Big Data Data Analytics

7 Data Analytics Mistakes Digital Marketers Make

It’s no question that a marketing campaign can gather tremendous amounts of data, however, if the data is not correctly interpreted the value may only be a fraction of its potential. According to a recent Gartner survey, marketers sensed their companies did not fully understand how to effectively leverage data analytics. Let’s dive into the common challenges and mistakes marketers face when it comes to their data analytics.

The 7 Most Commons Mistakes Digital Marketers Make

1. Confusing Data Metrics and Visualizations

A clear understanding of what metrics actually are rather than what they are “perceived’ to be is essential to any marketing campaign. Marketers should have a clear understanding of what the metric means, not purely what is presented in a visualization. For example, unless there is a precise understanding of what “views” represent as opposed to “visits”, analytical data can be easily misconstrued. 

Depending on training and expertise, some marketers may not necessarily be data experts. This highlights the need for strong background information when it comes to dashboards and data visualizations. Without proper context, it can be overwhelming when determining the correct course of action. It is imperative to not choose a visualization based upon the flashiest dashboard design but to understand the data behind the visual, this will ensure proper decision making and evaluation.

2. Relying on a Single Data Set

Data analytics requires collecting data and often there may be more than one tracking source for the data collection. Oftentimes different data tracking mechanisms may generate various data metrics from the same data collection. It is vital to work with numerous tracking sources for increased visibility across target audiences and campaign performance, whether they be internal or external. Aim to collect both qualitative and quantitative data for the most accurate and informative visibility.

3. Incorporating Data Too Late into the Creative Process

The marketer’s creative process should be the end result of the primary marketing objective. Though, the creative process can be more powerful when incorporating analytical data elements. 

Being able to drill down into your audience’s preferences and demographics is a winning process in creative production. Some key takeaways from incorporating data early in the creative process are:

 1. The earlier you can incorporate data analytics in the creative process, the better.

 2. Utilize the collected information to clearly define your key audience.

 3. Leverage data to create a road map of how to reach your targeted audience.

4. Concentrating Heavily on Vanity Metrics

A marketer understands many elements go into creating captivating content and copy. Though, the positive feedback for a video or campaign generating thousands of comments, likes, followers, or other vanity metrics may lead to a false sense of success. 

The key question and focus should continue to be towards quantifiable conversions and investment in the customer lifetime value. Access if the marketing efforts ultimately lead to loyal customers evangelizing the brand. The focus should remain on generating leads, then conversions, and sequentially creating loyal customers.

5. Not Asking Questions

Data analytics is very efficient in creating a comprehensive set of data, and studying a report or spreadsheet to form a clear picture can be daunting. The trick is to have an explicit focus on your end goals and intentions, asking questions is key to narrowing down the data points required to formulate a winning conclusion.

For example, when studying the data, the question may not be to see “how the website is performing” but rather asking “how much has our social traffic increased?” When questions are asked about specific data points, the answers should guide you to more productive conclusions.

6. Ignoring the Importance of Data Culture

Buy-in across the organization is critical to any successful analytics strategy. Commonly, few on the team have a clear understanding of the importance of being data-driven. High-level goals that data analytics will be a cornerstone for the marketing process should be known and understood across all levels of the organization. Try implementing an objective to embrace data analysis by defining obtainable goals and gradually increase awareness through training and workshops.

7. Failure to Create Actionable Insights

Actionable insights require looking beyond the surface level of standard metrics and KPIs. While not all conclusions may be useful, particularly without fully comprehending what they indicate, not diving deeper into analytical conclusions may lead to lost opportunities. Make sure to analyze the metrics in-depth for patterns and unique insights. By diving deeper into insights and taking an exploratory approach, successful strategies may begin to form. 

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