You’re losing leads to competitors you didn’t even notice—while you’re still manually scrolling comments, mentions, and DMs. Monitoring competitor activity across channels eats time, obscures which metrics matter, and leaves teams without repeatable processes for turning signals into timely engagement or qualified leads.
This automation-first, step-by-step playbook walks social, community, and growth managers through exactly how to analyse competitors and build a repeatable system in days, not months. You’ll get channel-specific metric checklists, recommended reporting cadence, dashboard examples, downloadable tracking templates, and ready-made automation workflows (comment replies, DM funnels, spam moderation, lead capture) so you can monitor at scale, respond authentically, and convert competitor signals into content and pipeline starting this week.
Why run a social competitor analysis (quick overview and goals)
Competitive analysis on social means systematically tracking what rival brands do across channels—content, engagement, comments, DMs and reaction speed—to turn observations into tactical wins. Main goals are benchmarking performance, discovering content that resonates, and detecting threats or opportunities (product launches, PR issues, promotions) before they affect your brand. For example, benchmarking might reveal a competitor’s short-form video drives 3× more comments than theirs on Instagram, which points to format and CTA experiments you can run.
Clear outcomes to measure success should be defined up front. Typical, measurable outcomes include:
Traffic lift: referral clicks or landing page visits attributed to social campaigns compared to baseline.
Engagement gains: increases in likes, comments, saves and share rate versus competitors.
Content ideas: number of validated concepts to test per month derived from competitor wins.
Faster reaction time: reduced time-to-response for competitor-triggered opportunities or threats (e.g., capitalizing on a competitor complaint).
Ownership on the social team should be explicit and practical. A recommended structure:
Owner: Social lead or growth manager — sets scope and OKRs.
Analyst: Tracks metrics, dashboards, and competitive benchmarks.
Creative: Turns insights into testable content ideas.
Customer care: Monitors and triages competitor-related mentions and DMs.
Tie the process to OKRs by mapping outcomes above to measurable targets (e.g., +15% engagement, 10 content tests/month, 24-hour response SLA). Use automation tools like Blabla to monitor comments and DMs, funnel alerts to the right owner, and maintain SLA-driven workflows so insights turn into action quickly.
Practical tip: classify competitors into tiers (A/B/C) and create keyword lists per tier (product names, campaign tags, complaint terms). Automate tagging and priority flags for mentions so the analyst and customer care get routed tasks. Deliver a weekly digest of competitor moves and three testable content ideas tied to OKRs.
Which social metrics to track — the essential KPIs for competitor comparison
Now that we understand why competitive analysis matters, let's define which social metrics to track so comparisons are actionable.
Start with core audience and growth metrics. Use follower growth rate rather than absolute follower counts: ((end - start) / start) × 100 over your chosen window. Measure velocity as followers gained per week or per post to detect momentum. Capture audience composition — age, location, language, top interests — to see who competitors attract. Practical tip: use a rolling 30-day window to smooth campaign spikes.
Next, engagement metrics. Track both post-level and account-level engagement rates. A post-level formula is (likes + comments + shares + saves) / impressions; account-level is the average across recent posts. Separately track comments per post to measure conversation depth, and saves/shares as indicators of utility and virality. Prefer interaction-to-reach ratios over per-follower metrics when reach varies across competitors.
For share of voice and visibility, quantify mention share, impression share and hashtag share. Mention share = competitor mentions / total mentions across your set. Estimate impression share by combining platform-reported impressions with third-party estimates; calculate hashtag share by counting hashtag uses over time. When aggregating across channels, weight each channel by business priority so the composite reflects where you compete most.
Response and sentiment metrics reveal service and reputation gaps. Measure response rate (% of comments and DMs replied to), median response time, and weekly sentiment distribution (positive/neutral/negative). Add qualitative themes — recurring complaints, praise, or misinformation — and track escalation volume: the percent of conversations moved to support or legal.
Include paid/ad metrics where feasible. Use public ad libraries to note active creatives, estimated impressions and ad copy frequency. Derive CPC/CTR proxies by dividing ad engagement by estimated impressions and watch landing engagement (click behavior, time on page, micro-conversions) to judge ad effectiveness.
Example: competitor A grew 8% month-over-month while competitor B grew 2% but averaged three times more saves per post — that indicates higher content value despite lower growth. For response benchmarks, a 90% response rate with a two-hour median time outperforms a 40% rate and 24-hour median. Watch repeated ad creative rotations as a sign of sustainable investment versus short test bursts.
Quick checklist:
Growth: follower growth rate, velocity, audience composition
Engagement: post & account engagement rates, comments per post, saves/shares, interaction-to-reach
Visibility: mention share, impression share, hashtag share (channel-weighted)
Response & Sentiment: response rate/time, sentiment distribution, qualitative themes, escalation volume
Paid: ad impressions/spend estimates, creative frequency, CPC/CTR proxies, landing engagement
Blabla helps by capturing and analyzing conversation metrics in real time — monitoring response rates, sentiment trends and escalation volume, surfacing comment and DM patterns, and applying AI replies or escalation rules so you can compare competitors’ conversational performance at scale.
Step-by-step, automation-first competitor analysis workflow (repeatable process)
Now that we understand which KPIs matter, let’s build a repeatable, automation-first workflow you can run on 30/90/365-day cadences.
1. Set objectives & scope. Be explicit before you collect data. Pick 3–7 direct competitors, prioritize 2–4 channels where those competitors are most active (for example: Instagram + TikTok for DTC brands, LinkedIn + Twitter/X for B2B), choose the KPIs you’ll track from Section 2, and lock time windows: 30 days for tactical shifts, 90 days for campaign learning, and 365 days for strategic benchmarking. Assign roles: a data owner to maintain the pipeline, a community owner to handle flagged conversations, and a growth owner to run experiments triggered by the analysis. Practical tip: start small — monitor one channel deeply for the first 30 days to validate thresholds and then scale outward.
2. Automate data collection. Manual scraping kills velocity. Use three complementary collection methods so you don’t miss signals:
Platform APIs: where available, pull post metadata, impressions, comment lists, and ad flags through official APIs to reduce sampling bias.
Scheduled scrapes: headless browser scrapers or third-party crawlers capture creative assets and comment threads on platforms with limited API access.
Social listening feeds: keyword and mention streams catch off-channel chatter, replies and brand mentions that don’t appear on the main post thread.
Sample Zap/Make/Blabla workflow (turn-key example):
Trigger: social listening feed or API reports a new competitor post or mention.
Action: call an API or scraper to collect post metadata, creative URL, and top comments.
Action: insert a standardized record into a central Google Sheet or cloud DB (Post table).
Action: forward collected comments and any inbound DMs referencing the competitor into Blabla for automated triage, AI-powered replies, spam/hate moderation and tagging.
Action: if the post exceeds viral thresholds, send a Slack alert and create a task in your project tracker with attached context.
This pipeline centralizes posts, metrics, and conversations while Blabla handles message automation—saving hours of manual reply work, increasing response rates, and protecting brand reputation by filtering spam and hateful content before it escalates.
3. Standardize data model & templates. A consistent schema makes analysis repeatable and automations reliable. Required fields to capture for each post:
post_id
timestamp (UTC)
channel (canonical values: instagram, tiktok, linkedin, facebook, youtube)
author_handle
reach / impressions
likes / comments / shares / saves
engagement_rate
creative_type (image, video, carousel, reel)
paid_flag (organic / paid)
caption_text (cleaned)
top_comments (ids & text) and sentiment_score
tags (product, promotion, complaint, crisis)
source_url
Create reusable spreadsheet dashboards and BI templates: a channel comparison tab, a trending creative tab (sorted by engagement velocity), and a viral candidates tab. Use canonical dropdowns for creative_type and tags to prevent messy labels.
4. Daily / weekly monitoring routines. Automate alerts and triage so human attention is focused where it matters. Example routines:
Hourly checks: automation evaluates rolling averages and flags anomalies (e.g., 3x comment velocity).
Daily digest: Slack or email summary of posts that passed thresholds, with direct links to the record and top comments.
Weekly exports: scheduled CSV or dashboard refresh for stakeholder briefings and OKR reviews.
Auto-tagging: any post classified as viral or crisis is assigned tags and routed to a review queue for the community manager.
5. Actions & playbooks. Turn insights into repeatable plays with automation triggers:
Content replication test: when a competitor format goes viral, auto-generate an experiment brief (creative_type, CTA, top comments) and queue a three-variant organic test for creative iteration.
Paid creative test: automatically create a campaign brief and UTM templates for paid teams when a competitor creative proves out.
Escalation to product / ops: if comments reveal product issues or regulatory risks, auto-create a ticket in your ops tracker with sample comments and source links.
Conversion play: route high-intent competitor DMs captured by the pipeline into Blabla flows that qualify leads and push warm prospects into CRM or sales queues.
When implemented, this workflow standardizes discovery, reduces manual triage, and moves teams from reactive monitoring to proactive testing. Blabla sits at the center of conversation automation—handling replies, moderation, tagging, and lead routing—so your analysts and community managers spend their time on insights and execution, not inboxes.
Monitor competitors’ comments, mentions and DMs at scale with automation
Now that we have an automated competitor data pipeline, let's monitor the live conversations that reveal intent, sentiment and opportunities.
What you can and cannot monitor
You can capture public mentions, comments, replies and tags on public profiles and posts. You cannot access private DMs, closed group posts or content behind paywalls without explicit permission — and you must avoid scraping user data in violation of platform terms. Best practices: use official APIs or permissioned webhooks, only store minimal metadata needed for triage, and log provenance so you can prove consent if needed.
Set up listening streams and triage rules
Create focused streams per competitor and per topic (product names, campaign hashtags, customer support terms). Use boolean keyword sets and account handles; include negative filters to remove noise (for example, exclude "job" or "hiring" when you only want product complaints). Define triage rules that:
tag intent as support, complaint, praise, or sales lead
score priority by sentiment, author reach, and explicit keywords (refund, broken, love)
route high-priority items to human review within your SLA
Practical tip: start with broad rules and refine with weekly audits — false positives are normal early on.
Capture & route conversations
Automate pushing captured comments and mentions into a shared inbox or ticketing system with context: original post, timestamp, author metrics and prior interactions. Typical routing logic:
Complaints with negative sentiment -> customer care queue, priority 1
Praise or product feedback -> product/marketing queue
Purchase intent or pricing questions -> sales queue, attach lead score
Blabla can act as the middle layer that ingests public comments and mentions, auto-tags them, enriches author data and forwards enriched tickets to your CRM or helpdesk while preserving full conversation history and audit logs.
Automated DM workflows & response templates
For DMs (only when permissioned or via your brand account), use canned replies with variable fields and conditional branching. Example workflows:
If DM contains "refund" → send an apology template requesting the order ID, then escalate to a human agent
If DM expresses interest in purchase → send a concise pricing summary, scheduling option, and create a lead card
Include human handoff triggers (time-to-respond exceeded, complex language detected) and require a mandatory edit step for high-sensitivity responses so brand voice stays authentic.
NLP and enrichment
Apply sentiment analysis, entity extraction (product names, competitor names) and author enrichment (follower count, prior ticket history). Automatic tags such as competitor_product:X or intent:lead let you build dashboards that surface trends (for example, spikes in complaints for feature Y). Practical tip: tune sentiment thresholds and periodically re-label a sample of messages to retrain models and reduce drift.
Track monitoring effectiveness with SLAs, first-response time, resolution rate and lead conversion; keep audit logs, retention policies and periodic privacy reviews to stay compliant regularly.
Tools, templates and turnkey automations you can use (including Blabla examples)
Now that we capture competitors' conversations at scale, let's map the practical tools, templates and turn‑key automations you can deploy.
Tool categories and purpose
Listening & social analytics: channel-native APIs and social listening platforms to capture mentions, sentiment and reach across channels.
Ad library trackers: Meta Ad Library, AdSpy and Pathmatics for creative, spend and placement visibility.
Automation platforms: Zapier, Make and native webhooks to move data between sources, sheets, CRMs and inboxes.
Inbox / ticketing: shared inboxes and helpdesk tools to assign and resolve captured conversations.
Creative asset trackers: simple DAM or spreadsheet catalogs that link creative IDs to post-level performance.
Turnkey templates (what to use now)
Downloadable spreadsheet schema: include fields for post_id, channel, timestamp, creative_id, reach, impressions, engagements, comments_csv_link, tags, sentiment, and priority.
Dashboard widgets: top-performing competitor creatives, mentions by sentiment, comments requiring escalation, ad creative vs organic engagement comparison.
Post-level CSV import template: columns ready for bulk import from APIs or exports so your analytics tool maps without manual remapping.
Prebuilt tag taxonomy: intent (support/sale/complaint/praise), bot/real, ad_vs_organic, competitor_name.
Sample integrations and automations (with Blabla)
Prebuilt competitor inbox: pipeline that pulls comments and mentions from Meta, X and TikTok into a central inbox, tags intent automatically, and surfaces high-priority threads—Blabla powers AI replies and moderation to auto-handle spam and triage low-touch messages.
Cross-channel listener: use the X API or TikTok/IG feeds to stream new posts into Google Sheets via Make, then trigger a Blabla workflow to monitor incoming comments and apply smart replies or escalate leads.
Alert workflow: when ad creative receives >X complaints or a spike in negative sentiment, a Zap pushes the creative_id and comments to Slack and creates a support ticket; Blabla inserts suggested reply drafts for faster response.
Ad-tracking integration tips
Pull Meta Ad Library JSON or CSV regularly, tag creatives by creative_id, then join with your comments table to compute engagement-per-ad and complaint rates.
Use AdSpy/Pathmatics exports to add spend and placement context; correlate spend spikes with changes in comment volume to judge creative impact.
Checklist for selecting tools
Data coverage: channels and historical depth you need.
API access: rate limits and export formats.
Automation support: native webhooks, Zapier/Make compatibility.
Inbox workflows: tagging, routing, SLA support and team assignment.
AI moderation & replies: quality, language support and brand voice control—Blabla saves hours by automating replies, increases response rates and protects brands from spam and hate.
Cost vs scale: predictable pricing as you ingest more comments and creative records.
Identify content gaps, benchmark performance and set realistic goals
Now that we’ve reviewed tools and turn‑key automations, let’s translate that intelligence into an action plan that finds content gaps, sets fair benchmarks and creates testable, time‑bound targets.
Content gap analysis — map and score what competitors own vs. what’s open: build a simple matrix with rows for content pillars (education, product, social proof, culture), columns for format (short video, carousel, static image, live), cadence and a performance column that pulls engagement-per-post and average comments. Color‑code cells where competitors consistently outperform you and add an “opportunity score” that weights format scarcity and engagement potential.
Practical example: if Competitor A publishes high-engagement 30‑second how‑to reels twice weekly and your brand has zero short videos, mark that pillar/format as high opportunity.
Attach signals: average comments, saves, shares, and DM mentions tied to each post so you can see which pieces drive conversations you can capture and convert.
Benchmarking methodology — normalize for audience and create fair comparators: calculate engagement-per-follower (total engagements / follower count) and reach-per-follower to remove scale bias. Compute percentiles (25/50/75) across competitors’ posts to create baseline visibility bands so you know what’s “above average” in your niche.
Calculate engagement-per-follower for each competitor post and take median to reduce outlier impact.
Compute share-of-voice = your brand mentions / total category mentions over the same window to measure visibility.
Use percentiles to set realistic thresholds: e.g., aiming for the 75th percentile engagement-per-follower is a strong but achievable target.
Set SMART goals and forecasts: translate percentiles into time‑bound objectives — for example, lift engagement rate from 0.8% to 1.2% (a 50% relative increase) in 90 days, or reach the 50th percentile of share-of-voice in 6 months. Define testing cadence: run two creative tests per week with 4‑week learning windows, then iterate on winners for another 4 weeks.
Creative replication playbook — what to copy, what to change, and how to test: deconstruct top competitor posts into elements: format, hook (first 3 seconds), CTA, visual style and value proposition. Replicate format, hook structure and CTA that drove engagement; differentiate on value prop and brand voice to avoid mimicry and to own the message. Always A/B test a single variable (e.g., CTA vs. hook) and consider paid boosts to reach statistically significant samples faster.
Example test: create Variant A that mirrors competitor hook + your brand voice and Variant B that changes hook but keeps CTA identical; boost both equally for a 7‑day test.
Metrics to monitor post‑test and how to iterate: track conversion signals (link clicks, form fills), organic reach lift, follower retention (percent of new followers still active after 30 days), comment sentiment and volume, and ROI on paid amplification ((revenue attributed − ad spend) / ad spend). Also monitor conversational signals: number of DM leads, lead quality tags, and average response time — areas where Blabla helps by automating replies, tagging intent and routing high-value conversations to sales so you can close the loop on tests and iterate faster.
Reporting cadence, deliverables, and ethical & policy considerations
Now that we defined benchmarks and goals, let's lock in a reporting rhythm and guardrails that make competitor insights operational, repeatable, and compliant.
Recommended cadence combines continuous monitoring with human review:
Real-time alerts: trigger immediate notifications for sudden spikes in competitor mentions, viral posts, or crisis signals.
Daily inbox triage: a short morning pass to clear priority mentions, route leads, and flag complaints for escalation.
Weekly operational dashboard: performance trends, top competitor moves, and quick wins for social ops.
Monthly strategy report: executive summary, KPI trends, content opportunities, and paid activity highlights.
Quarterly competitive review: deeper analysis, share-of-voice shifts, product implications and strategic recommendations.
Report structure — use a consistent slide outline so stakeholders scan quickly. Sample slide order:
Title + period and data sources
Executive summary: 3-line takeaway
Top wins and risks
KPI trends and benchmarks
Content opportunity heatmap
Paid activity summary (creative headlines, placements, spend signals)
Recommended actions with owners and deadlines
Appendix: raw data and filters
Distribution and handoffs: match report to role and automate task creation. Example assignments:
Executives: monthly strategy report (high-level slides)
Social ops: weekly dashboard + daily triage list (actionable tickets)
Product/UX: quarterly review + content opportunity heatmap
Customer care: immediate escalation for complaint threads
Practical tip: use Blabla to convert flagged comments into assigned tickets automatically, add tags, and notify owners—so insights become tasks without manual copying. You can also configure workflows that escalate negative sentiment to legal or customer care based on tag thresholds.
Ethical and compliance checklist:
Never access private competitor DMs; only analyze public comments and mentions.
Respect platform terms of use; avoid scraping prohibited endpoints.
Comply with privacy laws; anonymize personal data and document retention periods.
Log data sources, collection methods and access permissions for audits.
When to involve legal: consult legal before large-scale data collection, using competitor trademarks in campaigns, replicating creatives closely, or when retention or cross-border data flows are unclear; record reviews and approvals in your compliance log.
Practical example: set an alert threshold of three times baseline mention volume to auto-create a "Spike" ticket that assigns to social ops and pings legal if sentiment is negative; retain flagged records for 90 days by default, extend only with documented justification in the compliance log.
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