Smart Suggestions and browser-language matching with default-language fallback for the public card. |
| Handoff | Can the customer skip search or suggestions and reach a person? | Intercom documents an option to require search before starting a conversation. Zendesk advises matching automation to support goals and workflow. |
| Measurement | Can you see article views, helpfulness, conversations started, and self-service outcomes? | Intercom defines a self-serve session as one in which the customer viewed articles or received bot support and did not start a human conversation. |
| Client support | Does the behavior work on web, iOS, Android, or the SDK surface you use? | Zendesk’s capability documentation describes support across Web Widget and SDK surfaces, with platform-specific limitations. |
For Zendesk surface details, see Web Widget and SDK capabilities and About conversational support with messaging. Capabilities can vary by SDK version and platform, so do not assume a web behavior carries over unchanged to a native app.
Measure whether suggestions help—and improve them
Use measures that distinguish exposure from progress. An article impression shows that a suggestion appeared; an open shows that a customer chose it. Neither by itself proves the issue was solved. Where available, track:
- How often suggestions are shown and opened.
- Whether customers say an article helped or continue to report the problem.
- Whether a human conversation starts after an article view.
- Common questions, failed searches, and conversations that reveal missing, outdated, or hard-to-find content.
Intercom’s reporting documentation defines “Self-serve sessions resolved” as sessions where a customer viewed articles or received bot support and did not start a human conversation. Treat that as Intercom’s operational definition, not standalone proof that the customer’s underlying issue was resolved. Review the conversations and recurring questions behind the metric, then update the articles or suggestion rules. See Intercom’s Conversational support report documentation.
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How to choose the right pattern
- Choose a home search card when customers benefit from browsing before they type and you can keep the content easy to find and appropriately scoped.
- Choose question-based suggestions when customers describe their issue in the messenger and your platform can match those questions to current, eligible articles.
- Combine both when browsing and contextual answers serve distinct needs, provided the two surfaces do not crowd the conversation or create conflicting routes.
- Keep a human route prominent when the issue may be sensitive, unusual, unresolved by articles, or otherwise unsuitable for self-service.
Base the choice on the customer’s path, your article coverage, access rules, supported languages, handoff design, measurement, and the web or SDK clients you actually deploy. Vendor requirements are feature-specific and may vary by account cohort, plan, or SDK version; for example, Intercom’s documented article counts and display limits apply to its named features, not to all messengers.
Frequently Asked Questions
How many articles should an in-app messenger suggest?
There is no universal number. Intercom’s public Article search card documents up to four suggested articles, while Intercom Smart Suggestions and Zendesk Answer Bot documentation describe up to three. These limits apply to those documented features.
Can suggestions appear before a customer types?
Yes. Intercom documents a public Article search card with suggested articles on Messenger Home. Other products may expose different placements or controls.
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Do help article suggestions require a minimum number of articles?
That depends on the feature. Intercom says its Smart Suggestions require at least ten live articles and that those articles need to be in collections. The requirement should not be assumed for other products or for Intercom’s separate public-card feature.
Should customers have to search before they contact support?
Only if that fits the support workflow and the customer can still understand how to proceed when the articles do not help. Intercom documents a setting that can require search before a conversation starts; it is a product option, not a general best practice for every support team.
How do I know whether an article suggestion solved the issue?
Do not treat an impression or article open alone as proof of resolution. Combine article engagement with customer feedback and whether a human conversation follows. Intercom’s self-serve resolution definition specifically counts sessions with article views or bot support and no human conversation, so interpret it as that platform’s operational measure.
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