Daniel Reyes

The Pattern

Once or twice daily, Daniel runs what the data shows as a "upsell batch" — a session where he processes a list of customers for an upgrade or promotional offer. The keystroke data captured a 22-minute session with 13 consecutive Ctrl+C / Alt+Tab / Ctrl+V cycles in a single minute-bin. He copies customer records from a Google Sheet one row at a time, switches to Recharge, enters a promotional code, and repeats.

Decoded Workflow (Timestamped)

Raw keystroke capture from a batch session on Day 6:

10:14:03 — Google Sheets active: "Upsell List - March"
10:14:05 — Click cell B14, Ctrl+C (copy customer email)
10:14:07 — Alt+Tab to Recharge admin panel
10:14:09 — Ctrl+V (paste email into customer search)
10:14:12 — Enter (search), click customer result
10:14:18 — Tab to discount field, type "PINE20"
10:14:21 — Click "Apply Discount", wait for confirmation
10:14:24 — Alt+Tab back to Google Sheets
10:14:26 — Arrow down to next row, Ctrl+C
          ... (repeats 15-28 times per batch, 45-60 seconds per customer)

The same promo code (a team-specific string) appears in the keystroke data on every batch day. This is a campaign-based promo, not custom-per-customer — meaning the entire batch is templated and predictable.

Why This Matters

At 22-38 minutes per batch, 1-2 batches per day, this is 30-75 minutes of pure mechanical copy-paste daily. Lower bound: 2.5 hours per week. Upper bound: 6+ hours. This is not customer service — it is data entry. Recharge has an API. The entire workflow runs as a script.

What's Built

A Google Apps Script that reads the upsell Sheet, calls the Recharge API for each row, applies the promo code, and logs results back to the Sheet. Daniel clicks one button and reviews a summary. Zero copy-paste.

The Script (Paste Into Google Apps Script)

// === CONFIG ===
const RECHARGE_API_KEY = 'YOUR_RECHARGE_API_KEY_HERE';
const RECHARGE_BASE_URL = 'https://api.rechargeapps.com';
const PROMO_CODE = 'PINE20'; // Change per campaign
const EMAIL_COLUMN = 2;       // Column B = customer email
const STATUS_COLUMN = 5;      // Column E = result status
const HEADER_ROWS = 1;

function onOpen() {
  SpreadsheetApp.getUi()
    .createMenu('Upsell Tools')
    .addItem('Run Upsell Batch', 'runUpsellBatch')
    .addToUi();
}

function runUpsellBatch() {
  const sheet = SpreadsheetApp.getActiveSpreadsheet().getActiveSheet();
  const data = sheet.getDataRange().getValues();
  let processed = 0, failed = 0, skipped = 0;

  for (let i = HEADER_ROWS; i < data.length; i++) {
    const email = data[i][EMAIL_COLUMN - 1];
    const status = data[i][STATUS_COLUMN - 1];

    if (!email || status === 'Applied' || status === 'Failed') {
      skipped++;
      continue;
    }

    try {
      // Find customer in Recharge
      const custResp = UrlFetchApp.fetch(
        RECHARGE_BASE_URL + '/customers?email=' + encodeURIComponent(email),
        { headers: { 'X-Recharge-Access-Token': RECHARGE_API_KEY } }
      );
      const customers = JSON.parse(custResp.getContentText()).customers;

      if (!customers || customers.length === 0) {
        sheet.getRange(i + 1, STATUS_COLUMN).setValue('Not found');
        failed++;
        continue;
      }

      // Apply discount to first active subscription
      const subResp = UrlFetchApp.fetch(
        RECHARGE_BASE_URL + '/subscriptions?customer_id=' + customers[0].id + '&status=active',
        { headers: { 'X-Recharge-Access-Token': RECHARGE_API_KEY } }
      );
      const subs = JSON.parse(subResp.getContentText()).subscriptions;

      if (!subs || subs.length === 0) {
        sheet.getRange(i + 1, STATUS_COLUMN).setValue('No active sub');
        failed++;
        continue;
      }

      // Apply the discount
      UrlFetchApp.fetch(
        RECHARGE_BASE_URL + '/discounts',
        {
          method: 'post',
          headers: {
            'X-Recharge-Access-Token': RECHARGE_API_KEY,
            'Content-Type': 'application/json'
          },
          payload: JSON.stringify({
            discount: {
              code: PROMO_CODE,
              value: 20,
              value_type: 'percentage',
              applies_to_id: subs[0].id,
              applies_to_resource: 'subscription'
            }
          })
        }
      );

      sheet.getRange(i + 1, STATUS_COLUMN).setValue('Applied');
      sheet.getRange(i + 1, STATUS_COLUMN + 1).setValue(new Date().toISOString());
      processed++;
    } catch (e) {
      sheet.getRange(i + 1, STATUS_COLUMN).setValue('Error: ' + e.message.substring(0, 50));
      failed++;
    }

    Utilities.sleep(500); // Rate limiting
  }

  SpreadsheetApp.getUi().alert(
    'Batch Complete\n\n' +
    'Processed: ' + processed + '\n' +
    'Failed: ' + failed + '\n' +
    'Skipped: ' + skipped
  );
}Copy

The Pattern

Every single working day, Daniel spends time in an Etsy Live seller dashboard — a live-stream commerce channel that does not integrate with Zendesk. It generates its own inbox that he manually monitors. It produces zero metrics anywhere.

Decoded Workflow (Timestamped)

The data shows Etsy Live seller hub appearing in three distinct blocks daily:

09:12 — Etsy Seller Hub active (pre-session: checking messages, reviewing scheduled stream)
09:18 — Alt+Tab to Zendesk (handle incoming ticket)
09:24 — Alt+Tab back to Etsy (reply to customer DM)
  ... (morning block: ~30 min of monitoring interleaved with Zendesk)

13:04 — Etsy Live session begins (seller dashboard active, stream monitoring)
13:08 — Alt+Tab to Shopify (check product stock for live viewer question)
13:11 — Alt+Tab back to Etsy (respond in live chat)
  ... (afternoon block: 1.5-2 hours of active live session work)

16:22 — Etsy Seller Hub (post-session: handling follow-up messages, checking orders)
16:34 — Google Sheets (logging daily activity — Etsy numbers entered manually)

Why This Matters

This is a customer-facing sales channel that Daniel works every single day, and it generates zero metrics in any reporting system. Headcount decisions, workload assessments, and capacity planning are all being made with this channel completely invisible. If Pinewood & Co's Etsy Live business grows, Daniel's workload grows with it — but nobody can see it happening.

What's Built

A Zendesk channel tag, a Zapier zap config for routing Etsy messages into Zendesk, and a tracking setup that makes this channel visible in reporting within one week.

Zapier Integration Steps

The Pattern

The ticket count data shows a split personality. On normal days, Daniel handles 40-110 tickets at 8-12 minutes each — reasonable for genuine customer interactions. But on "high" days, the count jumps to 400+. At 8 hours of work, that's under 1.2 minutes per ticket — physically impossible for work that involves reading, reasoning, and writing.

What's Actually Happening on High Days

Two patterns in the data explain the inflation, both visible in the keystroke sequences:

• Batch operations inflate counts. The upsell batch sessions (Finding 2) generate a ticket-touch event in Zendesk for each row processed. 28 upsell items at 2 batches/day = 56 "tickets" that aren't customer interactions.
• "Touched" counts as "handled." The data shows inbox-scanning patterns where 200+ tickets are opened and triaged (snoozed, re-prioritized, or tagged) in rapid succession. Opening a ticket isn't handling it.

What's Built

A Zendesk Explore report configuration that separates real interactions from batch operations, plus tagging rules to keep the metrics clean going forward.

Zendesk Explore Report Setup

The Pattern

App-switching is the single most common event in Daniel's data. 4,487 Alt+Tab switches in 11 working days — 408 per workday. The window title data confirms these aren't random: each ticket requires 4-6 separate app visits because the tools don't talk to each other.

This Is a System Problem, Not a People Problem

4,487 Alt+Tab events could look like distraction. The data shows the opposite: every switch is purposeful. Daniel is not inefficient — he is bridging gaps between systems that should be integrated. Each ticket genuinely requires touching multiple tools, and none of them share data.

What's Built

Three integration configs that collapse six tabs into one. Total setup time: under an hour for Shopify + Recharge. The Etsy integration uses the Zapier config from Finding 3.

Integration 1: Zendesk + Shopify (10 minutes)

Integration 2: Zendesk + Recharge (15 minutes)

Integration 3: Etsy → Zendesk (see Finding 3)

The Zapier zap from Finding 3 routes Etsy messages into Zendesk as tickets. Combined with the Shopify and Recharge sidebar integrations, Zendesk becomes the single workspace for all customer channels.

The Pattern

Daniel spends 25-40 minutes every morning in Google Sheets manually entering ticket counts, resolution times, and upsell conversion numbers. The keystroke data shows it clearly: Zendesk tab → read number → Sheets tab → type number → repeat. On top of that, Calculator appeared 187 times in 11 working days — 17 times per day — for arithmetic that spreadsheet formulas handle instantly.

Evidence

Decoded Workflow (Timestamped)

09:17 — Google Sheets active: "CS Tracker 2024"
09:17 — Alt+Tab to Zendesk Explore (check yesterday's solved count)
09:18 — Alt+Tab to Sheets (type "87" into cell C14)
09:18 — Alt+Tab to Zendesk (check average resolution time)
09:19 — Alt+Tab to Calculator (type "487 / 87")
09:19 — Alt+Tab to Sheets (type "5.6" into cell D14)
09:20 — Alt+Tab to Recharge (check upsell conversion count)
09:21 — Alt+Tab to Calculator (type "23 / 87")
09:21 — Alt+Tab to Sheets (type "26.4%" into cell E14)
  ... (repeats for 25-40 minutes every single morning)

What's Built

A Google Apps Script that pulls yesterday's ticket data from the Zendesk API at 7:45 AM, calculates all the metrics, populates the tracking Sheet, and posts a Slack summary. Daniel starts his day with the report already done.

The Script (Paste Into Google Apps Script)

// === CONFIG ===
const ZENDESK_SUBDOMAIN = 'pinewoodco'; // your-subdomain.zendesk.com
const ZENDESK_EMAIL = '[email protected]';
const ZENDESK_API_TOKEN = 'YOUR_ZENDESK_API_TOKEN';
const SLACK_WEBHOOK_URL = 'YOUR_SLACK_WEBHOOK_URL'; // optional
const SHEET_NAME = 'Daily Metrics';

function pullDailyMetrics() {
  const yesterday = new Date();
  yesterday.setDate(yesterday.getDate() - 1);
  const dateStr = Utilities.formatDate(yesterday, 'America/New_York', 'yyyy-MM-dd');

  const auth = Utilities.base64Encode(ZENDESK_EMAIL + '/token:' + ZENDESK_API_TOKEN);
  const headers = { 'Authorization': 'Basic ' + auth };

  // Get solved tickets from yesterday
  const query = encodeURIComponent('type:ticket status:solved solved:' + dateStr);
  const resp = UrlFetchApp.fetch(
    'https://' + ZENDESK_SUBDOMAIN + '.zendesk.com/api/v2/search.json?query=' + query,
    { headers: headers }
  );
  const data = JSON.parse(resp.getContentText());
  const tickets = data.results || [];
  const ticketCount = tickets.length;

  // Calculate avg resolution time (hours)
  let totalResolutionHrs = 0;
  tickets.forEach(t => {
    if (t.created_at && t.updated_at) {
      const created = new Date(t.created_at);
      const solved = new Date(t.updated_at);
      totalResolutionHrs += (solved - created) / 3600000;
    }
  });
  const avgResolution = ticketCount ? (totalResolutionHrs / ticketCount).toFixed(1) : 0;

  // Write to sheet
  const sheet = SpreadsheetApp.getActiveSpreadsheet().getSheetByName(SHEET_NAME);
  const newRow = sheet.getLastRow() + 1;
  sheet.getRange(newRow, 1).setValue(dateStr);
  sheet.getRange(newRow, 2).setValue(ticketCount);
  sheet.getRange(newRow, 3).setValue(avgResolution + 'h');
  sheet.getRange(newRow, 4).setValue(new Date().toISOString());

  // Optional: Slack summary
  if (SLACK_WEBHOOK_URL && SLACK_WEBHOOK_URL !== 'YOUR_SLACK_WEBHOOK_URL') {
    UrlFetchApp.fetch(SLACK_WEBHOOK_URL, {
      method: 'post',
      contentType: 'application/json',
      payload: JSON.stringify({
        text: 'Daily CS Report for ' + dateStr +
              '\nTickets solved: ' + ticketCount +
              '\nAvg resolution: ' + avgResolution + 'h'
      })
    });
  }
}

// Set this as a daily time-driven trigger at 7:45 AMCopy

This isn't an efficiency finding — it's a revenue finding. Daniel's Etsy Live sessions appear in the behavioral data every single working day. He spends 1.5-2 hours daily in the Etsy Live seller dashboard — preparing, running, and following up on live selling sessions. His daily activity logs mention it as a highlight nearly every day.

But here's the thing: there are zero metrics being tracked on this channel. No ticket attribution, no revenue attribution, no conversion tracking. We can see from the window titles that he's active in the Etsy seller dashboard, but there's no integration between Etsy and Zendesk — so any customer questions from Etsy Live don't flow into his ticket metrics.

What the data shows:

This is a person generating direct revenue through live commerce and nobody can see it. If his Etsy Live sessions are driving even $500/week in sales (conservative for an active live seller), that's $26K/year from a channel with zero visibility. The actual number could be significantly higher.

What to do:

• Track Etsy Live revenue. Connect Etsy's analytics to your reporting. Know what Daniel is generating.
• Route Etsy customer questions into Zendesk. Right now they're handled separately — which means they don't count in his ticket metrics and he's doing more work than the numbers show.
• Consider giving Daniel MORE Etsy Live time, not less. If the automation in Findings 1-6 frees up 20+ hours/week of his time, the highest-value use of that recovered time might be more live selling — not more tickets. That's a revenue decision, not an efficiency decision.

The bigger point: This discovery found 22+ hours/week of recoverable time. Most of that comes from automating the grunt work. But the real ROI isn't in the hours saved — it's in where those hours go next. Daniel is already proving he can drive revenue through live commerce. Free him from the copy-paste and let him do more of what makes money.