What's the best way to analyze SEM experiment results?

Analyzing SEM experiment results can be crucial for understanding the effectiveness of your campaigns and making data-driven decisions for improvement. Here's a comprehensive guide to help you analyze your SEM experiment results effectively. 1. Define your goals and metrics 2. Choose your experiment type and design 3. Collect and visualize your data 4. Analyze and interpret your results 5. Communicate and apply your insights By following these steps and utilizing effective analysis techniques, you can gain valuable insights from your SEM experiments and make informed decisions to optimize your campaigns for improved performance.

The most important thing even before starting a SEM campaign and defining the KPI for it, is understanding of the business you are in and the consumer behaviour towards what you are offering (in terms of nascency of the market towards your product). E.g. If you're selling a very high value product in the Indian market and are thinking that the Indian consumer will do the transaction completely online, then that might not be the case. In that scenario your KPI needs to be driving leads rather than online conversions. Once the above is set than set up small experiments (like audience, age, location, or combinations, etc) and keep tweaking them until the desired result in terms of quality, quantity and cost is achieved.

The goal is always increasing the bottom line. Of course, there isn’t any bottom line without other goals, which we can call micro goals. Micro goals are those ones supporting the main goal, and depending on your marketing channel, they can take different forms: free subscription, lead generation, document download to name just a few.

Before delving into any SEM experiment, it's essential to establish clear objectives and select relevant metrics. These metrics should be specific, attainable, and aligned with your business objectives. For example, if your goal is to boost sales, you might employ metrics like CPA (Cost Per Acquisition) and ROAS (Return on Ad Spend). If your aim is to enhance brand awareness, metrics such as impressions, CTR (Click-Through Rate), and brand lift could be pivotal. Additionally, determining the minimum sample size, experiment duration, and desired statistical significance and confidence level is crucial.

"In SEM campaigns in Iran, precise targeting is paramount, considering the diverse business landscape. Differentiating between individual consumers and large organizations is crucial. Small companies often involve a single decision-maker, while larger ones require engagement with multiple stakeholders. Tailored SEM strategies, reflecting the unique needs of each target audience, are essential. Understanding the local business context, industry, and decision-making processes is vital for success. A flexible approach, recognizing that no two plans should be identical, ensures alignment with audience expectations and business goals.

When choosing your SEM experiment type, prioritize continuity and adaptability. Whether it's A/B, multivariate, or split testing, integrate them seamlessly into an ongoing process. Be aware that test periods can be affected by external factors like seasonality, unforeseen events (e.g., COVID-19), or evolving regulations. Stay vigilant and consider these variables when interpreting results. For instance, in A/B testing, external factors can alter the context of ad display, impacting audience behavior. Recognizing and incorporating these influences into your analysis ensures robust and actionable insights for optimizing your SEM strategies.

The choice of experiment type is pivotal. As a data analyst, I've seen how test selection impacts the clarity of insights. While A/B testing is straightforward, it's vital to keep other variables constant. Multivariate testing offers a view of element interactions but requires a larger sample for significance. Split testing, assessing holistic changes, is invaluable. However, external factors like seasonality can skew results. Design integrity, whether randomized or controlled, is crucial. Proper segmentation ensures actionable insights over skewed data.

For any kind of SEM campaign, it is a must to run A/B testing when you are working with a high budget. More or less even if the budget is small better to run A/B testing to check which Landing page work better with the quality of leads this mostly works with b2b concept and lead generation.

I believe one of the most obvious things that we all tend to ignore is that we're actually running experiments without realizing it. A lot of campaigns, landing pages, ad copy variations etc. But if you're really committed to run an experiment knowingly then i would say run a tight ship. No more of let's test everything and end up with no actionable insights. Test one thing like a simple button or CTA and see if it actually moves the needle. Then yoi move one to the next onw. In the end it's not about the number o things you test. I think that's the biggest trap. It's not about testing for testing sake. It's about your business benefiting from those tests. Testing is the means to get you to reach your goals. It's not the goal itself

When selecting your SEM experiment approach, emphasize continuity and flexibility. Whether you opt for A/B, multivariate, or split testing, integrate them seamlessly into an ongoing process. Keep in mind that external factors such as seasonality, unexpected events (e.g., COVID-19), or changing regulations can affect test durations. Stay alert and factor in these variables when assessing outcomes. For instance, in A/B testing, external factors may modify the context of ad displays, influencing audience behavior. Acknowledging and incorporating these influences into your analysis ensures resilient and actionable insights for enhancing your SEM strategies.

The clarity of data collection and representation is paramount. While tools like Google Ads and Analytics are foundational, integrating with platforms like Tableau can enhance insights. Dynamic dashboards, which adapt to data changes, are invaluable. Visualization's art lies in simplicity. Overloading visuals can obscure insights, so balance is key. Lastly, rigorous data checks are vital. Overlooking anomalies can misguide strategies, making regular audits essential.

During the data collection and visualization phase of A/B testing, harnessing tools like BigQuery for robust data processing is highly advantageous. These tools facilitate the monitoring of different version performances and provide insights into campaign optimization. Furthermore, data visualization & reporting tools such as Supermetrics, Looker Studio, or Tableau play a pivotal role in understanding and sharing A/B test results. They aid in presenting data in a clear and comprehensible manner, guiding campaign optimization decisions.

I would suggest another tool being that I put strong importance on security. GoodData is a cutting-edge solution that equips businesses with a complete data management and analysis arsenal. GoodData's software is an architectural marvel that paves the way for data integration, purification, in-depth analysis, and the swift dissemination of insights through robust reporting tools and dynamic applications. It's a highly scalable enterprise platform. But here's the real kicker: GoodData fortifies your data journey with an impenetrable shield of security. We're talking SOC 2, GDPR, and ISO 27001 standards compliance, backed by the formidable support of VPC Peering and AWS PrivateLink and HIPAA compliance is coming soon.

For Collecting the data and visualize the same from analytics and google ads, another tool we use to visualize the same is Looker a google product formally known as data studio.

We collect and visualize data from analytics and Google Ads using another tool called Looker, which was formerly known as Data Studio and is a product of Google.

The depth of SEM insights hinges on analytical rigor. Using tests like t-tests or ANOVA is vital, but understanding their context is key. KPIs like lift and p-value offer experiment snapshots. Yet, deeper dives, such as segment analysis, often unveil actionable insights. A cautious approach is paramount. Immediate conclusions can mislead; it's crucial to validate findings, especially unexpected ones, to ensure robust strategies.

When delving into the analysis and interpretation of SEM experiment results, it's essential to leverage statistical methods and techniques. Utilize statistical tests like t-tests, ANOVA, or chi-square tests to compare and evaluate different versions and variables. Calculate and report key performance indicators (KPIs) such as percentage change, lift, p-value, and confidence intervals. Additionally, scrutinize data for patterns, trends, or correlations, such as how different demographics responded to the experiment. Avoid making assumptions or hasty conclusions based on results and consider contradictory or unexpected findings.

"The depth of SEM insights relies on analytical rigor. While tests like t-tests or ANOVA are essential, it's crucial to grasp their context. KPIs like lift and p-value provide a quick overview of experiments, but delving deeper, as in segment analysis, often reveals actionable insights. A prudent approach is essential. Hasty conclusions can be misleading; it's vital to validate findings, especially those that are unexpected, to ensure the strength of your strategies.

The final and pivotal step in analyzing SEM experiment results is effectively communicating and applying your insights. Share findings and recommendations with your team, stakeholders, and clients using clear, concise language and visual aids. Explain the rationale and implications of your insights, while also acknowledging the experiment's limitations and potential risks. Apply these insights to optimize and enhance your SEM campaigns and strategies, whether it involves adjusting budgets, bids, keywords, or testing new variations. Document and archive your experiment results and plan the execution of subsequent experiments.

Can be obvious but don't forget, run your experiments on new campaigns so you don't impact your "stable" active ones. Also we found very useful to create an experiment log to document tests details, results, and learnings.

Absolutely, effective communication is key. Profound insights can lose impact if not aptly conveyed. Using clear language and visuals ensures stakeholders grasp findings. Beyond sharing results, explaining the 'why' behind insights aids informed decisions. Applying these insights, be it through bid adjustments or keyword tweaks, drives optimal ROI. And never underestimate documentation's value; it's a foundation for future experiments, ensuring iterative improvement.

The last crucial step in analyzing SEM experiment results is to communicate and use your findings effectively. Share what you've learned with your team, stakeholders, and clients using clear language and visuals. Explain the reasons and consequences of your insights, and be sure to acknowledge any limitations or risks associated with the experiment. Use these insights to improve your SEM campaigns and strategies, whether that means adjusting budgets, bids, keywords, or trying new variations. Keep a record of your experiment results and plan for future experiments.

Here are three points that I like to add at the end: 1. Iterative Learning: Every SEM experiment, whether successful or not, provides a learning opportunity. It's essential to view each experiment as a stepping stone, refining our approach based on past learnings. 2. Cross-Functional Collaboration: Engaging with teams beyond marketing, such as product or sales, can offer fresh perspectives. Their feedback can provide new angles for SEM strategies. 3. Holistic View: While SEM is powerful, it's one part of the broader digital marketing ecosystem. Considering how SEM insights can inform or be informed by channels like SEO, Social Media, or Email Marketing can lead to a more cohesive strategy.

Lastly, in the ever-evolving landscape of online marketing, there exist additional contemplations that should not be overlooked. A/B testing should merely constitute one facet of an ongoing refinement process, yet it often remains a discreet yet integral element. Endeavor to evade this oversight by adhering to this guide. As we look ahead, it is crucial to remain attuned to technological advancements and emerging trends in the marketing arena. Beyond A/B testing, harnessing evolving domains such as artificial intelligence and big data analytics can enrich our online marketing strategies.