Here’s a practical, organized rundown of the types of tools you can use to track and improve user acquisition metrics, what they do, the acquisition metrics they help with, and how to use them together. I’ll finish with a short recommended stack by company stage and a few integration / measurement tips.

1. Core analytics (product + funnel tracking)

• Google Analytics 4 (GA4): web + app funnels, acquisition channels, event tracking, basic cohort/retention. Good free option for web-centric apps.
• Mixpanel: event-based analytics, funnels, cohort retention, user-level profiles, A/B experiment tracking. Strong for product/engagement insights.
• Amplitude: advanced event analytics, behavioral cohorts, path analysis, conversion/retention modeling. Excellent for product-led growth teams.

What they help measure: sessions, new users, signups, activation rate, conversion funnels, retention, DAU/MAU, churn, cohort LTV.

2. Attribution & mobile app install tracking

• Branch: deep links + attribution, click-to-app behavior, cross-channel attribution.
• AppsFlyer / Adjust / Kochava: mobile attribution, fraud detection, campaign measurement across ad networks.
• Singular: unified attribution + cost aggregation and analytics.

What they help measure: paid channel attribution, cost per install (CPI), cost per acquisition (CPA), ROAS, campaign-level LTV.

3. Advertising & acquisition platforms

• Google Ads, Meta Ads Manager, TikTok Ads, LinkedIn Ads, Snap Ads: run and measure paid acquisition, lookalike audiences, conversion tracking.
• DSPs and programmatic platforms for larger budgets.

What they help measure: impressions, CTR, CPC, CPA, ROAS.

4. Customer data infrastructure / event routing

• Segment, Rudderstack: collect events once and route to analytics, marketing, and data warehouses.
• Benefits: keep instrumentation clean, avoid duplicated tracking across tools.

What they help measure: consistent user identities, unified event stream, easier analysis and attribution.

5. Experimentation & conversion rate optimization (CRO)

• Optimizely, VWO, Google Optimize (deprecated but historically used), Split.io (feature flags + experimentation).
• A/B and multivariate testing for landing pages, sign-up flows, pricing pages.

What they help measure: uplift in conversion rate, statistically significant test results, best-performing variants.

6. Session replay & qualitative analytics

• Hotjar, FullStory, LogRocket, Smartlook: session recordings, heatmaps, funnel drop-off analysis, on-site surveys.
• Use to diagnose UX issues causing drop-off and to generate test hypotheses.

What they help measure: friction points, where users abandon funnels, qualitative reasons for drop-off.

7. Marketing automation & growth engagement

• HubSpot, Marketo, Braze, Iterable: automated email/SMS/push campaigns, lead scoring, Drip flows, lifecycle messaging.
• Mailchimp for simpler email workflows.

What they help measure: email open/click rates, nurture conversion rates, activation via lifecycle messaging.

8. BI, reporting & data warehouse

• Looker, Tableau, Power BI for dashboards and executive reporting.
• Store raw data in BigQuery, Snowflake, Redshift to compute LTV, CAC, cohort analysis.

What they help measure: LTV:CAC, cohort revenue curves, attribution-adjusted ROI, unit economics.

9. Fraud detection & cost aggregation

• Apps like FraudBlockers integrated with attribution providers or Singular for cleaning ad data and normalizing cost across networks.

What they help measure: inflate-free CPI/CPA metrics, true ROAS.

Key acquisition metrics to track (minimum set)

• New users / new signups (volume by channel)
• CAC (Customer Acquisition Cost) by channel and campaign
• Conversion rate at each funnel step (visitor → signup → activation → paid)
• Activation rate (first success milestone)
• Retention (D1, D7, D30) and churn
• ARPU / ARPPU and LTV (cohort-based)
• ROAS and payback period
• Funnel drop-off points, CPI (mobile)

How to use tools to improve acquisition (practical playbook)

1. Instrument consistently: use Segment/Rudderstack + event schema to send identical events to analytics, attribution, BI, and marketing tools.
2. Map funnels: set up funnel reports in Amplitude/Mixpanel/GA4 and attribute conversions to channels (use attribution tool + UTM conventions).
3. Diagnose with qualitative tools: replay sessions/heatmaps (FullStory/Hotjar) on high-drop pages to find UX friction.
4. Hypothesize & test: run A/B tests (Optimizely / Split) on landing pages, CTA copy, sign-up flow. Prioritize tests by potential impact and traffic.
5. Optimize channels: compare CAC, LTV, ROAS across channels using attribution + cost aggregation (Singular/AppsFlyer). Shift budget to highest ROAS and test creative/targeting.
6. Automate conversion & activation: trigger targeted onboarding flows via Braze/HubSpot to increase activation and reduce early churn.
7. Measure long-term impact: compute cohort LTV in your warehouse and compare to CAC for sustainable growth decisions.
8. Guard against fraud & duplicate installs: rely on attribution providers and cost aggregation tools to ensure clean data.

Recommended stacks by stage

• Early / Seed (low budget, fast learning):
  • Analytics: Mixpanel (free tier) or GA4
  • Attribution: Branch (basic) or none (manual UTM tracking)
  • CRO: Hotjar for qualitative insight
  • Marketing: Mailchimp or HubSpot free tier
  • CDP: no CDP yet; instrument events carefully
• Growth / Series A–B:
  • Analytics: Amplitude or Mixpanel
  • Attribution: Branch + AppsFlyer (mobile) or Singular
  • CDP: Segment or Rudderstack
  • CRO: Optimizely / VWO
  • Automation: Braze or Iterable
  • Warehouse: BigQuery / Snowflake + Looker for BI
• Enterprise / scale:
  • Full attribution stack (AppsFlyer/Singular/Kochava), advanced fraud protection, data warehouse, Looker/Tableau, in-house experimentation platform + enterprise Optimizely/Split, enterprise marketing automation (Braze/Marketo).

Practical integration & measurement tips

• Standardize UTM & event naming conventions across marketing and product teams.
• Use server-side event collection for reliability and to reduce ad-blocker loss.
• Always calculate CAC and LTV on the same cohort definitions and currency/time windows.
• Tag experiments and campaigns in analytics (experiment IDs) so lift can be tied back to acquisition.
• Prioritize measuring incrementality (e.g., holdout groups) for high-spend channels—last-click attribution can be misleading.
• Track cost data alongside attribution (use a cost-aggregation tool or ETL ad platform cost into your warehouse daily).

Quick checklist to get started this week

• Implement core event schema (identify user, sign_up, activate, purchase).
• Plug events into an analytics tool (Mixpanel/Amplitude) and create a signup → activation funnel.
• Add UTM tracking on all acquisition links and confirm channel attribution in GA4.
• Install Hotjar/FullStory on the top-converting pages to collect session recordings.
• Run one prioritized A/B test on the highest-traffic funnel drop-off.

If you want, I can:

• Recommend a specific stack based on your product type (SaaS, mobile app, ecommerce) and monthly active users.
• Draft an event tracking schema template you can implement. Which would you like?