What are your customers telling you about your brand? Are you using their feedback to optimize your strategies? This Complete Data Analytics Guide will show you exactly what you do to benefit from the information which is in front of you.
Let us look closer into the Data Analytics process and what it can do for you.
What Is Data Analysis?
In Digital Marketing, Data Analysis refers to the process of measuring metrics which affects business success. These parameters include website traffic, leads, conversion, bounce rate and sales. Data Analysis enables a marketer or business owner to identify the key influencers; and who among the leads can become potential customers.
One of the goals of Data Analysis is to improve the digital success rate of a company by understanding the behavior of online traffic. In addition, Data Analysis helps track online performance and provides reports. In this guide, information for analysis comes from varied sources including company websites, emails, social media, press releases, blogs and other mediums.
Qualitative Data Analysis
Qualitative Data Analysis uses data categorized by description instead of numerical value. This analytic type uses unquantifiable information to arrive at a particular conclusion.
Brand image, a customer’s emotional attachment to a product, loyalty, product recall or a company’s reputation are measured through Qualitative Data Analysis. Data are gathered through interviews, testimonials, comments, audio or video recordings.
Quantitative Data Analysis
Evaluation of information involving data with numerical value is referred to as Quantitative Data Analysis. This type of evaluation involves statistical descriptions such as average, mean, median, standard deviation, traffic count, volume and other measurements.
Quantitative Data Analysis is very systematic, and therefore, turns raw data into useful information which may affect the growth and development of a company.
- Hello Marketers – Are You Ready for Data Analytics?
- Content Marketing Strategy and Potential Role of Big Data
Data Analytics Objectives
This complete data analytics guide will help you understand the process of evaluating information obtained from digital efforts. In addition, it allows marketers to predict future trends. With this, a company will be able to avoid possible risks.
We have gathered information from researchers and companies using Data Analytics to improve their business performance. Here are some of the results:
- Prediction of future trends, change, fashion and upcoming happenings.
- Identification of performance concerns that may need urgent and essential action.
- Availability of data results to faster and better decision making process.
- Awareness of customer preferences, habits, attitude and response to your brand.
- Competitive edge of a company from competitors.
- Achievement of company objectives in the long run.
- Enhancement of overall user experience.
To achieve better results, you need to have the relevant data to analyze and evaluate. You can obtain data through a well-thought-of data collection process. Here is how you can do exactly that.
What do you need the data for? To identify a problem that requires a solution. Additionally, this solution can be a set of opportunities that a company can use for growth and development. Problem identification is also the initial step, and failure to identify this concern, will lead to possible challenges along the data generation process and actual analysis of information.
What are your main priorities? These priorities are your goals. Hence, review the SWOT analysis of your company and identify the concerns which require the most attention and immediate action.
How do you achieve your goals? This step refers to your methods and approach for data collection. Further, you have to identify what direction to take to solve a problem or take advantage of an opportunity. Ask yourself the following questions:
- How to collect relevant data?
- Who will conduct the data generation?
- Where is the information coming from?
- What categories will be used for the data being collected?
- How will this contribute to the achievement of goals?
Different Types of Data Analytic Tools
- Adobe Marketing Cloud
- Google Analytics
- Google Website Optimizer
- Open Web Analytics
- Site Meter
- Spring Metrics
- Stat Counter
- Check My Links
- Google Analytics
- Google Webmaster Tools
- Hubspot’s Website Grader
- Moz Pro Tools
- Raven Tools
- Adobe Social
- Crimson Hexagon
- Digimind Social
- Falcon Social
- Google Analytics
- Simply Measured
- Socialbakers Analytics
- Brand Mentions
Step by Step Complete Data Analytics Guide
After you have gathered enough information, you can therefore make the data work for you.
1. Data Cleansing
Segregate what works and what doesn’t. This step includes identifying information that is valuable to the desired results. You also have to set your parameters when creating the objectives. Thus, it is up to you as a marketer or business owner which data you should keep.
Data cleansing is also about correcting incomplete records, correcting wrong spellings and typographical errors, removing corrupt files, and further enhancing the entire database.
This step is considered critical because of the process of retaining and removing data. The challenge occurs when one maintains the wrong information and removes the relevant data. This could lead to erroneous interpretations and conclusions at the end of the analytical process.
Finally, you should be able to have a set of information that has the following characteristics:
- Valid. The data should come from credible sources and with a reasonable basis.
- Accurate. The details must be correct, leaning towards the desired output.
- Complete. There should be no missing items and values which are well designated.
- Consistent. Information must be standard and uniform across specific parameters.
2. Data Hypothesis
With clean data at hand, a marketer or business owner can make initial sets of hypothesis. A hypothesis refers to the proposed explanation or “educated guess” from a limited source of information.
Merriam-Webster defines a hypothesis as “an assumption or concession made for the sake of argument,” or “a tentative assumption made to draw out and test its logical or empirical consequences.”
A data hypothesis is subject to testing or experiments. It is a simple definition of a relationship or trend among variables. Every prediction aims to answer a question or take advantage of an opportunity.
Test your hypotheses to prove the relevance of the data gathered. There is no other way to test evidence than by conducting experiments. Through experimentation, you can validate theories, enhance patterns, determine trends or just prove something you think is right.
In the online world, a sea of information is surrounding you, and it is up to you to determine its usefulness. You can experiment with marketing data, social media followers, the responsiveness of audience to your data, attractiveness of your design and a whole lot more.
Don’t be afraid to test your hypothesis. You pay for your ads, hire someone to manage your social media sites. Also, you buy tools to make everything work properly. Make sure that you achieve the desired ROI by always testing your hypothesis. Don’t throw away your money!
Sample experiments in digital marketing include running a social media campaign, publishing a PR, creating an experimental content, doing online surveys, or incorporating changes on the website (e.i. Color, layout, additional call-to-action, etc.). These experiments vary according to your needs.
Optimize your digital marketing efforts after identifying the results of your experiments. Once you have the facts to support which strategies drive more traffic to your website and increase conversion rates; it is easier for you to pinpoint which techniques to optimize.
Spend money on strategies that work. Invest in the right audience and expand towards the correct geographical location. This complete data analytics guide allows you on focus on aspects of your digital marketing efforts that create the most significant positive impact on your ROI.
Optimization includes focusing on the right keywords, on which content gets the most clicks, email messages, website color and layout that works well for customers, etc. By knowing your brand, you will know what part of your digital marketing process to optimize.
A successful data analysis does not end in a single run. You must repeat the process to innovate and continue developing the digital marketing plan.
This complete data analytics guide shows you that the sooner you can repeat the process, the more you get from your dataset, and the better your outcomes will be. With numerous iterations, you will come closer to the objectives of data analytics and therefore, hit or even exceed the target ROI for the organization.
The complete data analytics guide shows you just how vital information can be. Data analysis does not only help organizations understand customer trends and improve internal strategies, but it also aids in validating business intelligence. Through data analysis, you can therefore troubleshoot problems and rid yourself of digital marketing strategies which do not work.