marketing strategy and 6 common mistakers in data analytics

6 Common Mistakes in Data Analysis for Marketers to Avoid

With the evolving business landscape and the increasing significance of online presence, the role of marketers has undergone a significant transformation. Today, marketers need to possess not only creativity and innovation but also digital marketing and analytics skills.

In 2021, companies worldwide spent a staggering $521 billion on digital advertising, and this figure is projected to reach $876 billion by 2026. This underscores the critical need for marketers to leverage analytics to track return on investment (ROI) effectively.

While marketers now have access to various tools and technologies for marketing analysis, it remains paramount for them to acquire data analytics skills and develop a data-driven mindset. This ensures the accuracy and relevance of data, both in its collection and application in marketing strategies.

common mistakes in data analysis

In this article, we aim to shed light on 6 common mistakes in data analysis frequently observed among marketers. By highlighting these pitfalls, our objective is to steer you away from potential detours and toward more effective, well-informed marketing strategies.

6 Common Data Analysis Mistakes for Marketers to Avoid

1. Neglecting Data Quality

We have been emphasizing the importance of data quality.

Many times businesses are thrilled with the amount of data they have collected but neglect the quality of the data source. This led to faulty findings and wrong insights with potentially disastrous consequences. Not only can companies lose millions due to erroneous data insights, but it can also damage relationships with customers and suppliers.

Before conducting data analysis or seeking insights, marketers must prioritize checking the quality of the data source.

Is the data source reliable? How was it collected?

Is it comprehensive and representative of my users, or only convey partial truth?

Is this data real-time or outdated? Are there any missing or duplicated values?

If you are collecting data internally, you need to also make sure certain standards are being followed across the organization to prevent any biased data.

You also want to make sure the technical part is set up properly to prevent any wrong reporting of data.

We have previously shared that high-quality data should cover all 8 dimensions mentioned below. You can read more in this post on how to collect high-quality data.

data quality and in preventing common mistakes in data analysis

2. Analyze with Insufficient Data

After talking about data quality, we need to talk about quantity as well.

To gain a deeper understanding of customer behavior or analyze specific trends, marketers need a sufficient volume of data points for analysis.

It is essential to collect data that is fully representative of users, rather than relying on a subset.

Additionally, data should be collected over a period long enough to account for periodic or seasonal effects on user behavior. For example, a gaming company may observe increased user activity during weekends and holidays.

Avoid relying solely on one metric to understand customers. A single metric may not reflect the complete truth about customer behavior.

For instance, when you are celebrating that the customer satisfaction rate has increased, you might have neglected the possibility that there has been a high churn rate and only a small portion of the satisfied customers have remained.

To obtain a comprehensive picture of customers or product offerings, it is encouraged to use various forms of data collection, including both qualitative and quantitative methods.

Qualitative data collection involves surveys or descriptive questions that elicit detailed responses, while quantitative surveys focus on numerical answers related to frequency, ratings, preferences, and more.

focused group studies to prevent data analysis mistakes

In addition to tracking online user behavior, consider conducting surveys, face-to-face interviews, or post-service calls to gain direct insight into customer opinions and experiences. This holistic approach provides a more comprehensive understanding, enabling you to optimize your products or services accordingly.

You can read more on how to collect each type of data in this post.

Lastly, most of the marketing efforts are done through multiple channels now. Instead of relying solely on a single web analytics platform, such as Google Analytics, it is crucial to factor in customers’ behavior on different channels and devices.

Consolidating data from various sources into a single platform with a Business Intelligence (BI) Tool can prevent the issue of data silos. It can also help to improve efficiency and provide a centralized source of truth for the marketing team.

Read here on how to select the right BI tool for your business.

Also, users’ behaviors on phones and desktops are different. Don’t forget to factor that in as well.

3. Using Data to Confirm Assumptions Instead of Being Objective

A common mistake, even among data analysts, is approaching analysis with preconceived assumptions.

This biased approach affects judgment and increases the likelihood of falling into confirmation bias.

Confirmation bias occurs when we favor information that confirms or strengthens our existing beliefs, filtering out evidence that contradicts them.

Instead of using data to confirm existing ideas, marketers should formulate hypotheses and use data to test and accept new possibilities.

Insights derived from an objective approach are more reliable and avoid biases.

Objectivity is key to uncovering true patterns and deriving meaningful insights. Therefore, before conducting analysis, set the golden standard of objectivity and let the data speak.

4. Obsessed with Vanity Metrics

What are vanity metrics?

Vanity metrics are the metrics that boost one’s ego but fail to provide actionable insights.

vanity metrics on data analysis mistakes

Marketers often fall into the trap of focusing on metrics that make them feel good, such as website page views, total registered users, or social media page likes.

However, these numbers do not provide detailed information about webpage viewers or the level of engagement of newly registered users. They do not offer guidance for optimizing operations or planning the next steps.

The key here is still, to always remember what is your business objective.

Data analysis should serve a purpose. Without well-defined objectives, you risk getting lost in the sea of data, vanity metrics, and struggling to derive actionable insights.

Before tracking a metric, ask how it will improve business decisions or guide the achievement of objectives.

If the metric cannot answer these questions, it is necessary to brainstorm and identify relevant metrics that provide insights into key business questions.

5. Over-Analyzing the Data

Another common mistake observed among marketers is over-analyzing data.

As the data size increases, it is natural to start noticing correlations or insights through visual inspection. Marketers may then become fixated on these perceived trends or correlations, investing significant effort in analyzing the relationships and causes behind them.

Another scenario is that when some marketers see an abnormality in data, they instantly become alert and start to analyze the reason behind the fluctuation, but do not consider the other possibilities. For instance, seasonal or periodical fluctuation, external factors (political, competitors, etc.), internal factors (product update, change in the feature, etc.), or possible technical problems.

When marketers are too obsessed with the metrics alone, they start to lose sight of the bigger picture.

We understand that it’s not easy to develop an analytical mindset from the start. All of us have the tendency to find correlations between things and have cognition bias.

To prevent over-interpretation, you need to deliberately train yourself in analytical thinking or engage a professional data expert to help you accelerate the efficiency in obtaining data insights.

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Talk to us today

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6. Ineffective Data Visuals

Lastly, the misuse of data visuals can be detrimental.

Ineffective visuals can take various forms.

One common issue is cramming excessive information into a single dashboard, leading to data overload that overwhelms the audience and hinders the identification of key insights.

Another type is using ineffective visuals without considering the principles of data storytelling.

data storytelling to prevent data analysis mistakes

There is a whole discipline dedicated to data storytelling, and data visuals should adhere to its principles. Effective data storytelling focuses on simplicity, enabling the audience to understand information easily with minimal effort and prompting action.

For marketers juggling creativity and analytical roles, it can be challenging, especially without a data background or training, to navigate the tools and approach business data with a critical and analytical mindset.

Our aim is to help alleviate this burden and assist in streamlining the data process, enabling marketers to focus on implementing strategies based on valuable insights. Schedule a consultation with us today to discover how we can assist you in achieving marketing analytics success.

datasi data consultancy book consultation

Talk to us today

Find out how we can help your business to build a successful data strategy.

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