Job Application Tracker: Data-Driven Guide 2025

Low callbacks and guesswork make job searching exhausting. This guide shows you exactly how to build a tracker, use clear formulas, and run a weekly routine that lifts interviews and offers without burning more hours.

Many job seekers see a 2–4x improvement in interview rates after they start measuring their funnel and acting on the data.

What ‘job application success’ really means (and the core formula)

Job application success means turning the right applications into interviews and offers efficiently. At the simplest level, your success rate is offers divided by applications, expressed as a percentage.

1. Core formula: Success Rate = Offers / Applications × 100%.
2. In Google Sheets: =IFERROR(Offers/Applications,0).
3. Format the cell as a percentage so you can compare trends at a glance.

Most candidates benefit from tracking the steps between applications and offers. That helps you fix the right part of the funnel next.

You’ll use a short set of metrics (below) to diagnose whether it’s targeting, resume alignment, follow-ups, or interview skills. By breaking results into stages, you’ll know where to focus first and what to change next week. The outcome is a faster, clearer path from applications to signed offers.

Key metrics to track (the job-search funnel)

Your job search is a funnel: Applications → Responses → Interviews → Offers. Tracking a few ratios and times reveals where to improve first.

Start with these job application success metrics and compute them weekly to spot patterns early and respond before weeks slip by.

1. Applications sent per week
2. Job application response rate
3. Time to first response
4. Application → Interview ratio
5. Interview → Offer rate
6. Time spent per application
7. Source/channel conversion by LinkedIn, job boards, company sites, referrals
8. Resume version and keyword match rate

Applications sent per week

Weekly application volume keeps your pipeline healthy and predictable. Pick a target you can sustain without sacrificing quality.

1. Most job seekers do best at 8–15 tailored applications per week for mid-level roles. Go higher for entry-level and lower for senior roles.
2. Example: If you aim for 12 per week, block 90 minutes daily to source three roles and tailor two resumes.
3. If you routinely miss the target, reduce sources or automate data entry so quality stays high.

The takeaway: volume matters, but only at a level where you can customize and follow up.

Treat this as a capacity plan, not a brute-force sprint. Lock time on your calendar and assign specific days to sourcing vs tailoring.

Protect quiet hours for deep work on two high-priority roles.

1. If you’re overshooting volume but conversions are flat, you’re likely under-customizing.
2. If volume is low and conversions are decent, increase sourcing in your best channels.

This balance helps you maintain momentum without burnout.

Response rate and time to first response

Response rate shows how often employers acknowledge your applications.

1. Formula: Response Rate = Responses / Applications × 100%.
2. Time to First Response is the average days between Applied_Date and the first recruiter reply.

In Sheets, add a First_Response_Date column. Then use:

1. Average array formula: =IFERROR(AVERAGE(IF(LEN(First_Response_Date),First_Response_Date-Applied_Date)),)
2. Or compute per row: =IF(LEN(First_Response_Date),First_Response_Date-Applied_Date,"")

If your response rate is under 20% and time-to-response exceeds 10 business days, improve targeting, keyword alignment, and follow-up cadence.

Faster employer responses often correlate with higher fit and stronger conversions down the funnel. Shortening time-to-response is a leading indicator that your messaging and resume match the role.

If timing is slow across all channels, review job-family fit. If it’s slow in one channel, adjust your approach there first. Use this metric as your early-warning system for targeting and resume relevance.

Application → Interview ratio

This is your primary quality signal: how many applications turn into interviews.

1. Formula: App→Interview Ratio = Interviews / Applications × 100%.
2. Worked example: 80 applications, 10 interviews → 12.5%.
3. In Sheets: =IFERROR(Interviews/Applications,0).

If this ratio is low, your resume is probably mismatched to the job description or you’re applying to roles outside your range.

Fixes include tighter role targeting, stronger ATS resume optimization, and testing resume versions by job family. Start by mirroring must-have skills in the top third of your resume and tailoring accomplishments to the job’s scope.

Track App→Interview by Source and Resume_Version to isolate which change moves the needle. Once you see a lift, keep the winning pattern and iterate small improvements.

Interview → Offer rate

Down-funnel performance indicates your interview preparation and storytelling quality.

1. Formula: Interview→Offer Rate = Offers / Interviews × 100%.
2. Example: 12 interviews, 3 offers → 25%.

If this metric lags, focus on structured prep: practice STAR stories, technical drills, and closing questions. Ask interviewers for feedback and note themes in your tracker to target the highest-impact prep next week.

Treat each interview as data. Tag feedback by competency (e.g., strategy, communication, technical depth) and look for repeating gaps.

Schedule two mock interviews per week, refine your answers, and re-measure after 2–3 weeks. When this rate climbs, your pipeline becomes more efficient without increasing application volume.

Time spent per application

Track the minutes you invest to tailor and submit each application.

1. Formula: Time per Application = Total Minutes Spent / Applications.
2. Example: If you spent 600 minutes to send 12 applications, that’s 50 minutes each.

Pair this with conversion by channel to compute minutes per interview and per offer—your practical job search ROI. Reallocate time to channels and roles that deliver interviews faster.

Use this metric to avoid busywork. If keyword editing is consuming time without lifting App→Interview, shift effort to referrals or targeted outreach.

Conversely, if a carefully tailored resume doubles conversion for a role family, lean into that craft. Aim to reduce minutes per interview while keeping quality high.

Source/channel conversion (job boards, LinkedIn, referrals)

Not all sources are equal. Attribute each application to a source and compare App→Interview and Interview→Offer by channel.

Example: Over 4 weeks, referrals convert to interviews at 25%, company career sites at 15%, LinkedIn at 10%, job boards at 6%. Shift time toward your highest-yield sources and use different follow-up rhythms by channel. Recruiters consistently report that referrals are among the highest-converting sources for qualified candidates.

Break this down weekly, then re-allocate time every two weeks based on results. Keep a control slice of time for experimentation so you can discover new winners without derailing throughput.

If one source surges or dips, check for seasonality or role-family mix before overreacting. Channel clarity keeps your pipeline productive.

Resume keyword match and version tracking

ATS screens for relevant keywords and clear experience signals. Maintain a Resume_Version field and a Keyword_Match_Score (manual 1–5 rating or a count of critical terms found in your resume).

Quick keyword method in Sheets: list 8–12 must-have keywords for the role in a Keywords cell, then score with: =SUM(COUNTIF(SPLIT(LOWER(My_Resume_Text)," "),LOWER(TRIM(SPLIT(Keywords,",")))))

Track App→Interview by Resume_Version to A/B test headlines, skills sections, and summaries. Over 2–4 weeks, keep the better-performing version for that job family and iterate.

Keep versions focused: one for each job family or seniority tier. Document what changed (headline, skills, metrics added) so you can link improvements to specific edits.

Alternate versions systematically to avoid bias from channel mix or timing. This disciplined approach turns resume tweaks into measurable gains.

Set up your tracking system (templates included)

You’ll create a simple, reliable job application tracker, then enrich it with formulas and tags. Start in Google Sheets for speed; move to Notion or Airtable if you want richer relations and views.

Everything below is copy-pasteable, and you can extend it later to power a Looker Studio or Power BI job search dashboard. The goal is a system you’ll actually maintain weekly.

Build once, then let the tracker do the heavy lifting. Standard columns and statuses make your metrics trustworthy.

Formulas and helper fields save time on calculations and reduce manual errors. With consistent data entry, your insights will be accurate enough to guide decisions confidently.

Google Sheets template: columns, data types, and formulas

Create a sheet with these columns:

1. Application_ID
2. Company
3. Role_Title
4. Location
5. Source
6. Job_URL
7. Applied_Date
8. Status
9. First_Response_Date
10. Interview_Date
11. Offer_Date
12. Outcome
13. Resume_Version
14. Keywords_Targeted
15. Keyword_Match_Score
16. Time_Spent_Min
17. Notes

Use Data Validation to lock Status to: Applied, Responded, Interviewing, Rejected, Offer, Withdrawn. Add a Week_Start helper for cohorting and group by it in pivots.

Core formulas you can paste:

1. App→Interview Ratio (summary cell): =IFERROR(COUNTIF(StatusRange,"Interviewing")/COUNTIF(StatusRange,"<>"),0)
2. Interview→Offer Rate (summary cell): =IFERROR(COUNTIF(StatusRange,"Offer")/COUNTIF(StatusRange,"Interviewing"),0)
3. Response Rate: =IFERROR(COUNTIF(StatusRange,"Responded")/COUNTIF(StatusRange,"<>"),0)
4. Days to First Response (per row): =IF(LEN(First_Response_Date),First_Response_Date-Applied_Date,"")
5. Week_Start (helper column for cohorts): =Applied_Date-WEEKDAY(Applied_Date,2)+1

Pro tip: generate a unique Application_ID with: =LOWER(REGEXREPLACE(Company&"-"&LEFT(Role_Title,10)&"-"&TEXT(Applied_Date,"yyyymmdd"),"[^a-z0-9-]","")) so you can deduplicate later.

Notion or Airtable schema: statuses, tags, relations

Set up a main Applications database with properties:

1. Company (Relation)
2. Role_Title
3. Source (Select)
4. Status (Select)
5. Applied_Date (Date)
6. First_Response_Date (Date)
7. Interview_Date (Date)
8. Offer_Date (Date)
9. Outcome (Select)
10. Resume_Version (Select)
11. Keyword_Match_Score (Number)
12. Time_Spent_Min (Number)
13. Notes (Text)
14. Week_Start (Formula)

Create related databases for Companies and Contacts. In Airtable, add automations to set Week_Start automatically and roll up Interviews and Offers per company.

Use Board views by Status and Calendar views by Interview_Date. Add Grouped views by Source or Resume_Version to spot winners quickly.

Save filtered views for each job family so you can A/B test cleanly. With relations in place, you’ll see patterns by company and contact without duplicating data. The structure makes weekly reviews faster and more reliable.

Standardizing statuses and avoiding duplicates

Standard statuses make your metrics trustworthy. Map any employer ATS updates back to your set: Applied, Responded, Interviewing, Offer, Rejected, Withdrawn.

1. If the ATS shows “Assessment,” treat it as Responded.
2. “On-site scheduled” maps to Interviewing.

Consistent mapping ensures apples-to-apples conversion ratios across roles and sources.

Avoid duplicates by enforcing a unique Application_ID and checking new entries against it.

1. In Sheets, use Data → Remove Duplicates weekly.
2. In Airtable, add an automation that flags rows with matching Company + Role_Title + Applied_Date.
3. Keep one row per role per company, even if multiple recruiters email you.

Clean data prevents inflated counts and misleading conversion rates.

From application to insight: a weekly workflow

A consistent routine turns raw data into better decisions. Block 45–60 minutes once a week to tag, compute, review, and choose next actions.

This cadence ensures you iterate on your resume and channel mix before wasting weeks on low-yield tactics. With a repeatable workflow, you’ll see signal faster and act with confidence.

Set a recurring calendar event and prepare a short checklist. Close all other tabs, open your tracker, and move through updates before analysis.

Keep notes on what you changed last week so you can judge results fairly. Small, measured adjustments compound quickly.

Tagging sources and resume versions (UTM/email tracking optional)

Always fill Source and Resume_Version when you log an application.

1. For referrals, use Source = Referral and note the person in Notes.
2. For LinkedIn Easy Apply, use LinkedIn.
3. For company portals, use Company Site.

Optional: use email plus-addressing to track outreach—firstname+indeed@yourmail.com—for simple attribution via filters. In Gmail, auto-label confirmations by source and use a rule via Zapier or Make to append new applications to your sheet with Source already set.

This habit removes guesswork later. When you compare channels or resume variants, you’ll trust the data and make cleaner decisions.

If you change a tagging rule, document it in the sheet so historical metrics remain interpretable. Consistent tagging is the foundation of good analytics.

Weekly review checklist: compute, compare, decide

Run this checklist every week:

1. Update statuses and dates for the week’s applications and interviews.
2. Review App→Interview and Interview→Offer overall and by Source and Resume_Version.
3. Calculate minutes per interview by channel: (SUM Time_Spent_Min for channel) / Interviews from channel.
4. Identify one low-performing step to fix and one high-performing area to double down.
5. Plan 1–2 changes: resume tweak, channel shift, new follow-up cadence, or interview prep block.

You’ll make faster progress by changing one lever at a time and measuring its effect for at least two weeks.

Keep notes on what you changed and when. Compare current metrics to your 4-week average to avoid reacting to a single noisy week.

If a change helps, keep it. If not, revert and test the next lever. Discipline here drives steady improvement.

Follow-up rules by response timing and company type

Follow-ups lift response rate when they’re timely and respectful.

1. Job boards or company portals: follow up at 5 business days and again at 12–14 days if no reply. Ideally email a recruiter or hiring manager.
2. Referrals and warm introductions: check in at 3–4 days, then again at 10 days with a crisp update or new accomplishment.
3. Enterprise vs startup: enterprises move slower—extend your patience window by a week. Startups move quickly—follow up sooner and offer scheduling flexibility.
4. Stop after 2–3 follow-ups to avoid diminishing returns.

Log each follow-up in Notes so you can correlate cadence with response rate. Use templates to save time but personalize a line or two to show fit.

If a channel responds better to LinkedIn messages than email, lean into that channel’s norms. Consistent, respectful follow-up is one of the simplest ways to boost top-of-funnel results.

Dashboards that matter: pivots, cohorts, and trends

Visuals reveal patterns that raw rows hide. You only need a handful of charts: weekly application volume, source conversion, and trendlines for App→Interview and Interview→Offer.

Start simple, then add filters for role family, seniority, and resume version to compare like with like. As you iterate, dashboards turn weekly reviews into quick, focused decisions.

Use pivots to summarize by Source and Week_Start. Add sparklines or trendlines for fast visual context.

Keep the dashboard on a single page so you actually use it. When a chart moves, you’ll know which lever to pull next.

Looker Studio or Power BI: source conversion and trend charts

In Looker Studio, connect your Google Sheet. Create a bar chart with Source on the dimension and a calculated field for App→Interview: COUNT_DISTINCT(IF(Status="Interviewing",Application_ID,NULL)) / COUNT_DISTINCT(Application_ID)

Add a time-series chart with Week_Start on the X-axis and both conversion rates as metrics. Use filters for Role_Title contains, Resume_Version, and Source to isolate results by job family and channel without changing the data.

In Power BI, import your sheet and mark date columns as Date. Create measures like:

1. Interviews = DISTINCTCOUNT(Applications[Application_ID]) filtered to Status = "Interviewing"
2. AppToInterview = DIVIDE([Interviews],[Applications])

Plot by Week_Start. Add slicers for Source and Resume_Version to see which combinations drive momentum. Save a template so weekly refreshes are one click.

Cohort analysis: week-start groups and time-to-interview

Cohort analysis lets you compare groups of applications sent in the same week. Use Week_Start to group rows, then compute conversion by cohort to see if your changes are working.

To measure speed, calculate Time_to_Interview = Interview_Date - Applied_Date for rows with interviews. Chart median Time_to_Interview by cohort—if it’s dropping, your targeting and messaging are improving.

Seasonality shows up too, which helps set realistic expectations during slow hiring periods.

Annotate cohort charts with the dates you launched resume versions or channel shifts. If a cohort performs unusually well, check for role mix or referral density before generalizing.

Repeat the same view for Interview→Offer to confirm end-to-end impact. Together, these charts validate whether you’re fixing the right stage.

Make better decisions with thresholds (the action matrix)

Thresholds translate metrics into action. Use them to know when to optimize, when to shift channels, and when to double down.

Adjust numbers to your market, but start with these ranges and rules of thumb. Clear thresholds prevent overreacting to noise and underreacting to real trends.

If app→interview < X%: retarget roles, refine keywords, adjust resume

These ranges suggest a top-of-funnel issue:

1. Entry-level: below 10%
2. Mid-level: 12–18%
3. Senior: 6–12%

First, tighten targeting to roles where your core skills and scope match the job description. Next, do ATS resume optimization: mirror high-signal keywords, reorder bullets by impact, and add metrics.

Finally, A/B test Resume_Version by job family for two weeks. If one version beats the other by 5+ percentage points with 50–100 applications per variant, keep it.

Confirm the change with a fresh two-week cohort so you’re not chasing a fluke. If channel mix is skewing results, rebalance before drawing conclusions.

Once ratio lifts into range, maintain focus on that job family and scale in your highest-yield sources. Stability beats constant tinkering.

If interview→offer < Y%: upgrade prep, storytelling, and closing

Below 20% across multiple processes flags interview performance gaps. Focus on structured answers (STAR), stronger examples mapped to the job’s competencies, and closing with next-step and value-prop questions.

Run two mock interviews per week, record yourself, and log feedback themes in Notes. If technical or case interviews are the blocker, schedule targeted drills and a practice loop 48 hours before each round.

Expect a measurable lift in 2–3 weeks.

Track interview outcomes by stage (screen, panel, onsite) to localize the issue. If screens are fine but panels stall, deepen role-specific stories. If onsites lag, strengthen cross-functional narratives and closing.

Revisit your prep plan weekly until conversion rises above the threshold. Keep what works and standardize it as your playbook.

ROI of time: when to stop a channel and double down elsewhere

Compute Minutes per Interview by channel: Sum(Time_Spent_Min where Source=X) / Interviews where Source=X. Also track Minutes per Offer.

If a channel’s Minutes per Interview is 2–3x worse than your best channel for two consecutive weeks, pause it. Reallocate those hours to referrals, company sites, or targeted outreach.

Keep a small budget of time for experimentation so you can discover new winners without sacrificing throughput.

Make exits and reallocations time-bound. Pause low-ROI channels for two weeks, then retest briefly to confirm the result holds.

Document changes and check the next cohort to validate gains. This keeps your search efficient and focused on what works.

Advanced tactics (optional): automation, A/B testing, privacy

Once your basics are solid, use light automation to save time, run real A/B tests, and protect your data. Each tactic compounds the gains from your weekly workflow.

Implement one at a time, measure the effect for two weeks, then keep or revert. Small, controlled experiments beat big, messy overhauls.

Treat these as force multipliers, not prerequisites. Start with the tactic that removes your biggest friction—usually auto-logging or clean A/B testing.

As your system matures, add the next layer. The result is more signal with less effort.

Zapier and email rules to auto-log applications

In Gmail, create labels like “App/LinkedIn,” “App/CompanySite,” and “App/Referral,” then build filters that auto-label confirmations. Use Zapier’s New Labeled Email trigger to append a row in Google Sheets with Source, Company, Role, and Applied_Date.

For Notion or Airtable, use web clipper or form submissions to add applications from your browser with one click. Automation reduces manual data entry and keeps timestamps accurate, which improves your response-time metrics.

Start with one channel, confirm data quality, then extend to others. Add basic error handling (e.g., flag missing fields) so bad rows don’t pollute your metrics.

Reconcile automated entries during your weekly review. Clean automation saves time without sacrificing accuracy.

Designing resume A/B tests and reading results confidently

Define a clear hypothesis: “Resume_V1 will increase App→Interview for Product Manager roles by 6 percentage points vs Resume_V2.” Randomize fairly by alternating versions each day or by source.

Run until each version has at least 100 applications in the same job family and similar source mix. If the difference is ≥5 percentage points and stable week to week, adopt the winner and iterate a new challenger.

For more rigor, use a two-proportion test in a spreadsheet or an online calculator. Control for source so you don’t confuse channel effects with resume effects.

Document version differences and lock other variables during the test window. If results are inconclusive, extend the sample or narrow the role family for a cleaner signal.

When you find a winner, update your default resume and keep a backlog of next hypotheses. Iteration turns into predictable gains.

Privacy, consent, and ethical tracking for your search

Store only what you need to make decisions—avoid sensitive data like SSNs, birthdates, and confidential interview content tied to names. Keep your tracker private, use strong passwords and MFA, and restrict sharing to trusted mentors if necessary.

In jurisdictions with data rights (e.g., GDPR/CCPA), avoid storing any nonpublic company information and never record interviews without consent. If you track compensation discussions, redact names and keep estimates general to protect both you and your contacts.

Revisit permissions and sharing settings monthly. If you export or back up data, encrypt it.

Treat referrals’ information with the same care you expect others to give yours. Good security preserves relationships and your reputation.

Benchmarks and goals (by role level and job family)

Benchmarks vary by market and season, but ranges help you set expectations and trigger action. For many job seekers:

1. App→Interview: Entry 8–15%, Mid 12–20%, Senior 6–12%; Referrals often exceed 20–40% regardless of level.
2. Interview→Offer: Entry 15–25%, Mid 20–35%, Senior 25–40%.

Channel patterns: referrals convert best, company sites and warm introductions are next, LinkedIn mid-pack, job boards lowest but highest volume. Weekly goals to start: 10–15 quality applications, at least 3 referral or warm-intro attempts, and two hours of interview prep tied to your next round.

Use these as starting points, not hard rules. If you’re below range for two weeks, apply the action matrix above. If you’re above, scale what’s working.

Review by job family to avoid mixing very different funnels. Aim for steady, incremental improvement.

Common pitfalls and how to fix them

Good data beats more effort. Avoid these traps so your metrics stay meaningful and your actions stay smart.

Small sample sizes and misleading spikes

A single good week can distort your ratios. Don’t change direction based on fewer than 30–50 applications or 5–10 interviews.

Use 4-week rolling averages for your conversion metrics to smooth noise. If you make a big change (new resume), annotate the date in Notes so you can compare pre/post cohorts cleanly.

When in doubt, gather more data. Hold changes steady for at least two weeks before judging.

If numbers swing, check for role mix or channel shifts as the real cause. Patience here prevents whiplash decisions.

Inconsistent tagging or status updates

If Source, Status, or dates are blank or messy, your insights will be wrong. Lock your Status picklist and define a short tagging guide in the first sheet row.

Schedule a 10-minute “admin block” after each application session to log entries and update any replies. Run a weekly audit: filter for blanks in Source, Resume_Version, and Applied_Date and fill them before you review metrics.

This discipline pays off in cleaner dashboards and better calls on what to change. If you collaborate with a mentor, share the tagging guide so feedback aligns with your data.

Over time, consistency reduces review time and error rates. Your metrics become a reliable compass.

Mini case study: from 5% interviews to 20% in 30 days

A mid-level marketing manager applied to 120 roles in 4 weeks and got 6 interviews (5%). She built the tracker above, tightened targeting to lifecycle marketing roles, and A/B tested two resumes by job family.

She also shifted time from job boards (6% App→Interview) to referrals and company sites, booked two mock interviews weekly, and followed up at 5 and 12 days. In the next 30 days she sent 80 applications and logged 16 interviews (20%), with referrals converting at 32% and company sites at 18%. Minutes per interview fell from 180 to 65, and she accepted an offer in week 6.

The sequence mattered: fix targeting and resume first, then reallocate channels, then tune interview prep. She changed one lever at a time and measured for two weeks before moving on.

The compounding effect produced faster interviews and a stronger offer set. This playbook is repeatable across roles and markets.

FAQs

1. How do I measure job application success? Track offers divided by applications as your headline rate, plus App→Interview and Interview→Offer to see where to improve. Compute them weekly and by source and resume version.
2. What is a good application to interview ratio? Entry-level 8–15%, mid-level 12–20%, senior 6–12%. Referrals can exceed 20–40% across levels.
3. How to build a job application tracker in Google Sheets? Create columns for Company, Role, Source, Applied_Date, Status, dates for responses/interviews/offers, Resume_Version, Time_Spent_Min, and Notes; add data validation and the formulas listed above.
4. How to analyze job application sources for highest conversion? Filter by Source and compute App→Interview and minutes per interview; double down on sources with the best ratios for two weeks and reassess.
5. How to track resume versions and A/B test them? Add a Resume_Version field and alternate versions by day or source; keep each test to one job family until each version has ~100 applications.
6. What metrics should I track weekly? Applications, response rate, time to response, App→Interview, Interview→Offer, minutes per interview, and conversion by Source and Resume_Version.
7. How to create a job search dashboard in Looker Studio? Connect your Sheet, add charts for source conversion and weekly trends, and filter by Week_Start, Source, and Resume_Version.
8. How to standardize ATS statuses in my tracker? Map employer-specific states to your set: Applied, Responded, Interviewing, Offer, Rejected, Withdrawn; document the mapping in your tracker.
9. How many applications should I send per week to get interviews? Most mid-level seekers see momentum at 8–15 tailored applications per week with 3+ warm-intro attempts.
10. How to use Zapier to automate a job application tracker? Trigger on labeled confirmation emails and append rows to Sheets or Notion/Airtable with Source and dates prefilled.
11. What is a good interview to offer conversion rate? Aim for 20–35% depending on level; if you’re below 20% for several processes, invest in targeted interview prep.
12. How to calculate job search ROI (time vs results)? Compute minutes per interview and per offer by channel; shift time from channels that are 2–3x worse than your best.

Templates and resources

1. Column starter pack for Sheets: Application_ID, Company, Role_Title, Location, Source, Job_URL, Applied_Date, Status, First_Response_Date, Interview_Date, Offer_Date, Outcome, Resume_Version, Keywords_Targeted, Keyword_Match_Score, Time_Spent_Min, Week_Start, Notes.
2. Copy-paste formulas: ratios, response time, and cohort Week_Start listed in the Sheets section above.
3. Notion/Airtable schema: Applications, Companies, and Contacts databases with Relations, Status picklists, and views by Source and Week_Start.
4. Dashboard quick start: Looker Studio connection to Google Sheets; Power BI import and measures for AppToInterview and InterviewToOffer.
5. A/B testing checklist: hypothesis, randomization plan, sample size (~100 apps per variant), 2-week minimum run, source balance, and simple two-proportion check.
6. Privacy checklist: store minimal data, strong passwords/MFA, private sharing, no confidential employer info, consent for recordings, and redacted compensation notes.

References and further reading:

1. LinkedIn Talent Blog and employer benchmark studies on source effectiveness and funnel conversion.
2. Jobvite and Lever hiring reports for general conversion patterns across channels and roles.
3. Google Sheets and Looker Studio documentation for calculated fields and time-series charts.