“Data is a precious thing and will last longer than the systems themselves.” – Tim Berners-Lee, inventor of the World Wide Web.
This is why we all love data…
Marketing data analysis is a technique, where marketers extract insights from all the available business data to create better marketing plans. Being a vital process of the business, it shows how the business has done in the past and what can be done to drive results in future.
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Marketing data analysis focuses on internal as well as external factors. Taking into consideration the strengths and weaknesses of the company, they will tell you how your business can compete in the market. During this process, marketers try to extract information from all the channels of marketing, consolidate the data into a unified marketing view.
Marketing data analysis is important because:
- You use the data to back-up your goals and visions .
- You want to check for profitability.
- It helps to know what the market has to offer in the future.
- You understand your customers and competitors.
- With the help of accurate data analysis, you can take calculated risks for your business.
While you gather data from plenty of resources, you need to clean the qualitative data so that you are left only with precise and actionable data. Data cleaning can be overwhelming. While it can be interesting for some marketers, the others will find it rather tedious.
Either way, a properly formatted data set is all you need to make it more analyzable. Thankfully, we have some simple and flexible functions that will help you make the process easier.
A few data cleansing tips:
- Develop a Data Quality Plan
So, you have the data set in your hand. The first thing you need to do is to set your expectations with the data. Creating KPIs will be the best shot. Think of:
- What are these KPIs?
- How are you going to meet them?
- Can you track the health of your data?
- What measures you must take to maintain data hygiene?
Once you answer these questions, you will easily identify data errors, the incorrect data and reach the root cause of the problem.
- Standardize data at the point of entry
Be careful right from the start. You already know where the data is coming from, so your duty is to standardize the data from the point of entry. The more unhealthy data you invite to your CRM, the more difficult it is going to become.
This is a step that you need to follow much before the actual cleaning process. Keep a close watch on the data entry points, which will make it easier for you to trap the duplicate or useless data.
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- Check the accuracy of the data
So, it is time to validate the data in your hand. But, how?
Today, there are plenty of tools that will help you and there is no need to mess it up with manual work. You can look for various data hygiene tools like Xplenty, Tibco Clarity, RingLead and so on, which will help you substantiate the accuracy of your data.
For marketers, this step is critical because the quality of the data is directly correlated to the quality of the marketing campaigns.
- Identify Duplicates
While dealing with customers on various platforms, it is easier to add plenty of duplicate information on the CRM. Duplicates stuff your database and they also cost you too much when generating and implementing marketing campaigns. They also prevent you from having a unified customer view.
You must make all the efforts to reduce duplicity from the system.
Quick tip: ensure that your data is healthy, check the data entry, validate it and scrub it for any duplicates.
- Append Data
Finally, after all the hard work you have some concrete data in your database. A simple example could be the name, email address, location and age of a customer.
Think of adding more information such as phone number, title, their tech stack and so on.
Without the complete information of your customers, you will create “whitespace”. Never let that happen to your database. Use the right tools to help.
Any effective data cleaning tactic should have the following key practices:
- You should have the ability to remove major errors and inconsistencies working with single data and while you combine multiple data at a time.
- It is important to implement tools that will help you reduce manual inspection errors, streamlining the entire process.
- Deployment should happen in conjunction with schema-related data transformations and particular mapping functions.
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