What are the best ways to test and learn from your programmatic campaigns and experiments?

First, we must ask ourselves, am I a knowledgeable digital marketer? The share of those who still cannot measure the digital investments they make today is very large. Of course, if you look at the LinkedIn universe, everyone is riding a horse with AI. In this kind of approach, my suggestion is that a proper tracking mechanism should be in place first. I wouldn't invest where I can't measure. If this system is in place, you need to clearly discuss and agree with your team on what you are going to test. My advice is to start with a setup that is as uncomplicated as possible. Finally, it is important to remember that it is very important to test AB, not ABCDEF.

Most people here provide AI-generated generic answers to these questions. But let's be honest, Today, a natural potential or delusion of having infinite knowledge can follow a specific title. This has an interesting effect on others: People with a lower title tend to accept the idea without any data to support it. To avoid this, plan the test-and-learn structure and agenda for the related business period. This will minimize the effect. Rough benefit of this: A or B has a 50% chance of winning (of course, someone will say that it depends on that variable, but unfortunately, that is not the point). Assuming that one out of every two tests minimizes the opportunity cost, this means that for every two tests, we are improving our business

Nos meus projetos, aplico direcionamentos baseados no planejamento que desenvolvi. No entanto, nos primeiros dias, gosto de permitir que o algoritmo da plataforma atue nas campanhas, aplicando sua otimiza??o. Se observo algo fora do roteiro, ajusto de imediato; caso contrário, fa?o pequenas interven??es. Após alguns dias de veicula??o da campanha e com as sugest?es de melhorias doa plataforma, passo a atuar de forma mais incisiva, realizando interven??es com o objetivo de otimizar cada vez mais a campanha e atingir os objetivos tra?ados inicialmente.

Start with A/B testing for simpler variables and progress to multivariate or incrementality testing as your campaigns grow. Combining methods can provide deeper insights. Choose tools that integrate well with your platforms for smoother analysis, and regularly review outcomes to refine your strategy.

So, you've got your goal. Now, it's time to figure out how to test different things to see what works best. There are two main ways to do this: A/B testing: This is like comparing two different versions of something, like two different ad headlines. Multivariate testing: This is like mixing and matching different things to see what combo works best. For example, you could try different headlines and images together. There are special tools to help you do these tests, like Google Optimize, Adobe Target, and Optimizely.

Set clear success criteria for your hypothesis and consider testing multiple hypotheses at once. Plan for post-experiment analysis by outlining how results will guide your decisions.

Before starting the test, you should have a clear idea of what you think will happen. This is called a hypothesis. For example: "If I use more targeted audience segments, my CPA will decrease." Make sure your tests have a control group (the standard ad) and a test group (the one where you apply changes), so you can compare results and see the effects of your changes.

Si usas AdManager lo primero que hay que hacer cada día es ver si todos los line item están en verde en cuestión de delivery. Lo siguientes es sacar un reporte de viewability y CTR que son las métricas más comunes que piden los clientes. A partir de aquí lo que uno quiera mirar, hasta KPIs por dispositivos, horas del día, creatividad...

Ao analisar o histórico de campanhas, foco em pontos centrais para gerar insights valiosos para projetos futuros. Primeiro, identifico os canais e plataformas mais eficazes para otimizar a aloca??o do or?amento e maximizar o retorno. Em seguida, analiso quais criativos geraram maior reten??o e engajamento, entendendo melhor o público para criar materiais mais eficazes. Também reviso as a??es menos eficazes, buscando melhorias contínuas. Por fim, mesmo que a campanha atinja suas metas, estou sempre em busca de otimizar os resultados e aumentar a margem de sucesso.

Once you’ve set up your test, let it run long enough to gather meaningful data. Don’t stop too soon, but don’t let it go for too long either, because other factors (like seasonality or market changes) might affect your results. Track your data over time and make sure you’re monitoring your KPIs closely

Once your experiment is finished, it's time to see what worked and what didn't. Look at the numbers and compare the different versions of your ad to see which one performed better based on what you wanted to achieve (your goals). For example, if you changed your "Buy Now" button to "Learn More" and more people clicked on it, you might wonder why. Was it the word "Learn More"? Was it where you put the button? Or maybe it was who you showed the ad to? The most important thing is to learn from your results. Figure out what worked, what didn't, and why. This knowledge is gold! You can use it to make your next ad even better.

Once your experiments are complete, analyze the data to determine whether the results support your hypothesis. Use statistical analysis methods, such as significance testing, to ensure that your findings are robust. Look for patterns in the data, considering both quantitative outcomes (like CTR increases) and qualitative insights (such as shifts in audience behavior). Identify any unexpected results and explore their implications.

Apply Your Insights to Optimize Your Campaigns Implement Adjustments: Leverage the insights you've gathered to fine-tune your campaigns for improved results. Example: If a specific image outshines others, make it the benchmark for your future campaigns across various platforms. Ongoing Optimization: Testing and learning should be continuous—consistently refine and adjust based on fresh data.

Use the insights gained to refine your campaigns. For example, if an ad variant outperformed others, consider scaling it across more of your budget. Continuously optimize by iterating on what works and discarding what doesn’t. Set up a feedback loop where insights from one experiment inform the next, allowing for ongoing improvement and adaptation to market changes.

Here’s What Else to Consider Iteration: Always be testing. What works today might not work tomorrow, so continually run new experiments. Example: After optimizing your CTA, you might move on to testing different audience segments or ad formats. Audience Segmentation: Different audiences may respond differently. Consider segmenting your tests for more targeted insights. External Factors: Stay aware of external influences, like seasonality or market trends, that might skew your results. Example: During a holiday season, consumer behavior can change drastically, so factor that into your analysis.

Nosotros nos encontramos más en el otro lado, en la monetización, y se nota que los espacios publicitarios cuando son nuevos suelen tardar 2-3 semanas de media en alcanzar los máximos resultados, a no ser que se tenga mucho mucho tráfico que entonces los algoritmos de optimización pueden llegar a su máximo aprendizaje en 1-2 días.

Siempre siempre siempre, hay que definir el KPI a medir, el tiempo y porqué se está haciendo el experimento, si no tienes claro esas tres preguntas no hagas ningún experimento.