Atticus Li shares five real enrollment flow A/B tests from NRG Energy that collectively projected over $1M in annual revenue — with exact metrics, behavioral mechanisms, and the decision frameworks behind each test. This is the "show your work" post.
Why I'm Publishing Real Numbers
Most experimentation content on the internet falls into two categories: theoretical frameworks with no data, or case studies with vague results like "significant improvement in conversions." Neither is useful to a practitioner who needs to understand what a real test looks like from hypothesis to revenue projection.
I'm publishing exact metrics from five enrollment flow experiments because I believe the experimentation field gets better when people share real work. These tests ran across NRG Energy's retail brands — Reliant, Green Mountain Energy (GME), and Direct Energy — during my time building the experimentation program to 150+ total experiments.
For each test, I'll share: the hypothesis, the behavioral mechanism that informed it, the results, the revenue projection methodology, and what I'd do differently in retrospect. All five followed Atticus Li's PRISM Method — Probe, Revenue Rank, Implement, Score, Multiply.
The Pre-Work That Makes Tests Succeed
Before diving into the five experiments, I need to explain what happens before a test gets greenlit. This pre-work is what separates tests that generate insight from tests that waste traffic.
Minimum Detectable Effect (MDE) calculations: For every test, I calculate the smallest lift we'd need to detect given the available traffic and test duration. If the MDE is 15% but we're hypothesizing a 3% lift, the test can't detect the expected effect. It shouldn't run.
Revenue per customer projections: Each brand has a known average revenue per enrolled customer. This lets me translate a conversion rate lift into annual dollar impact before the test even starts. If the projected impact doesn't justify the test slot, we test something else.
Behavioral hypothesis: Every test starts with an observed behavior, not an opinion. "I think this page should look different" is not a hypothesis. "Session replay data shows 35% of users abandon the enrollment flow at step 3, with dead-click analysis suggesting confusion about form fields" is a hypothesis.
This pre-work takes 2-4 hours per test. It's the highest-ROI time I spend because it prevents bad tests from consuming limited traffic.
Experiment 1: Streamlined Enrollment Flow (Reliant)
Brand: Reliant Energy Hypothesis: The existing enrollment flow contains unnecessary steps, redundant form fields, and visual distractions that create friction for users who have already decided to enroll. Removing these elements will reduce abandonment and increase enrollment completions.
Behavioral mechanism: Contentsquare session replay analysis revealed a pattern I've seen at every company I've worked with: users who had already selected a plan were dropping off during the enrollment form. They weren't comparison shopping anymore — they had made their decision. But the form was asking them to re-confirm information, navigate through unnecessary steps, and deal with visual noise that slowed them down.
The behavioral principle here is cognitive load reduction. Once a user has committed to a decision, every additional step is friction that gives them a reason to reconsider or postpone. The hypothesis was grounded in specific observations: users were pausing at form fields that asked for information already captured in earlier steps, and back-button usage spiked at the review screen, suggesting users were unsure whether they'd entered everything correctly.
What we changed: We removed redundant form fields, eliminated an unnecessary confirmation step, reduced visual distractions on the enrollment screens, and streamlined the progress indicator to make the remaining steps feel shorter.
Results:
- 12% lift in enrollment confirms (p < 0.001, 99% Bayesian probability)
- ~100 additional enrollments in 46 days
- Projected annual revenue: ~$299K
Why this worked: The lift came almost entirely from reducing abandonment in the middle of the flow, not from increasing traffic to the flow. Users who entered the enrollment process were already high-intent. The friction wasn't preventing interest — it was preventing completion. Removing that friction converted existing demand into revenue.
What I'd do differently: I would have run a more granular test — isolating the form field reduction from the step elimination to understand which change drove the majority of the lift. The bundled approach gave us a clear winner but limited our ability to prioritize future optimizations within the flow.
Experiment 2: Homepage Acquisition Phone Number + Mobile Sticky Nav (Green Mountain Energy)
Brand: Green Mountain Energy Hypothesis: GME's customer base includes a significant segment that prefers phone enrollment over digital. Making the phone number more prominent on the homepage and adding a sticky mobile navigation bar will increase call-driven enrollments without cannibalizing web enrollments.
Behavioral mechanism: This hypothesis came from an unexpected data intersection. Call center data showed that a meaningful percentage of enrollments happened via phone. Web analytics showed that users who eventually called often visited the website first — they used the site for research but preferred the phone for the actual transaction.
Cross-referencing these datasets revealed that GME's customer demographic skewed older than other NRG brands. This segment was comfortable browsing online but trusted a phone conversation for a purchase decision that involved a long-term energy contract. The phone number on the existing homepage was small, in the header, and disappeared when you scrolled on mobile.
What we changed: We placed a prominent, high-contrast phone number in the hero section. We added a utility selector widget to the homepage. And we implemented a sticky navigation bar on mobile that kept the phone number visible regardless of scroll position.
Results:
- 300% lift in incremental call sales
- 948 incremental sales in 57 days
- Projected annual revenue: ~$523K
Why this worked: This is the test that taught me the most about assumptions. The entire digital optimization industry is focused on reducing friction in online flows. But for this customer segment, the lowest-friction path to enrollment was picking up the phone. The website's job wasn't to convert them online — it was to give them the information they needed and make it dead simple to call.
The 300% lift in call sales did not come at the expense of web enrollments. Web enrollment rates held steady. This was pure incremental revenue from a channel we'd been accidentally suppressing by making the phone number hard to find.
What I'd do differently: I would have instrumented call tracking from day one of the test instead of relying on the call center's existing tracking. Better attribution data would have given us more confidence in the incremental nature of the call lift.
Experiment 3: HBE Free Copy and Visual Improvements (Direct Energy)
Brand: Direct Energy Hypothesis: The home bundle enrollment (HBE) page's copy and visual design were confusing users about the value proposition, particularly on mobile. Redesigning the copy to emphasize the "free" benefit and improving visual hierarchy would increase mobile enrollment completions.
Behavioral mechanism: Dead-click analysis in Contentsquare told the story here. Users were tapping on elements that weren't interactive — specifically on copy blocks that described pricing benefits. This is a strong signal that users were trying to learn more about something that interested them but couldn't. The design was creating interest it wasn't fulfilling.
Additionally, heatmap data showed that mobile users weren't reaching the CTA below the fold. They were reading the copy, getting confused or losing interest, and leaving. The copy itself was technically accurate but used industry language that required energy market knowledge to parse.
What we changed: We restructured the copy to lead with the clearest benefit statement — "free" — in plain language. We improved visual hierarchy so the eye naturally moved from headline to benefit to CTA. We reduced the amount of copy above the fold to ensure the CTA was visible without scrolling on most mobile devices.
Results:
- 60% mobile enrollment lift
- 70 incremental enrollments in 36 days
- Projected annual revenue: ~$177K
Why this worked: The original page was optimized for completeness — it included every detail about the home bundle offer. The new page was optimized for action — it included just enough information to get a user who was interested to take the next step. The 60% mobile lift (versus a smaller desktop lift) confirmed that the problem was primarily a mobile layout issue where critical content was hidden below the fold.
What I'd do differently: I'd run a follow-up test that isolated copy changes from visual changes. The 60% lift was compelling, but we couldn't attribute it cleanly to the copy rewrite versus the visual restructuring. Understanding which lever drove more of the lift would inform how we approach other brand pages.
Experiment 4: Hero Layout Optimization (Green Mountain Energy)
Brand: Green Mountain Energy Hypothesis: Restructuring the hero section on mobile to prioritize product information and CTA visibility will increase enrollment starts and improve the path from hero engagement to the product comparison chart.
Behavioral mechanism: Heatmap data showed a clear problem on mobile: users were not scrolling past the hero section. The hero occupied the entire viewport on most phone screens, and the CTA to explore plans was below the fold. Users who did scroll showed high engagement with the product chart, suggesting the content below the hero was effective — it just wasn't being seen.
This is a classic "first screen" problem. On desktop, the hero and the first section of content are both visible without scrolling. On mobile, the hero takes up everything, and users have to actively scroll to discover that there's more to the page. If the hero doesn't compel a scroll or provide a visible CTA, you lose users who never see your best content.
What we changed: We restructured the hero layout on mobile to reduce its viewport footprint, ensuring that the top of the next section (including the plan comparison CTA) was visible without scrolling. We also adjusted the visual hierarchy within the hero to direct attention toward the primary CTA.
Results:
- 7% secondary lift in enrollment starts
- 35% improvement in product chart views-to-enrollment rate
- Projected annual revenue: ~$212K
Why this worked: The 7% lift in enrollment starts came from more users seeing and clicking the CTA. The 35% improvement in chart-to-enrollment rate was the more interesting finding — it suggested that the hero restructuring didn't just drive more traffic to the product chart, it drove higher-quality traffic. Users who engaged with the new hero layout were more purchase-ready by the time they reached the product chart.
This is why Atticus Li's PRISM Method emphasizes looking beyond the primary metric. The primary metric (enrollment starts) showed a modest improvement. The secondary metric (chart-to-enrollment rate) revealed the deeper behavioral shift: the new layout was qualifying users, not just directing them.
What I'd do differently: I'd test progressive disclosure on the hero — showing a teaser of plan pricing directly in the hero to eliminate the scroll gap entirely. The current test proved the concept; a follow-up could optimize it further.
Experiment 5: Product Chart Value Prop CTAs (Green Mountain Energy)
Brand: Green Mountain Energy Hypothesis: Adding value proposition CTAs directly in the product comparison chart will increase enrollments by reducing the cognitive gap between plan evaluation and enrollment action.
Behavioral mechanism: The product chart was where users made their plan selection decision. Analytics showed strong engagement with the chart — users compared plans, toggled between options, and spent significant time on the page. But the enrollment CTA was separated from the chart. Users had to finish evaluating plans, scroll past the chart, and find the enrollment button.
This separation created a cognitive gap. Users were in evaluation mode while looking at the chart. By the time they reached the enrollment CTA, they had left evaluation mode and needed to re-motivate themselves to take action. The hypothesis was that embedding CTAs with value prop messaging directly in the chart would capture users at their moment of peak interest.
What we changed: We added value proposition CTAs — brief messaging highlighting key benefits — directly within the product comparison chart, adjacent to each plan option. The CTA appeared in context, while the user was actively evaluating that specific plan.
Results:
- 3% enrollment lift
- 35% improvement in product chart-to-enrollment rate
- Projected annual revenue: ~$86K
Why this worked: The 3% overall enrollment lift was modest, but the 35% improvement in chart-to-enrollment rate told the real story. Users who were actively comparing plans converted at a dramatically higher rate when the CTA was embedded in their evaluation context. The value prop messaging served as a bridge between "I'm comparing" and "I'm enrolling."
This test illustrates a principle I come back to repeatedly: small percentage lifts on high-volume pages translate to meaningful revenue. A 3% lift sounds unimpressive in a case study. $86K in projected annual revenue sounds like a business decision worth making.
What I'd do differently: I'd test different value prop messages by plan type. The winning variant used the same messaging framework across all plans. Tailoring the value prop to the specific differentiator of each plan (price for the budget plan, renewable sourcing for the green plan) could compound the effect.
What Ties All Five Experiments Together
Looking across these five tests, several patterns emerge that inform how I prioritize and design experiments:
Pre-test analysis prevented waste: Every test had MDE calculations confirming the test could detect a meaningful lift. Every test had revenue-per-customer projections establishing that the potential impact justified the test slot. None of these tests were shots in the dark — they were calculated bets with known risk parameters.
Behavioral mechanisms identified before testing: I didn't test changes and then figure out why they worked. I identified the behavioral problem first — through session replays, heatmaps, dead-click analysis, and cross-referencing with call center data — and then designed interventions targeting that specific behavior. This is what separates hypothesis-driven experimentation from random variant testing.
Revenue projection as the universal language: Every test result is tied to projected annual revenue. This is what makes the experimentation program legible to finance. The CFO doesn't care about conversion rate lifts. The CFO cares about "$1.2M in projected annual revenue from five tests." That's the number that grows the program, the team, and the tools.
The PRISM Method as connective tissue: Atticus Li's PRISM Method provided the framework for every test. Probe (behavioral analysis), Revenue Rank (MDE and revenue projections), Implement (test design and deployment), Score (statistical analysis and segmentation), Multiply (scaling winners and informing the backlog). The consistency of the framework is what made it possible to run 100+ tests per year — each test follows the same process, reducing the cognitive overhead of managing a high-velocity program.
Cumulative impact compounds: Any single test on this list is a solid win. But the real value is the portfolio. Five tests projecting $1.2M+ collectively is the kind of number that changes the conversation with executive stakeholders. It moves experimentation from "that thing the analytics team does" to "a core revenue lever." And that shift is what enables the program to keep growing — more budget, more test slots, better tools, eventually more headcount.
The Invitation
If you're running experiments and not tying every test to revenue projection, you're leaving program growth on the table. The tests themselves might be excellent. But without financial framing, the people who control budget won't understand why your work matters.
I'm happy to walk through how to build revenue-per-customer models for your experimentation program, or how to structure your executive reporting to drive buy-in. Reach out at [email protected].
The data is only as valuable as the decisions it enables. Make sure the decision-makers can see what you see.