You can find a creator for any niche in under 10 minutes — if you know how to search TikTok. If you’re a social media manager, growth marketer, community lead, or creator, you already know the pain: native search is shallow, digging through comments and DMs is manual and exhausting, and keeping track of candidates across campaigns feels impossible. Those limits mean missed trends, wasted outreach, and slow campaign velocity.
This 2026 playbook fixes that. Inside you’ll get copy-paste search queries, export and organization workflows, plug-and-play outreach templates, and hands-on automation SOPs to scale comment and DM funnels. Read on to learn concrete steps to find creators, capture and export results, personalize outreach at scale, and measure outcomes — everything you need to turn scattered discovery into repeatable, measurable campaigns.
Why TikTok Search Matters for Growth Teams (overview)
As introduced above, TikTok search spans multiple surfaces beyond the native search box. This section focuses on why treating those surfaces as distinct discovery channels is a high-impact growth lever: it converts passive discovery into predictable outcomes by making queries repeatable, scorable, and automatable.
Systematic search is a growth lever because it converts passive discovery into predictable outcomes. With repeatable queries and filters teams can:
Discover creators and micro-influencers aligned to a campaign
Surface trends before they mainstream and stitch content into briefs
Find authentic UGC, product mentions and sentiment in comments
Feed outreach lists and qualification pipelines with actionable leads
Practical tip: build one canonical query per objective. For creator outreach include keywords from niche, follower range, and engagement thresholds. For trend spotting monitor a set of top 20 hashtags and three rising sounds weekly. Export findings to a sheet or CRM and tag by intent.
This tutorial moves beyond one-off lookups. Instead of manually hunting, you’ll get SOPs, query templates and automation patterns that turn discovery into measurable campaigns. For example, a discovery workflow can flag brand mentions, route them to Blabla for AI-powered replies and moderation, and escalate warm conversations to sales via conversation automation. That loop lets teams measure discovery-to-conversion metrics and iterate fast.
Measure success with small set of KPIs: discovery-to-outreach rate, response time, conversion rate from DM to lead, and average deal value from creator-driven sales. Practical SOP: run saved queries daily, tag results by intent, assign to outreach owners, and use Blabla to auto-qualify comments and DMs with smart replies; exported tags feed your CRM so each discovery step becomes a measurable campaign stage and optimize.
TikTok’s Native Search: How to Find Videos, Hashtags, Sounds, and Locations
Now that we understand why TikTok search matters, let’s walk through TikTok’s native search surfaces so you can find videos, hashtags, sounds, and local content efficiently.
Step-by-step: use the search bar and the tab row (Top, Users, Videos, Sounds, LIVE) to focus results. Practical flows:
Keyword (Videos) search: Type a short phrase like "sustainable fashion" and open the Videos tab. Results show matching clips; use Sort to toggle relevance versus upload date. Use this when you’re hunting trend examples or UGC mentioning a topic.
Hashtag search: Prefix with # (for example, "#unboxing"). The hashtag page gives volume metrics, top and recent videos, and related tags—useful for campaign ideation and measuring hashtag reach.
Sound search: Go to Sounds, enter a sound name (e.g., "rainy day ambience") and view all videos using that audio. Use sound search to discover replicable formats and creators already adopting a trend.
Using filters and operators inside TikTok: after running a search, press Filters to refine. Common filter combinations and when to use them:
Sort by Relevance / Upload date: Relevance finds high-engagement or algorithmic picks; Upload date surfaces newest posts—use upload date to track emerging trends.
Video length: Short vs long helps when you want snackable UGC vs in-depth reviews.
Original sound: Toggle to find videos using custom audio, handy for spotting creators who made original content about your brand.
Verified accounts: Enable to narrow to official creators and partners for outreach or brand-safe examples.
Searching by location and local content: TikTok surfaces place pages and geotags when creators tag a venue or city in captions or the video’s place label. Tactics:
Search city or venue names directly ("Seattle coffee").
Use location-based hashtags (#NYCFood, #AustinEats) to gather hyperlocal content and event coverage.
Check creator profiles for place badges and filter by upload date to see current local activity.
Query templates you can paste into the search bar (modify for your brand):
"[brand name] review" — find product reviews.
"#[city] #[event]" — surface local event content (example: "#Austin #SXSW").
"[keyword] challenge" — hunt for participatory trends.
"#[campaign hashtag]" — track campaign performance and discover creators who used it.
Tip: combine a match query with Filters—search "brand X review" then filter by Upload date and Verified to get recent, authoritative takes. When you surface comments, DMs, or creator profiles ready for outreach, Blabla helps by automating replies, moderating conversations, and converting those interactions into measurable outreach workflows without touching posting or scheduling features.
Now that we’ve covered TikTok’s native search surfaces, let’s focus on finding and qualifying the creators, comments, DMs, UGC, and brand mentions that feed outreach pipelines.
Creator discovery starts with targeted profile queries and expands with co-occurrence and sound-based lists. Practical steps:
Profile searches: scan bios for niche keywords, location, and contact cues; record follower ranges (micro: 10k–100k, mid: 100k–1M, macro: 1M+) to match campaign objectives.
Hashtag co-occurrence: search a primary hashtag, then note secondary tags that repeatedly appear on high-performing creators; use those co-occurrence pairs to find creators outside the obvious pool.
Sound-based creator lists: open a trending sound page and export or manually compile the creator handles who consistently use that sound — ideal for product launches tied to a challenge.
Filters for follower ranges and niches: prioritize creators whose engagement rate and content style match your brief, not just follower count; sample three recent videos to check consistency.
Comments often hide intent and opportunities but TikTok’s native UI limits export and bulk search. Manual techniques:
Regularly snapshot top-performing videos’ comments and paste into a simple spreadsheet.
Tag comments by intent (purchase question, complaint, influencer offer) and sentiment (positive/neutral/negative).
Look for:
Buying intent: phrases like “where to buy,” “price,” “link?”
UGC requests: “can I use this?” or “how did you make this?”
Reputation risks: repeated negative themes or product defects.
DMs and privacy: TikTok’s native DM search is very limited and doesn’t support organization-wide keyword routing. To capture inbound DMs:
Use your business inbox settings and notify a shared team mailbox.
Integrate an inbox aggregator or a platform like Blabla to ingest messages, apply keyword filters, tag conversations, and automate initial replies while preserving privacy.
Be mindful of consent and PII—route sensitive leads to secure CRM channels.
Finding UGC and brand mentions at scale combines searches with saved queries. Techniques:
Hashtag scrapes: export recent posts on your branded tags and filter by engagement.
Caption and mention scans: search your brand name and common misspellings in captions or comments.
Stitch/duet detection: look for keywords “stitch”/“duet” in captions or use sound/page patterns to identify derivative content.
Save queries and run them weekly to surface recurring UGC and convert mentions into outreach opportunities.
Track outcomes: log outreach attempts, conversion rates, and creative formats tied to each creator to refine selection criteria and saved query relevance over time regularly.
Scaling Discovery: SOPs, Query Templates, and Automation Workflows
Now that we’ve moved from discovery to qualification, let’s scale the pipeline with repeatable SOPs, query templates, and automation workflows.
Create a simple SOP that turns raw search results into outreach-ready contacts. Example daily/weekly cadence:
Daily: run high-recall searches (brand mentions, product keywords, rising sounds) and triage new comments and DMs.
Twice weekly: enrich shortlisted creators with follower counts, avg engagement, and recent post cadence.
Weekly: run trend-spotting queries and refresh outreach lists.
Triage rules (quick filters to accept/reject for outreach):
Accept: creator used product organically, positive sentiment, engagement rate >2%, audience aligns with target persona.
Flag for manual review: ambiguous sentiment, potential partnership fit but low-quality video.
Reject: spam, hate, or safety risk.
Qualification checklist to add to each candidate entry:
Follower range (or reach estimate)
Avg likes/views per video
Content category and tone
Recent posting frequency
Explicit product mention or UGC intent
Contact method available
Reusable query templates make searches repeatable. Store these as plain-text templates your team can paste into scrapers and exporters. Examples:
Product niche discovery: "keyword OR #hashtag AND (original sound OR duet) since:7d" returns recent native content mentioning product terms.
Sentiment mining: "brandname OR productname AND (love OR recommend OR hate OR disappointed)" returns high-intent comments for outreach or support.
Trend spotting: "sound:top OR #challenge AND views>100k since:3d" returns emerging formats for creative briefs.
Automation workflows convert search outputs into action. A scalable flow looks like:
Scheduled scrape: run saved queries nightly to collect new posts, creators, and comments.
Deduplication: drop repeat handles or videos captured on multiple queries.
Enrichment: attach follower counts, average engagement, audience demo (where available), and last-post date.
Scoring & queueing: apply rules (eg. score >70 → auto-queue micro-outreach; 40–70 → manual review).
Outreach sequences: queue messages or creative briefs into your CRM or outreach tool.
Blabla fits naturally into step 4–5: its AI-powered comment and DM automation can handle initial outreach messages and conversational qualification, saving hours of manual replies, increasing response rates, and filtering out spam or abusive messages so your human team focuses on high-value creators.
Measure and close the loop with campaign tags and a few core metrics:
Response rate: replies to initial outreach or AI-first messages.
Conversion: creators who accept briefs, produce content, or drive sales.
Content produced: number of UGC pieces and associated views/engagement.
Time-to-first-reply: speed from discovery to first contact.
Iterative refinement: every two weeks, review top and bottom performers, adjust query keywords, update triage rules, and retrain AI reply templates. Example: if "how to use X" queries yield high conversion, add that phrase to high-priority templates.
Practical tip: version-control your templates and SOPs in a shared doc and store query names inside your scraping config so changes propagate automatically.
Sample scoring formula: score = (engagement rate * 50) + (recency bonus * 20) + (explicit product mention? 30 : 0). Tweak weights by campaign and log results for A/B testing every cycle.
Log outcomes per-tag and export weekly reports to inform creative briefs and budget allocation for next quarter.
Tools and Integrations to Automate TikTok Search, Outreach, and Engagement
Now that we have SOPs and automation blueprints in place, let's map the tools and integrations that make those processes reliable, compliant, and scalable.
Start with five tool categories you should evaluate:
Native TikTok API (where available) — the most reliable source for rate-limited structured data and authorised account actions.
Third‑party scrapers — useful when API access is limited; choose ones with respectful throttling and export features.
Social listening platforms — surface trends, sentiment, and high‑intent comments across wider web and social channels.
Influencer marketplaces — fast discovery and standardised creator metrics for outreach lists and budgets.
Outreach CRMs and automation tools — manage sequences, follow-ups, task assignments, and campaign reporting.
Core automation patterns to implement and the practical guardrails for each:
Scheduled queries — run templated searches at off‑peak hours; store incremental results to avoid duplicates and preserve API quota. Example: nightly keyword sweep appending new results to a deduped queue.
Comment ingestion — capture comments into a moderation queue with intent tags (question, praise, complaint). Use human review triggers for high‑intent or negative sentiment items.
Trigger‑based DMs/outreach sequences — fire personalised messages only after enrichment (follower count, recent content) and cooldown timers to avoid spammy behaviour.
Rate/limit management — implement exponential backoff, distributed query windows, and monitor quota dashboards to stay compliant.
How Blabla fits into this stack (practical examples):
Use Blabla to ingest comments from scheduled search templates, automatically tag intent with AI, and push actionable items into your CRM or spreadsheet.
Enrich creator rows with Blabla’s profile summaries and engagement signals, then auto-queue personalised outreach sequences based on your SOP rules.
Let Blabla run AI‑powered replies for routine questions and moderate spam/hate comments before they reach your agents — saving hours of manual moderation and increasing response rates.
Export campaign lists from Blabla as CSV or deliver via webhooks to downstream CRMs for reporting and paid activation.
Tool evaluation checklist and recommended stack:
Data freshness and update frequency.
Scalability and parallel query support.
Privacy, consent, and compliance posture.
Integration options: CSV export, webhooks, Zapier or native connectors.
Cost per query and error handling SLAs.
For most growth teams a pragmatic stack is: TikTok API for authorised actions, a social listening platform for trend context, Blabla for comment/DM automation and enrichment, plus an outreach CRM for multi‑channel sequences.
Operational tip: run end‑to‑end tests on a small segment before scaling. For example, pick 50 creators discovered via your nightly queries, enrich them with Blabla, and run a seven‑day outreach drip with manual review on negative signals. Track reply rate, conversion to leads, and time saved to justify scaling and adjust throttling to protect account health and monitor errors.
Saving, Exporting, Organizing, and Measuring Search Results for Campaigns
Now that we reviewed tools and integrations, let’s focus on how to save, structure, and measure discovery outputs so they become actionable campaign assets.
Start with practical methods to store and organize results. Use saved searches with clear names, consistent tagging, and deduplication rules. A simple naming convention prevents confusion: YYYY-MM-DD_channel_query_niche (for example, 2026-01-04_tiktok_sneakers_UGC). For creators and videos create a canonical ID combining platform ID and handle, such as tt_123456789_janedoe, and use that as the primary key across exports. Tagging examples: high-intent, product-mention, reply-needed, spam-filtered. Deduplicate by matching canonical ID and URL, and prefer the freshest record by last_seen timestamp.
Export strategies should match how teams work downstream. Standard formats and fields make integrations predictable:
CSV export: lightweight snapshot for analytics. Include fields: id, handle, display_name, followers, engagement_rate, first_seen, last_seen, sentiment, tags, outreach_status.
Google Sheets or Airtable: live collaboration and lightweight enrichment. Use import scripts or CSV syncs to append new rows and preserve history.
Webhooks: push discoveries into CRMs, outreach platforms, or task queues in real time. Send a compact payload with canonical_id, top_tags, and last_seen to avoid reprocessing.
Practical tips: always export an incremental delta (changes since last_run timestamp) to avoid duplicates; include source_query and search_params for provenance; and version your exports with a run_id.
Measure discovery-to-conversion with a simple funnel and dashboards that record state transitions. A recommended pipeline is: discovered → qualified → contacted → responded → converted. Track these KPIs:
Discovery volume per day/week
Qualification rate (qualified/discovered)
Outreach velocity (median hours from discovered → contacted)
Response rate (responses/contacted)
Conversion rate and cost per conversion
Benchmarks to aim for: respond to high-intent signals within 48 hours, target a response rate improvement of 20–40% by using personalized templates, and reduce outreach latency each week. Attribution best practices: attach UTM or unique promo codes to outreach links, store message IDs, and capture the discovery query so conversions can be traced back to the original search.
Blabla simplifies organization and measurement by providing campaign boards, automated exports, and enrichment fields like engagement_rate and recency. For example, set up a campaign board with columns: New discovery, Qualified, Outreach queued, Engaged, Closed — Blabla can auto-move items when an AI reply is sent or when a DM arrives. Configure automated weekly exports to a Google Sheet or webhook to your CRM, and use Blabla’s built-in reporting to monitor outreach velocity and response rate. The platform’s AI replies and moderation filters save hours of manual follow-up, increase response rates with smart personalization, and protect brand reputation by filtering spam.
Checklist:
Define canonical ID and naming convention
Standardize export fields and incremental delta
Create dashboard for the discovery→conversion funnel
Automate exports and use Blabla campaign boards to close the loop
Review metrics weekly.
Best Practices, Common Mistakes, and Next Steps for Growth Teams
Now that we understand saving and measuring discovery outputs, let’s finish with practical best practices, pitfalls, a compact 7‑step playbook, and experimentation ideas.
Best practices: schedule and standardize searches into short iterative queries, prioritize high‑intent signals (product mentions, purchase queries, link clicks), and maintain ethical and privacy standards—never scrape private profiles or reuse DMs without consent. Keep queries concise: start with a 2–4 keyword seed, then expand with modifiers. Example: “wireless earbud review” + “price” for buying intent. Use human‑in‑the‑loop checks for automation to catch nuance.
Common mistakes to avoid:
Relying solely on raw follower counts — favor engagement rate and recent view velocity.
Ignoring dedupe and enrichment — duplicates waste outreach bandwidth; reference these steps briefly and move on in your workflow.
Spamming creators with templated pitches — personalize using context from their videos.
Violating privacy norms — don’t message contacts who opted out.
7‑step mini‑SOP to run your first automated discovery→outreach campaign:
Define campaign intent and KPI.
Create 3 concise query templates (niche, intent, sentiment).
Run scheduled queries and sample results manually.
Enrich and score candidates by intent and engagement.
Segment into priority tiers.
Send staged, personalized outreach; monitor responses.
Measure conversion and iterate weekly.
Next steps and experiments: A/B test outreach subject lines and timing, run trend‑incubation cycles (seed content, monitor pickups), and build continuous discovery pipelines that feed weekly experiments. Use Blabla’s AI moderation and smart‑reply layer to keep outreach respectful and scalable while protecting reputation. Plan monthly retros to regularly adjust queries, scoring, messaging templates, and KPIs.
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