Automated AI Systems - Proven Data Driven for YouTube
Automated AI systems use APIs and machine learning to collect and analyze viewer behavior at scale, turning raw metrics into psychological insights like attention triggers and drop-off causes. For creators, this means faster content decisions, repeatable testing, and data-driven optimization that boosts retention and engagement on YouTube.
Why Automated AI and APIs Matter for Viewer Psychology Insights
Understanding what viewers feel and do while watching your videos is core to growth. Manual checks miss patterns; automated AI systems and api automation pull signals across many videos, revealing consistent psychological cues-what hooks viewers in the first 10 seconds, when curiosity fades, and which thumbnails trigger clicks. These scalable insights let creators iterate faster and produce content that aligns with real viewer motivations.
What is the YouTube Analytics API and why should I use it?
The YouTube Analytics API is a programmatic way to fetch channel and video metrics like watch time, impressions, and retention. For creators, it automates data collection so you can spot trends, validate hypotheses, and run repeatable tests to improve viewer psychology-driven content choices.
Do I need to know Python or coding to use api automation for YouTube?
No, you can start without coding by using no-code tools like Zapier or Google Sheets with Apps Script. Basic Python helps for more automation and predictive models, but many creators successfully use spreadsheets and visual tools to automate key tasks.
How do I handle YouTube Analytics API quota and limits?
Manage quota by batching requests, caching results, and prioritizing essential metrics. Request only needed dimensions and time ranges, schedule off-peak pulls, and consolidate queries. This reduces quota usage while keeping your automated insights fresh and reliable.
Next Steps and How PrimeTime Media Helps
If you want a quick, creator-focused setup, PrimeTime Media provides templates, automation recipes, and onboarding support that simplify the YouTube Analytics API and api automation for creators aged 16-40. Try a tailored setup to pull retention curves, thumbnail CTR tests, and automated alerts-so you can spend more time making content. Contact PrimeTime Media for a walkthrough and setup assistance.
PrimeTime Advantage for Beginner Creators
PrimeTime Media is an AI optimization service that revives old YouTube videos and pre-optimizes new uploads. It continuously monitors your entire library and auto-tests titles, descriptions, and packaging to maximize RPM and subscriber conversion. Unlike legacy toolbars and keyword gadgets (e.g., TubeBuddy, vidIQ, Social Blade style dashboards), PrimeTime acts directly on outcomes-revenue and subs-using live performance signals.
Continuous monitoring detects decays early and revives them with tested title/thumbnail/description updates.
Revenue-share model (50/50 on incremental lift) eliminates upfront risk and aligns incentives.
Optimization focuses on decision-stage intent and retention-not raw keyword stuffing-so RPM and subs rise together.
👉 Maximize Revenue from Your Existing Content Library. Learn more about optimization services: primetime.media
Key Benefits
Faster decisions: automated pipelines analyze hours of video metrics in minutes.
Consistent signals: spot repeatable engagement patterns across uploads.
Personalized growth: tailor content to audience preferences backed by data driven evidence.
Operational scaling: use api automation to integrate analytics into your publishing workflow.
Core Concepts Explained for Beginners
APIs and YouTube Metrics
An API (Application Programming Interface) lets you programmatically request data from YouTube. For viewer psychology, the YouTube Analytics API provides metrics such as watch time, audience retention, impressions, click-through rate, and more. These metrics are the raw signals AI models use to infer psychological patterns like attention span and topic interest.
What “Data Driven” Means Here
Data driven means decisions are based on measured viewer behavior rather than intuition. Instead of guessing which thumbnail will work, you examine historic impressions and click-through trends, test variations, and rely on statistically significant results. This approach uses analytics tutorial methods and api automation to repeat tests at scale.
AI Models and Psychological Signals
AI models translate numeric metrics into interpretable insights: segments where retention dips indicate confusion or boredom; spikes at specific moments signal surprise or delight. Simple models can be rule-based (if retention falls by X% then flag), while more advanced ones predict retention using features from title, thumbnail, and early watch behavior.
7 Data-Driven API Steps for YouTube Beginners
Follow these seven clear, actionable steps to set up an automated pipeline that turns YouTube metrics into viewer psychology insights. Each step is approachable for creators with beginner coding or no-code tool experience.
Step 1: Define outcome and KPI - choose the viewer psychology signals to measure (e.g., first 15s retention, mid-video drop-off, thumbnail CTR).
Step 2: Create API access - get a YouTube Analytics API key via Google Cloud Console and enable the YouTube Analytics API following the authorization flow.
Step 3: Pull baseline data - use simple queries to fetch metrics like watchTime, averageViewDuration, impressions, and CTR for recent uploads.
Step 4: Build or use a data store - save API responses in a spreadsheet, Google BigQuery, or a CSV file so you can compare videos historically.
Step 5: Automate ingestion - schedule api automation with a script or no-code tool (Zapier, Make) to fetch daily or weekly metrics automatically.
Step 6: Run simple analysis - compute relative retention curves, compare first 10-30 seconds, and flag videos with abnormal drop-offs or high early retention.
Step 7: Test hypotheses and iterate - create two or three variants (thumbnails, intros) and measure results using the same automated pipeline to determine what psychologically resonates.
Step 8: Add alerts and dashboards - set up notifications for sudden drops in impressions or retention and visualize trends via a dashboard to speed decision-making.
Step 9: Scale with models - once you have enough labeled examples, try simple predictive models that score new video concepts for likely retention and CTR.
Step 10: Operationalize learnings - feed proven formats and hooks into your content calendar, and use api automation to continuously monitor and optimize.
Practical Examples for Creators
Example 1 - Thumbnail Testing with API Automation
Automate fetching impressions and CTR for thumbnails across similar videos. Use a scheduled script to collect data daily, calculate CTR lift for each thumbnail variant, and choose the winner after the test reaches a stable sample. This is a data driven way to pick thumbnails without guesswork.
Example 2 - Intro Hook Optimization
Track audience retention at 0-10s and 10-30s segments to see which intro styles keep viewers. Use the YouTube Analytics API metrics to flag videos with early drop-offs. Then create two short intro formats and run controlled tests, measuring retention improvements via your automated pipeline.
Tools and Integrations for Beginners
Google Cloud Console - enable the YouTube Analytics API and get credentials.
Google Sheets + Apps Script - beginner-friendly storage and automation.
No-code platforms (Zapier, Make) - schedule API calls without coding.
Simple ML tools - Google AutoML or even spreadsheet-based models for early predictive work.
Dashboards - Google Data Studio or Looker Studio for visualization.
Common Constraints and How to Handle Them
Keep in mind YouTube API quota limits, privacy constraints, and data sampling. Use batching, cache results, and prioritize the most actionable metrics. If you need help optimizing quota usage or setting up automation, PrimeTime Media pairs creators with practical templates and setup help so you stay focused on content rather than plumbing.
Helpful Links and Further Learning
Official documentation and industry insights will help you implement best practices and stay compliant:
Decide which psychological signals matter to your channel (hooks, curiosity, surprise).
Enable YouTube Analytics API and obtain an API key.
Set up daily or weekly automated pulls into a spreadsheet or datastore.
Visualize early retention segments and impressions to find patterns.
Run small A/B tests informed by the data and iterate.
Beginner FAQs
Proven Data Driven AI Systems for YouTube Analytics API
Automated AI Systems and APIs for Scaling Viewer Psychology Insights on YouTube
Automated AI systems combine data driven pipelines and YouTube APIs to ingest behavioral signals, predict retention and optimize content workflows. This approach scales viewer psychology insights by automating data collection, model training, and deployment to turn analytics into actionable content decisions across teams and tools.
Think with Google - research and insights on viewer behavior and trends.
Hootsuite Blog - social media management and analytics best practices.
Next steps checklist for intermediate creators
Register a service account and secure YouTube Analytics API key and OAuth credentials.
Implement automated pulls and store results in a queryable data warehouse.
Create one predictive model for a single retention KPI and run a programmatic A/B test.
Automate recommendation delivery to your editors and measure impact over 4-8 weeks.
Contact PrimeTime Media for a custom pipeline and playbook to accelerate implementation.
PrimeTime Advantage for Intermediate Creators
PrimeTime Media is an AI optimization service that revives old YouTube videos and pre-optimizes new uploads. It continuously monitors your entire library and auto-tests titles, descriptions, and packaging to maximize RPM and subscriber conversion. Unlike legacy toolbars and keyword gadgets (e.g., TubeBuddy, vidIQ, Social Blade style dashboards), PrimeTime acts directly on outcomes-revenue and subs-using live performance signals.
Continuous monitoring detects decays early and revives them with tested title/thumbnail/description updates.
Revenue-share model (50/50 on incremental lift) eliminates upfront risk and aligns incentives.
Optimization focuses on decision-stage intent and retention-not raw keyword stuffing-so RPM and subs rise together.
👉 Maximize Revenue from Your Existing Content Library. Learn more about optimization services: primetime.media
Why this matters for creators
Gen Z and Millennial creators (16-40) need repeatable systems to move fast. Manual analysis of retention and CTR only scales so far - automation for content teams unlocks predictable improvements by surfacing micro-behaviors (drop-off points, rewatch loops, CTA timing) and operationalizing them into scripts, thumbnails, and templates that drive watch time and subscriber growth.
Core components of a data driven YouTube analytics automation stack
Data ingestion: YouTube Analytics API and YouTube Data API pulls for views, watch time, impressions, traffic sources, and audience retention.
Event enrichment: Combine comment sentiment, chapter interactions, and playback locations to map psychology signals to content moments.
Storage and schema: Time-series and event tables with video_id, timestamp, retention_percent, user_cohort, and impression_source.
Modeling layer: Predictive retention models (survival analysis, RNNs), clustering for personas, and uplift models for CTA placement.
Automation and orchestration: Cron jobs, serverless functions, and api automation for model retraining, alerting, and content A/B rollout.
Dashboarding and ops: Actionable dashboards showing predicted drop-off, best-performing thumbnails, and recommended edits for editors and hosts.
Data sources and metrics to prioritize
YouTube Analytics API metrics: watchTime, views, averageViewDuration, audienceRetention, impressions, clickThroughRate.
Impression and CTR signals: thumbnail and impression metadata to connect creative variations to click behavior.
Session-level signals: number of videos per session, time to next video, and playlist interactions.
Engagement cues: comments sentiment, like/dislike ratios, and rewatch loops from playback data.
External data: social shares, short-form traffic drivers, and Google Trends context to detect trending hooks.
Implementation checklist - build an end-to-end automated pipeline
Step 1: Define outcomes and KPIs - choose target metrics (e.g., increase average view duration by X% or reduce first-minute drop-off by Y) and map them to model outputs.
Step 2: Register and secure access - create a service account, request a YouTube Analytics API key and OAuth credentials; understand quota limits and set up secure storage for keys.
Step 3: Ingest raw metrics - schedule automated exports using the YouTube Analytics API to pull daily and hourly metrics: impressions, views, watchTime, averageViewDuration, retention reports.
Step 4: Enrich events - join playback timestamps with comment sentiment, chapter markers, and thumbnail variants to create labeled training datasets.
Step 5: Build predictive models - train retention and churn models using sequences (RNN/LSTM) or survival analysis; validate on holdout videos and run A/B simulations for interventions.
Step 6: Automate deployment - wrap models in REST endpoints, use api automation to deploy with CI/CD pipelines, and expose recommendations to editors via dashboards or Slack alerts.
Step 7: Instrument experiments - create programmatic A/B tests for thumbnails, intros, and CTAs; capture impressions and conversions via the API and calculate lift statistically.
Step 8: Monitor quotas and costs - track YouTube Analytics API quota usage, retry logic, and batching to avoid quota exhaustion and unexpected billing.
Step 9: Continuous learning loop - schedule retraining with new labeled outcomes, monitor concept drift, and push updated recommendations to content teams automatically.
Step 10: Operationalize insights - map model outputs to concrete playbooks for editors, hosts, and thumbnail designers, and measure impact on channel growth metrics.
Tools, libraries, and stack recommendations
APIs and SDKs: Official YouTube Analytics API and Google APIs Client Libraries (Python, Node.js).
Data storage: BigQuery or PostgreSQL for event tables; object storage for raw exports.
Processing: Apache Airflow or Prefect for orchestration; serverless (Cloud Functions) for lightweight transforms.
Modeling: TensorFlow/Keras or PyTorch for sequence models; scikit-learn for classical baseline models.
Dashboarding: Looker Studio or Metabase for non-technical teams, integrated with alerting channels like Slack.
Monitoring: Prometheus/Cloud Monitoring for API quota and pipeline health.
Best practices for working with YouTube Analytics API
Use incremental pulls and caching to reduce quota consumption; aggregate at hourly or daily granularity where possible.
Batch requests and use proper pagination to avoid hitting quota limits; implement exponential backoff on 429s and 5xx errors.
Map API metric definitions to business KPIs - use official docs to ensure correct metric selection: YouTube Help Center.
Secure keys and rotate credentials routinely; separate production and staging projects to avoid cross-environment disruption.
Scaling insights into content operations
Translate model predictions into simple, repeatable playbooks. Example: if predicted drop-off >20% at 25 seconds, automatic recommendation could be “trim intro to 5 seconds, surface key hook at 8 seconds.” Ship these as tasks in your CMS or project management tool to ensure editors implement changes.
Quantitative benchmarks and sample results
Benchmarks from mid-sized channels using automation for retention improvements show:
3-8% lift in average view duration within 6-8 weeks after automated thumbnail A/B testing and intro optimizations.
5-12% increase in session depth when suggestions target video sequencing and playlist placement.
Reduced manual QA time by up to 60% through automated alerts for high-risk videos and confidence-ranked recommendations.
Security, privacy, and policy considerations
Follow YouTube API terms and user privacy rules. Do not store personally identifiable information beyond what you need. Adhere to the YouTube Creator Academy best practices for using data and ensure compliance with data retention policies: YouTube Creator Academy.
Integration examples and code pointers
For Python builders, use the Google API client library to query the YouTube Analytics API and store results into BigQuery. Search for examples like "youtube analytics api python" and "youtube analytics api example" in the official docs. Remember to handle "youtube analytics api quota" and "youtube analytics api key" securely and implement retries for 429 errors.
PrimeTime Media combines creator-first playbooks with engineering-grade automation for YouTube. We help creators set up api automation, deploy predictive retention models, and convert insights into team-ready playbooks. Want a streamlined pipeline and custom playbook built for your channel? Reach out to PrimeTime Media to plan your automation rollout and content ops integration.
Intermediate FAQs
What is the YouTube Analytics API best used for in automation?
The YouTube Analytics API is best for automated reporting and feeding time-series and per-video metrics into ML pipelines. Use it to programmatically pull impressions, watch time, retention curves, and CTR so models and dashboards can run without manual exports, enabling realtime or near-realtime interventions.
How do I avoid hitting YouTube Analytics API quota limits?
Reduce quota usage by batching requests, using incremental pulls, caching results, and aggregating metrics at daily/hourly granularity. Implement exponential backoff for 429 responses, distribute requests across multiple authorized projects carefully, and monitor quota consumption to adjust polling frequency.
Can predictive retention models be trained using YouTube Analytics API data?
Yes - export playback timestamps, retention percent, and event-enriched labels to build sequence models or survival analyses. Combine with comment sentiment and thumbnail variant metadata to train models that predict drop-off timing and recommend content edits or CTA placement.
What practical automation for content teams improves retention fastest?
Automated thumbnail A/B testing, intro trimming suggestions based on predicted drop-off, and placement of hooks at detected rewatch loops deliver quick wins. Pipeline automation that turns model recommendations into editorial tasks reduces implementation friction and accelerates measurable retention gains.
Data driven API Automation - Viewer Psychology for YouTube
Automated AI systems and APIs let creators build continuous, data driven pipelines that ingest YouTube engagement signals, train predictive retention models, and push actionable insights into content operations. This reduces guesswork, scales psychological testing across hundreds of videos, and turns viewer behavior into repeatable creative decisions and growth triggers.
How do I manage youtube analytics api quota for continuous pulls?
Distribute pulls across time windows, batch metric requests, and cache unchanged results. Prioritize essential dimensions, use incremental date ranges, and implement exponential backoff. Monitor quota usage with alerts at 60% and 90% to avoid disruptions and schedule heavy batch jobs in off-peak hours to conserve quota.
Can you explain youtube analytics api metrics needed for retention modeling?
Key metrics: averageViewDuration, watchTime, audienceRetention (histograms), views by trafficSourceType, impressions, and impressionCTR. Combine with event timestamps to construct session-level retention curves. These metrics let you model early-session drop-offs and long-term retention drivers effectively.
Is youtube analytics api python suitable for production ingestion?
Yes. The official Python client supports authenticated pulls and pagination. Use it for scheduled ETL jobs, but move heavy transformations to scalable batch platforms. Add caching, retries, and quota-aware logic to make Python ingestion robust for production.
How do I handle youtube analytics api impressions to tie CTR to retention?
Join impression logs to subsequent watch events by timestamp windows and traffic source. Weight CTR by impression volume and control for thumbnail changes. Use cohorting to isolate the effect of impressions on immediate and long-term retention for causal analysis.
What contingencies exist for youtube analytics api quota spikes?
Implement rate limits and request backoff, cache frequent queries, and degrade gracefully (serve last-known insights). Maintain a quota budget, prioritize critical pulls, and schedule noncritical analytics during low-usage windows to prevent operational outages.
PrimeTime Advantage for Advanced Creators
PrimeTime Media is an AI optimization service that revives old YouTube videos and pre-optimizes new uploads. It continuously monitors your entire library and auto-tests titles, descriptions, and packaging to maximize RPM and subscriber conversion. Unlike legacy toolbars and keyword gadgets (e.g., TubeBuddy, vidIQ, Social Blade style dashboards), PrimeTime acts directly on outcomes-revenue and subs-using live performance signals.
Continuous monitoring detects decays early and revives them with tested title/thumbnail/description updates.
Revenue-share model (50/50 on incremental lift) eliminates upfront risk and aligns incentives.
Optimization focuses on decision-stage intent and retention-not raw keyword stuffing-so RPM and subs rise together.
👉 Maximize Revenue from Your Existing Content Library. Learn more about optimization services: primetime.media
Why automated AI systems matter for viewer psychology on YouTube
Creators who move from ad-hoc analytics to automated AI pipelines unlock three advantages: continuous signal capture (real-time watch and impression trends), statistically robust behavioral models (predictive retention and engagement), and operational scaling (alerts, A/B rollouts, and automated edits). For Gen Z and Millennial creators, this means making smarter creative choices faster and at scale.
Core components of a scalable system
Data ingestion: scheduled pulls from the YouTube Analytics API and supplemental telemetry (ad impressions, search queries, CTR logs).
Signal engineering: normalize retention curves, impression-weighted CTR, impression cohorts, and audience microsegments.
Feature stores: store per-video and per-user features for model re-training and quick queries.
Deployment pipelines: CI/CD for model updates, automated A/B rollout control, and inference endpoints.
Alerting and dashboards: confidence-based alerts for dips, and prescriptive recommendations for edits or reuploads.
Governance: quota, privacy, and cost controls for YouTube Analytics API usage.
Architectural checklist for automation for YouTube viewer psychology
Use this checklist to move from prototype to production-grade automation for YouTube insights.
Authenticate and centralize credentials (service accounts, rotated secrets, and restricted IAM).
Implement rate-limited ingestion using exponential backoff to respect YouTube API quotas and reduce failed pulls.
Capture raw metrics and derived signals (audience retention curves, impression-to-watch funnels, impression-weighted CTR, first 15 seconds drop-off).
Store time-series in a cost-efficient store (BigQuery, ClickHouse or similar), partitioned by channel and upload week.
Build feature pipelines for model-ready datasets with deterministic replay for reproducibility.
Train models in resolved experiments, log metadata for each training run, and version both models and transformer code.
Deploy model endpoints behind autoscaling inference services and throttle usage to preserve cost predictability.
Anomaly detect using ensemble thresholds and push alerts to Slack, email, or ops dashboards.
Automate experiment rollouts with canary percentages and automatic rollback rules based on retention lift confidence.
Monitor API quota usage and model inference cost as separate SLOs with alerts at 60% and 90% thresholds.
Data sources and API specifics
Primary source: the YouTube Creator Academy and the YouTube Help Center explain policy constraints and best practices for data usage. Engineers should use the YouTube Analytics API to fetch channel and video-level metrics, cross-referenced with YouTube Data API for metadata. For authoritative insights on audience behavior, consult research from Think with Google.
Advanced signal engineering
Retention signatures: convert watch percentage histograms into compact signatures using PCA or wavelet transforms for clustering.
Impression context: join impressions with traffic sources and impression timestamps to see which impressions produce short vs long watch sessions.
Micro-cohorting: split viewers by watch-start time, device, and geography to test hypothesis-specific lifts with statistical power.
Attribution windows: define consistent attribution windows for recommendations, external embeds, and playlists to avoid polluted signals.
Behavioral triggers: synthesize triggers like "15-second drop spike after scene change" for automated editing suggestions.
Modeling approaches for retention and engagement
Choose models that balance interpretability and predictive power. Start with generalized additive models and move to ensemble learners or sequence models for temporal dynamics. Use survival analysis for retention risk and uplift modeling to measure the causal effect of thumbnails, intros, and CTAs. Always back every model with statistical significance and practical effect size thresholds.
Productionizing inference and alerts
Design inference to be lightweight and actionable. Batch predictions on new uploads and trigger alerts only for high-confidence predictions (e.g., predicted first-minute retention below 25% with p < 0.05). Tie alerts to playbooks: if predicted retention is low, auto-generate a prioritized checklist (test a new thumbnail, A/B openers, shorten intro, add chapter marks).
Scaling operations and governance
Quota management: track youtube analytics api quota usage weekly and schedule heavy pulls during off-peak windows.
Cost controls: move heavy preprocessing into batch jobs on cheap compute and reserve autoscaling for inference only.
Privacy: anonymize user-level identifiers and aggregate where possible to stay within platform rules.
Audit logging: maintain immutable logs for data pulls and model inferences to diagnose regressions.
Practical automation recipes
Step 1: Create a service account and get authorized access to the YouTube Analytics API with a rotated key store.
Step 2: Schedule incremental data pulls for relevant metrics: views, watchTime, averageViewDuration, impressions, and impressionCTR.
Step 3: Normalize timestamps and map to upload IDs, then compute retention histograms and impression-weighted CTR per video.
Step 4: Enrich records with metadata (title, tags, thumbnail hash, chapters) using the YouTube Data API.
Step 5: Build feature pipelines that output training tables with stable cross-validation splits and deterministic seed control.
Step 6: Train predictive models (survival, uplift, and classification) and evaluate with AUC, calibration, and uplift metrics.
Step 7: Deploy models to an inference endpoint with autoscaling and canary rollout capability.
Step 8: Wire alerts to Slack for high-impact predictions and to ops dashboards for monitoring. Include automated runbooks for each alert type.
Step 9: Automate experiments: schedule thumbnail variations, track randomized cohorts, and measure lift using predefined success metrics.
Step 10: Iterate on models monthly, keep training lineage metadata, and archive raw signals to allow retrospective re-training when metrics drift.
Integration examples and code pointers
Engineers often build lightweight ingestion scripts in Python using the YouTube Analytics API Python client. Key knobs: use pagination for large date ranges, throttle requests to stay within the YouTube Analytics API quota, and cache calls when metric calculations are deterministic. For concrete examples, consult the YouTube Analytics API documentation and community examples when implementing metric queries and report definitions.
Experimentation and continuous learning
To test psychological hypotheses, set up randomized experiments (e.g., different thumbnail treatments) and use uplift models to isolate causal effects. Track leading indicators (clicks, first minute retention) and trailing indicators (watch time, subscribes). Use sequential testing with pre-specified stopping rules to protect from false positives.
Operational tips for creators 16-40
Automate simple actions: thumbnail swaps, scheduled reuploads, or metadata tweaks triggered by alert rules - saves time and protects creative flow.
Use production dashboards that translate model outputs into plain-language recommendations for non-technical creators.
Balance automation and intuition: keep a human-in-the-loop for final creative decisions and to safeguard community authenticity.
Learn the basics of analytics with practical tutorials like PrimeTime Media’s blog and expand to advanced APIs once comfortable.
Think with Google - insight-driven research on viewer behavior and trends.
Hootsuite Blog - social media operational insights and measurement tips.
Deployment and maintenance checklist
Implement monitoring for model drift and metric-level regression testing.
Schedule monthly retraining windows and quarterly architectural reviews.
Maintain API key rotation and IAM least-privilege policies.
Document playbooks for each automated recommendation so creators understand and trust the system.
Track A/B experiment metadata and store randomization seeds for auditability.
PrimeTime Media advantage and CTA
PrimeTime Media specializes in helping creators adopt production-grade automation while keeping creative control. We translate predictive models into simple playbooks creators can act on. If you want a tailored assessment or a pipeline blueprint for your channel, PrimeTime Media can audit your metrics and design an automated system-request a channel automation consult to get started.