As a marketer, you may not be a data analyst or data scientist but to succeed, you need to develop a strong affinity with data. That’s because successful marketing depends on data as light bulbs depend on electricity. In recent times, data analysis has become an essential part of digital marketing roles and without it you’ll be working in the dark.
Although big organizations have dedicated analysts that handle their marketing analytics, you might find yourself in digital marketing positions where you are expected to be both the marketer and analyst.
Even in organizations with data analysts, having data skills can make collaboration easier between the marketing team and the analytics team. By understanding how to analyze marketing data, marketers can also singlehandedly measure and report on their organization’s marketing program, and draw insights that can help to improve the program.
So what data skills are essential for digital marketing analysis, and where do they apply in a marketer’s daily data practice? Let’s find out by considering the following.
Spreadsheet is one of the basic data analysis skills a marketer needs, and this goes beyond importing and exporting data or using keyboard shortcuts. There are many spreadsheet functions that you can use to find the information you need from data. For example, the VLOOKUP and HLOOKUP functions work great for comparing various sets of data vertically and horizontally across data cells. This can be helpful in keyword planning, to know if a proposed keyword already exists in your database.
There is also the “Pivot Table” that can be used to review, analyze, summarize, and extrapolate important trends from your data by characterizing and arranging tabular data. Another interesting Spreadsheet skill is programming with macros to automate data analysis. With macros, you can program a series of commands and execute them at the click of a button, thus making your work faster.
Since your work will require collaborating with teams, you can learn to work on Google Sheets. It is flexible and you can connect it to other data sources such as Google Analytics. This can help to build more dynamic reporting dashboards that you can send to team members, managers, and clients.
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As a marketer, you need access to how your campaigns are performing, and how users are interacting with your ads and landing pages. However, one of the essential factors for getting accurate performance metrics is a cohesive tagging strategy.
Tagging is used to connect ad campaigns to their target URLs. Each ad is assigned a unique identifier which makes it possible to track the source of the traffic on a webpage to a specific ad. It also provides more information on each user. If you run several campaigns on ad platforms like Facebook, Twitter, and LinkedIn, you will need adequate tagging to get rich data from web analytics.
Failure to tag your ads to the target URLs can result in huge gaps in data collection which makes it difficult, if not impossible to measure marketing campaigns accurately. Therefore, learning how to create tagging and tracking strategies on ad platforms is important for accurate web analytics.
Understanding how to use structured query languages (SQL) to interact with the databases and obtain useful information is another essential digital skill for marketers. Marketing analytics requires obtaining specific information that can only be accessed by querying databases. For example, an important marketing insight might be to know the geographical location of specific users; therefore, the ability to use SQL can help to unlock such insight.
With applications like MS Access and MSQL, marketers can also develop, maintain, and update data reports from marketing programs and share them with the marketing team or senior managers in a way that is clear and understandable.
SQL is limited when it comes to pulling all the data you need from multiple marketing platforms into one database. Besides, the kind of marketing analytics that effectively measures marketing performance is complex. Learning to code with REST API is a critical skill for marketing analytics that can help you with such tasks. It is incredibly essential to automatically pull marketing data from several ad platforms into one database, and programmatically analyze them to discover business insights.
Therefore, to pull data from Facebook, Twitter, LinkedIn, and Email marketing platforms, you will need to connect to their Graph APIs using the Python Request module. Request is a simpler, more user-friendly library that is used to make all kinds of HTTP requests including passing parameters in URLs, sending custom headers, and SSL Verification. It allows you to add headers, form data, multipart files, and parameters using simple Python dictionaries. It also similarly accesses the response data.
Once you have pulled data from various APIs, you will need another Python module, PYODBC to insert the pulled data into a single database. That way, you can query the database to get any information that you need.
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ETL is an acronym for three database functions: extract, transform, and load. ETL tools are used to automatically extract data from multiple and different types of sources, transform (convert) the extracted data to a required form for placement into another database, and load (write) the data into the target database.
If you can’t code with Rest API, using ETL tools can be a viable alternative for handling your data migration. They have built-in integrations that work with major APIs. The challenge with these tools however is that they are premium tools and might be expensive. However, if you are a power user, you will have value for your money. That is if you are dealing with large volumes of data, and multiple data sources or you need to convert large databases from one format or type to another.
Examples of paid ETL tools include Xplenty, Stitch, Talend Data Integration, Fivetran, Microsoft SSIS, Alooma, Panoply, Azure Data Factory, and more. There are also free open source ETL tools if you can understand how their backend works and be part of the community of developers. Examples include Airbyte, Apache Camel, Apache Kafka, and more.
Another very challenging area in marketing data analytics that you also need to understand is combining marketing data with CRM data. Not all sales are closed on a webpage, B2B sales especially often require several CRM efforts.
Being able to tie these sales to the marketing campaigns that produced the leads is essential for measuring marketing performance. But this is not an easy task and that is why understanding how to code with Rest API or use ETL tools are required so that you can obtain all the data that you need.
The edge you have as a digital marketer with proficiency in data analytics is that you understand the business and also know how to obtain the data you need to measure and improve your marketing program. For example, you can use data to track customers’ journeys and unravel what’s most effective and leading to more conversion. That way, you can also develop new growth and objectives for your campaigns.
Most importantly, combining analytical skills with business and marketing domain knowledge will help you to demystify technical data, and create dashboards and reports to effectively communicate marketing performance to your team or senior managers.
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