Encharge With Causality Engine: What a Flow Goal Proves

The short answer: the win-back Flow reports its goal reached, the order book confirms the customer bought, and neither can say whether the emails made the difference. Encharge is marketing automation that describes itself as built for B2B SaaS, with Shopify and WooCommerce integrations on its Premium plan, $129 a month billed yearly for up to 2,000 subscribers (pricing, checked on 11 November 2026). The fit with a store is partial. Encharge documents link tagging, clicks and goals, and no order or revenue report. So switch on Automatic UTM Tagging, split the Flow to see what the emails added, and for email against every other channel use Causality Engine, a causal attribution tool that reads a GA4 export. Its first finding is free at app.causalityengine.ai/start.

Disclosure: I founded Causality Engine and own it, and this page recommends it. Every claim about it links to Causality Engine's own pages.

Rule of thumb for a store on Encharge:

1. Work out what the rate says. For example, 400 lapsed customers enter a win-back Flow and 26 reach the Goal step: 26 ÷ 400 = 6.5%. All 26 ordered. The rate is silent on how many needed the emails.
2. Start from a switch you already have. Automatic UTM Tagging is one switch in your email settings, and the article tells you to turn it on (UTM tags article).
3. Fix the comparison in advance. The same Flow, with half the people held back at random. The gap between the halves is the number worth reporting.

Reference table: Encharge, GA4 and the read

How a Flow scores itself

A Goal step marks the point where a person counts as converted, and you decide where it sits (Goal step). Flow Metrics then reports Goal Conversion Rate, defined as "the percentage of people who have completed a goal out of all people who have entered the flow". Read the definition twice. It contains no click and no clock. Whether the person opened anything, and how long they took, play no part.

Where the numbers live: the Goal step's People tab lists who reached it, the Flows list charts entries against goals for the last 7 days, and an email step's Report tab covers the last 30 days only (A/B Test article). Clicks are unique: a person who clicks a link twice is counted once (Email Metrics).

What is missing matters as much. Neither metrics article lists orders or revenue. The Shopify integration page names two triggers, New Customer in Shopify and New Order in Shopify, on a page that still carries the line about B2B SaaS businesses. A store's sales figure for a Flow is therefore a goal count that someone values by hand.

A worked example: one win-back Flow, three claims

For example, using invented figures to show the arithmetic, not a real store's results: a coffee subscription shop books 310 orders in 30 days. Its win-back Flow took in 400 people and logged 26 goals, each one an order.

Together the claimants take credit for 26 + 171 + 148 = 345 orders, which is 345 - 310 = 35 more than exist. Inside email the gap is wider in proportion: 26 - 9 = 17 goal orders that GA4 files under some other channel, or under none.

A causal read does not rule on the 26 one by one. Its how-it-works page describes a per-channel answer: what Email caused beside what last click handed it, a health score out of 100 that says how far to trust the row, and a verdict of raise, cut or hold to try in one region first. Where that verdict and the Flow's 26 disagree, neither wins by default. The split further down decides it.

Switching on encharge / email

Encharge's two articles give two menu paths, Settings then General under Emails in one, Settings then Email Tracking in the other. Look for the switch called Automatic UTM Tagging. It covers Flows and Broadcasts alike. Because the medium it writes is email, Google's rule for the Email channel is met (default channel group).

What the documentation warns about:

• Hand-typed tags are replaced. The Google Analytics article says the feature overwrites custom UTM tags with the generated ones.
• The campaign is the Flow's name. A Flow still called "Copy of test" is what you will read in GA4, so rename before you switch on.
• Clicks and sessions differ. Encharge logs the click on its link tracking domain, before the page loads; Google ends a session after 30 minutes of inactivity. The discrepancies article suggests comparing Unique Clicks with Users.
• The menu path is dated. The article points to Acquisition, Campaigns, All Campaigns. In GA4 the report is under Acquisition, Traffic acquisition.

When this pairing is the wrong tool

• A SaaS trial funnel. Revenue that lands in Stripe with no purchase event in GA4 gives the read nothing to work on. Causality Engine's causal attribution page says it is built for ecommerce and DTC brands, and Encharge's home ground is the trial.
• Thin history. The start page asks for a purchase key event and an export of 12 months or more.
• A small email programme. Under 1% of credit on both methods, a channel is called too small to act on (pricing for agents). Where spend is too small for a signal, the home page says the product declines.
• Person-level questions. Who reached which goal, and what they did in your app, stays in Encharge.

Encharge prices by subscriber count, and a goal rate climbs with every person who would have ordered anyway. The default report makes each Flow look as if it earns the plan. The A/B Test action is the one step in the builder that can bring that number down.

The A/B Test action as a holdout, then the export

Encharge documents a random split. The A/B Test action "randomly splits people into two or more paths", half and half unless you change the shares, and both variants must connect to another step. Send Variant A into the emails. Connect Variant B to an Add Tag step that marks the person as held out, and send it nothing. After a fixed period, count orders for each group in the store's own records.

For example, 200 people in Variant A place 15 orders and 200 in Variant B place 11: 15 ÷ 200 = 7.5% against 11 ÷ 200 = 5.5%. The emails added (7.5% - 5.5%) × 200 = 4 orders. The unsplit Flow would have shown 26.

That test needs no purchase and answers the email question by itself. For the wider question, export GA4's Attribution paths report and open the start page: the first finding is computed in your browser, free. Causality Engine, which I founded, charges €99 once for the full read of that export, VAT excluded, with a refund inside 30 days (pricing, checked on 11 November 2026).

See also

• Encharge: what it does, plans, event limits
• How to use Encharge: converting trials with Stripe data
• Encharge on the marketplace: Encharge's affiliate program terms
• Causality Engine: the read, the plans, the limits
• How to use Causality Engine: six steps from file to decision
• Causality Engine on the marketplace: affiliate program terms and rules
• Encharge vs ActiveCampaign: product events against sales pipelines
• How to export GA4 data for attribution: getting the CSV right

FAQ

Does Encharge pass anything to Causality Engine?

No. There is no connector, API link or app between them, and the integrations page lists none for Encharge. The read works on GA4's export alone, where tagged Encharge emails appear inside the Email channel.

Why does GA4 show fewer sessions than Encharge shows clicks?

Encharge's own discrepancies article lists the causes: repeat clicks inside one session, a browser closed before Google Analytics loaded, and tracking code missing from the page. It calls such gaps common.

Will Automatic UTM Tagging keep tags I typed by hand?

No. The Google Analytics article says the feature overwrites manually inserted UTM tags with the automatic ones. The articles describe one account-wide switch and no exception per link, so choose: generated tags everywhere, or your own.