You're faced with conflicting consumer data for brand positioning. How can you make informed decisions?

I’ve experienced similar scenarios many times specially when you have multiple data sources including 1P and Zero party data. You’ll see differences in the customer persona. For example a dipstick or a survey data on predominantly upper funnel consumer might conflict with the first party data of the middle and lower funnel consumer data. Therefore in my perspective it’s important to audit many data sources that has a significant importance in the consumer journey, and try to analyze and understand from a human perspective.

Data Validation Source Evaluation: Assess the credibility and reliability of the data sources. Give more weight to data from reputable sources or those with a larger sample size. Consistency Check: Check for consistency within the data sets. Look for patterns or discrepancies that can help identify which data points are more reliable.

This is something most people miss - that data can often be misleading if not organized and filtered properly. Screen your data in such a way that it only directly corelates to your brand strategy- then only you can make sound decisions on it.

If you’re faced with conflicting consumer data for brand positioning, making informed decisions involves a systematic approach to reconcile the differences and gain a clear direction. Break down the data by different consumer segments to understand if the conflicting information comes from distinct demographic or psychographic groups. This can help identify if the brand appeals differently to various segments.

This might be the most challenging yet enjoyable part of the brand strategy process. To make sense of conflicting data, I find it easier to group consumer segments before I look for patterns. This makes identifying key psychographic traits much simpler. In a digital world where quantifiable data rules, I think qualitative tests prove the best for identifying human-related insights. When engaging the right target audience, focus groups and in-depth interviews can uncover invaluable human-related insights that data audits and pattern recognition alone might miss.

Use not just your data but historical brands that are similar to yours. Find out patterns where their brand seemed to be breaking too and what they did to rectify and come back stronger. Try to see beyond what the consumers are asking for into instead why they're asking for it.

Analyze historical data to identify trends and patterns. This can help determine if the conflicting data is an anomaly or part of a broader trend. Look for trends over time. In this business tends happen to change overnight.

You may be getting conflicting data because you're trying to target two different audiences. One may be an excellent fit for your products. And the other may be a bad fit. Look at your data and see if there are particular groups that correlate strongly with a particular view of the positioning. This may reveal a segment that just isn't a good fit for your brand. Not every brand is for everyone.

Segmentation Analysis Segment Differentiation: Analyze data based on different consumer segments (e.g., demographics, psychographics, geographic regions). Conflicts might be due to differing behaviors across segments. Target Audience Focus: Prioritize data from the segment that aligns most closely with your target audience for brand positioning.

Understanding the market context is essential when faced with conflicting consumer data. The same data can imply different things depending on market trends, competitor actions, and cultural shifts. Evaluating external factors can help interpret conflicting data to align with current market dynamics, ensuring your brand plays the right role at the right time. I've found that taking a holistic view often clarifies seemingly contradictory data. By considering the broader market environment and identifying patterns or disruptions, you can make more informed decisions that resonate with your target audience. This approach not only refines your brand positioning but also ensures it remains relevant and compelling in a dynamic marketplace.

Hypothesis Testing Form Hypotheses: Based on your initial analysis, form hypotheses about why the data conflicts exist. Experimentation: Design experiments or A/B tests to test these hypotheses in real market conditions and gather empirical evidence.

Develop hypotheses based on the conflicting data and design A/B tests or pilot programs to see which positioning resonates more with your target audience. Use the results to make data-driven decisions.