WhatsApp · Revenue forecaster

Forecast year-1 WhatsApp revenue in under two minutes.

Plug in your channel volumes — sessions, email, SMS, social, in-store — and we’ll model 12 months of revenue across the four streams a Merx-style WhatsApp programme drives.

Your inputs

Numbers stay in your browser — nothing is sent to a server.

Industry default: £60

Average monthly visitors to your store. WhatsApp click-through: 0.44% × 90% opt-in = 0.40%

Total email subscribers at year start. WhatsApp click-through: 0.42% × 90% opt-in = 0.38%

Active SMS opt-ins. WhatsApp click-through: 5.50% × 90% opt-in = 4.95%

Followers across Instagram, TikTok, etc. WhatsApp click-through: 0.38% × 90% opt-in = 0.34%

Footfall or QR-code scans in physical stores. WhatsApp click-through: 0.35% × 90% opt-in = 0.32%

How it works

Three steps, one slide-ready PDF

  1. Step 1

    Enter your inputs

    Pick your industry and currency, set AOV, and quote the five channel volumes — sessions, email, SMS, social, and store walkthroughs.

  2. Step 2

    See the 12-month breakdown

    The cohort engine runs four independent revenue streams across 12 monthly cohorts and surfaces the hero number plus a per-stream chart.

  3. Step 3

    Download the slide-ready PDF

    Export a one-page PDF with your inputs, the hero number, the chart, and a methodology link — ready to drop into a deck.

What each stream means

Methodology →

Three forecasting traps we see

  • The double-counted subscriber

    A naive forecast multiplies your list size by a welcome CVR and an abandon-recovery CVR and a broadcast CVR — and counts the same subscriber three times. The cohort engine attributes each customer purchase to exactly one stream by subtracting overlapping buyers between welcome, recovery, reorder, and broadcast before reporting the total.

  • The flat-ramp forecast

    A spreadsheet that says “you’ll add 4,000 subscribers in year 1” usually divides by 12 and reports a flat monthly number. Real opt-ins decay — your email list contributes a finite annual pool spread across a 60/25/10/5 tail, while your sessions and store footfall recur. The chart you see is the actual cohort shape, not a smoothed average.

  • The slider-cooked forecast

    Most calculators let users tune every input until the output matches what they wanted to see. We expose three sliders — sends per month, marketing investment, and AOV — because those are business decisions the e-com director actually controls. Welcome CVR, abandon rate, and churn stay locked to the per-industry priors so the model can’t be self-cooked.

Frequently asked questions

How are the conversion rates and uplifts sourced?
Welcome-flow CVRs come from the published Klaviyo e-commerce benchmark family. Cart-abandonment rates come from the Baymard Institute's long-running checkout research. Broadcast CVRs are anchored to the public benchmarks published by WhatsApp BSP vendors (Wati and Charles). The Merx-specific priors — AOV defaults, opt-in rates, recovery uplift, repeat uplift — come from our own customer cohort. Every prior is listed with its source on the methodology page.
What's the difference between this and the calculator on hellomerx.com?
The marketing-site calculator on hellomerx.com is a one-screen napkin sketch — it multiplies your monthly revenue by a single uplift number. This forecaster runs a 12-month cohort engine across four independent streams (welcome, recovery, reorder, broadcast), and it subtracts overlapping buyers between streams so the four numbers never double-count the same purchase. Two different tools, two different jobs.
Can I import data from Shopify or Klaviyo?
Not yet. The forecaster takes five channel volumes as direct inputs — sessions, email list, SMS subscribers, social followers, and store walkthroughs — which most operators can quote from memory. A future iteration may add an OAuth import for Shopify and Klaviyo, but we deliberately shipped the manual-input version first so the model is auditable end-to-end.
Why is there no slider for welcome CVR or abandon rate?
Those are model commitments, not inputs. The whole point of the forecaster is that the per-industry priors are independently sourced and stable; if we let every user re-tune them, the output would be a self-cooked forecast that tells you whatever you wanted to hear. The three sliders we do expose — sends per month, marketing investment, and AOV — are business decisions, not model parameters.
How does the four-stream split avoid double-counting?
Each cohort of new opt-ins enters the welcome stream once. The recovery stream applies an incremental WhatsApp uplift on top of the brand's existing cart-recovery flow, not the full recovered revenue. The reorder stream caps at a single reorder per cohort in year 1. The broadcast stream subtracts the buyers already attributed to the other three streams before applying its conversion rate. Each customer purchase is attributed to exactly one stream.
Does the forecast assume a particular WhatsApp tool or BSP?
No. The cohort engine is BSP-agnostic — the priors describe the channel, not a vendor. The model assumes you can run a welcome flow, recover abandoned checkouts, send broadcasts, and segment your list, which all major BSPs (Wati, Charles, Gupshup, Twilio, MessageBird, plus the Cloud API direct) support.
What currency is the forecast in? Is there FX conversion?
You pick GBP, USD, or EUR at the top of the form, and the result is rendered with that symbol. The engine itself is currency-agnostic — there's no FX conversion. The AOV you enter and the revenue you see are in the same currency, which is what almost every internal forecast needs.
Is my input data sent to a server or kept private?
The forecast itself runs entirely in your browser — your inputs and the result are computed client-side, with no upload. The only data we receive is what you submit through the lead form when you choose to download the PDF: your name, email, company, phone, and a snapshot of the inputs and result so we can follow up with a tailored conversation. See the privacy policy for retention details.

Want the methodology behind every number?

The forecaster gives you the number. The assumptions page shows every prior, every formula, and every citation source — so a finance lead can sanity-check the model before you put it in front of a CEO.

Read the methodology

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