You're faced with conflicting data analytics results for brand performance. How do you make sense of it all?

Data is a guide, not gospel. It happens more often than we think! The key is context. Numbers alone don’t tell the full story, look at the source of the data, timeframe, and variables used. For example, imagine one report shows high website traffic but another reflects low conversion rates. Instead of getting caught up in the contradiction, ask: What’s driving traffic? Is it the right audience? Break the data down into pieces, and focus on actionable insights.

to start resolving this conflict we have to step back and see the Bigger Picture: -Seek a balance between the insights from different data sets that align with business goals, recognizing that each may have its strengths and weaknesses to cross-verify the results and identify consistent patterns, to reduce the impact of anomalies. -prioritize data sources then segment/break-down analysis based on your needs -identify the factors contributing to the conflicting results and improve data accuracy -normalize data given to ensure consistency in results outcomes -continuous monitoring and review methods to select the best A/b testing approach and address any discrepancies as they arise -prepare for various possibilities to make informed decisions

Data isn't the gospel truth, it's just wayfinding. Don't get too hung up on it. Go back to the heart of every brand - the Brand Purpose. What is the data telling you in the context of the purpose? What's aligned to the purpose, what's in conflict with it. If you anchor your decisions in line with the purpose, you'll be fine.

In the face of conflicting data analytics results, a data audit acts like a detective, uncovering hidden clues within your data. For instance, if social media engagement metrics show a decline while website traffic is booming, a data audit might reveal that a tracking code error is underreporting social interactions. Or, if customer surveys paint a positive picture but sales are stagnant, the audit might identify a sampling bias in the survey respondents. By examining every step of the data's journey, from collection to analysis, a data audit can reconcile these inconsistencies and reveal the true story behind your brand's performance.

To avoid any bugs or data issues, it’s crucial to prioritize maintenance and consistently monitor your CRM. Regular maintenance is essential to ensure performance isn't affected. In case of bugs, it's important to manage them efficiently and work closely with your team to prevent any negative impact on performance and audience.

With following parameters we can analyze data conflicts for brand performance: Verify Sources: Check the reliability of data sources. Ensure Consistency: Confirm data definitions and periods match. Review Methodologies: Compare the methods used in each analysis. Compare Metrics: Focus on key KPIs and look for trends or anomalies. Check Segmentation: Review how data segmentation might affect results. Consult Stakeholders: Seek input from team members for additional context. Analyze Causes: Investigate the root causes of discrepancies.

Next, I would look for patterns and trends within the conflicting data. This would involve reviewing both sets of results in detail, breaking them down by key metrics, and observing if there are recurring insights across different variables (e.g., time periods, regions, or customer segments).

Es importante que puedas identificar tendencias o patrones de error que pueden ser comunes en los datos que estas analizando. La calidad de tus resultados de análisis depende mucho de de la calidad de la información y las tendencias de error a veces pueden enmascarar estos errores, siempre un double check no viene mal.

After your data audit, **identify patterns** in the conflicting data. Even if numbers don't match perfectly, trends can still reveal useful insights. For example, if two sources show different engagement levels but both spike on weekends, that’s a pattern to pay attention to. These consistencies can help you focus on what’s important, rather than getting stuck on small differences in the data.

To resolve conflicting brand performance data, verify source reliability, ensure consistency, compare methodologies and KPIs, check segmentation, consult stakeholders, identify trends, and investigate discrepancies to uncover root causes.

**Contextual analysis** is key. Look at the conflicting data within the bigger picture of your market and recent brand activities. Maybe one tool didn’t fully capture a marketing campaign, or external events affected consumer behavior, causing unusual data. By considering these factors, you can better understand why certain numbers don’t align and make more sense of the data.

I absolutely agree with emphasizing on "contextual analysis" because wider context ,according to the market you cover , should be recognised as an important data. Focusing always on same analysis is something boring and is not giving "real data" and therefore analysis conclusions can go "wrong way"

Context matters. Analyze the conflicting data within the context of your market and recent brand activities. Perhaps a marketing campaign was only captured partially by one analytics tool, or external events influenced consumer behavior, leading to irregular data. Understanding the broader context can help explain why certain data points seem to contradict each other

I would then apply contextual analysis to interpret the data within the broader brand and market landscape. Understanding external factors such as economic conditions, marketing campaigns, or changes in consumer behavior can help explain why the data might appear contradictory.

Analyse conflicting data in the broader market context and recent brand activities. External events or incomplete campaign capture may cause irregularities. Contextual analysis clarifies discrepancies and helps make sense of the data.

With **hypothesis testing**, you can explore why data discrepancies exist. If, for example, one dataset shows high engagement but another shows low conversion rates, form a hypothesis. Maybe there's an issue with website usability or a mismatch in campaign targeting. Test these ideas to uncover the real causes and turn them into actionable insights.

Crie hipóteses que expliquem por que essas inconsistências estão ocorrendo. Um possível cenário pode ser que, apesar de um alto engajamento com o conteúdo da marca, o processo de compra no site não seja intuitivo ou fácil. Outro motivo pode ser um público segmentado que interage com a marca, mas não está pronto para converter em compras.

Duda y plantéate la realidad de tu duda. Es importante realizar pruebas adicionales o encuestas significativas y relacionadas a la actividad general que te permitan recopilar más datos. Esto puede ayudar a confirmar o refutar cualquier hallazgos contradictorio que veas en tu análisis de data.

Formulate hypotheses to explain potential reasons for data discrepancies, then test them. For instance, if one dataset shows high brand engagement but another reveals low conversion rates, hypothesize why this gap exists. It could stem from website usability issues or a misalignment in campaign targeting. By systematically testing these hypotheses, you can uncover the root causes behind the discrepancies and turn those insights into actionable strategies for improvement.

Create hypotheses based on potential reasons for the data discrepancies and test them. For example, if one set of data suggests high brand engagement while another shows low conversion rates, hypothesize why this might be the case. Could it be an issue with website usability or perhaps a targeting misalignment in your campaigns? Testing these hypotheses can lead to actionable insights.

Confronting data with the field, with customers, and with those who interact daily with clients is a way to better understand the data and find concrete solutions to address the problem.

Hay expertos en investigación que no sólo aman los datos sino que saben leerlos y explicarlos con una sencillez que no deja pie a las dudas. Te hacen ver la solución claramente donde solo habia ruido e incógnitas. Si encuentras esa persona no dejes de consultarle nunca.

Seek data analytics experts help - they can take a look at your numbers and help you make sense of them. Think of them as a second pair of eyes that can help you: -> See things from a new angle -> Identify blind spots -> Find solutions that fit your brand's unique needs -> Turn data into actionable insights -> Boost performance A fresh perspective can be just what you need to take your business to the next level.

Consult data analytics experts for fresh perspectives, identifying patterns and solutions. Engage with customers and field professionals to align data with real-world insights, improving brand performance analysis and addressing issues effectively.

Il peut être utile de solliciter un avis externe. Consulter des experts en analyse de données apporte une nouvelle perspective et permet de mieux interpréter les chiffres. Ces professionnels peuvent identifier des tendances ou des problèmes que vous auriez pu manquer, et suggérer des approches ou des méthodes d’analyse différentes, mieux alignées sur les objectifs de votre marque.

Based on the insights gathered, refine your brand strategies. Focus on the most consistent and actionable data, aligning your approach with the patterns and expert advice to improve future brand performance.

Finally, use your insights to refine your branding strategies. If the data shows some approaches are working better than others, pivot accordingly. This might involve reallocating resources to more effective channels or refining your messaging to better connect with your audience. Let data guide your decisions, but stay flexible and ready to adapt.

From my perspective as a brand designer and founder, it's essential to let your data-driven insights shape your branding strategies. If the data highlights successful approaches and reveals areas that need improvement, it's time to make adjustments. This could involve shifting resources to more effective channels or fine-tuning your messaging to better connect with your audience. While data is a valuable guide, staying adaptable and ready to pivot when needed is key to staying ahead in branding.

Follow the data but also understand how you make use of your marketing channels. There are many reasons why certain channels perform better than others, but none usually work best alone without the other. For example, paid social conversions can be fantastic, but it doesn't mean you'd completely deny doing paid search. Search is a result of social's awareness. Understanding THE ROLE each channel plays in your marketing funnel is crucial to seeing how the buyer's journey truly plays out. Some are influencers and others are drivers. Follow the data, but even more so, follow the mind of the buyer. Because seasons change, messages and promotions change, etc. but the mind of the buyer, is quite consistent.

De acordo com insights e novas informações redefina a rota. Entenda que é normal mudar, sendo que agora há informações que antes não tinha. Mude canais, mude o tom, mas muito cuidado em não mudar a personalidade da sua marca, pois a estratégia muda, mas a marca, de forma costumeira, sempre tem já um DNA.