Stop guessing which changesactually move revenue.
We've seen the challenges of a redesign that doesn't meet expectations before. Traffic dipped, conversions shifted, and no one can say whether the change helped or hurt. BLKDG provides A/B testing services that let data decide what ships.
Get Your Free Growth Audit
A free, no-obligation look at exactly why you aren't getting found or converting online, with a clear roadmap for how to fix it.
Why opinion-based optimization is costing you more than you think
Design by committee feels collaborative. In practice, it means the loudest voice or the most senior title decides what ships. No one measures the outcome. The change gets absorbed into the site and becomes the new baseline — whether it improved performance or degraded it. Over months and years, your site accumulates dozens of untested decisions. Some helped. Some hurt. The net effect is unknowable, and every new change starts from a baseline you cannot trust.
Why your analytics cannot tell you what your redesign actually did
You launched the new page and compared this month to last month. Conversions went up. Was it the redesign, the seasonal demand, the email campaign that drove extra traffic, or the paid media budget increase that happened the same week? Before-and-after comparisons cannot isolate the effect of a single change because too many variables shift between time periods. Only a controlled experiment — where a portion of traffic sees the original and a portion sees the variant at the same time — can attribute the outcome to the change itself. Without that, your analytics can describe what happened but never explain why.
The Optimization Problem Is a Revenue Problem
Changes Ship but Results Are Ambiguous
Your team redesigned the product page, updated the checkout flow, and rewrote the homepage copy. Metrics shifted in various directions, but no one can draw a straight line from any specific change to a specific outcome. The redesign might have improved things. It might have made them worse. The conversation moves on to the next project because there is no mechanism to settle the question. Every change goes live with uncertainty baked in.
Some of Your Changes Are Hurting Revenue and You Cannot See Which
Not every change improves performance. Industry data consistently shows that roughly half of all A/B tests produce no meaningful lift, and a meaningful percentage actively decrease conversions. When you ship changes without testing, you are statistically guaranteed to have deployed changes that are costing you money right now. The damage is invisible because you never measured it. Your conversion rate could be higher today if certain past changes had never shipped.
Your Competitors Are Compounding Wins While You Reset to Zero
A competitor running a disciplined A/B testing program ships three to five validated winners per month. Each winner lifts their baseline. The next round of tests starts from a higher conversion rate. Over 12 months, their compounding improvements create a gap you cannot close with a single redesign. They are not smarter. They are not more creative. They just test everything and only ship what works. That discipline compounds into a structural advantage that grows every month.
A testing program audit shows you exactly where untested changes are costing revenue and which experiments to run first.
We analyze your site through heatmaps, session recordings, funnel data, and analytics to identify the highest-impact testing opportunities. The output is a prioritized experiment roadmap ranked by potential revenue impact — not a generic list of best practices.
Three Pillars of a Testing Program That Compounds
Every Test Starts With Evidence, Not Opinion
We do not test random ideas. Every experiment begins with research — heatmap and session recording analysis, funnel analysis, user behavior data, and analytics review. We identify where users hesitate, abandon, or fail to convert, then form a hypothesis about why. The hypothesis predicts a specific outcome: "Changing X will improve Y by Z because the research shows..." That structure is what separates a testing program from random experimentation.
Statistically Valid Tests That Produce Trustworthy Results
We run A/B and multivariate tests with proper sample sizes, statistical significance thresholds, and test durations calculated before launch. Traffic is split randomly so external variables affect both groups equally. Results are read at the confidence level your business requires — not called early because the graph looks good on day three. When we declare a winner, you can trust it. When we declare no difference, that learning is equally valuable.
Winners Get Deployed. Losers Become Lessons. The Baseline Rises.
Every winning variant is implemented permanently into your site. Every losing variant is documented with the hypothesis, the data, and the learning it produced. The next round of tests starts from the new, higher baseline. Over time, this compounding effect creates a measurable gap between where your conversion rate was and where a program of disciplined testing has pushed it. That is the difference between one-off optimization and a CRO program that builds on itself.
A/B Testing by a Team That Treats Every Experiment as a Revenue Decision
Most agencies run tests. We run a testing program. The difference is that every experiment we design connects to a hypothesis, every result connects to a decision, and every decision connects to your revenue.
BLKDG builds and operates A/B testing programs for ecommerce and lead generation businesses. We do not hand you a test plan and disappear. Using advanced tools like Klaviyo & Optimizely, we run the research, form the hypotheses, build the variants, configure the experiments, monitor the results, and implement the winners. When testing reveals friction points that connect to deeper issues — checkout optimization, funnel leaks, or UX problems visible in session recordings — we fix those too. Testing is the method. Revenue growth is the outcome.
You Run the Business. We Run the Experiments That Grow It.
You should not need to calculate sample sizes, configure audience splits, or interpret confidence intervals. You should not be debating whether to call a test at 90 percent significance or wait for 95. That is our job.
A/B testing sits at the intersection of behavioral psychology, statistics, front-end development, and conversion strategy. It requires understanding why users behave the way they do, designing experiments that isolate a single variable, building variants that do not break the experience, and reading results with the discipline to wait for statistical validity. Hand us the optimization problem. We design the experiments, run the tests, and deliver results you can trust, along with the implemented changes that move your conversion rate.
What an A/B Testing Engagement Covers
Research and Hypothesis Development
Every testing engagement starts with behavioral research. We analyze heatmaps, session recordings, funnel data, and analytics to identify where users drop off, hesitate, or fail to convert. Each observation becomes a hypothesis with a predicted outcome. The output is a prioritized test backlog ranked by expected revenue impact — not a list of things that might be interesting to try.
A/B and Multivariate Test Design
We design experiments that isolate the variable being tested. For A/B tests, that means a single change between the control and the variant. For multivariate tests, that means a structured approach to testing multiple elements simultaneously while maintaining the statistical power to attribute results. Sample sizes are calculated before launch. Test duration is determined by traffic volume and the minimum detectable effect that matters to your business. Nothing is left to chance.
Variant Development and QA
Test variants are built to production quality, not quick mockups that break on mobile or introduce layout shifts. We develop variants that render correctly across devices, load without impacting page performance, and integrate cleanly with your existing analytics and tracking. A broken variant does not just invalidate the test. It costs you conversions on the traffic that saw it.
Experiment Execution and Monitoring
Tests are launched with proper traffic allocation, monitored for data quality issues, and checked for sample ratio mismatches that would invalidate results. We do not set and forget. We monitor experiments daily to catch technical issues early — broken tracking, uneven splits, or external events that contaminate results. If a test needs to be paused or adjusted, we catch it before bad data accumulates.
Statistical Analysis and Decision Framework
Results are analyzed against pre-determined significance thresholds. We report the confidence level, the effect size, the revenue impact projection, and whether the result is actionable or inconclusive. Inconclusive results are not failures — they are data that informs the next hypothesis. We never call a winner early because the graph looks promising. We never declare a loser without the statistical power to confirm it.
Winner Implementation and Program Iteration
Winning variants are implemented permanently into your production site. The new baseline is documented. The test results, hypotheses, and learnings are cataloged in a testing knowledge base that informs every future experiment. Each test cycle makes the next one smarter.
Not sure what to test first? That is exactly what the research phase answers.
We analyze your site behavior data, identify the highest-impact testing opportunities, and build a prioritized experiment roadmap. Every test idea is tied to a hypothesis and ranked by potential revenue impact.
Research. Test. Ship Winners.
We Research Your Site to Find the Highest-Impact Opportunities
We analyze heatmaps, session recordings, funnel data, and analytics to understand where users are dropping off, where they hesitate, and where the friction lives between your traffic and your conversions. Every observation is turned into a hypothesis with a predicted outcome. The output is a prioritized experiment backlog: specific tests ranked by expected revenue impact, with the research evidence behind each one. This is the foundation of every A/B testing engagement we run — and the reason our tests produce actionable results instead of ambiguous data.
We Run Controlled Experiments With Statistical Rigor
Every test is designed with a calculated sample size, a pre-determined significance threshold, and a test duration based on your traffic volume and the minimum detectable effect that matters to your business. Traffic is split randomly so external factors affect both groups equally. We monitor experiments daily for data quality issues and never call a result before it reaches statistical validity. When we say a variant won, the data supports it. When we say a variant lost, that learning is just as valuable for the next experiment.
We Ship Winners and Build on Every Result
Winning variants are implemented permanently into your production site. The conversion rate improvement becomes your new baseline. Losing variants and inconclusive results are documented with the full hypothesis, data, and learning — feeding the next round of experiments with evidence about what your audience responds to and what it does not. Each testing cycle starts from a higher baseline than the last. Over six months, the compounding effect of validated wins creates a measurable gap between where your conversion rate was and where disciplined testing has pushed it. That is the program your competitors are running while you are debating opinions.
Before and After
The Cost of Letting Opinion Decide What Ships
Research consistently shows that a significant percentage of site changes have no positive effect or actively decrease conversions. If you have shipped 20 changes in the past year without testing, simple probability says several of them made things worse. Those changes are on your site today, suppressing your conversion rate by an amount you cannot quantify because you never measured it. Your <a href="/conversion-rate-optimization-audit">conversion rate</a> is lower than it should be and you have no way to identify which changes are responsible.
A business running a testing program ships three to five validated winners per month. Each winner raises the baseline. Over 12 months, those compounding improvements create a structural advantage in conversion rate, revenue per session, and customer acquisition cost. You are competing against that compounding effect with a site built on untested opinions. The gap widens every month.
Every visitor who lands on your site and does not convert represents a missed opportunity. When your conversion rate is suppressed by untested changes, you need more traffic to hit the same revenue target. That means higher ad spend on <a href="/conversion-funnel-analysis-services">the same funnel</a>, more investment in SEO for the same return, and a higher customer acquisition cost across every channel. Testing is the multiplier that makes all of your traffic acquisition more efficient.
A redesign costs time, money, and organizational energy. Without testing, you cannot prove that the new version performs better than what it replaced. If you cannot measure the ROI of the redesign, the next one is even harder to justify. Over time, stakeholders lose confidence in optimization because no one can demonstrate that previous investments produced a return. Testing changes that dynamic entirely.
Before you ask.
Every test starts with research, not opinion. We analyze heatmaps, session recordings, and funnel data to identify where users drop off, hesitate, or fail to convert. Each observation becomes a hypothesis with a predicted outcome. The output is a prioritized experiment backlog ranked by expected revenue impact. The first test is always the one with the highest potential return, not the one that is most convenient to build.
Test duration is calculated before launch based on your traffic volume and the minimum detectable effect that matters to your business. Low-traffic sites need longer test windows to reach statistical validity. High-traffic sites can produce conclusive results in one to two weeks. We set the duration upfront and do not call results early because the graph looks promising on day three, which is the most common way agencies produce false positives.
An A/B test isolates a single variable — one element changed between a control and a variant. A multivariate test changes multiple elements simultaneously and uses statistical modeling to attribute the outcome to each element. A/B tests are simpler to run and easier to interpret. Multivariate tests are more efficient when you need to understand how multiple changes interact and you have enough traffic to detect effects across more combinations. We choose the format based on your hypothesis and traffic volume, not a preference for complexity.
An inconclusive test is not a failed test. It tells you the change you made does not have a meaningful effect on your specific audience — which is valuable information. That learning gets documented in a testing knowledge base and informs the next hypothesis. We never ship a variant based on a directional trend that has not reached statistical significance, and we never declare a test inconclusive before it has the traffic to produce a real answer.
Yes. We run experiments across Shopify and custom ecommerce platforms. For Shopify, we work within the testing tools that integrate cleanly with the platform without introducing layout shifts or tracking gaps that would contaminate results. Every variant we build is tested across devices and browsers before it goes live — a broken mobile experience on a test variant does not just invalidate the experiment, it costs you conversions on the traffic that saw it.
Each winning variant ships permanently and becomes the new baseline. The next round of tests starts from a higher conversion rate. Over six months, a disciplined testing program — three to five validated wins per quarter — creates a gap between your current conversion rate and where you started that no single redesign could achieve. Losing tests and inconclusive results get cataloged too, because what does not work for your audience is just as valuable as what does. The program gets smarter with every experiment.