Run more tests, learn faster, and boost impact with these 6 GenAI tactics you can apply today.
GenAI Quick-Win Playbook Experimentation for
Run more tests, learn faster, and boost impact with these 6 GenAI tactics you can apply today.
Introduction
Modern businesses thrive on fast, data-driven experimentation to outpace competition.
Research shows
that
teams running more tests often see greater success, and generative AI (GenAI) can make it dramatically
cheaper and faster to create and validate new experiment ideas .
Traditional A/B testing is powerful but slow – designing variations, crafting hypotheses, coding experiments,
and analyzing results can take weeks. GenAI changes the game by acting as your always-on co-pilot,
speeding up each step from hypothesis to insight.
This playbook offers platform-agnostic, AI-powered tactics to boost testing velocity and decision speed. You
can apply these with public AI tools like ChatGPT, Claude, Google’s Gemini, or your private internal models, to
turbocharge your experimentation program while staying compliant with your organization’s data privacy
and AI policies.
(These tactics apply across e-commerce, retail, travel, media, or any industry where rapid experimentation
and learning matter.)
GenAI Quick-Win Playbook for Experimentation
1
Tactic 1:
Rapid Hypothesis Generation with AI
What it is:
Use GenAI to brainstorm and refine experiment ideas and
hypotheses in minutes. Instead of long ideation sessions,
an AI assistant can propose creative, data-driven test
ideas on the fly.
How to use it:
Provide AI with context about your business goal or
challenge, and ask for possible hypotheses or A/B test
ideas. For better results, include relevant (non-sensitive)
data or user research insights so AI can tailor its
Example Prompt:
suggestions. It can generate a list of potential changes
“You are a conversion rate optimization expert.
to test, each with a rationale.
Our product page has a high bounce rate.
Based on best practices, suggest 5 A/B test
ideas to improve engagement, with a brief
hypothesis for each.”
GenAI Quick-Win Playbook for Experimentation
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Tactic 1: Rapid Hypothesis Generation with AI
When to use:
Tips:
This tactic is ideal at the ideation stage – when your
•
Give the AI some constraints or focus areas
team needs fresh ideas or when you’re not sure what to
(e.g. “ideas for mobile users” or “low-cost
test next. Also useful when optimizing a specific metric
changes”) to get more relevant suggestions.
(e.g., add-to-cart rate) and want AI to propose
•
Use a refinement loop. After AI suggests ideas,
targeted, hypothesis-driven changes.
ask follow-up questions on the most promising
ones (e.g. “How would that improve UX?”) to
Why it works:
further develop the hypothesis.
AI can tap into a wide knowledge base of proven
•
Keep human insight in the mix. Use the AI’s
strategies, sparking ideas you might miss. In fact,
ideas as a springboard in team discussions,
modern AI systems can even suggest targeted
not a final decision. Human intuition plus AI
experiments based on your data and goals, essentially
breadth makes for better hypotheses.
automating the brainstorming process. You’ll still want to
vet the AI’s ideas for feasibility and impact, but this
approach ensures you start with a strong list of
hypotheses aligned to your objectives.
GenAI Quick-Win Playbook for Experimentation
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Tactic 2: Automated Variant Generation (Copy, Design, & More)
Example Prompt:
What it is:
“Our current headline is: ‘Discover the Future of
Use GenAI to create multiple content or design variations
Travel Planning’. Generate 3 alternative
for your experiments quickly. This includes generating
headlines that are shorter and create urgency,
alternative headlines, product descriptions, images (with
to A/B test which gets more sign-ups.”
image-generation models), layout suggestions, or even
code snippets for UI changes – all without a dedicated
creative team or developer for each variant.
How to use it:
Once you have a hypothesis (e.g. “a more concise headline
will increase signup”), ask the AI to produce several versions
of the content or element you want to test. For text changes,
provide the original copy and describe the tone or
approach for variants. For design or layout, you can
describe the current state and the desired change (the AI
can suggest HTML/CSS or design ideas in words). You can
even have the AI generate entire experiment recipes – for
example, suggesting multiple page variations at once.
GenAI Quick-Win Playbook for Experimentation
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Tactic 2: Automated Variant Generation (Copy, Design, & More)
Tips:
•
Be specific in prompts:
e.g., “Generate a lighter,
friendly version of this text” or “Suggest a
completely different layout for the signup form.”
When to use:
The more detail on what kind of change you
want, the better the variants.
Employ AI variant generation during the experiment design
phase, right after you decide what to test. It’s especially useful
•
Quality-check and edit:
AI content is a first
when you need many creative options fast – for instance, testing
draft. Review generated copy for brand
personalized messages for different audiences, or localizing
compliance, tone, and factual accuracy
content to different regions without hiring translators. In high-
(especially for product details). Edit any quirks
velocity testing, AI can supply a steady stream of variations so
before deploying to real customers.
you’re never stuck waiting for new creative.
•
Use multi-modal AI for design:
Some advanced
Why it works:
tools (or internal models) can generate images
GenAI (especially large language models) excels at producing
or design mockups from text descriptions. If
natural-sounding text in different styles, tones, and languages.
available, leverage these to get visual
It can mimic your brand voice or even a famous personality if you
variations (e.g., different hero banner images)
prompt it to, ensuring variations stay on-brand. AI doesn’t get writer’s
to test, accelerating graphic design work.
block – you can instantly get tagline alternatives, UI text tweaks, or
color/theme suggestions.
According to industry experts
, today’s AI
•
Don’t forget edge cases:
Ask AI to also suggest
can go beyond one-off suggestions and recommend complete
a “radical” variation outside your usual comfort
experiments with multiple variations built-in optimizely.com. This
zone – this could surface an out-of-the-box
means when the AI suggests a test idea, it can provide the specific
idea that yields a surprising win. Just ensure it’s
variants to try, saving your team creative effort.
still practical to implement.
GenAI Q uick- W in P laybook for Experimentation
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Tactic 3: Experiment Design & Setup Guidance
What it is:
Have the AI assist in planning your experiment setup – from
identifying the right success metrics to estimating sample
size or duration. Think of it as an on-demand experiment
advisor that helps ensure your test is well-designed and
statistically sound before you launch.
How to use it:
Describe your experiment idea and ask the AI what metrics or
KPIs to track, or how to set up the test. For example, you might
prompt the AI with your hypothesis and ask: “What metrics
should I measure, and what audience segment and test
Example Prompt:
length would you recommend for reliable results?” While a
“We plan to test a new checkout flow vs. the
public AI won’t perform precise power calculations, it can
current flow. Draft an experiment design: list
suggest considerations (e.g. “use conversion rate as primary
the hypothesis, primary metric, guardrail
metric, ensure a few thousand visitors per variant, run at least
metrics (like time on page or customer
2 weeks to account for weekly cycles”). If you have internal
support contacts), and an estimate of how
data, you could input simplified stats (traffic, current
long to run the test with ~10,000 users/week.”
conversion rate, desired lift) and ask the AI to estimate
needed sample size or highlight if the test might run too long.
GenAI Quick-Win Playbook for Experimentation
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Tactic 3: Experiment Design & Setup Guidance
Tips:
•
Ask the AI to explain its reasoning.
For example,
if it suggests “run the test for 3 weeks,” have it
explain why. This helps you learn and also verify
When to use:
if the advice makes sense.
Use this tactic before launching an experiment, after you have a
•
Use AI to generate a pre-test checklist:
hypothesis and variants. It’s great for teams less familiar with
e.g., “List 5 things to verify before starting this
experimentation best practices, or whenever you’re unsure about
test (tracking, segmentation, etc.).” This
test parameters. It’s like having a statistician or seasoned A/B
ensures you cover all setup steps (like QAing
testing expert on call to sanity-check your plan.
that both A and B variants render correctly).
Why it works:
•
Combine with human expertise:
If you have a
GenAI has ingested vast knowledge on experimentation and
data scientist, have them review the AI’s
statistics. It can guide you through selecting the right metrics,
recommendations. The AI can do the heavy
audience size, and duration for confident results optimizely.com.
lifting on routine computations or recalling best
For instance, it might warn you (as a human expert would) if your
practices, freeing your experts to focus on
chosen metric is too rare to reach significance, or suggest a more
critical decisions.
sensitive metric. It brings up factors like metric relevance,
•
Remember AI’s limits:
For precise statistical
statistical power, and business alignment, which are key to good
needs (like exact sample size calculation),
experimental design. Essentially, AI can help prevent common
you’ll still use traditional formulas or tools. Use
mistakes such as testing too short, picking a misleading metric, or
AI for directional guidance and education
forgetting to isolate variables.
rather than final authority on stats.
GenAI Quick-Win Playbook for Experimentation
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Tactic 4: AI-Powered Experiment Orchestration & Execution
Example Prompt:
What it is:
“We have 3 major experiments (details below)
Use AI to streamline the execution phase of experimentation –
but limited staff. Suggest an optimal rollout plan
from prioritizing which tests to run first, to automating some
(order and timing) to run all three as quickly as
development tasks, and even monitoring experiment status.
possible without quality issues. Include
This tactic treats the AI as a project manager or engineer that
reasoning.”
helps you run more tests with less manual effort.
How to use it:
If you have a backlog of experiment ideas, feed a summary of them to the AI and ask it to prioritize based on potential impact,
effort, likelihood of success and opportunity cost. For example, “Here are 5 test ideas with their goals and estimated impact; in
what order should we run them and why?” The AI can objectively assess and recommend an order (e.g. run the high-impact,
low-effort ones first). It can also identify if certain tests conflict (overlapping audience or page) and suggest a sequencing to
avoid interference.
For execution, you can leverage AI to automate development or content tasks. For instance, ask the AI to generate code snippets:
“Write a JavaScript snippet to change the ‘Add to Cart’ button color to blue on variant pages.” or “Provide HTML/CSS for a new
layout where the image is on the left of text.” Even if you’re not a developer, the AI’s output can jump-start implementation which
you then hand to engineering or use in your testing tool. Additionally, AI can help schedule or coordinate tests: you might instruct
it to create a calendar or plan (e.g. “Generate a timeline to run these 3 experiments this quarter without overlap”).
GenAI Quick-Win Playbook for Experimentation
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Tactic 4: AI-Powered Experiment Orchestration & Execution
When to use:
Apply this during the planning and execution phase – after ideas are generated and designed, and before/during
development. It’s particularly useful for large organizations managing many experiments across teams, or small teams
trying to maximize output with limited dev help. If you ever feel bottlenecked by engineering bandwidth or overwhelmed
by scheduling, AI orchestration can help break the logjam.
Why it works::
AI can analyze multiple constraints and objectives faster than a human, offering a neutral perspective on prioritization.
It adds an “unquestionable level of objectivity” by, for example, assessing past test outcomes to predict which new tests
might win. Moreover, by letting AI handle routine tasks, you free up human resources. Studies found teams get the highest
impact when each engineer isn’t overloaded with too many tests – AI can help you reach that sweet spot by doing some
heavy lifting (like producing template code or content, or even auto-launching a test at a scheduled time). Some
experimentation platforms even integrate AI to automatically launch subsequent tests when a prior one ends, and to
analyze results instantly. While full hands-off automation should be used carefully, these capabilities mean you can run
higher-velocity test programs with the same team size.
GenAI Quick-Win Playbook for Experimentation
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Tactic 4: AI-Powered Experiment Orchestration & Execution
Tips:
•
Always QA AI-generated code or test configurations in a safe environment. The AI might produce functional code
90% of the time, but subtle bugs or mismatches with your tech stack could occur. Treat it as a junior developer’s work –
review and test it.
•
Use AI to draft experiment documentation — for example, prompts like "Write a one-page test plan for X experiment" or
"Create a results template report." This ensures every test is well-documented without burdening the team.
•
If your organization runs experiments across departments (product, marketing, etc.), use AI as a knowledge hub.
Team members can query a chat-style AI, “How do I set up a proper test on the pricing page?”, to get instant answers.
This reduces repeated questions and training, enabling more people to execute tests correctly (democratizing
experimentation).
•
Monitor performance. If you let AI auto-pilot parts of execution (say, auto-implement simple changes), keep an eye on
key metrics and system logs. You want a human in the loop for critical junctures (e.g., final “go live” decision or rolling
back a test if metrics tank unexpectedly).
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Tactic 5: AI-Assisted Results Analysis & Insights
What it is:
Example Prompt:
After or during an experiment, use GenAI to analyze the
“Our test results: Variant A – 5% conversion,
performance and translate results into plain-language insights.
Variant B – 6% conversion (20% lift, p=0.03).
This includes summarizing which variant won and by how
Bounce rate dropped 5% on B. Analyze these
much, explaining statistical significance, uncovering patterns in
results and suggest what we should do next.”
subgroups, and even suggesting follow-up actions – all in an
easily digestible format for stakeholders.
How to use it:
Provide the AI with the key results of your experiment. This could be absolute numbers or a summary: for example, “Variant A
conversion rate 12.5%, Variant B 13.7%, p-value 0.04, B is 10% higher on conversions.” You can paste in more detailed metrics or
observation notes if available (e.g. “mobile users responded better than desktop”). Then ask the AI to interpret the results and
draw conclusions. A prompt could be: “Here are the A/B test results... Summarize the outcome and highlight any insights, as if
reporting to a product manager. Include whether the result is statistically significant and actionable.” The AI will then generate a
narrative: e.g., “Variant B increased conversions by 10%, which is statistically significant, suggesting the new checkout flow is likely
better. Notably, the improvement was biggest for mobile users, indicating our changes resonated more on small screens. Next,
we should consider rolling this out or testing on other pages…”
The AI can also answer ad-hoc questions about the data: “Did any segment perform differently?” (if you provide segment data)
or “What might be reasons Variant B outperformed A based on the data?” Essentially, it can serve as a data analyst that speaks
human language.
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Tactic 5: AI-Assisted Results Analysis & Insights
When to use:
Use AI analysis the moment you have results in hand. This is great for rapidly turning raw data into a first report or for exploring
the data for patterns. It’s also helpful in live experiment monitoring – e.g., mid-test, feed partial data to AI to see if it notices
trends (but be careful to not act on interim analysis without statistical confidence). When you need to present results to non-
technical stakeholders, AI can draft the narrative for you, saving time on report writing.
Why it works:
Interpreting experiment data can be complex, especially for those not fluent in statistics. GenAI excels at explanation and
summarization. It can translate data into clear takeaways (“Variant B increased conversions by 15%”) and highlight what
worked best (“The simplified form drove the biggest impact”). It’s like having an analyst who instantly writes the “so what?”
of the test. AI can also suggest next steps (“Test this form design on other landing pages”), connecting the dots from result
to action. By catching hidden patterns (maybe noticing, say, that new users reacted differently than returning users if you
provided that data), it ensures you don’t miss insights that could inform future experiments. This accelerates decision speed –
teams can go from data to decision in hours instead of days.
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Tactic 5:
AI-Assisted Results Analysis & Insights
Tips:
•
Double-check the summary against the data.
AI might occasionally misstate a number or misinterpret significance.
Always verify critical claims (e.g., whether something is truly statistically significant) with your analytics source.
•
Use the AI to create different outputs for different audiences.
A detailed analysis for the team, a one-paragraph executive
summary for leadership, and even a catchy headline for an internal newsletter (“Test X increased sign-ups by 10%,
driving $Y in potential annual revenue.”). This way, communication is tailored but consistent, and you only spent minutes
generating it.
•
Ask for visual suggestions.
While a text-based AI won’t create charts, it can recommend what kind of chart or table would
best illustrate the results (“a bar chart comparing conversion rates with error bars for confidence intervals”). This can
guide you or a designer to quickly produce supporting visuals.
•
Leverage AI to combine experiment data with other context.
For example, “Our A/B test showed a 10% lift. How does this
compare to typical results in our industry?” The AI might know or infer that 10% is quite high for, say, retail e-commerce
tests, which you can use to add color in your report.
GenAI Quick-Win Playbook for Experimentation
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Tactic 6: ROI Estimation & Business Impact Forecast
What it is:
Example Prompt:
Use GenAI to connect your experimentation efforts to business
Our experiment improved click-through rate
value. This tactic involves asking AI to help estimate the Return
from 4% to 5%. Calculate the potential ROI:
on Investment (ROI) or potential impact of a test (before or
baseline 1 million impressions/month,
after you run it), translate results into dollars or key business
conversion rate 10%, avg value $30. Include
KPIs, and even draft a business case for scaling a successful
assumptions and any risks.”
experiment or personalization. Essentially, AI helps quantify
“why this test matters” in financial or strategic terms.
How to use it:
Before running a test, you might supply the AI with assumptions (baseline conversion rate, traffic, average order value, etc.) and
have it project outcomes. For example: “If our new feature increases conversion by 5% (from 10% to 10.5%) on 100,000 monthly
visits with $50 average order, estimate the annual revenue lift and ROI if implementation cost is $20,000.” The AI can outline the
math and give a rough estimate (in this case, roughly $300k extra revenue/year). It will also list assumptions or factors (e.g.
“assuming traffic remains constant and the lift is sustained”). This turns abstract percentages into concrete value, supporting
prioritization decisions.
After an experiment, do the same to calculate realized impact: “Variant B won. Based on actual +2% absolute lift on conversion
(from 10% to 12%), what is the projected impact on quarterly revenue?” The AI will crunch the approximation and possibly
compare it to costs. Additionally, you can ask the AI to frame the results as an ROI narrative: “Help me write a brief for executives
on why this experiment’s result is important.” It might respond with: “This change is expected to drive an additional $X in sales
next quarter, a 20x return on the implementation investment, demonstrating the high ROI of data-driven experimentation.”
GenAI Quick-Win Playbook for Experimentation
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Tactic 6: ROI Estimation & Business Impact Forecast
When to use:
Use AI for ROI analysis before pitching or implementing a test change, and right
after you have results to summarize impact. It’s especially valuable when you
need to justify experimentation resources to finance or leadership – tying
experiments to revenue, cost savings, or customer lifetime value. In enterprise
settings, this helps speed up decision-making on whether to roll out a tested
change (by showing the payoff) or whether a particular personalization
initiative is worth the effort.
Why it works:
AI is great at synthesizing data points into a coherent story. It can do back-of-the-
envelope calculations and, more importantly, articulate the business significance.
Many enterprise leaders prioritize AI projects that deliver measurable value – by
having the AI explicitly connect an experiment to value, you ensure your testing
program speaks the language of the business. This tactic also uncovers factors
affecting ROI. For example, the AI might note, “If the lift only applies to new users, the
overall revenue impact will be lower,” or “Ensure no increase in cost per acquisition,
so net ROI remains high.” These pointers are gold when assessing an experiment’s
true impact. Plus, AI’s ability to quickly iterate scenarios (e.g. “What if the lift is half of
expected?”) allows you to do sensitivity analysis with minimal effort.
GenAI Quick-Win Playbook for Experimentation
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Tactic 6:
ROI Estimation & Business Impact Forecast
Tips:
•
Provide realistic input data.
The quality of ROI estimates depends on accurate assumptions (traffic, conversion rates,
margins, etc.). If you’re unsure, ask colleagues or use analytics to get the numbers, then feed them to the AI.
•
Use ranges.
Have the AI calculate best-case, expected-case, and worst-case impact. E.g., “if uplift is 2% vs 5% vs 8%” –
this helps decision-makers understand the spectrum of outcomes.
•
Cross-verify any calculations.
Large language models can sometimes make arithmetic mistakes or misinterpret
percentages. It’s wise to double-check critical calculations with a calculator or spreadsheet. Treat the AI’s math as
helpful draft work that you will verify.
•
Emphasize strategic benefits.
ROI isn’t only dollars – you can ask the AI to list qualitative impacts (e.g., “improved user
experience, strengthens brand loyalty, learning can be applied to other pages”). This ensures your business case covers
both tangible and intangible benefits.
•
Keep enterprise compliance in mind if using real financial data in a prompt (see the checklist on Page 17). If needed,
round or anonymize figures when using a public model, or use a secure internal model for this analysis.
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Data Privacy & AI G overnance Ch ec k list
Before you unleash GenAI on your experimentation workflow, run through this checklist to ensure privacy, security, and
compliance are in check:
Align With Your Organization's AI Policy: Verify that using AI in this manner adheres to your company’s AI usage
guidelines. Many large firms have policies on which AI tools are approved, data handling requirements, and review
processes. Get approval if needed and choose tools (public or internal) that are sanctioned for enterprise use.
Protect Sensitive Data: Never input confidential or personally identifiable information (PII) (customer names, emails, etc.)
into a public AI tool. Anonymize or aggregate experiment data before sharing with an external model. For example, use
relative metrics (“Variant B had +5% lift”) instead of raw user counts. If you must use customer data to personalize with AI,
opt for a self-hosted or private model where data stays within your controlled environment.
Use Privacy-Friendly Settings: If using public LLM services, enable features that prevent data retention. For instance,
OpenAI allows users to turn off chat history (so prompts aren’t used to train the model) – use such features. Ensure any
vendor contracts include data privacy clauses (no storing or re-using your data). Data privacy missteps can derail AI
initiatives – 21% of failed enterprise AI projects cite data privacy issues as a cause, so lock this down upfront.
E ns u re I n f or m ation Sec u rity: Treat AI like any other software from a security perspective. Avoid sharing code or URLs that
could expose system vulnerabilities unless you’re using a trusted internal tool. If you have an internal generative model,
keep it behind your firewall and enforce access controls. Monitor AI usage for any unusual activity – for example, if an AI
integration is making external requests, ensure they’re expected and secure.
GenAI Quick-Win Playbook for Experimentation
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Data P rivacy & AI G overnance Checklist
Compliance and Legal Check: If you operate in regulated industries (finance, healthcare) or regions with strict laws
(GDPR, CCPA), consult your legal team before using AI on certain data. Ensure that any automated content generation
still complies with advertising standards, accessibility requirements, and so on. For example, if AI writes product copy, it
should not inadvertently make false claims or violate regulations – incorporate legal review where appropriate.
Human Oversight and Quality Control: Maintain a human-in-the-loop for all critical decisions and content. AI can
accelerate work, but humans must verify outputs. Establish a process to review AI-generated hypotheses, variations,
and analysis for sense-checking. Watch out for AI “hallucinations” (confident but incorrect statements) – especially in
analysis or ROI calculations. Always validate conclusions with real data.
Documentation and Traceability: Keep records of how you used AI in experimentation. Save important prompts and
the AI’s outputs (especially any analysis that influenced a decision). This documentation helps with transparency – if a
result is questioned later, you can show the supporting AI-generated insight and that it was reviewed by a human. It
also aids in auditing the process for compliance and learning what works best with the AI.
B ias and E thical Considerations: GenAI may carry biases from its training data. When using it to generate content for
experiments or personalization, review outputs for bias or sensitive issues (e.g., make sure language works for all
demographics and doesn’t inadvertently exclude or offend). Ensure your use of AI aligns with your company’s ethical AI
guidelines – for instance, if your brand has rules about diversity and inclusion in messaging, check AI outputs against them.
By following this checklist, you can harness the speed of GenAI safely and responsibly. The goal is to supercharge your
experimentation program without compromising ethics, security, or result integrity. With the guardrails above, you’re set to
innovate at high velocity while keeping trust and compliance intact.
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This is J ust the S tart
You’ve unlocked six quick-win tactics—now imagine what could be achieved by wiring these same capabilities into your
day-to-day tools, training them on your business context and customer data.
This type of investment could allow you to:
Automate variant generation based on your brand voice
•
Build and test ideas in minutes, not days or weeks
•
Analyze results instantly, segmented by customer cohorts
•
Our clients are already seeing dramatic results:
•
A Fortune 100 financial services client increased their AI model testing velocity by 300% by leveraging advance analytics
techniques
•
A B2B sales team leveraged GenAI to build sales assets automatically from CRM data, reducing human time by 50x and
costs by 13%
•
A global travel & hospitality brand increased cross sell unit sales by 21% by leveraging AI-powered product recommendations
•
A health and beauty retailer increased email open rates by more than 40% and drove millions in incremental online revenue
by transitioning to AI-powered product recommendations
Contact Us
Ready to accelerate the impact of your experimentation program with generative AI? Let’s talk.
Concord | concordusa.com
952-241-1090
info@concordusa.com
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