Guide
How to Track Views, Clicks, Checkout and Sales
Build a weekly creator funnel in under 90 minutes so you know whether to fix traffic, store clarity, checkout, or the offer—without buying another analytics stack.
Chapters
What you’ll get
- A six-event measurement map from content view to net sale.
- A source naming convention for organic, email, paid, partner, and direct traffic.
- A weekly scorecard with the four conversion rates that expose the weakest step.
- A diagnosis matrix that turns each funnel pattern into the next action.
- A one-experiment brief with owner, timeframe, success threshold, and stop rule.
- An interactive funnel calculator preserved below for testing scenarios before you change the live journey.
Quick win in 10 minutes
Take the last complete seven-day window and write down six numbers: views, store visits, product clicks, checkout starts, purchases, and net sales. The smallest step-to-step ratio is the first place to investigate.
Do not improve the largest number first. Improve the transition that loses the highest share of qualified people, provided the sample is large enough to support a decision.
By channel
| Channel | Tracking focus |
|---|---|
| Organic social | Track content views → profile or link action → Tapmy visit → sale. |
| Tag each campaign link and compare visits, checkout starts, and purchases. | |
| Paid ads | Keep campaign parameters consistent and evaluate cost against net sales. |
| Direct or partner traffic | Use one source label per partner, placement, or offline link. |
Choose your starting point
- You have traffic but few store visits: begin with Chapter 1, then jump to Chapter 4.
- Your numbers disagree across tools: begin with Chapter 2 before interpreting any rate.
- You have a clean week of data: use Chapter 3 and the calculator, then diagnose in Chapter 4.
- You keep changing several things at once: go directly to Chapter 6 and write one experiment brief.
Operating rhythm
- Use one complete reporting window and record the same six funnel events.
- Compare the current window with a like-for-like previous window.
- Name the weakest transition and the evidence that supports that diagnosis.
- Choose one experiment with one primary metric.
- Run it long enough to produce a useful sample, then keep, revise, or stop it.
- Record the result so the next decision starts with evidence rather than memory.
Build the measurable journey in Tapmy
Use the chapter assets to create or improve the live offer in Tapmy. Start with the smallest change that can produce a measurable customer behavior; do not rebuild the entire store to answer one funnel question.
Interactive funnel calculator
Change the funnel inputs to see how one constraint affects purchases and revenue before you choose the experiment.
Worked example: Maya’s launch dashboard
Maya sells a $29 content-planning template. Her launch video receives 12,000 views. The store receives 540 visits, 210 people open the product, 42 start checkout, and 25 purchase.
- Store visit rate: 540 ÷ 12,000 = 4.5%
- Product click rate: 210 ÷ 540 = 38.9%
- Checkout-start rate: 42 ÷ 210 = 20%
- Purchase completion rate: 25 ÷ 42 = 59.5%
- Revenue: 25 × $29 = $725 before fees, refunds, or taxes
Her largest strategic opportunity is the handoff from product view to checkout start. Instead of posting more immediately, Maya adds a preview carousel, a plain-language list of files, and a “best for” section. She then measures the same rate during the next comparable launch window.
Frequently asked questions
How often should I check analytics?
Use a weekly decision review. Check briefly during a launch for broken links or payment issues, but avoid changing strategy after every hour of normal variation.
Which metric matters most?
Completed purchases and sustainable revenue are outcomes, but the weakest preceding rate tells you what to improve. No single top-of-funnel metric explains the full journey.
What if Tapmy and a social platform show different totals?
Platforms may use different event definitions, time zones, attribution windows, or bot filters. Keep each metric tied to its named source and compare trends consistently rather than forcing totals to match.
Should I track followers?
How do I know whether a change worked?
State the affected metric before shipping, compare similar periods, and check that another important outcome did not worsen. Treat small results as directional until the pattern repeats.