How would you resolve conflicting data sources when analyzing marketing campaign performance?

Analysis of data can be misleading without taking into consideration external factors, a project i was managing had terrible conversion rate as per the client, but when I examined the historical data he is using, turned out that he was mainly focusing on data that was collected during Covid times, but customers now changed behavior or behavior is normal again so such data can be misleading

Conflicting data sources can create confusion and hinder decision-making when analyzing marketing campaign performance. Rather than immediately questioning the data's validity, it’s essential to adopt a holistic approach that considers context, methodologies, and the broader picture.

Conflicting data sources is quite common in real scenarios but can be tricky to handle sometimes. To resolve such conflicts, one should follow a comprehensive approach. First, clearly understand the conflicting metrics between the sources. Second, understand the methodology /logic through which data is ingested into the database. After knowing the root causes of the difference, move forward with the source that aligns more with the business

I usually have problems when comparing data from Google Ads with the client's sales data. This is often due to how attributions and conversion tracking are done. First, I try to rule out these common scenarios. If the problem isn't related to attributions, I then check the client's data pipeline.

Conflicting data sources are quite recurrent in the real life; to counter we may have following approaches. -Validate the information keeping the right context filters (i.e. time period, business filters) -Benchmark the information against the source of truth (Data tables which are being utilized by financial departments, mind the context filters) -check the data pipelines for the sources. P.S: Context filters are the criteria deployed for observing information, every department may have their own set of refining criteria's, hence it's important to understand the information from the same lens

We should prioritize key performance indicators (KPIs) and evaluate the quality and reliability of the data. It's essential to focus on overall trends instead of individual data points, cross-check insights across different channels, and ensure consistent time frames. If discrepancies remain, we can validate results by running A/B tests.

I would have done that other way around. Like Russians scientist do. Out of 10 scientist 1 scientist try to prove whole working wrong. In process loop holes surfaces in existing working. 9 scientist try to resolve that loop holes. In process quality check also taken care of & chances of success is increase. So 9 vs 1 but its team of 10 working for same objective.

Here it is: - Start by checking if the data sources are using different time zones. Discrepancies in time zones can lead to misaligned data, causing conflicts when comparing metrics from different sources. - Examine whether tracking parameters, such as UTM codes, are consistently implemented across all campaigns. Inconsistent or missing tracking parameters can lead to inaccurate data collection. Examples: - If your Google Analytics is set to Pacific Time but your CRM is set to Eastern Time, the data might not align. Adjust the settings so they match or convert the data to a uniform time zone before comparison. - Ensure that all MKT campaigns use consistent UTM parameters for tracking.

Pour résoudre les sources de données conflictuelles lors de l'analyse des performances des campagnes marketing, commencez par identifier les différences entre les plateformes et comprendre leurs méthodologies d'attribution. Vérifiez la qualité des données en contrôlant les paramètres de suivi et les intégrations. Standardisez les métriques et les définitions pour garantir une comparaison cohérente. Comparez les données brutes pour repérer les écarts majeurs et, de préférence, utilisez une plateforme centralisée pour harmoniser les rapports. Enfin, documentez le processus, communiquez les résultats aux parties prenantes, et ajustez les stratégies en conséquence.

The way I have done this a couple of times is to make sure there are no errors in how the data is collected, then check that each source measures things the same way, and aligns the periods that I am looking at. Then, compare the data and adjust for accuracy.

Ensure that tracking codes, such as UTM parameters, Google Analytics tags, and pixel codes, are correctly placed across all platforms and touchpoints. This alignment ensures that data is collected uniformly across all channels. For example; Implement Google Tag Manager to centrally manage and deploy tracking tags across your website and landing pages. This ensures that all pages are tracked consistently, reducing the likelihood of missing or incorrect tags.

I’ve seen firsthand how important it is to standardize data when analyzing campaign performance. • Consistency: Aligning metrics and formats across the board helps ensure everyone is working with the same data. • Accuracy: Standardizing reduces discrepancies, leading to more reliable insights. • Efficiency: It streamlines the analysis process, making it easier to uncover what’s really driving results.

Be aware that this may not be possible with tools that use different modelling, or algorithms where we don't know the exact logic (e.g. Google Analytics 4 black boxes). It's obviously important to standardise data as far as possible, but also to know where this isn't possible - and if that matters - we're often looking for trends and patterns of behaviour rather than exact numbers, and those can still work without 100% reconciliation.

Após a análise, identificaria quais fontes de dados são mais confiáveis e relevantes para o objetivo específico da análise. Daria prioridade às fontes diretamente vinculadas aos KPIs da campanha, um método interessante é o ICE Score para definir qual ferramenta utilizar.

If possible, use the same tools or methods for data collection across all channels. For example, if you’re using Google Analytics, ensure it’s implemented consistently across all pages and campaigns.

To standardize data from conflicting sources, ensure all data is measured in consistent units and formats. Align reporting periods, normalize metrics, and apply the same definitions and calculations across sources.

After identifying data inconsistencies, the next step is to standardize your data. This involves aligning your data collection methods, ensuring all tracking codes are correctly placed, and using consistent definitions for metrics across all platforms. Think of it as setting the rules for a game—everyone must follow the same guidelines to ensure a fair and accurate comparison of results. Standardization is crucial for gaining clear insights and making informed decisions about your marketing campaign's effectiveness.

Align the data sources by adjusting for differences in tracking windows, attribution models, or audience segments. Use data transformation techniques such as time alignment (e.g., adjusting for different time zones or reporting delays) or unit conversion (e.g., currency, time, or value formats). Make sure the tracking parameters are rightly places such as UTM parameters, GTM tags, event level attributes.

Establish a common framework for your data. This involves aligning metrics, timeframes, and definitions across all sources. Ensure that all data is processed uniformly, which might include converting data formats or adjusting for different time zones.

Importante saber qual o algoritmo a ser padronizado para a coleta de dados, qual o público alvo, período e população para alcançar a acuracidade.

🔗 Resolve Conflicting Data Sources with Effective Data Integration 🔗 ◘ Use Integration Tools: Combine data sets from various sources for a unified view. ◘ See the Full Picture: An integrated approach provides a complete view of campaign performance. ◘ Make Informed Decisions: Unified data helps in making more accurate and informed decisions. In my experience, data integration is crucial when dealing with conflicting data sources in marketing analytics. I used integration tools to merge data sets, allowing us to see the full picture of our campaign performance. This unified approach not only resolved discrepancies but also enabled us to make more informed and accurate decisions, driving better outcomes for our marketing efforts.

Centralizar los datos en una plataforma como un DMP o CDP es esencial para una estrategia de marketing coherente y precisa. Coca-Cola, por ejemplo, utiliza un CDP para unificar datos de clientes en tiempo real, permitiéndoles ajustar sus campañas a nivel global con mayor efectividad. Netflix, por su parte, centraliza datos de usuarios de diferentes dispositivos en un DMP, lo que les permite personalizar recomendaciones y optimizar sus campañas publicitarias. Esta centralización garantiza que todas las decisiones de marketing se basen en datos actualizados y precisos.

A data partner can offer tremendous ROI in this space. If your situation does not lend itself to a simple out of the box product consider consulting with an experienced data consultancy who views your unique situation rather than one who attempts of retrofit a 1 size fits all solution. There is often low hanging fruit in terms if investment in your data instrastructure that a good consultancy will highlight and propose a roadmap to leverage your data for tangable commercial return. Marketing departments often lead the way for the wider business, as a single view of the customer is a solid proof of concept and always drives growth at pace.

One thing I will keep in mind in this scenario is the importance of aligning data sources to improve cross-channel attribution, which helps me understand the full customer journey. Additionally, resolving conflicts enhances reporting accuracy, making it easier to communicate results to stakeholders and justify marketing investments. This process also fosters better collaboration between teams (including cross functional ones) by creating a unified view of campaign performance, ultimately driving more effective and efficient marketing strategies.

To integrate conflicting data sources, map and merge data into a unified schema, reconcile differences by aligning metrics and definitions, and use data integration tools to ensure consistency and accuracy across sources.

Integrating data from various sources is like merging different dialects of the same language. To do this effectively, you may need tools or platforms that can combine data sets into a unified view. This approach ensures that you can see the complete picture, eliminating gaps and overlaps, which allows for more informed and accurate decisions regarding your marketing campaign's performance. By harmonizing these diverse data sources, you can better understand trends, optimize strategies, and achieve more reliable outcomes.

Data Integration can be technically challenging, we can either rely on our internal techincal teams such as Data Engineers or Data Scientists. Or we can utilise tools such as Funnel.io which can help in data integrations into one single platform such as google sheets or any reporting tool as Tableau.

Integrate data from all sources into a single, coherent dataset. Use data integration tools or platforms that can merge and harmonise information, ensuring that all data is comparable and ready for analysis.

Centralizar os dados por meio de uma única plataforma de dados é um caminho para tratar os dados e ter a informação com mais acuracidade.

At the same time, keep track of results that don’t seem to fit the overall pattern. They usually are early warning signs. You might not recognize what they mean at this moment. But you will likely find them popping up in other data. And they frequently are the most important results, once you understand what they mean.

To analyze holistically, aggregate data from all sources, look for overarching trends, and consider the context of each source. Focus on overall patterns rather than isolated discrepancies.

With your data now aligned, it’s time for a holistic analysis. Go beyond the numbers and consider the broader context—what was happening in the market during your campaign? Were there external factors, such as economic shifts or industry trends, that might have impacted performance? This comprehensive approach allows you to understand your campaign's effectiveness more deeply, revealing insights that numbers alone can't provide. By considering both data and context, you gain a more nuanced understanding, leading to more informed decisions and better strategies moving forward.

Once your data is integrated, analyse it as a whole. Look for overarching trends and patterns rather than focusing on isolated data points. A holistic approach provides a clearer picture of your campaign's overall performance.

Conforme informado, entender o período dos dados coletadas e as variáveis externas são fatores de extrema relevância para conseguir a acuracidade nos dados que darão êxito na campanha.

El análisis predictivo y el machine learning son claves para convertir los conflictos de datos en información procesable. Al utilizar estas herramientas, puedes identificar qué canales tienen un rendimiento inferior o qué tipos de mensajes resuenan mejor con tu audiencia. Por ejemplo, Amazon emplea machine learning para predecir comportamientos de compra y ajustar recomendaciones, mientras que Spotify usa análisis predictivo para personalizar listas de reproducción, mejorando la experiencia del usuario. Uber, por su parte, detecta anomalías en tiempo real y ajusta sus estrategias. Aplicar estos métodos no solo corrige discrepancias, sino que también refina tus estrategias de marketing y mejora el rendimiento de futuras campañas.

🔍 Identify Key Trends: Look for consistencies that can reveal which channels or strategies are performing well and which need adjustment. 📊 Segment Analysis: Understanding how different segments respond can help tailor your messaging and optimize targeting efforts. 🎯 Refine Strategies: Use the insights gained from the analysis to refine your marketing strategies. Whether it’s adjusting your messaging, reallocating your budget to high-performing channels, or optimizing your targeting, these data-driven decisions will enhance future campaigns. 🔄 Continuous Improvement: Regularly revisit your data sources and insights to ensure that your strategies remain aligned with evolving audience behaviors and market conditions.

An important aspect is "Insight Prioritisation". Here's a framework for it: 1. Impact: Estimate the potential effect on KPIs like revenue, conversion rates, or customer acquisition cost etc 2. Feasibility: Assess how easily the insight can be turned into action 3. Urgency: Determine if the insight addresses an immediate problem or opportunity 4. Cost-benefit ratio: Weigh the expected benefits against the costs of implementation By following this, you can focus your efforts on the changes that will drive the improvements.

To derive actionable insights, prioritize the most reliable data, identify key trends and patterns, and focus on metrics that align with your campaign goals. Use these insights to inform strategic decisions and adjustments.

The ultimate goal in resolving data conflicts is to gain actionable insights. What can you learn from the discrepancies once they are resolved? Perhaps certain channels are underperforming, or your audience responds better to a particular type of message. Tip: Use these insights to refine your marketing strategies and enhance the performance of future campaigns. By continuously analyzing and adapting, you can stay ahead of the curve and achieve better results.

From your analysis, extract insights that are practical and can drive decision-making. Ensure these insights are backed by consistent and reliable data, and are aligned with your campaign goals.

La optimización es un proceso que mezcla la creatividad, el análisis, la observación y la experimentación. Estar atento a los detalles, en las métricas clave, que pueden marcar la diferencia es una apuesta de quienes buscan el crecimiento, y ahí enfocan todas sus energías para lograrlo.

Regularly measure the return on investment (ROI) of your campaigns and adjust your strategies accordingly. Use these measurements to refine your data tracking and analysis processes to better align with your business goals.

How do you make sure teams don’t have conflicting data in future? One way is to run workshops that have the objective of bringing together whole departments. This is in order to show that there is data that might work better segmented in one way for A or B, but also that there is data which should be able to be used across departments, for example, as key performance indicators.

For continuous improvement, regularly review and reconcile data sources, refine data collection methods, and adjust analysis processes based on feedback and evolving needs.

Treat this process as ongoing. Continuously refine your data collection, integration, and analysis methods. Regularly update your standards and tools to adapt to new data sources and evolving campaign needs.