Common audience analysis mistakes and how to avoid them

Accurate audience analysis is the backbone of any successful marketing strategy. However, many marketers make common mistakes that can lead to incorrect conclusions and ineffective campaigns. Here are the most frequent audience analysis mistakes and how to avoid them.

1. Starting with the wrong audience definition

Mistake: Not applying any filters when defining your audience.

Explanation: The first step in any audience analysis is defining the audience. While there is no "wrong" audience definition per se, it's essential to be fully aware of what you are analyzing. Every piece of data you gather will depend entirely on your input, so always keep potential biases in mind.

Example: If you're analyzing Tesla fans, simply selecting the interest "Tesla" might result in a lot of irrelevant data due to the broad qualification of interests on platforms like META.

Solution: Always refine your audience definition. For instance, add a second level of interest like "electric car OR electric vehicles" to ensure you're capturing people genuinely interested in Tesla as a company. Similarly, when using tools like Audiense Insights, consider adding filters like followers of the CEO, other electric car-related accounts, or bio keywords like "Tesla" or "EV."

Learn more about creating precise audience segments using Audiense.

2. Setting the wrong benchmark/baseline

Mistake: Using an incorrect benchmark that skews results.

Explanation: The importance of benchmarks in audience analysis cannot be understated. In tools that use comparative analysis, the benchmark dictates what information is highlighted. Setting the wrong benchmark can lead to misunderstandings.

Example: If you're analyzing a target audience of UK football fans and set the benchmark to worldwide, your results will represent the UK population more than football fans.

Solution: Align your benchmark with your analysis goal. To learn about the behavior of British football fans, set the benchmark to the UK population. To compare British and American football fans, set the benchmark to American football fans.

Check out our guide on setting effective benchmarks in audience analysis.

3. Mistaking the hierarchy of information

Mistake: Starting with lower-priority data types.

Explanation: In audience analysis tools, different types of information, such as segmentation, interests, and topics, are not all equal. It's essential to start with the right data type to avoid making generalizations from outliers.

Example: In Soprism, start with segmentation to understand the audience as a whole. Segmentation includes profiles created to provide a broad understanding, like personality insights and lifestyle.

Solution: Begin with segmentation to understand the audience broadly, then delve into interest criteria for more details. Using criteria and topics first might lead to generalizing from atypical data points. In Audiense Insights, start with bio keywords and the influencers and brands tabs, as segmentation and other information are often inferred from these sources. Bio keywords represent high-quality data as they are self-reported by audience members.

Discover how bio keywords can enhance your audience insights.

4. Not looking at the whole picture

Mistake: Focusing on single criteria or segments.

Explanation: To get the best audience analysis, consider the results as a whole rather than in isolation. A common mistake is to focus on a single criterion, segment, or account, leading to a monolithic view of the audience.

Example: Analyzing a US audience might reveal interests like "English courses," "language education," and "English tests," along with TV channels from Spain and a "Latino culture lover" segment. These pieces of information suggest the target audience might have recently moved to the US, even if not explicitly stated.

Solution: Consider the results holistically. Group different pieces of information to understand the audience comprehensively. Conversely, avoid assuming that a single data point defines the entire audience. For instance, a single left-wing politician in the top following does not necessarily indicate a left-wing audience.

5. Never updating your data

Mistake: Relying on outdated data.

Explanation: Key audience data can change significantly over time. Running campaigns based on a single report from a year ago is not advisable. Trends within a market can shift dramatically even from quarter to quarter.

Solution: Regularly update your audience analysis, ideally monthly. This allows you to track the impact of your campaigns, understand core audience elements that remain constant, and anticipate significant market shifts.

6. Not keeping a critical eye on the data

Mistake: Dismissing data that contradicts your knowledge.

Explanation: Sometimes, the data you receive may not align with your market knowledge. A mistake would be to dismiss the data outright or ignore your insights. It's crucial to investigate discrepancies.

Solution: Investigate discrepancies between data and market knowledge. Ask yourself:

• Is your audience definition accurate, or can it be improved?
• Is this a single piece of information that differs, or is everything different?
• Has there been a recent event or news that might have influenced the data?

Trust the data, but also trust your knowledge. Seek explanations for unexpected results and don't hesitate to ask questions if you're struggling with your analysis.

By avoiding these common audience analysis mistakes, you can ensure more accurate insights and more effective marketing strategies. Remember, understanding your audience is a continuous process that requires careful definition, benchmarking, holistic analysis, regular updates, and critical thinking.

Ready to elevate your audience analysis? Sign up for Audiense today and start transforming your marketing strategies with accurate insights.