Performance Analytics Skill
Frameworks for measuring, reporting, and optimizing marketing performance across channels and campaigns.
Tracking plan (RU) — события и цели под воронку
Для настройки плана аналитики, событий и целей под реальную воронку (курс/консалтинг/SaaS, лидген через контент) — см. references/tracking-plan-ru.md. Это event-library + tracking-plan, переложенный с GA4/GTM на Яндекс Метрику (цели вместо conversions, параметры вместо custom dimensions) и привязанный к метрикам воронки, которые реально важны: подписчики контент-канала, trial → paid, заявки на консультацию, лиды продукта.
Сначала собери контекст: оффер, ICP, воронка (→ references/funnel-another-db.md). Данные тянутся через yandex (Metrika API) и вашу продуктовую аналитику (сегменты, воронки) — НЕ дублировать их API-клиенты. Эксперименты над метриками — через ab-testing-ru.
Сквозная когортная аналитика на масштабе (12-шаговая когортная воронка, Predict LTV, one-screen дашборд, 9-шаговый процесс еженедельного анализа, выбор модели атрибуции под цикл сделки) вынесена в отдельный скилл full-funnel-analytics-ru. Этот скилл покрывает метрики отдельных каналов и tracking plan; full-funnel — слой когортного анализа поверх них.
Performance-аналитика (RU) — справочники по каналам
Дополнительные references с эталонными ориентирами и определениями. Каждый — самодостаточный справочник с cross-links, чтобы не дублировать соседние скиллы.
| Reference |
Что внутри |
Когда читать |
references/perf-metric-definitions-ru.md |
Словарь метрик: ROMI/ROAS/CPL/CPA/CAC/LTV/AOV/CR/CPQL + ROMI vs ROAS, дерево метрик, 40-80+ касаний/день, 5-7 касаний на покупку, UTM-структура (5 параметров + динамические подстановки Яндекса) |
«что такое ROMI/ROAS», «определения метрик», «UTM-структура», «динамические параметры» |
references/attribution-models-direct.md |
5 моделей атрибуции для Директа (Last Click / First Click / Last Non-Direct / Multi-Touch / Last Non-Brand) + правила выбора + автоцели vs JS-события, микро/макроконверсии, целевая доля 100%, корреляция Пирсона |
«модели атрибуции», «Last Non-Brand Click», «автоцели vs js-события», «как засчитать конверсию» |
references/server-side-tracking-stack.md |
Decision matrix server-side (Stape / GTM Server / Яндекс SSP / self-hosted / no-code) + per-platform CAPI cross-links + обзор сервисов сквозной аналитики ($1k+/мес класс) |
«server-side tracking stack», «server-side GTM», «Stape», «чем собирать серверные события» |
references/learning-phase-cross-platform.md |
Пороги обучения на одной оси: Meta 50 событий/7-10 дней vs Google 5-7 дней / ≥50 конв/нед vs Yandex 10 конв/14-21 день vs Telegram (нет фазы) vs VK (ручной) + универсальный паттерн «что сбивает обучение» |
«фаза обучения по платформам», «cross-platform learning phase», «сколько событий нужно алгоритму», «что сбивает обучение» |
references/lookalike-quality-events.md |
Cross-platform паттерн «Lookalike на качественных событиях»: VK CS / Meta 1-3-5% / Yandex LAL / Google Demand Gen LAL — строить ядро на оплатах/квал-лидах, не на кликах |
«lookalike на качественных событиях», «на чём строить похожую аудиторию», «LAL на оплатах» |
Границы (не дублировать):
- Когортная воронка на масштабе, Predict LTV, Zero Day Revenue, 9 шагов анализа, выбор атрибуции под цикл сделки →
full-funnel-analytics-ru.
- Пошаговая no-code настройка CAPI, 7 параметров матчинга, Event ID, Test Events, офлайн-конверсии →
capi-no-code-setup.
- Готовый справочник цифр/бенчмарков по всем каналам →
ad-benchmarks-ru.
- Аукционная оптимизация конкретного канала →
yandex-direct-pro-ru, google-ads-pro-ru, telegram-ads-pro-ru, vk-ads-pro-ru, meta-ads-launch-ru.
Key Marketing Metrics by Channel
Email Marketing
| Metric |
Definition |
Benchmark Range |
What It Tells You |
| Delivery rate |
Emails delivered / emails sent |
95-99% |
List health and sender reputation |
| Open rate |
Unique opens / emails delivered |
15-30% |
Subject line and sender effectiveness |
| Click-through rate (CTR) |
Unique clicks / emails delivered |
2-5% |
Content relevance and CTA effectiveness |
| Click-to-open rate (CTOR) |
Unique clicks / unique opens |
10-20% |
Email content quality (for those who opened) |
| Unsubscribe rate |
Unsubscribes / emails delivered |
<0.5% |
Content-audience fit and frequency tolerance |
| Bounce rate |
Bounces / emails sent |
<2% |
List quality and data hygiene |
| Conversion rate |
Conversions / emails delivered |
1-5% |
End-to-end email effectiveness |
| Revenue per email |
Total revenue / emails sent |
Varies |
Direct revenue attribution |
| List growth rate |
(New subscribers - unsubscribes) / total list |
2-5% monthly |
Audience building health |
Social Media
| Metric |
Definition |
What It Tells You |
| Impressions |
Number of times content was displayed |
Content distribution and reach |
| Reach |
Number of unique users who saw content |
Audience breadth |
| Engagement rate |
(Likes + comments + shares) / reach |
Content resonance |
| Click-through rate |
Link clicks / impressions |
Traffic driving effectiveness |
| Follower growth rate |
Net new followers / total followers per period |
Audience building |
| Share/Repost rate |
Shares / reach |
Content virality and advocacy |
| Video view rate |
Views / impressions |
Video content hook effectiveness |
| Video completion rate |
Completed views / total views |
Video content quality and length fit |
| Social share of voice |
Your mentions / total category mentions |
Brand visibility vs. competitors |
Paid Advertising (Search and Social)
| Metric |
Definition |
What It Tells You |
| Impressions |
Times ad was shown |
Budget utilization and targeting breadth |
| Click-through rate (CTR) |
Clicks / impressions |
Ad creative and targeting relevance |
| Cost per click (CPC) |
Total spend / clicks |
Cost efficiency of traffic generation |
| Cost per mille (CPM) |
Cost per 1,000 impressions |
Awareness cost efficiency |
| Conversion rate |
Conversions / clicks |
Landing page and offer effectiveness |
| Cost per acquisition (CPA) |
Total spend / conversions |
Full-funnel cost efficiency |
| Return on ad spend (ROAS) |
Revenue / ad spend |
Revenue generation efficiency |
| Quality Score (search) |
Google's relevance rating (1-10) |
Ad-keyword-landing page alignment |
| Frequency |
Average times a user sees the ad |
Ad fatigue risk |
| View-through conversions |
Conversions from users who saw but did not click |
Display/awareness campaign influence |
SEO / Organic Search
| Metric |
Definition |
What It Tells You |
| Organic sessions |
Visits from organic search |
SEO effectiveness and content reach |
| Keyword rankings |
Position for target keywords |
Search visibility |
| Organic CTR |
Clicks / impressions in search results |
Title and meta description effectiveness |
| Pages indexed |
Number of pages in search index |
Crawlability and site health |
| Domain authority |
Third-party authority score |
Overall site strength |
| Backlinks |
Number of external sites linking to you |
Content authority and off-page SEO |
| Page load speed |
Time to interactive |
User experience and ranking factor |
| Organic conversion rate |
Organic conversions / organic sessions |
Content quality and intent alignment |
| Top entry pages |
Most-visited pages from organic search |
Content driving the most organic traffic |
Content Marketing
| Metric |
Definition |
What It Tells You |
| Pageviews |
Total views of content pages |
Content reach and distribution |
| Unique visitors |
Distinct users viewing content |
Audience size |
| Average time on page |
Time spent on content pages |
Content engagement and depth |
| Bounce rate |
Single-page sessions / total sessions |
Content-audience fit and UX |
| Scroll depth |
How far users scroll on a page |
Content engagement through the piece |
| Social shares |
Times content was shared on social |
Content resonance and virality |
| Backlinks earned |
External links to content |
Content authority and SEO value |
| Lead generation |
Leads attributed to content |
Content conversion effectiveness |
| Content ROI |
Revenue attributed / content production cost |
Overall content investment return |
Overall Marketing / Pipeline
| Metric |
Definition |
What It Tells You |
| Marketing qualified leads (MQLs) |
Leads meeting marketing qualification criteria |
Top-of-funnel effectiveness |
| Sales qualified leads (SQLs) |
MQLs accepted by sales |
Lead quality |
| MQL to SQL conversion rate |
SQLs / MQLs |
Marketing-sales alignment and lead quality |
| Pipeline generated |
Dollar value of opportunities created |
Marketing impact on revenue |
| Pipeline velocity |
How fast deals move through pipeline |
Campaign urgency and quality |
| Customer acquisition cost (CAC) |
Total marketing + sales cost / new customers |
Efficiency of customer acquisition |
| CAC payback period |
Months to recover CAC from revenue |
Unit economics health |
| Marketing-sourced revenue |
Revenue from marketing-originated deals |
Direct marketing contribution |
| Marketing-influenced revenue |
Revenue from deals where marketing touched |
Broader marketing impact |
Reporting Templates and Dashboards
Weekly Marketing Report
Quick-scan format for team standups:
- Top 3 metrics with week-over-week change
- What worked this week (1-2 bullet points with data)
- What needs attention (1-2 bullet points with data)
- This week's priorities (3-5 action items)
Monthly Marketing Report
Standard stakeholder report:
- Executive summary (3-5 sentences)
- Key metrics dashboard (table with MoM and target comparison)
- Channel-by-channel performance summary
- Campaign highlights and results
- What worked and what did not (with hypotheses)
- Recommendations and next month priorities
- Budget spend vs. plan
Quarterly Business Review (QBR)
Strategic review for leadership:
- Quarter performance vs. goals
- Year-to-date trajectory
- Channel ROI analysis
- Campaign performance summary
- Competitive and market observations
- Strategic recommendations for next quarter
- Budget request and allocation plan
- Key experiments and learnings
Dashboard Design Principles
- Lead with the metrics that map to business objectives (not vanity metrics)
- Show trends over time, not just point-in-time snapshots
- Include comparison context: prior period, target, benchmark
- Use consistent color coding: green (on track), yellow (at risk), red (off track)
- Group metrics by funnel stage or business question
- Keep dashboards to one page/screen — detail goes in appendix
- Update cadence should match decision cadence (real-time for paid, weekly for content)
Trend Analysis and Forecasting
Trend Identification
When analyzing performance data, look for:
- Directional trends: is the metric consistently going up, down, or flat over 4+ periods?
- Inflection points: where did performance change direction and what happened then?
- Seasonality: are there predictable patterns by day of week, month, or quarter?
- Anomalies: one-time spikes or drops — what caused them and are they repeatable?
- Leading indicators: which metrics change first and predict future outcomes?
Trend Analysis Process
- Chart the metric over time (at least 8-12 data points for meaningful trends)
- Identify the overall direction (upward, downward, flat, cyclical)
- Calculate the rate of change (is it accelerating or decelerating?)
- Overlay key events (campaigns launched, product changes, market events)
- Compare to benchmarks or targets
- Identify correlations with other metrics
- Form hypotheses about causation (and plan tests to validate)
Simple Forecasting Approaches
- Linear projection: extend the current trend line forward (useful for stable metrics)
- Moving average: smooth out noise by averaging the last 3-6 periods
- Year-over-year comparison: use last year's pattern as a baseline, adjusted for growth rate
- Funnel math: forecast outputs from inputs (e.g., if we generate X leads at Y conversion rate, we will get Z customers)
- Scenario modeling: create best case, expected case, and worst case projections
Forecasting Caveats
- Short-term forecasts (1-3 months) are more reliable than long-term
- Forecasts based on fewer than 12 data points should be flagged as low confidence
- External factors (market shifts, competitive moves, economic changes) can invalidate trend-based forecasts
- Always present forecasts as ranges, not exact numbers
Attribution Modeling Basics
What Is Attribution?
Attribution determines which marketing touchpoints get credit for a conversion. This matters because buyers typically interact with multiple channels before converting.
Common Attribution Models
| Model |
How It Works |
Best For |
Limitation |
| Last touch |
100% credit to last interaction before conversion |
Understanding final conversion triggers |
Ignores awareness and nurture |
| First touch |
100% credit to first interaction |
Understanding top-of-funnel effectiveness |
Ignores nurture and conversion drivers |
| Linear |
Equal credit to all touchpoints |
Fair representation of all channels |
Does not reflect relative impact |
| Time decay |
More credit to touchpoints closer to conversion |
Balanced view favoring recent interactions |
May undervalue awareness |
| Position-based (U-shaped) |
40% first, 40% last, 20% split among middle |
Valuing both discovery and conversion |
Somewhat arbitrary weighting |
| Data-driven |
Algorithmic credit based on conversion patterns |
Most accurate representation |
Requires significant data volume |
Attribution Practical Guidance
- Start with last-touch attribution if you have no model in place — it is the simplest and most actionable
- Compare first-touch and last-touch to understand which channels drive awareness vs. conversion
- Use position-based (U-shaped) as a reasonable middle ground for most B2B companies
- Data-driven attribution requires high conversion volume to be statistically meaningful
- No model is perfect — use attribution directionally, not as absolute truth
- Multi-touch attribution is better than single-touch, but any model is better than none
Attribution Pitfalls
- Do not optimize one channel in isolation based on single-touch attribution
- Awareness channels (display, social, PR) will always look bad in last-touch models
- Conversion channels (search, retargeting) will always look bad in first-touch models
- Self-reported attribution ("how did you hear about us?") provides useful qualitative color but is unreliable as quantitative data
- Cross-device and cross-channel tracking gaps mean attribution data is always incomplete
Optimization Recommendations Framework
Optimization Process
- Identify: which metrics are underperforming vs. target or benchmark?
- Diagnose: where in the funnel is the problem? (impressions, clicks, conversions, retention)
- Hypothesize: what is causing the underperformance? (audience, message, creative, offer, timing, technical)
- Prioritize: which fixes will have the biggest impact with the least effort?
- Test: design an experiment to validate the hypothesis
- Measure: did the change improve the metric?
- Scale or iterate: roll out wins broadly; iterate on inconclusive or failed tests
Optimization Levers by Funnel Stage
| Funnel Stage |
Problem Signal |
Optimization Levers |
| Awareness |
Low impressions, low reach |
Budget, targeting, channel mix, creative format |
| Interest |
Low CTR, low engagement |
Ad creative, headlines, content hooks, audience targeting |
| Consideration |
High bounce rate, low time on page |
Landing page content, page speed, content relevance, UX |
| Conversion |
Low conversion rate |
Offer, CTA, form length, trust signals, page layout |
| Retention |
High churn, low repeat engagement |
Onboarding, email nurture, product experience, support |
Prioritization Framework
Rank optimization ideas on two dimensions:
Impact (how much will this move the metric?):
- High: directly addresses the primary bottleneck
- Medium: addresses a contributing factor
- Low: incremental improvement
Effort (how hard is this to implement?):
- Low: copy change, targeting adjustment, simple A/B test
- Medium: new creative, landing page redesign, workflow change
- High: new tool, cross-team project, major content production
Priority order:
- High impact, low effort (do immediately)
- High impact, high effort (plan and resource)
- Low impact, low effort (do if capacity allows)
- Low impact, high effort (deprioritize)
Testing Best Practices
- Test one variable at a time for clean results
- Define the success metric before launching the test
- Calculate required sample size before starting (do not end tests early)
- Run tests for a minimum of one full business cycle (typically one week for B2B)
- Document all tests and results, regardless of outcome
- Share learnings across the team — failed tests are valuable information
- A test that confirms the status quo is not a failure — it builds confidence in your current approach
Continuous Optimization Cadence
- Daily: monitor paid campaigns for budget pacing, anomalies, and disapproved ads
- Weekly: review channel performance, pause underperformers, scale winners
- Bi-weekly: refresh ad creative and test new variants
- Monthly: full performance review, identify new optimization opportunities, update forecasts
- Quarterly: strategic review of channel mix, budget allocation, and targeting strategy
1---2name: performance-analytics-23description: Метрики маркетинг-каналов и tracking plan: атрибуция, server-side GTM. Триггеры: «ROMI vs ROAS», «UTM-структура», «фаза обучения по платформам».4---56# Performance Analytics Skill78Frameworks for measuring, reporting, and optimizing marketing performance across channels and campaigns.910## Tracking plan (RU) — события и цели под воронку1112Для настройки плана аналитики, событий и целей под реальную воронку (курс/консалтинг/SaaS, лидген через контент) — см. **`references/tracking-plan-ru.md`**. Это event-library + tracking-plan, переложенный с GA4/GTM на **Яндекс Метрику** (цели вместо conversions, параметры вместо custom dimensions) и привязанный к метрикам воронки, которые реально важны: подписчики контент-канала, trial → paid, заявки на консультацию, лиды продукта.1314Сначала собери контекст: оффер, ICP, воронка (→ `references/funnel-another-db.md`). Данные тянутся через `yandex` (Metrika API) и вашу продуктовую аналитику (сегменты, воронки) — НЕ дублировать их API-клиенты. Эксперименты над метриками — через `ab-testing-ru`.1516> **Сквозная когортная аналитика на масштабе** (12-шаговая когортная воронка, Predict LTV, one-screen дашборд, 9-шаговый процесс еженедельного анализа, выбор модели атрибуции под цикл сделки) вынесена в отдельный скилл **`full-funnel-analytics-ru`**. Этот скилл покрывает метрики отдельных каналов и tracking plan; full-funnel — слой когортного анализа поверх них.1718## Performance-аналитика (RU) — справочники по каналам1920Дополнительные references с эталонными ориентирами и определениями. Каждый — самодостаточный справочник с cross-links, чтобы не дублировать соседние скиллы.2122| Reference | Что внутри | Когда читать |23|-----------|-----------|--------------|24| `references/perf-metric-definitions-ru.md` | Словарь метрик: ROMI/ROAS/CPL/CPA/CAC/LTV/AOV/CR/CPQL + ROMI vs ROAS, дерево метрик, 40-80+ касаний/день, 5-7 касаний на покупку, **UTM-структура** (5 параметров + динамические подстановки Яндекса) | «что такое ROMI/ROAS», «определения метрик», «UTM-структура», «динамические параметры» |25| `references/attribution-models-direct.md` | 5 моделей атрибуции для Директа (Last Click / First Click / Last Non-Direct / Multi-Touch / Last Non-Brand) + правила выбора + **автоцели vs JS-события**, микро/макроконверсии, целевая доля 100%, корреляция Пирсона | «модели атрибуции», «Last Non-Brand Click», «автоцели vs js-события», «как засчитать конверсию» |26| `references/server-side-tracking-stack.md` | Decision matrix server-side (Stape / GTM Server / Яндекс SSP / self-hosted / no-code) + per-platform CAPI cross-links + обзор сервисов сквозной аналитики ($1k+/мес класс) | «server-side tracking stack», «server-side GTM», «Stape», «чем собирать серверные события» |27| `references/learning-phase-cross-platform.md` | Пороги обучения на одной оси: Meta 50 событий/7-10 дней vs Google 5-7 дней / ≥50 конв/нед vs Yandex 10 конв/14-21 день vs Telegram (нет фазы) vs VK (ручной) + универсальный паттерн «что сбивает обучение» | «фаза обучения по платформам», «cross-platform learning phase», «сколько событий нужно алгоритму», «что сбивает обучение» |28| `references/lookalike-quality-events.md` | Cross-platform паттерн «Lookalike на качественных событиях»: VK CS / Meta 1-3-5% / Yandex LAL / Google Demand Gen LAL — строить ядро на оплатах/квал-лидах, не на кликах | «lookalike на качественных событиях», «на чём строить похожую аудиторию», «LAL на оплатах» |2930**Границы (не дублировать):**31- Когортная воронка на масштабе, Predict LTV, Zero Day Revenue, 9 шагов анализа, выбор атрибуции под цикл сделки → **`full-funnel-analytics-ru`**.32- Пошаговая no-code настройка CAPI, 7 параметров матчинга, Event ID, Test Events, офлайн-конверсии → **`capi-no-code-setup`**.33- Готовый справочник цифр/бенчмарков по всем каналам → **`ad-benchmarks-ru`**.34- Аукционная оптимизация конкретного канала → `yandex-direct-pro-ru`, `google-ads-pro-ru`, `telegram-ads-pro-ru`, `vk-ads-pro-ru`, `meta-ads-launch-ru`.3536## Key Marketing Metrics by Channel3738### Email Marketing3940| Metric | Definition | Benchmark Range | What It Tells You |41|--------|-----------|----------------|-------------------|42| Delivery rate | Emails delivered / emails sent | 95-99% | List health and sender reputation |43| Open rate | Unique opens / emails delivered | 15-30% | Subject line and sender effectiveness |44| Click-through rate (CTR) | Unique clicks / emails delivered | 2-5% | Content relevance and CTA effectiveness |45| Click-to-open rate (CTOR) | Unique clicks / unique opens | 10-20% | Email content quality (for those who opened) |46| Unsubscribe rate | Unsubscribes / emails delivered | <0.5% | Content-audience fit and frequency tolerance |47| Bounce rate | Bounces / emails sent | <2% | List quality and data hygiene |48| Conversion rate | Conversions / emails delivered | 1-5% | End-to-end email effectiveness |49| Revenue per email | Total revenue / emails sent | Varies | Direct revenue attribution |50| List growth rate | (New subscribers - unsubscribes) / total list | 2-5% monthly | Audience building health |5152### Social Media5354| Metric | Definition | What It Tells You |55|--------|-----------|-------------------|56| Impressions | Number of times content was displayed | Content distribution and reach |57| Reach | Number of unique users who saw content | Audience breadth |58| Engagement rate | (Likes + comments + shares) / reach | Content resonance |59| Click-through rate | Link clicks / impressions | Traffic driving effectiveness |60| Follower growth rate | Net new followers / total followers per period | Audience building |61| Share/Repost rate | Shares / reach | Content virality and advocacy |62| Video view rate | Views / impressions | Video content hook effectiveness |63| Video completion rate | Completed views / total views | Video content quality and length fit |64| Social share of voice | Your mentions / total category mentions | Brand visibility vs. competitors |6566### Paid Advertising (Search and Social)6768| Metric | Definition | What It Tells You |69|--------|-----------|-------------------|70| Impressions | Times ad was shown | Budget utilization and targeting breadth |71| Click-through rate (CTR) | Clicks / impressions | Ad creative and targeting relevance |72| Cost per click (CPC) | Total spend / clicks | Cost efficiency of traffic generation |73| Cost per mille (CPM) | Cost per 1,000 impressions | Awareness cost efficiency |74| Conversion rate | Conversions / clicks | Landing page and offer effectiveness |75| Cost per acquisition (CPA) | Total spend / conversions | Full-funnel cost efficiency |76| Return on ad spend (ROAS) | Revenue / ad spend | Revenue generation efficiency |77| Quality Score (search) | Google's relevance rating (1-10) | Ad-keyword-landing page alignment |78| Frequency | Average times a user sees the ad | Ad fatigue risk |79| View-through conversions | Conversions from users who saw but did not click | Display/awareness campaign influence |8081### SEO / Organic Search8283| Metric | Definition | What It Tells You |84|--------|-----------|-------------------|85| Organic sessions | Visits from organic search | SEO effectiveness and content reach |86| Keyword rankings | Position for target keywords | Search visibility |87| Organic CTR | Clicks / impressions in search results | Title and meta description effectiveness |88| Pages indexed | Number of pages in search index | Crawlability and site health |89| Domain authority | Third-party authority score | Overall site strength |90| Backlinks | Number of external sites linking to you | Content authority and off-page SEO |91| Page load speed | Time to interactive | User experience and ranking factor |92| Organic conversion rate | Organic conversions / organic sessions | Content quality and intent alignment |93| Top entry pages | Most-visited pages from organic search | Content driving the most organic traffic |9495### Content Marketing9697| Metric | Definition | What It Tells You |98|--------|-----------|-------------------|99| Pageviews | Total views of content pages | Content reach and distribution |100| Unique visitors | Distinct users viewing content | Audience size |101| Average time on page | Time spent on content pages | Content engagement and depth |102| Bounce rate | Single-page sessions / total sessions | Content-audience fit and UX |103| Scroll depth | How far users scroll on a page | Content engagement through the piece |104| Social shares | Times content was shared on social | Content resonance and virality |105| Backlinks earned | External links to content | Content authority and SEO value |106| Lead generation | Leads attributed to content | Content conversion effectiveness |107| Content ROI | Revenue attributed / content production cost | Overall content investment return |108109### Overall Marketing / Pipeline110111| Metric | Definition | What It Tells You |112|--------|-----------|-------------------|113| Marketing qualified leads (MQLs) | Leads meeting marketing qualification criteria | Top-of-funnel effectiveness |114| Sales qualified leads (SQLs) | MQLs accepted by sales | Lead quality |115| MQL to SQL conversion rate | SQLs / MQLs | Marketing-sales alignment and lead quality |116| Pipeline generated | Dollar value of opportunities created | Marketing impact on revenue |117| Pipeline velocity | How fast deals move through pipeline | Campaign urgency and quality |118| Customer acquisition cost (CAC) | Total marketing + sales cost / new customers | Efficiency of customer acquisition |119| CAC payback period | Months to recover CAC from revenue | Unit economics health |120| Marketing-sourced revenue | Revenue from marketing-originated deals | Direct marketing contribution |121| Marketing-influenced revenue | Revenue from deals where marketing touched | Broader marketing impact |122123## Reporting Templates and Dashboards124125### Weekly Marketing Report126Quick-scan format for team standups:127- **Top 3 metrics** with week-over-week change128- **What worked** this week (1-2 bullet points with data)129- **What needs attention** (1-2 bullet points with data)130- **This week's priorities** (3-5 action items)131132### Monthly Marketing Report133Standard stakeholder report:1341. Executive summary (3-5 sentences)1352. Key metrics dashboard (table with MoM and target comparison)1363. Channel-by-channel performance summary1374. Campaign highlights and results1385. What worked and what did not (with hypotheses)1396. Recommendations and next month priorities1407. Budget spend vs. plan141142### Quarterly Business Review (QBR)143Strategic review for leadership:1441. Quarter performance vs. goals1452. Year-to-date trajectory1463. Channel ROI analysis1474. Campaign performance summary1485. Competitive and market observations1496. Strategic recommendations for next quarter1507. Budget request and allocation plan1518. Key experiments and learnings152153### Dashboard Design Principles154- Lead with the metrics that map to business objectives (not vanity metrics)155- Show trends over time, not just point-in-time snapshots156- Include comparison context: prior period, target, benchmark157- Use consistent color coding: green (on track), yellow (at risk), red (off track)158- Group metrics by funnel stage or business question159- Keep dashboards to one page/screen — detail goes in appendix160- Update cadence should match decision cadence (real-time for paid, weekly for content)161162## Trend Analysis and Forecasting163164### Trend Identification165When analyzing performance data, look for:1661671. **Directional trends**: is the metric consistently going up, down, or flat over 4+ periods?1682. **Inflection points**: where did performance change direction and what happened then?1693. **Seasonality**: are there predictable patterns by day of week, month, or quarter?1704. **Anomalies**: one-time spikes or drops — what caused them and are they repeatable?1715. **Leading indicators**: which metrics change first and predict future outcomes?172173### Trend Analysis Process1741. Chart the metric over time (at least 8-12 data points for meaningful trends)1752. Identify the overall direction (upward, downward, flat, cyclical)1763. Calculate the rate of change (is it accelerating or decelerating?)1774. Overlay key events (campaigns launched, product changes, market events)1785. Compare to benchmarks or targets1796. Identify correlations with other metrics1807. Form hypotheses about causation (and plan tests to validate)181182### Simple Forecasting Approaches183- **Linear projection**: extend the current trend line forward (useful for stable metrics)184- **Moving average**: smooth out noise by averaging the last 3-6 periods185- **Year-over-year comparison**: use last year's pattern as a baseline, adjusted for growth rate186- **Funnel math**: forecast outputs from inputs (e.g., if we generate X leads at Y conversion rate, we will get Z customers)187- **Scenario modeling**: create best case, expected case, and worst case projections188189### Forecasting Caveats190- Short-term forecasts (1-3 months) are more reliable than long-term191- Forecasts based on fewer than 12 data points should be flagged as low confidence192- External factors (market shifts, competitive moves, economic changes) can invalidate trend-based forecasts193- Always present forecasts as ranges, not exact numbers194195## Attribution Modeling Basics196197### What Is Attribution?198Attribution determines which marketing touchpoints get credit for a conversion. This matters because buyers typically interact with multiple channels before converting.199200### Common Attribution Models201202| Model | How It Works | Best For | Limitation |203|-------|-------------|----------|------------|204| Last touch | 100% credit to last interaction before conversion | Understanding final conversion triggers | Ignores awareness and nurture |205| First touch | 100% credit to first interaction | Understanding top-of-funnel effectiveness | Ignores nurture and conversion drivers |206| Linear | Equal credit to all touchpoints | Fair representation of all channels | Does not reflect relative impact |207| Time decay | More credit to touchpoints closer to conversion | Balanced view favoring recent interactions | May undervalue awareness |208| Position-based (U-shaped) | 40% first, 40% last, 20% split among middle | Valuing both discovery and conversion | Somewhat arbitrary weighting |209| Data-driven | Algorithmic credit based on conversion patterns | Most accurate representation | Requires significant data volume |210211### Attribution Practical Guidance212- Start with last-touch attribution if you have no model in place — it is the simplest and most actionable213- Compare first-touch and last-touch to understand which channels drive awareness vs. conversion214- Use position-based (U-shaped) as a reasonable middle ground for most B2B companies215- Data-driven attribution requires high conversion volume to be statistically meaningful216- No model is perfect — use attribution directionally, not as absolute truth217- Multi-touch attribution is better than single-touch, but any model is better than none218219### Attribution Pitfalls220- Do not optimize one channel in isolation based on single-touch attribution221- Awareness channels (display, social, PR) will always look bad in last-touch models222- Conversion channels (search, retargeting) will always look bad in first-touch models223- Self-reported attribution ("how did you hear about us?") provides useful qualitative color but is unreliable as quantitative data224- Cross-device and cross-channel tracking gaps mean attribution data is always incomplete225226## Optimization Recommendations Framework227228### Optimization Process2291. **Identify**: which metrics are underperforming vs. target or benchmark?2302. **Diagnose**: where in the funnel is the problem? (impressions, clicks, conversions, retention)2313. **Hypothesize**: what is causing the underperformance? (audience, message, creative, offer, timing, technical)2324. **Prioritize**: which fixes will have the biggest impact with the least effort?2335. **Test**: design an experiment to validate the hypothesis2346. **Measure**: did the change improve the metric?2357. **Scale or iterate**: roll out wins broadly; iterate on inconclusive or failed tests236237### Optimization Levers by Funnel Stage238239| Funnel Stage | Problem Signal | Optimization Levers |240|-------------|---------------|---------------------|241| Awareness | Low impressions, low reach | Budget, targeting, channel mix, creative format |242| Interest | Low CTR, low engagement | Ad creative, headlines, content hooks, audience targeting |243| Consideration | High bounce rate, low time on page | Landing page content, page speed, content relevance, UX |244| Conversion | Low conversion rate | Offer, CTA, form length, trust signals, page layout |245| Retention | High churn, low repeat engagement | Onboarding, email nurture, product experience, support |246247### Prioritization Framework248249Rank optimization ideas on two dimensions:250251**Impact** (how much will this move the metric?):252- High: directly addresses the primary bottleneck253- Medium: addresses a contributing factor254- Low: incremental improvement255256**Effort** (how hard is this to implement?):257- Low: copy change, targeting adjustment, simple A/B test258- Medium: new creative, landing page redesign, workflow change259- High: new tool, cross-team project, major content production260261Priority order:2621. High impact, low effort (do immediately)2632. High impact, high effort (plan and resource)2643. Low impact, low effort (do if capacity allows)2654. Low impact, high effort (deprioritize)266267### Testing Best Practices268- Test one variable at a time for clean results269- Define the success metric before launching the test270- Calculate required sample size before starting (do not end tests early)271- Run tests for a minimum of one full business cycle (typically one week for B2B)272- Document all tests and results, regardless of outcome273- Share learnings across the team — failed tests are valuable information274- A test that confirms the status quo is not a failure — it builds confidence in your current approach275276### Continuous Optimization Cadence277- **Daily**: monitor paid campaigns for budget pacing, anomalies, and disapproved ads278- **Weekly**: review channel performance, pause underperformers, scale winners279- **Bi-weekly**: refresh ad creative and test new variants280- **Monthly**: full performance review, identify new optimization opportunities, update forecasts281- **Quarterly**: strategic review of channel mix, budget allocation, and targeting strategy
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