Your product page probably does a decent job explaining what you sell. But is it answering the questions customers are asking themselves and never typing into a chat widget? Adrian Stewart, co-founder of Scale Messaging, has a framework for finding those gaps, and it can change the way you approach A/B testing your messaging.

In this episode of IntelliJAMS, Alex and Adrian dig into the "Frequently Unasked Questions" framework: four categories of questions shoppers silently ask themselves while browsing your site. They cover where most Shopify brands fall short on messaging, why reducing friction is easier than building motivation (but both matter), when urgency tactics actually work versus when they erode trust, and how to build hypotheses around messaging that you can test and learn from.

Timestamps:
0:00 - Intro and the Frequently Unasked Questions framework
0:44 - Why "unasked" questions matter more than FAQs
1:57 - The four categories: understanding, motivation, difference, trust
4:26 - Motivation vs. friction and how they drive behavior
6:35 - Why motivation is harder to build than friction is to reduce
7:40 - Urgency as a bonus category (and when it gets hacky)
9:56 - Fake scarcity vs. real scarcity: the windscreen wiper example
11:22 - Difference: competing within your range, against competitors, and against inertia
14:09 - How to figure out which unasked questions to prioritize
17:13 - Message, expression, and placement: the three layers of testing
19:02 - Why one test usually leads to five more questions
20:15 - Where to start: trust is the fastest win, difference is the biggest opportunity
24:07 - Recap and where to find Adrian

Topics covered:

The Frequently Unasked Questions framework (understanding, motivation, difference, trust)

Why "difference" is the most overlooked messaging gap on product pages

The motivation-to-friction ratio and how it affects conversion

When urgency and scarcity tactics help vs. hurt your brand

How traffic source (paid social vs. search) should shape your messaging strategy

Building messaging hypotheses you can A/B test

Message vs. expression vs. placement as three layers of experimentation

Why trust is the fastest win for most e-commerce brands

Ready to start testing your messaging? Join GEM Academy for free courses and a community of brands sharing what works: https://www.skool.com/intelligems-academy-1535

Connect with Adrian / Scale Messaging:

Website: https://scalemessaging.com
LinkedIn: https://www.linkedin.com/in/adrianjstewart/

Connect with Intelligems:

Website: https://intelligems.io
Blog: https://intelligems.io/blog
LinkedIn: https://www.linkedin.com/company/intelligems

Post-purchase upsells sit at the exact moment a customer has already committed — credit card swiped, conversion done. That means there's zero risk of hurting conversion and pure upside potential for your AOV.

In this episode, we break down why post-purchase might be the highest-leverage Shopify A/B testing opportunity most brands haven't explored yet.
In this episode of IntelliJAMS, Adam and Alex explore the economics of post-purchase upsells — from a CBD brand that grew AOV 20% overnight to the consumption psychology that makes "buy more now" actually better for long-term LTV.

Timestamps:
0:00 - Intro
0:22 - Why post-purchase upsells are worth your attention
1:04 - "Be close to the money" — does post-purchase qualify?
1:52 - What brands are doing with post-purchase today
2:11 - The CBD gummies case study: 20% AOV lift overnight
3:32 - Why there's zero downward pressure on conversion
5:05 - The consumption psychology surprise: more supply = faster consumption
6:09 - Scarcity vs. abundance mindset and second-order effects
8:04 - Three post-purchase upsell strategies that work
9:35 - Matching upsell strategy to your margin profile
10:26 - Using post-purchase for inventory clearance and sell-through
11:02 - How Intelligems handles post-purchase testing and measurement
12:28 - Why post-purchase tests can run in parallel with everything else

Topics covered:

The economics behind post-purchase upsells (zero incremental CAC, no conversion risk)

A real-world CBD brand case study: 50% off second pack, 40% take rate, 20% AOV increase

Three post-purchase strategies: same product discount, complementary products, and clearance/inventory sell-through

Why consumption scales with supply — and what that means for repurchase rates

How to match your upsell strategy to your margin profile

Running post-purchase tests in parallel without interaction effects

How Intelligems measures incrementality, revenue, and profit on post-purchase offers

Want to start testing post-purchase upsells (or anything else)? Join GEM Academy for free courses and a community of brands sharing what works: https://www.skool.com/intelligems-academy-1535

Connect with Intelligems:

Website: https://intelligems.io
Blog: https://intelligems.io/blog
LinkedIn: https://www.linkedin.com/company/intelligems

Running 10 tests a month sounds productive, but if none of them are tied to a real business question, you're just taking swings for the sake of swinging. In this episode Adam and Alex dig into why "how many tests should I run?" is often the wrong question, and what to ask instead when you're building an experimentation program on Shopify.

In this episode of IntelliJAMS, Adam and Alex explore the difference between test output and test outcomes, how to run concurrent tests without confounding your results, and why elevating the conversation from "number of tests" to "strategic priorities" changes everything.

Timestamps:
0:00 - Intro
0:27 - The question everyone's asking right now
0:52 - Why "how many tests" is the wrong question
2:23 - Outputs vs. outcomes: reframing productivity
3:10 - Test the plan, don't plan the tests
3:51 - You can't predict how many tests it'll take
4:54 - Running concurrent tests without confounding results
5:53 - Scoping metrics to the right part of the funnel
6:48 - Every test needs a hypothesis
7:30 - One test often leads to five new questions
8:15 - Real example: ending a checkout upsell test early
9:11 - How to push back on "I need 10 tests this month"
11:49 - Elevating the conversation from output to strategy
13:28 - Final thoughts: see the forest for the trees

Topics covered:

Why counting tests is an output metric, not an outcomes metric

How to reframe testing around strategic business goals

Running concurrent A/B tests on Shopify without confounding data

Segmenting tests by funnel stage and visitor type

How one test can lead to five new questions

Pushing back when stakeholders demand a test quota

Real-world example: killing a checkout upsell test 24 hours in

The "test the plan, don't plan the tests" framework

Want to sharpen your experimentation skills? Join GEM Academy for free courses and a community of brands sharing what works: https://www.skool.com/intelligems-academy-1535

Website: https://intelligems.io
Blog: https://intelligems.io/blog
LinkedIn: https://www.linkedin.com/company/intelligems