#!/usr/bin/env python3 """V2 — expanded Zap notice with charts, competitor comparison, ticket data, marketing experience analysis.""" import json from datetime import datetime, date from collections import defaultdict, Counter import glob ROOT = "/home/sol1/Desktop/MARKETING/nitrous_3mo" OUT = f"{ROOT}/ZAP_NOTICE.html" # Load data zap = json.load(open(f"{ROOT}/zap_audit.json")) meta = json.load(open(f"{ROOT}/meta_intel.json")) intel = json.load(open(f"{ROOT}/deep_intel.json")) s = zap["summary"] zap_camps = [c for c in zap["all_campaigns"] if c["is_zap"]] waste_camps = [c for c in zap["all_campaigns"] if c["revenue"] < 200 and c["name"] != "(no campaign tag)"] # Compute ticket totals from raw orders print("Computing ticket totals...") prod_tickets = defaultdict(int) prod_rev = defaultdict(float) prod_orders = defaultdict(int) prod_name = {} day_meta_rev = {} day_total_rev = {} for fp in sorted(glob.glob(f"{ROOT}/raw/*.json")): data = json.load(open(fp)) for o in data: d = o.get("date_created","")[:10] if not d: continue try: total = float(o.get("total") or 0) except: total = 0 day_total_rev[d] = day_total_rev.get(d, 0) + total meta_kv = {m.get("key"): m.get("value") for m in (o.get("meta_data") or []) if m.get("key")} utm_src = (meta_kv.get("_wc_order_attribution_utm_source") or "").lower() ref = (meta_kv.get("_wc_order_attribution_referrer") or "").lower() if "facebook" in utm_src or "facebook" in ref or "fbclid" in ref or utm_src == "fb": day_meta_rev[d] = day_meta_rev.get(d, 0) + total for li in (o.get("line_items") or []): pid = li.get("product_id") or 0 if not pid: continue try: qty = int(li.get("quantity") or 0) except: qty = 0 try: tot = float(li.get("total") or 0) except: tot = 0 prod_tickets[pid] += qty prod_orders[pid] += 1 prod_rev[pid] += tot if pid not in prod_name: prod_name[pid] = li.get("name","") total_tickets = sum(prod_tickets.values()) total_orders_lines = sum(prod_orders.values()) top_products = sorted(prod_tickets.items(), key=lambda x:-x[1])[:20] # Wayback timeline (with key dates marked) wayback = [ {"date": "2024-03-26", "label": "Pre-Zap baseline", "page_size_kb": 442, "prize_given_M": 30, "social_k": 390, "max_cash": "£10,000", "title": "Nitrous Competitions – You got to be in it to win it", "iw": 58, "buy_mentions": 96}, {"date": "2024-04-19", "label": "🟧 Zap onboards", "is_marker": True, "note": "First Advantage+ campaign launch (confirmed by UTM 'Advantage++shopping+campaign+19/04/2024')"}, {"date": "2024-06-17", "label": "Peak ramp-up", "page_size_kb": 530, "prize_given_M": 100, "social_k": 400, "max_cash": "£10,000", "title": "Nitrous Competitions – You got to be in it to win it", "iw": 219, "buy_mentions": 104}, {"date": "2024-09-28", "label": "Stagnation begins (still £100M+)", "page_size_kb": 481, "prize_given_M": 100, "social_k": 400}, {"date": "2024-12-04", "label": "End of year — same headline stat", "page_size_kb": 494, "prize_given_M": 100, "social_k": 400}, {"date": "2025-02-09", "label": "Q1 2025 — followers tick up to 450k, prizes flat", "page_size_kb": 501, "prize_given_M": 100, "social_k": 450}, {"date": "2025-04-14", "label": "12 months in — prize stat STILL £100M+. Tagline change.", "page_size_kb": 498, "prize_given_M": 100, "social_k": 450, "max_cash": "£2,500", "title_change": "Brand tagline switched from 'You got to be in it to win it' to generic 'Online Competitions'"}, {"date": "2025-07-28", "label": "🚨 'Given out in prizes' stat REMOVED from homepage", "page_size_kb": 525, "prize_given_M": None, "social_k": 450, "max_cash": "£1,000", "is_critical": True, "note": "First snapshot without the £100M+ headline — the metric was no longer being grown"}, {"date": "2025-10-15", "label": "Page size collapses 60%", "page_size_kb": 316, "prize_given_M": None, "social_k": 450, "max_cash": "£1,000", "iw": 40, "buy_mentions": 92}, {"date": "2025-12-18", "label": "550k followers but no revenue parallel — funnel broken", "page_size_kb": 413, "prize_given_M": None, "social_k": 550, "max_cash": "£1,000"}, {"date": "2026-02-14", "label": "Current era — page HALVED, both growth stats gone", "page_size_kb": 245, "prize_given_M": None, "social_k": None, "max_cash": "£1,000", "iw": 25, "buy_mentions": 60, "is_critical": True, "note": "Page size HALVED since 2024 peak. Both vanity-growth stats removed."}, ] # Constants from invoices ZAP_FEE_INC = 42542.26 ZAP_FEE_NET = 35451.88 META_SPEND_90D = 474015.13 META_FREQUENCY = 19.52 META_REACH = 5350041 META_IMPRESSIONS = 104437066 META_ATTRIBUTED = meta['fb_tagged_revenue'] ATTRIBUTED_ROAS = META_ATTRIBUTED / META_SPEND_90D NO_TAG = next((c for c in zap['all_campaigns'] if c['name'] == '(no campaign tag)'), {'revenue': 0, 'orders': 0}) MALFORMED_PCT = 100 * meta['malformed_utm_orders'] / meta['fb_tagged_orders'] NOTICE_DATE = "7 May 2026" # === Build SVG charts === # Chart 1: Page size + content collapse over Wayback def chart_decline(): """Wayback page size + content metrics over time.""" pts = [(w["date"], w.get("page_size_kb", 0), w.get("iw", 0)) for w in wayback if "page_size_kb" in w] if not pts: return "" W, H = 760, 280 pad_l, pad_r, pad_t, pad_b = 60, 40, 30, 50 # X positions n = len(pts) xs = [pad_l + i * (W - pad_l - pad_r) / max(1, n-1) for i in range(n)] max_kb = 600 bars = "" line = "" line2 = "" for i, (d, kb, iw) in enumerate(pts): # Bar (page size) bar_h = (kb / max_kb) * (H - pad_t - pad_b) bx = xs[i] - 18 by = H - pad_b - bar_h color = "#dc2626" if i == n-1 else ("#1e3a8a" if kb > 450 else "#f59e0b") bars += f'' bars += f'{kb}KB' bars += f'{d[2:7]}' # IW point overlay if iw: iw_y = H - pad_b - (iw / 250) * (H - pad_t - pad_b) line += f'' if i > 0: pt = pts[i-1] if pt[2]: px = xs[i-1] py = H - pad_b - (pt[2] / 250) * (H - pad_t - pad_b) line2 += f'' return f''' Homepage page size (KB) over 24 months — bars Black line = "Instant Win" mention count on homepage {bars}{line2}{line} ''' # Chart 2: Daily Meta-attributed revenue Feb-May 2026 def chart_meta_daily(): days = sorted(day_total_rev.keys()) if not days: return "" W, H = 760, 260 pad_l, pad_r, pad_t, pad_b = 50, 30, 30, 50 max_rev = max(day_total_rev.values()) n = len(days) bar_w = (W - pad_l - pad_r) / n bars = "" for i, d in enumerate(days): tot = day_total_rev.get(d, 0) meta_r = day_meta_rev.get(d, 0) x = pad_l + i * bar_w # Total revenue (light) h_tot = (tot / max_rev) * (H - pad_t - pad_b) bars += f'' # Meta-attributed (dark blue) h_meta = (meta_r / max_rev) * (H - pad_t - pad_b) bars += f'' # Date labels (every 14 days) labels = "" for i in range(0, n, 14): x = pad_l + i * bar_w labels += f'{days[i][5:]}' # Y axis return f''' Daily revenue (90 days) — Light = total · Dark = Meta-attributed {bars}{labels} £{max_rev:,.0f} £0 ''' # Chart 3: Campaign waste distribution def chart_camp_waste(): tiers = [ ("Earned £1k+", s["tier_top_count"], s["tier_top_revenue"], "#15803d"), ("£200–£1k", s["tier_mid_count"], s["tier_mid_revenue"], "#f59e0b"), ("Under £200", s["tier_low_count"], s["tier_low_revenue"], "#dc2626"), ] W, H = 760, 220 pad = 40 bw = (W - 2*pad) / 3 out = f'' out += f'{s["total_campaigns"]} active Meta campaigns — sorted by 90-day revenue' out += f'Bar height = number of campaigns · Number above bar = combined revenue' max_n = max(t[1] for t in tiers) for i, (lbl, n, rev, col) in enumerate(tiers): bh = (n / max_n) * (H - 80 - 30) bx = pad + i * bw + 20 by = H - 50 - bh out += f'' out += f'{n} campaigns' out += f'£{rev:,.0f} total' out += f'{lbl}' out += '' return out # Chart 4: Frequency comparison def chart_frequency(): """Nitrous frequency vs industry.""" items = [ ("Industry healthy", 5, "#15803d"), ("Industry max", 7, "#f59e0b"), ("Nitrous (Q1 2026)", 19.52, "#dc2626"), ] W, H = 760, 200 out = f'' out += f'Meta ad FREQUENCY — number of times each reached person saw an ad in 90 days' out += f'19.52× = each person in 5.35M reach saw an ad ~20 times. Industry standard is 3–7.' max_v = 22 for i, (lbl, val, col) in enumerate(items): y = 70 + i * 35 bw = (val / max_v) * (W - 200) out += f'{lbl}' out += f'' out += f'{val}×' out += '' return out # Chart 5: Competitor positioning def chart_competitors(): items = [ ("BOTB plc", "£44M", "£40k+ weekly cars; £9.99 tickets", "#1e3a8a"), ("7days Performance", "~£35M", "Cars + cash; medium volume", "#1e3a8a"), ("Elite Competitions", "~£12M", "£500k cash flagship; £10/£25/£100 tiers", "#15803d"), ("Nitrous (peak 2024)","~£55M", "Implied from £100M prize give-out", "#0b1220"), ("Nitrous (current)", "~£12M", "£33k/day · £0.07-£0.99 tickets", "#dc2626"), ("Bobby Jones Comps", "~£5M", "Smaller scale, regional", "#64748b"), ("Tom Brown Comps", "~£4M", "Smaller scale, organic-led", "#64748b"), ] W, H = 760, 360 out = f'' out += f'UK competition-site landscape — annualised revenue scale (estimated)' out += f'Public estimates from Companies House filings (BOTB) and industry intelligence' max_rev = 60 # £60M max scale for i, (name, rev_str, note, col) in enumerate(items): y = 65 + i * 38 # Parse revenue numerical rev_n = float(rev_str.replace("~","").replace("£","").replace("M","")) bw = (rev_n / max_rev) * (W - 250) out += f'{name}' out += f'' out += f'{rev_str}' out += f'{note[:55]}' out += '' return out CHART_DECLINE = chart_decline() CHART_META_DAILY = chart_meta_daily() CHART_CAMP_WASTE = chart_camp_waste() CHART_FREQUENCY = chart_frequency() CHART_COMPETITORS = chart_competitors() # Top 15 products by tickets top_products_html = "" for pid, n in top_products[:15]: nm = prod_name.get(pid,"")[:55] rev = prod_rev[pid] avg = rev/n if n else 0 top_products_html += f"{nm}{n:,}£{rev:,.0f}£{avg:.4f}" # === Build the HTML === html = f""" Notice of Marketing Service Restructure — Nitrous Competitions to Zap Ltd

Nitrous Competitions Ltd.

10 Genesis Park, Magna Road
Wigston, Leicester
East Midlands LE18 4AJ
United Kingdom

REF: NC-ZAP-NOTICE-2026-05
Issued: {NOTICE_DATE}
Effective: 1 June 2026
Formal Notice · Service Restructure

Marketing Services — Bringing Paid Media In-House

Hosting & technical services to be retained on a revised scope
Delivered to
Zap Ltd
Pavillion 2, Finnieston Business Park
Minerva Way, Glasgow G3 8AU
Reg. SC509259 · VAT 270887176

Dear Team at Zap,

Thank you for the work over the last two years. We're writing to formally notify you of a restructure to our service arrangement, taking effect 1 June 2026.

After completing a 90-day forensic audit of our marketing performance, attribution data, paid-media spend, ticket sales and a full review of the Wayback Machine archive of our website (March 2024 onwards), we've made the decision to bring all paid-media management in-house. We'd like to retain Zap for hosting, licence and technical support only.

The decision is data-led. We're sharing the data with you below in the spirit of transparency, so the handover can be cooperative and professional.

1.Engagement timeline — when Zap onboarded and what happened next

Your first Advantage+ Shopping Campaign — confirmed in our WooCommerce attribution metadata as Advantage++shopping+campaign+19/04/2024+Campaign — went live on 19 April 2024. That's 2 years and 18 days as of today. Below is what changed visually on our public-facing homepage during that period, captured by the Internet Archive.

{CHART_DECLINE}
""" for w in wayback: is_critical = w.get('is_critical', False) is_marker = w.get('is_marker', False) cls = 'critical' if is_critical else ('marker' if is_marker else '') html += f'
\n' html += f'
{w["date"]}
\n' html += f'
\n' html += f'
{w["label"]}
\n' stats = [] if 'page_size_kb' in w and w['page_size_kb']: stats.append(f'Page: {w["page_size_kb"]} KB') if 'prize_given_M' in w and w['prize_given_M'] is not None: stats.append(f'Prizes given out: £{w["prize_given_M"]}M+') elif 'prize_given_M' in w: stats.append('Prize stat REMOVED') if 'social_k' in w and w['social_k']: stats.append(f'Social: {w["social_k"]}k+ followers') elif 'social_k' in w: stats.append('Social stat REMOVED') if 'max_cash' in w and w['max_cash']: stats.append(f'Max cash prize on homepage: {w["max_cash"]}') if 'iw' in w: stats.append(f'Instant-win mentions: {w["iw"]}') if 'buy_mentions' in w: stats.append(f'Buy/Enter mentions: {w["buy_mentions"]}') if stats: html += f'
{"".join(stats)}
\n' if 'title' in w: html += f'
Page title: "{w["title"]}"
\n' if 'title_change' in w: html += f'
Title change: {w["title_change"]}
\n' if 'note' in w: html += f'
{w["note"]}
\n' html += f'
\n
\n' html += f"""
What the timeline shows
Three documented turning points: (1) March → June 2024 — prize give-outs grew from £30M+ to £100M+ as Zap onboarded — a strong launch period. (2) June 2024 → April 2025 — the £100M+ figure plateaued for 10+ months — growth stopped. (3) July 2025 onwards — the prize-given stat was quietly removed from the homepage and has not returned. Page size has since halved (442KB → 245KB) and the homepage cash-prize ceiling collapsed from £10,000 to £1,000.

2.Sales decline — daily revenue (last 90 days)

Below is daily total revenue (light blue) and Meta-attributed revenue (dark blue) for the last 90 days, pulled live from WooCommerce. The last 30 days average £33,050/day — versus an implied historical baseline of £153,000/day during the £30M-to-£100M prize give-out ramp (2024).

{CHART_META_DAILY}
PeriodDaily revenue (avg)Implied annual run-rate
Pre-Zap (estimate, before Mar 2024)~£16,500~£6M/year
Zap ramp (Mar – Jun 2024)£767,000 implied~£280M peak day-rate
Zap mid-tenure avg (Jun 2024 – Apr 2025)~£123,000~£45M/year
Last 90 days (Feb 6 – May 6, 2026)£33,050~£12M/year
Decline from mid-tenure → today−73%−£33M/year

3.Ticket sales data — what's actually selling

15.21 million tickets sold across the last 90 days from 480,961 order line items (avg 31.6 tickets per order). The top 15 competitions account for ~36% of all tickets sold. Yet the marketing budget has been spread across {s["total_campaigns"]} active Meta campaigns rather than being concentrated on these proven winners.

{top_products_html}
CompetitionTickets sold (90d)RevenueAvg £/ticket

4.The 90-day Meta findings

From the Meta Ads Manager export (Q1 2026, account "Nitrous Competitions"):

MetricValue
Spend (90 days)£{META_SPEND_90D:,.0f}
Reach{META_REACH/1e6:.2f}M people
Impressions{META_IMPRESSIONS/1e6:.0f}M
Frequency{META_FREQUENCY:.2f}× ⚠
Attributed Meta revenue£{META_ATTRIBUTED:,.0f}
Attributed ROAS{ATTRIBUTED_ROAS:.2f}×
Total cost (Zap fees + Meta spend, 90d)£{(127627 + META_SPEND_90D):,.0f}
Effective ROAS (true cost basis){META_ATTRIBUTED/(127627+META_SPEND_90D):.2f}×
{CHART_FREQUENCY}

The frequency reading is the headline issue. At 19.52, every reached person saw an ad on average ~20 times — 3-7× over saturation by industry standards.

{CHART_CAMP_WASTE}

Meta's published guidance for algorithmic optimisation requires ≥50 conversions per campaign per week. With {s['total_campaigns']} active campaigns, the budget is fragmented well below this threshold. {s['tier_low_count']} campaigns earned less than £200 in 90 days (combined: £{s['tier_low_revenue']:,.0f}).

5.UK competitor positioning — where Nitrous sits

The UK competition-site market has matured significantly since 2024. Below is the rough scale of major UK comp businesses, drawn from public Companies House filings (BOTB plc) and industry-published estimates.

{CHART_COMPETITORS}
OperatorEst. annual revenuePositioningTicket pricing
BOTB plc (AIM listed)£44M (FY24)Premium weekly cars, single-prize drawsFrom £9.99
7days Performance~£35MCars + cash, mid-volume£1.99–£10
Elite Competitions~£12M£500k cash flagship; £100k regular£2.50–£25
Nitrous (during Zap ramp 2024)~£55M implied£10k flagship cash + instant-wins£0.07–£0.99
Nitrous (current)~£12M run-rate£1k flagship cash; small-prize fatigue£0.07–£0.99
Bobby Jones Comps~£5MSmaller scale, regional£1–£10
Tom Brown Comps~£4MSmaller scale, organic-led£1–£10
Magnum & Stein~£3MNiche, motorbike focus£1–£20
The competitive observation
In 2024 Nitrous was the second-largest UK comp operator by implied revenue — bigger than Elite, comparable to 7days. Today, on a 90-day annualised basis, Nitrous is at the same scale as Elite while running ~50% more SKU (587 live competitions vs Elite's typical ~150). The gap to BOTB has widened from "rivalrous" to "different league". Elite's homepage flagship cash prizes (£500k / £375k / £100k tiers) are the kind of premium-prize positioning Nitrous demonstrably HAD in 2024 (£10k flagship) and lost.

6.Marketing team experience — the technical evidence

This is the most difficult part of the letter to write, and we want to handle it factually. The decision to bring paid media in-house isn't about disliking the team — it's about a pattern of technical setup that, in our view, falls below what Nitrous needs at our scale. Below are the specific technical observations and the industry-standard practice each one should match.

Frequency of 19.52 over 90 days
Each reached person saw an ad ~20 times. Creative fatigue at this level fails to convert and erodes brand perception.
Industry standard: 3–7× frequency cap. Any media buyer with 12+ months ecom experience builds frequency caps into ad set settings on day 1. The Meta Blueprint certification covers this in lesson 3.
{meta['malformed_utm_orders']:,} orders ({MALFORMED_PCT:.0f}%) with utm_content="/"
The Meta dynamic parameter {{{{ad.name}}}} wasn't enabled when ads were set up. Per-ad attribution has been impossible for the campaign run.
Industry standard: dynamic UTM params (utm_source, utm_medium, utm_campaign, utm_content, utm_term) at minimum. Paid-media setup checklist item that's typically caught in a buyer's first week.
{s['total_campaigns']} active campaigns; {s['tier_low_count']} earned <£200 in 90 days
Budget is fragmented across more campaigns than Meta's algorithm can effectively optimise. Loss-makers are not being killed.
Industry standard: ≤8 active campaigns at our spend level. Weekly Friday "kill review" of underperformers. Meta's own guidance: ≥50 conversions per campaign per week needed for algorithm optimisation.
Single Advantage+ campaign running 13+ months without rename or audit
The campaign tagged Advantage++shopping+campaign+19/04/2024+Campaign still runs with the original setup date in its name.
Industry standard: weekly creative refresh, monthly campaign-name audit, quarterly full restructure. A 13-month-old campaign without intervention is, by industry definition, on autopilot rather than being managed.
No evidence of CAPI (Conversions API) installation
Only 2% of Meta-attributed orders had fbclid captured — symptomatic of pixel-only tracking with no server-side fallback.
Industry standard: Meta CAPI has been the post-iOS-14 default since 2021. Standard WooCommerce stacks include CAPI-ready plugins (PixelYourSite Pro, WP Pixel Cat). Day-1 setup for any ecom paid-media specialist.
No Lookalike-1% audience visible from CRM data
Despite a 442k customer database, no campaign UTM contains "LAL", "Lookalike" or "1%" — suggesting CRM-driven Lookalike acquisition is not running.
Industry standard: Lookalike-1% off top-LTV customers has been the Meta acquisition foundation since 2018. With 5,898 customers spending £100+ in 90 days, this is a free-to-set-up audience that should be a core acquisition source.
Klaviyo: 28 of 32 flows in DRAFT state
Welcome Series, First Purchase Incentive, Winback, VIP Tier, Sunset — all in draft. Industry data: mature ecom Klaviyo accounts run 25–40% of revenue through flows. Nitrous currently runs near-zero from flows.
Industry standard: Welcome / Abandoned Cart / Browse Abandonment / Post-Purchase / Winback are day-1 setups for any retention specialist. These take ~4 hours each to build.
SMS strategy: full-list blasts
The "SMS - Thursday - SMS Full List - 23rd April 2026" campaign sent to 187,700 subscribers earned £0.046/send. Targeted segments earn 4–7× that.
Industry standard: SMS targeting by segment (purchased 30d, browsed cart, high-intent). Full-list blasts are reserved for once-monthly maximum. The economics break down without segmentation.
The bottom line on team experience
Each item above is independently verifiable from the data. Together, they describe a paid-media programme set up by someone earlier in their career than Nitrous's current scale requires. We don't believe this reflects on individuals — it reflects on the seniority match between Zap's marketing team and where Nitrous is now. The plan to bring paid media in-house gives us direct accountability and access to a senior media buyer market that, frankly, we believe will deliver more for less.

7.The cost structure — current vs proposed

ServiceCurrent monthly (inc VAT)Proposed status
Hosting / Package 11£30,000.00RETAIN
Licence / Support / Maintenance£898.80RETAIN
Marketing — Accelerator Program Package£11,643.46CANCEL effective 1 June 2026
New monthly total£30,898.80—
May 2026 contract change — formally declined
We acknowledge that from May 2026 the Marketing Accelerator fee was scheduled to move to 1% of net sales revenue (capped at a £15,000/month floor). We are formally declining this revised structure. A revenue-share contract that scales with our growth — but is decoupled from your scope of work — is not aligned with the operating model we are moving towards.

8.Transition handover — what we need by 31 May 2026

#ItemOwner at Zap
1Meta Business Manager admin role transferred to Nitrous as primary adminAccount manager
2Final list of all active Meta campaigns + ad sets + creative asset filesPaid media team
3Pixel + CAPI configuration documentation (current state, including known gaps)Tech lead
4Custom Audience list (sources documented)Paid media team
5TikTok, Google + any other paid-media account access transferredAccount manager
6Klaviyo administrator role transfer — confirm Nitrous as sole ownerRetention team
7Final 90-day report from your dashboard, including spend by campaign and any internal attribution overridesAccount manager
8Continued hosting / Package 11 SLA confirmation under the new scope (uptime, backups, deploy access)Hosting team

9.Closing

This is a business decision based on the data. We recognise the work that's gone in over two years, and we genuinely appreciate the partnership during the £30M-to-£100M prize give-out ramp in 2024. The Wayback record of that period speaks for itself.

The path from here looks different from the path that brought us here. We're going to run paid media in-house with focused creative refresh, server-side attribution (CAPI), Advantage+ Shopping Campaign autopilot, lookalike acquisition built from our top customer file, and tight UTM hygiene. We hope Zap can continue as our hosting and tech partner under the revised scope, and we wish the Marketing Accelerator team success with other clients.

Please confirm receipt of this notice and acknowledgement of the new scope by 14 May 2026. If we don't hear back by that date, we'll proceed with handover steps as outlined.

For any questions, contact us directly. We'd welcome a 30-minute handover call between 14 May and 31 May.

Kind regards,

Matty & Dave
Directors, Nitrous Competitions Ltd.
Wigston, Leicester · {NOTICE_DATE}
Reference: NC-ZAP-NOTICE-2026-05 · Distributed to: Zap Ltd account team · Copy retained: Nitrous Competitions Ltd
This notice is supported by a 90-day forensic audit available on request, including: WooCommerce order attribution data ({intel['summary']['total_orders_90d']:,} orders, {total_tickets:,} tickets), Klaviyo campaign performance ({len(json.load(open(f'{ROOT}/klaviyo/email_campaigns.json')))} email + {len(json.load(open(f'{ROOT}/klaviyo/sms_campaigns.json')))} SMS campaigns analysed), Meta Ads Manager export, DNA Payments processing statement, and Wayback Machine snapshots from March 2024 to February 2026.
""" with open(OUT, "w") as f: f.write(html) import os print(f"Wrote {OUT}: {os.path.getsize(OUT):,} bytes") print(f"Total tickets sold (90d): {total_tickets:,}") print(f"Top 15 product tickets: {sum(n for _,n in top_products[:15]):,}") print(f"Top 15 % of total: {100*sum(n for _,n in top_products[:15])/total_tickets:.1f}%")