How do you use analytics to identify and prioritize opportunities for experimentation?

1 We start by identifying our objectives which could be increase revenue, improve customer satisfaction, optimize marketing effectiveness 2 Set up SMART goals based on business objectives 3 Set up KPI ensuring they are measurable 4 Using analytical tools to analyze data related to KPI looking for trends, patterns correlations in data to find out area of improvement 5 From insights generated from data identify potential hypotheses or area to improve KPI 6 We prioritize experimentation opportunities based on potential impact on KPI alignment with business objective 7 We design hypotheses or assumption about potential changes 8 We design experiment, variables to be tested, sample size, duration and success criteria and implement experiment

I primarily use Digital Analytics, specifically GA4, BigQuery, GTM etc. Here is my approach: Firstly, I create and implement an event analytics plan that captures user actions and details about them. I pay special attention to the user journey, identifying areas where users drop off. Next, I focus on the frequent/significant actions and develop a plan for experimentation. Eg. if I notice a significant drop-off at a particular stage in the funnel, I hypothesize solutions to mitigate this. Third, I prioritise experiments based on potential business impact, ease of implementation, without lot of dev work, and areas where I feel most confident I have seen really positive results from this approach. I hope you will find it useful as well

completely agree with the importance of defining clear objectives and success criteria before identifying and prioritizing opportunities for experimentation. It is crucial to have a well-defined plan in place to ensure that your experiments are aligned with your business goals and KPIs. However, it's also essential to continuously monitor and analyze the results of your experiments to ensure that you're achieving the desired outcomes. This will allow you to adjust your strategy and approach as needed to optimize your experimentation program and drive better business results. Therefore, regularly reviewing your KPIs and experiment results will help you stay on track towards achieving your objectives.

Before you start looking for opportunities the most important thing is understanding the data and the integrity behind it. For this to be possible you need to understand the why and purpose behind the data. What I have found is if you go into a meeting without an analyst reviewing a data set you will generally have 20 different opinions as to why it looks that way. The job of the analyst is to stay true to the purpose, without any bias and be the data driven expert.

It's crucial to understand the context in which the data is generated and consumed. Get to know your stakeholders' analytics goals, and ask detailed questions to get a deeper understanding of their needs. Once the objectives are clear, explore the data, using simple but effective statistics (revenue sizing, averages, median, standard deviation, time series analysis, linear regression, etc.) to get an idea of what specific combination of KPIs might be worth deep diving. It's important to put the opportunities in a feasibility matrix that takes into account the business value, the cost, and the tech feasibility. Run this exercise with the stakeholders to get a holistic set of opportunities that are aligned with the business.

Benchmark data is crucial to evaluating KPI’s, it serves as a standard reference point, allowing organizations to measure, compare, and improve their performance. Ensure you have a solid foundation for establishing your own organizational benchmarks (not just industry benchmark data) before you begin testing. Your own benchmarks will help guide informed strategic decisions; understanding your baseline will help pinpoint weaknesses or optimize experiments.

Analyzing data is both an art and a science. Like any artistic medium, you must choose your instrument: in this case tools and processes. Often, several analytic techniques could be deployed. But your job is to choose one that tells the best story

One of my favorite techniques is to combine quantitative and qualitative data to get a more complete picture of user behavior. The combination of data sources allows me to uncover deeper insights and make more accurate recommendations. I believe that it's important to be curious and explore different analytical methods. There are always new tools and techniques emerging, and it's essential to stay up-to-date and experiment with different approaches to find what works best for your specific needs. By being open-minded and willing to learn, you can continue to develop your skills as a business analyst and make a valuable contribution to your organization.

Having data driven experimentation and testing is an important focus for any company. Part of why data and insights are so important is because it allows us to constantly make improvements and find new things to test out. Taking the existing data and finding out why a website visitor may be leaving the funnel earlier helps us start to hypothesize and create tests to help improve the funnel completion.

In my experience in influencer marketing industry, I've witnessed how diving into engagement metrics like click-through rates, conversion rates, and audience demographics, can help you pinpoint influencers or content types that resonate most with your target audience. It helps you in recognizing patterns and trends, helping you prioritize influencers or campaigns that have the potential for higher ROI.

After analyzing your data, craft hypotheses for experimentation. A hypothesis is a testable statement predicting how altering a variable will impact an outcome. For instance, "Adding a testimonial section to the landing page will boost the conversion rate by 10%." Ensure your hypothesis is evidence-based, aligns with your objectives, and is measurable for effective experimentation.

Set aside different samples to generate and test your hypotheses in order to avoid bias. Do this from the beginning if you are working with a large enough dataset by splitting it randomly into at least 2 subsets, 3 if your work will involve machine learning.

Generating hypotheses involves predicting expected relationships in your data. Understand your dataset, identify variables, and form clear research questions. Review existing literature to inform your predictions. Craft SMART null (H_0) and alternative (H_1) hypotheses. Consider directionality: one-tailed for a specific effect, two-tailed for any difference. Predict expected outcomes if your alternative hypothesis holds. Hypotheses guide analysis, with results supporting or rejecting them. Stay open to adjustments based on findings for a dynamic approach.

Look for underutilized segments or micro-behaviors using clustering techniques and predictive models—this helps reveal hidden user patterns. Use A/B test results, attribution modeling, and engagement scoring to pinpoint friction points in customer journeys. Advanced behavioral analytics can uncover latent variables influencing conversions. Leverage heatmaps and session recordings to validate qualitative data and generate high-impact hypotheses. Prioritize testing on elements with high interaction but low success rates to maximize ROI.

Set clear objectives. Then define KPIs which are measurable. Use analytics tool to measure the said KPIs. Identify potential hypothesis. Priorities the experiments based on potential impact on business objectives. Design the hypothesis and test it.

In my experience, analytics can be used to identify and prioritize opportunities for experimentation by first analysing existing data to uncover patterns, trends, and anomalies. This involves leveraging advanced data analysis tools and techniques, such as predictive modelling and segmentation analysis, to pinpoint areas with the most significant potential for improvement or innovation. Prioritization can then be based on factors like expected impact, feasibility, and alignment with strategic goals of our business.

The true value of a hypothesis lies not only in its testability but also in its impact on the business. When evaluating a hypothesis, consider the potential benefits it brings. Ask yourself: If this hypothesis is accepted and implemented, how much value will it generate for the business? Will it lead to increased revenue, improved engagement, or enhanced customer satisfaction? Quantifying this value is essential. Equally important are the costs associated with implementing the change. These costs include development time, resources and any potential risks. Consider both direct costs and indirect costs. Estimating benefits versus costs helps set priorities. Focus on hypotheses that promise substantial gains while minimizing expenses.

Prioritizing hypotheses is entirely dependent on your objectives. For example, if you are prioritizing minimizing draw down risk as compared to maximizing return of a trading strategy, you would focus more on volatility reduction approaches and pertinent hypotheses than you otherwise would. One could then use frameworks such as RICE (Reach, Impact, Confidence, Effort) to prioritize amongst multiple hypotheses targeting a single objective.

In my earlier role, we implemented a dynamic prioritization framework that integrated data analytics with business objectives. We evaluated hypotheses not just on potential impact and feasibility but also on alignment with long-term strategic goals and resource availability. This approach allowed us to pivot quickly and allocate resources more effectively, ensuring that our experimentation efforts consistently drove meaningful business outcomes. By continuously refining our prioritization criteria based on feedback and results, we were able to maintain agility and focus, ultimately leading to completing 163 projects over 4 years and delivering a 12x ROI.

Apply advanced frameworks like PXL, which factors in psychological triggers and user motivation beyond just ease or impact. Use regression analysis on historical test data to predict ROI and leverage machine learning to model potential gains across segments. Prioritize high-traffic, high-impact areas using multivariate scoring, incorporating time-to-value as a key metric. Consider risk-adjusted ROI to account for variability in test outcomes, and factor in long-term user retention impacts, not just short-term conversion rates. Prioritize quick wins, but don’t ignore high-effort, high-potential innovations.

The key to A/B testing is to ensure that you test only one variable at a time. This means changing and comparing a single element between your A and B variants while keeping all other aspects of the test the same. Testing one variable at a time allows you to accurately attribute any changes in results to that specific change, making it easier to determine what caused improvements or setbacks.

Based on the results of the hypotheses, we can start with the design of the experiment. Plan experiments based on goals and resources—A/B, multivariate, or split tests work. Set parameters like sample size, duration, audience, and control/variation groups as these parameters can alter the evaluation metrics. Pre-plan analysis and reporting for structured results interpretation. Defining variables, using random assignment for unbiased participant allocation, maintaining adequate sample size, and applying controls for causation. Use designs like pre-post and counterbalanced, randomized conditions to avoid sequence effects. Collect quantitative and qualitative data, apply statistical tests, and draw conclusions aligned with hypotheses.

Define Research Questions and Hypotheses: Clearly articulate the research questions or hypotheses you want to investigate. Ensure they are specific, testable, and relevant to your objectives. Formulate null and alternative hypotheses that can be tested statistically. 2. Determine Experimental Variables: Identify the independent and dependent variables in your experiment. The independent variable is manipulated to observe its effect on the dependent variable. Control for extraneous variables that could confound the results by either holding them constant or randomizing their effects. 3. Choose Experimental Design: Select an appropriate experimental design based on the nature of your research questions and variables.

The test plan is very important : it helps to define what type of test we are doing, all the parameters (device, segment), the KPIs, the date range and the details about control and variation designs. In the test plan, I also add the exploratory analysis (that brings me to this hypothesis) at the beginning, and at the end the results and recommandations. The test plan is shared to the stakeholders before the test is launched so that everybody is aligned on the KPIs.

Design experiments that will effectively test your prioritized hypotheses. Ensure that your experimental design includes clear metrics for success and control groups where necessary, enabling you to accurately measure the impact of each intervention.

Execute your experiments, closely monitoring the results to see if they meet your success criteria. Use the insights gained to refine your approach, iterating on successful strategies and discarding those that don't deliver the expected outcomes. Continuous iteration helps you optimize and scale successful initiatives.

Don't dismiss anomalies; anomalies are pivotal in A/B testing as they reveal unexpected insights. They represent data points that deviate significantly from the norm, often highlighting hidden opportunities or issues. By closely examining anomalies, you can identify what drives exceptional or underperforming results in specific user groups or test variants. This knowledge empowers you to adapt and optimize your strategies, enhancing the effectiveness of your A/B testing process and leading to better-informed decisions and improved outcomes.

Once your experiment is live, monitor the results regularly, but avoid making conclusions too early. Allow your tests to gather enough data before making any decisions. After running the experiment, analyze the results—did the changes have the desired impact? Whether your hypothesis was confirmed or disproven, use the learnings to iterate. Successful experiments can be refined and scaled, while unsuccessful ones offer insights for future tests. Experimentation is a continuous process of learning, optimizing, and iterating to achieve the best outcomes 🚀.