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Abstract

In an increasingly data-driven world, businesses seek to enhance their strategies and performance through effective optimization methods. One such method is A/B testing, a potent tool enabling the comparison of different versions of products or services to determine superior performance. This research paper delves into the fundamentals of A/B testing and its potential to drive improved outcomes. By investigating user funnels and journeys, opportunities for improvement emerge, forming the foundation for hypothesis development. The hypothesis, a crucial element, involves educated conjectures driven by data, research, or experience, closely linked to specific problems or opportunities. Designing a robust test includes power analysis for sample size calculation, as well as considerations such as randomization, control variables, and appropriate statistical analyses. Subsequently, the paper delves into statistical tests like t-test, z-test, and chi-squared test, determining the statistical significance of observed differences. Interpretation of A/B test outcomes involves statistical significance, effect size, user behavior analysis, practical significance, and replicability. The paper concludes by envisioning the role of AI in reshaping A/B testing, automating tasks, processing real-time data, and testing multiple hypotheses efficiently. This revolution offers data professionals unparalleled insights and possibilities for the future
IJARSCT ISSN (Online) 2581-9429
International Journal of Advanced Research in Science, Communication and Technology (IJARSCT)
International Open-Access, Double-Blind, Peer-Reviewed, Refereed, Multidisciplinary Online Journal
Volume 3, Issue 1, August 2023
Copyright to IJARSCT DOI: 10.48175/IJARSCT-12486 550
www.ijarsct.co.in
Impact Factor: 7.301
Optimizing Product Choices through A/B Testing
and Data Analytics: A Comprehensive Review
Shashank Agarwal
Senior Decision Scientist, CVS Health, Chicago, IL, USA
ORCID: 0009-0003-7679-6690
Abstract: In an increasingly data-driven world, businesses seek to enhance their strategies and
performance through effective optimization methods. One such method is A/B testing, a potent tool enabling
the comparison of different versions of products or services to determine superior performance. This
research paper delves into the fundamentals of A/B testing and its potential to drive improved outcomes. By
investigating user funnels and journeys, opportunities for improvement emerge, forming the foundation for
hypothesis development. The hypothesis, a crucial element, involves educated conjectures driven by data,
research, or experience, closely linked to specific problems or opportunities. Designing a robust test
includes power analysis for sample size calculation, as well as considerations such as randomization,
control variables, and appropriate statistical analyses. Subsequently, the paper delves into statistical tests
like t-test, z-test, and chi-squared test, determining the statistical significance of observed differences.
Interpretation of A/B test outcomes involves statistical significance, effect size, user behavior analysis,
practical significance, and replicability. The paper concludes by envisioning the role of AI in reshaping A/B
testing, automating tasks, processing real-time data, and testing multiple hypotheses efficiently. This
revolution offers data professionals unparalleled insights and possibilities for the future
Keywords: A/B testing, Digital Products, Artificial Intelligence, Hypothesis Testing, Business Intelligence
I. INTRODUCTION
As the world becomes increasingly data-driven, businesses and organizations constantly look for ways to optimize their
strategies and improve their performance.
AB testing is a powerful tool in their arsenal, which allows them to test different product or service versions to see
which performs better. In this research paper on AB testing, we'll explore the basics of this powerful technique and how
it can drive better results and outcomes for your business.
Are you a marketer, product manager, or business owner? Then understanding AB testing can help you make more
informed decisions and achieve greater success.
II. WHAT IS A/B TESTING AND WHEN TO USE IT?
A/B testing, or split testing, is a technique used to compare two versions of a product or service to determine which one
performs better. This is done by dividing your audience into two clusters and showing each group a different version of
IJARSCT ISSN (Online) 2581-9429
International Journal of Advanced Research in Science, Communication and Technology (IJARSCT)
International Open-Access, Double-Blind, Peer-Reviewed, Refereed, Multidisciplinary Online Journal
Volume 3, Issue 1, August 2023
Copyright to IJARSCT DOI: 10.48175/IJARSCT-12486 551
www.ijarsct.co.in
Impact Factor: 7.301
the product or service. You can then measure which version leads to better results, such as higher engagement, more
conversions, or increased revenue.
A/B testing is particularly useful when you want to make data-driven decisions and optimize your strategies. For
example, if you're launching a new website, you might want to test different designs, layouts, or copies to see which
version leads to higher engagement and conversions. Or, if you're running a marketing campaign, you might want to
test different messaging, offers, or calls to action to see which generates more leads or sales.
III. USER FUNNEL AND USER JOURNEY
User funnel and user journey are important considerations in A/B testing, as they help to identify areas of potential
improvement and guide the testing process.
When conducting A/B tests, it's important to consider how different product or service variations will impact the user
funnel and user journey. For example, suppose you're testing a new website. In that case, you might want to consider
how the changes will impact the user's journey through the site, from initial navigation to completing a purchase.
Similarly, suppose you're testing different marketing messages or calls to action. In that case, you'll want to consider
how those variations impact the user funnel and whether they are more effective at driving conversions at each stage.
By focusing on the user funnel and journey, you can identify areas of potential improvement and create hypotheses for
testing. For example, suppose you notice users dropping out of the funnel at a certain stage. In that case, you might
hypothesize that a different approach or message would be more effective at keeping them engaged and moving toward
conversion.
3.1 Choice of Primary/Success Metric
The choice of primary/success metric is a critical consideration in A/B testing, as it determines the criteria by which
you'll evaluate the success or failure of the test.
It should be connected to your business goal: The primary/success metric should be tied directly to the
business goal or objective you're trying to achieve with the test. For example, if your goal is to increase
revenue, your primary/success metric might be total sales or revenue per user. If your goal is to increase user
engagement, your primary/success metric might be time spent on site or the number of page views per session.
Meaningful and measurable: It's important to choose a primary/success metric that is both meaningful and
measurable. This means that the metric should be tied to a specific business outcome and that you should be
able to collect and analyze data on that metric reliably and accurately.
Consider the secondary metrics: In addition, it's important to consider secondary metrics as well. While the
primary/success metric should be the main criterion for evaluating the test, secondary metrics can provide
additional insights and help identify potential improvement areas.
IV. THE HYPOTHESIS OF THE TEST
A hypothesis is a statement that defines what you expect to achieve through an A/B test. It's essentially an educated
guess that you make based on data, research, or experience. The hypothesis should be based on a specific problem or
opportunity that you've identified, and it should propose a solution that you believe will address that problem or
opportunity.
For example, let's say you're running an e-commerce website and notice that the checkout page has a high abandonment
rate. Your hypothesis might be: "If we simplify the checkout process by removing unnecessary fields and reducing the
number of steps, we will increase the checkout completion rate and reduce cart abandonment."
The hypothesis should be specific, measurable, and tied directly to the primary/success metric you chose for the test.
This will enable you to determine whether the test was successful or not based on whether the hypothesis was proven or
disproven.
IJARSCT ISSN (Online) 2581-9429
International Journal of Advanced Research in Science, Communication and Technology (IJARSCT)
International Open-Access, Double-Blind, Peer-Reviewed, Refereed, Multidisciplinary Online Journal
Volume 3, Issue 1, August 2023
Copyright to IJARSCT DOI: 10.48175/IJARSCT-12486 552
www.ijarsct.co.in
Impact Factor: 7.301
V. DESIGN OF THE TEST (POWER ANALYSIS)
The design of an A/B test involves several key components, including sample size calculation or power analysis, which
helps to determine the minimum sample size required to detect a statistically significant difference between the two
variations.
Power analysis is important because it ensures you have enough data to confidently detect a meaningful difference
between the variations while minimizing the risk of false positives or negatives.
To conduct a power analysis, you'll need to consider several factors, including:
the expected effect size (the size of the difference you expect to see between the variations)
the level of statistical significance you want to achieve (typically 95% or 99%)
the statistical power you want to achieve (typically 80% or higher)
Using this information, you can calculate the minimum sample size required to achieve the desired level of statistical
power.
In addition to power analysis, the design of the A/B test should also include considerations such as randomization
(ensuring that users are randomly assigned to each variation), control variables (keeping all other variables constant
except for the one being tested), and statistical analysis (using appropriate statistical methods to analyze the results and
determine statistical significance).
VI. CALCULATION OF SAMPLE SIZE, TEST DURATION
Calculating the appropriate sample size and test duration for an A/B test is important in ensuring that the results are
accurate and meaningful. Here are some general guidelines and methods for calculating sample size and test duration:
Sample size calculation: The sample size for an A/B test depends on several factors, including the desired
level of statistical significance, statistical power, and the expected effect size. Multiple online calculators can
be used to estimate the sample size
Test duration calculation: The test duration is determined by the number of visitors or users needed to reach
the desired sample size. This can be calculated based on the website or app's historical traffic data or estimated
using industry benchmarks. Once you have the estimated number of visitors or users needed, you can calculate
the test duration based on the website or app's average daily traffic or usage.
Balancing sample size and test duration: It's important to balance sample size and test duration, as
increasing the sample size will typically increase the test duration and vice versa. It's also important to ensure
that the test runs for a sufficient amount of time to capture any potential seasonal or day-of-week effects.
VII. STATISTICAL TESTS (T-TEST, Z-TEST, CHI-SQUARED TEST)
When conducting A/B testing, statistical tests determine whether the observed differences between the two variations
are statistically significant or simply due to chance. Here are some commonly used statistical tests in A/B testing:
T-test: A t-test is a statistical test that compares the means of two samples to determine whether they differ
significantly. It is commonly used when the sample size is small (less than 30), and the population standard
deviation is unknown.
Z-test: A z-test is a statistical test that compares the means of two samples to determine whether they differ
significantly. It is commonly used when the sample size is large (greater than 30), and the population standard
deviation is known.
Chi-squared test: A chi-squared test is a statistical test used to determine if there is a significant association
between two categorical variables. It is commonly used when the variables are independent, and the sample
size is large.
These tests help to determine the probability that the observed differences between the two variations are statistically
significant and not simply due to chance.
A significance level (typically 0.05 or 0.01) is set beforehand, and if the calculated p-value is lower than the
significance level, the observed differences are considered statistically significant.
IJARSCT ISSN (Online) 2581-9429
International Journal of Advanced Research in Science, Communication and Technology (IJARSCT)
International Open-Access, Double-Blind, Peer-Reviewed, Refereed, Multidisciplinary Online Journal
Volume 3, Issue 1, August 2023
Copyright to IJARSCT DOI: 10.48175/IJARSCT-12486 553
www.ijarsct.co.in
Impact Factor: 7.301
VIII. VALIDITY CHECKS
Validity checks are an important aspect of A/B testing and involve ensuring that the test results are valid and
meaningful. Here are some common validity checks used in A/B testing:
Pre-test data analysis: Before conducting an A/B test, it's important to analyze the pre-test data to ensure that
the two variations are similar regarding important variables. This helps reduce the risk of confounding
variables affecting the test results.
Randomization: Randomization is the process of randomly assigning users to each variation. This helps to
ensure that any observed differences between the variations are not due to differences in the characteristics of
the users.
Control variables: Control variables are variables that are kept constant across both variations. This helps to
ensure that any observed differences are due to the tested variable rather than other variables that may affect
the results.
Statistical analysis: Appropriate statistical analysis is necessary to ensure the test results are valid and
meaningful. This includes using appropriate statistical tests, setting appropriate significance levels, and
conducting appropriate sample size calculations.
Post-test data analysis: After the test, it's important to analyze the post-test data to ensure the results are valid
and meaningful. This includes checking for statistical significance, analyzing user behavior data, and checking
for unexpected results or anomalies.
IX. RESULT INTERPRETATION
Interpreting the results of an A/B test is a critical step in using the test to make informed decisions. Here are some
important considerations for interpreting the results of an A/B test:
Statistical significance: The first step in interpreting the results of an A/B test is to determine whether the
observed differences between the two variations are statistically significant. This involves conducting
appropriate statistical tests and comparing the p-value to the significance level.
Effect size: The effect size measures the magnitude of the observed differences between the two variations. A
large effect size indicates a large difference between the variations, while a small effect size indicates a small
difference. The effect size can be calculated using various methods, such as Cohen's d or Hedges' g.
User behavior data: Analyzing user behavior data to understand how the variations affect user behavior is
important. This includes click-through rates, conversion rates, and revenue per user. It's important to look at
the overall differences between the variations and any differences in user behavior across different segments
(such as different traffic sources or user demographics).
Practical significance: While statistical significance is important, it's also important to consider the practical
significance of the results. This involves considering the cost and feasibility of implementing the changes, the
potential impact on user experience and engagement, and the overall business goals and objectives.
Replicability: Finally, it's important to consider whether the results of the A/B test are replicable. This
involves considering factors such as the stability of the test results over time, the potential impact of external
factors such as seasonality or changes in user behavior, and the robustness of the statistical analysis.
X. LAUNCH / NO LAUNCH DECISION
The decision to launch or not launch a variation based on the results of an A/B test is critical. Here are some important
considerations for making this decision:
Statistical significance: The first and most important consideration is whether the observed differences
between the two variations are statistically significant. Suppose the p-value is less than the significance level
(usually set at 0.05). In that case, the differences are considered statistically significant, and you can
confidently decide based on the test results.
IJARSCT ISSN (Online) 2581-9429
International Journal of Advanced Research in Science, Communication and Technology (IJARSCT)
International Open-Access, Double-Blind, Peer-Reviewed, Refereed, Multidisciplinary Online Journal
Volume 3, Issue 1, August 2023
Copyright to IJARSCT DOI: 10.48175/IJARSCT-12486 554
www.ijarsct.co.in
Impact Factor: 7.301
Effect size: Even if the results are statistically significant, it's important to consider the effect size. A small
effect size may not justify the cost and effort of implementing the changes, while a large effect size may make
the changes a no-brainer.
User behavior data: Analyzing user behavior data to understand how the variations affect user behavior is
important. This includes click-through rates, conversion rates, and revenue per user. It's important to consider
the overall differences between the variations and any differences in user behavior across different segments
(such as different traffic sources or user demographics).
Practical considerations: When making a launch/no-launch decision, it's important to consider practical
factors such as the cost and feasibility of implementing the changes, the potential impact on user experience
and engagement, and the overall business goals and objectives.
Risks: Finally, it's important to consider potential risks of launching the variation. For example, there may be
technical or operational risks, or there may be risks associated with changing the user experience in a
significant way.
A/B testing is a powerful tool for optimizing digital products and services, and it can provide valuable insights into user
behavior and preferences. To conduct an effective A/B test, it's important to have a clear hypothesis, a well-designed
test, and a robust statistical analysis.
Additionally, choosing the right primary metric, calculating the appropriate sample size, and conducting validity checks
are critical steps in ensuring the accuracy and reliability of the results.
Interpreting the results of an A/B test requires careful consideration of various factors, including statistical significance,
effect size, user behavior data, practical significance, and replicability. Ultimately, the decision to launch or not launch
a variation based on the results of an A/B test should be based on a careful analysis of all of these factors, as well as
practical considerations and potential risks.
XI. FUTURE CONSIDERATIONS
10. 1 Role of Artificial Intelligence in revolutionizing A/B testing
Modern marketing is undergoing a transformation driven by new technologies, particularly AI. This shift is
revolutionizing the way we conduct tests and gather data. This is hardly surprising considering the overwhelming
volume of data we encounter, necessitating the assistance of artificial intelligence.
AI, or artificial intelligence, has been a transformative influence across various industries, including marketing. Its
potential impact on A/B testing is substantial. By integrating AI with A/B testing, the scope expands beyond mere
comparisons to encompass sophisticated analyses.
AI-powered A/B testing platforms excel at processing extensive real-time data, identifying intricate trends and patterns
that might elude human perception, and offering more accurate predictions of future outcomes.
AI introduces automation to A/B testing, liberating your time while ensuring optimal marketing decision-making. The
era of dedicating prolonged hours to test setups, data analysis, and adjustments is over. Instead, you can focus on
devising impeccable strategies guided by AI-generated insights. This advantage is particularly beneficial in the fast-
paced, competitive realm of AI marketing.
AI also facilitates the execution of advanced and precise A/B tests, leading to a deeper comprehension of your audience
and yielding superior marketing results. Embracing AI in your marketing pursuits opens up novel possibilities and
elevates your approach to an unprecedented level.
A. AI testing plays a vital role in automation
Efficiency and error reduction are crucial. Modern tools automate repetitive tasks, allowing marketers to concentrate on
complex responsibilities. AI automates processes and handles data collection, reporting, and insightful analysis.
AI-driven platforms initially require human oversight but become more self-sufficient with time, demanding less
supervision and input for valuable outcomes.
International Journal of Advanced
International Open-
Access, Double
Copyright to IJARSCT
www.ijarsct.co.in
Impact Factor: 7.301
B.
Collect and analyze vast amounts of real
AI-
driven testing tools stand out because they can gather and process large re
capabilities. These systems can test multiple hypotheses and tailor individual experiences using machine learning
algorithms.
For marketers, this means the chance to broaden segmentation, targeting various customer groups
messaging and campaigns. AI’s potential expands horizons, enabling marketers to elevate strategies and engage
customers more effectively.
C.
Test multiple hypotheses efficiently within a single experiment
Machine learning and AI provide a d
istinct advantage: efficient testing of multiple hypotheses in a single experiment.
In an AI-
based experiment, the algorithm identifies hypotheses that result in performance improvements based on
analyzed data.
D.
Enhance and perform complicated tests with nu
Furthermore, AI can enhance and streamline complex tests with multiple variables conducted simultaneously within a
single experiment. This technology constructs new experiments by combining top
continuously testing until it identifies the most impactful one.
This suggests that AI could be the future of multivariate testing in marketing, allowing for the simultaneous assessment
of various combinations of variables and elements.
AI simplifies testi
ng multiple hypotheses and exploring diverse variables, increasing adaptability to different marketing
objectives. This efficient system empowers marketers to optimize the ROI of new campaigns, reaching new segments
through personalized messaging and campa
opportunities.
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Sheng J, Liu H, Wang B. Research on the Optimization of A/B Testing System Based on Dynamic Strategy
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7687-1_891.
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Lessons Learned from Enabling A/B testing as a Product Feature. I
Software Engineering: Software Engineering in Practice (ICSE
Shashank Agarwal is Healthcare data science expert whose experience cuts across various areas in
ma
rket access, artificial intelligence, brand analytics, predictive modeling, launch strategy, and
multi-
channel marketing in several Fortune 500 companies such as CVS Health, AbbVie, and
IQVIA.
IJARSCT
International Journal of Advanced
Research in Science
, Communication and
Access, Double
-Blind, Peer-
Reviewed, Refereed, Multidisciplinary Online Journal
Volume 3, Issue 1, August 2023
DOI: 10.48175/IJARSCT-12486
Collect and analyze vast amounts of real
-time data
driven testing tools stand out because they can gather and process large re
al-
time datasets, surpassing human
capabilities. These systems can test multiple hypotheses and tailor individual experiences using machine learning
For marketers, this means the chance to broaden segmentation, targeting various customer groups
messaging and campaigns. AI’s potential expands horizons, enabling marketers to elevate strategies and engage
Test multiple hypotheses efficiently within a single experiment
istinct advantage: efficient testing of multiple hypotheses in a single experiment.
based experiment, the algorithm identifies hypotheses that result in performance improvements based on
Enhance and perform complicated tests with nu
merous variables at the same time.
Furthermore, AI can enhance and streamline complex tests with multiple variables conducted simultaneously within a
single experiment. This technology constructs new experiments by combining top
-
performing hypotheses and
continuously testing until it identifies the most impactful one.
This suggests that AI could be the future of multivariate testing in marketing, allowing for the simultaneous assessment
of various combinations of variables and elements.
ng multiple hypotheses and exploring diverse variables, increasing adaptability to different marketing
objectives. This efficient system empowers marketers to optimize the ROI of new campaigns, reaching new segments
through personalized messaging and campaigns. The potential of AI in marketing promises exciting future
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Experiments Cannot (and Can) Tell You About How Customers Respond to Advertising, Available at
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Kohavi, Ron & Longbotham, Roger. (2017). Online Controlled Experiments and A/B Testing. 10.1007/978
R. Ros and P. Runeson, "Continuous Experimentation and A/B Testing: A Mapping Study,"
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BIOGRAPHIES
Shashank Agarwal is Healthcare data science expert whose experience cuts across various areas in
rket access, artificial intelligence, brand analytics, predictive modeling, launch strategy, and
channel marketing in several Fortune 500 companies such as CVS Health, AbbVie, and
ISSN (Online) 2581-9429
, Communication and
Technology (IJARSCT)
Reviewed, Refereed, Multidisciplinary Online Journal
555
time datasets, surpassing human
capabilities. These systems can test multiple hypotheses and tailor individual experiences using machine learning
For marketers, this means the chance to broaden segmentation, targeting various customer groups
with personalized
messaging and campaigns. AI’s potential expands horizons, enabling marketers to elevate strategies and engage
istinct advantage: efficient testing of multiple hypotheses in a single experiment.
based experiment, the algorithm identifies hypotheses that result in performance improvements based on
Furthermore, AI can enhance and streamline complex tests with multiple variables conducted simultaneously within a
performing hypotheses and
This suggests that AI could be the future of multivariate testing in marketing, allowing for the simultaneous assessment
ng multiple hypotheses and exploring diverse variables, increasing adaptability to different marketing
objectives. This efficient system empowers marketers to optimize the ROI of new campaigns, reaching new segments
igns. The potential of AI in marketing promises exciting future
Sheng J, Liu H, Wang B. Research on the Optimization of A/B Testing System Based on Dynamic Strategy
B Testing Goes Wrong: What Online
Experiments Cannot (and Can) Tell You About How Customers Respond to Advertising, Available at
Kohavi, Ron & Longbotham, Roger. (2017). Online Controlled Experiments and A/B Testing. 10.1007/978
-1-4899-
: A Mapping Study,"
2018 IEEE/ACM 4th
, Gothenburg, Sweden, 2018, pp. 35
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A/B testing. "
up performance: Evidence from A/B
A/B Testing: A Systematic Literature Review.
arXiv
Fabijan, A., Dmitriev, P., Arai, B., Drake, A., Kohlmeier, S., & Kwong, A. (2023, May). A/B Integrations: 7
2023 IEEE/ACM 45th International Conference on
Shashank Agarwal is Healthcare data science expert whose experience cuts across various areas in
rket access, artificial intelligence, brand analytics, predictive modeling, launch strategy, and
channel marketing in several Fortune 500 companies such as CVS Health, AbbVie, and
... Sengupta, Banerjee, and Chakrabarti (2022) discuss the role of AI in enhancing the efficiency of data mining models, particularly in the context of information retrieval [7]. Agarwal (2023) delves into the application of A/B testing in product optimization, underscoring the role of AI in automating tasks and processing real-time data [8]. Manias (2023) emphasize the importance of trusted AI in public governance, highlighting the need for transparency and accountability in AI systems [9]. ...
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The Internet connectivity of client software (e.g., apps running on phones and PCs), websites, and online services provide an unprecedented opportunity to evaluate ideas quickly using controlled experiments, also called A/B tests, split tests, randomized experiments, control/treatment tests, and online field experiments. Unlike most data mining techniques for finding correlational patterns, controlled experiments allow establishing a causal relationship with high probability. Experimenters can utilize the scientific method to form a hypothesis of the form “If a specific change is introduced, will it improve key metrics?” and evaluate it with real users.
Article
Recent scholarship argues that experimentation should be the organizing principle for entrepreneurial strategy. Experimentation leads to organizational learning, which drives improvements in firm performance. We investigate this proposition by exploiting the time-varying adoption of A/B testing technology, which has drastically reduced the cost of testing business ideas. Our results provide the first evidence on how digital experimentation affects a large sample of high-technology start-ups using data that tracks their growth, technology use, and products. We find that, although relatively few firms adopt A/B testing, among those that do, performance improves by 30%–100% after a year of use. We then argue that this substantial effect and relatively low adoption rate arises because start-ups do not only test one-off incremental changes, but also use A/B testing as part of a broader strategy of experimentation. Qualitative insights and additional quantitative analyses show that experimentation improves organizational learning, which helps start-ups develop more new products, identify and scale promising ideas, and fail faster when they receive negative signals. These findings inform the literatures on entrepreneurial strategy, organizational learning, and data-driven decision making. This paper was accepted by Toby Stuart, entrepreneurship and innovation.
Conference Paper
Background. Continuous experimentation (CE) has recently emerged as an established industry practice and as a research subject. Our aim is to study the application of CE and A/B testing in various industrial contexts. Objective. We wanted to investigate whether CE is used in different sectors of industry, by how it is reported in academic studies. We also wanted to explore the main topics researched to give an overview of the subject and discuss future research directions. Method. We performed a systematic mapping study of the published literature and included 62 papers, using a combination of database search and snowballing. Results. Most reported software experiments are done online and with software delivered as a service, although varied exemptions exist for e.g., financial software and games. The most frequently researched topics are challenges to conduct experiments and statistical methods for software experiments. Conclusions. The software engineering research on CE is still in its infancy. There are future research opportunities in evaluation research of technical topics and investigations of ethical experimentation. We conclude that the included studies show that A/B testing is applicable to a diversity of software and organisations.
Designing with data: Improving the user experience with
  • R King
  • E F Churchill
  • C Tan
King, R., Churchill, E. F., & Tan, C. (2017). Designing with data: Improving the user experience with
  • F Quin
  • D Weyns
  • M Galster
  • C C Silva
Quin, F., Weyns, D., Galster, M., & Silva, C. C. (2023). preprint arXiv:2308.04929.