¿Cuáles son las mejores maneras de probar y aprender de sus campañas y experimentos programáticos?

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

In AdTech, defining objectives and KPIs before launching a programmatic campaign is crucial. It’s about understanding what success looks like, whether it's increasing brand awareness or driving sales. Measuring these goals with precise KPIs is key to track progress and adjust strategies as needed. Remember, Ad Fraud is a significant challenge in digital advertising. Utilize solutions like ClearTrust to ensure your campaigns reach real audiences, maximizing ROI with their advanced AdFraud Mitigation technology.

The best ways to test and learn from your programmatic campaigns include setting clear objectives and KPIs, conducting A/B tests to compare different strategies, and using segmented audiences to gather diverse insights. Analyze performance data regularly to identify trends and areas for improvement. Document findings to refine future campaigns, and maintain an agile approach to adapt quickly based on what you learn. This continuous cycle of testing and learning helps optimize your efforts and maximize results.

Align your objectives and KPIs with both campaign goals and broader business outcomes. Set short-term and long-term KPIs to track immediate performance and overall impact. Including qualitative goals, like improving brand perception, can give a fuller picture of your campaign’s success.

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 otimização. Se observo algo fora do roteiro, ajusto de imediato; caso contrário, faço pequenas intervenções. Após alguns dias de veiculação da campanha e com as sugestões de melhorias doa plataforma, passo a atuar de forma mais incisiva, realizando intervenções com o objetivo de otimizar cada vez mais a campanha e atingir os objetivos traçados inicialmente.

Create engaging and high-quality ad creatives. Your ads should capture attention, convey your message clearly, and include a strong "call to action". A/B testing different versions of your creatives can help you determine which ones perform best.

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.

Decide on the most appropriate testing methods, such as A/B testing, multivariate testing, or incrementality testing. Choose tools that can handle the complexity of your campaigns, such as Google Optimize for A/B testing or Adobe Analytics for deeper insights. Consider using programmatic platforms that offer built-in testing capabilities, and ensure you have the right data analytics tools in place to measure outcomes accurately.

Developing a compelling value proposition is also crucial. Clearly articulate what makes your product or service unique and why your audience should choose you over competitors. This proposition should be consistently communicated across all campaign materials.

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.

Okay, let's break it down: First, make a prediction. Think about what you think will happen if you change something. For example, you might think, "If I change the ‘Buy Now’ button to ‘Learn More’, more people will click on it." That's your prediction. Next, set up your experiment. This means creating two versions of whatever you're testing. In our example, you'd have one version with "Buy Now" and another with "Learn More". Make sure everything else stays the same so you can really see if the button change makes a difference.

Craft a clear hypothesis for each experiment, stating what you expect to happen and why. For example, "If we increase the frequency of ads shown to users, we expect to see a 10% increase in conversion rates." Design experiments that isolate variables, ensuring that any observed changes can be confidently attributed to your modifications. Randomize your test groups to reduce bias and ensure reliable results.

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 alocação do orçamento e maximizar o retorno. Em seguida, analiso quais criativos geraram maior retenção e engajamento, entendendo melhor o público para criar materiais mais eficazes. Também reviso as ações 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

Ensure you have a process for optimization and iteration. Programmatic campaigns require ongoing adjustments to maximize their effectiveness. Be prepared to tweak your targeting, adjust bids, and test new creatives based on performance data.

Once you've set up your experiment, it's time to let it run its course. You'll want to give it enough time to collect solid data. For example, if you're testing out different ad copy, running the ads for at least a week can help smooth out any day-of-week differences that might skew the results. While your experiment is running, it's tempting to check in constantly to see how things are going. It's important to resist this urge! Jumping to conclusions too early can lead you down the wrong path. Instead, let the data accumulate over time. Think of it like baking a cake – you can't take it out of the oven too soon or it won't be cooked through.

Set up tracking and measurement tools. Implementing tracking pixels, conversion tags, and utilizing analytics platforms will allow you to monitor your campaign’s performance in real-time. Regularly analyze the data to gain insights into what’s working and what isn’t.

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.

El Social Listening complementa el análisis de datos programáticos al proporcionar insights cualitativos sobre las reacciones del público. Al evaluar sentimientos y emociones en las conversaciones, puedes contextualizar métricas como CTR o conversiones. Esto ayuda a entender el “por qué” detrás del rendimiento de los anuncios, optimizando futuras campañas con mensajes más alineados a las expectativas del consumidor

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.

Incorporar Social Listening en la fase de optimización permite iterar creatividades en función de la percepción pública. Al analizar comentarios en tiempo real, puedes ajustar mensajes, formatos o incluso audiencias, alineando la campaña con las emociones y necesidades detectadas. Este enfoque adaptable maximiza la relevancia y potencia el rendimiento programático de forma continua

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.

El Social Listening no solo informa la estrategia programática, sino que también actúa como termómetro de marca. Al monitorear cambios en el sentimiento y la conversación a lo largo del tiempo, puedes anticipar crisis o capitalizar oportunidades emergentes. Además, revela insights culturales y de comportamiento que inspiran campañas creativas, fortaleciendo la conexión emocional con la audiencia