Actionable Analytics, and Insights
Data is your biggest asset and all your digital marketing activities such organic search, paid search, social media marketing, email marketing are based on your target user’s databases. Your digital marketing is primarily based upon tons, and tons of your historical, and real time user data, that cannot be entirely counted upon as beneficial. Hence, in the given situation, data analytics becomes crucial for generating the highest possible revenue, while keeping the cost of digital marketing in control.
We perform actionable analysis to make smarter, and informed decisions while taking the raw data into account. We define a new method of collecting data and categorizing it in order to define the appropriate marketing strategies and channels.
Actionable analytics is a data processing technology that enables us to contextualize large amounts of data, categorize it, and identify trends and patterns in order to generate effective and profitable results from our digital marketing efforts. The entire process is performed in three steps.
The process starts with collecting a large amount of user raw activity data from multiple sources on a regular basis, contextualizing and converting it to relevant data, identifying trends and patterns, forecasting and influencing future developments, and leveraging correct actions to produce the improved version of structured data every month. However this step is more about gathering information, about the users. The step is basic but important. We use artificial intelligence technologies in analytics, for creating reports, and unify it to extract more value, for the best data-based predictions.
In this step, we get to know, about, ‘why?’ The objective is to get understanding of the root causes of observed user patterns. Diagnostic analytics generates and participates in questions concerning in out company’s goals and imperatives. We can see, for example, whether the report is trending upward or downward for a specific piece of content, page, or media at a specific location.
Here are a few examples:
Why are 90% of my visitors leaving my home page without taking any action, increasing the bounce rate?
There are various possibilities: Is site architecture becoming inefficient? Or does the page require a more effective call-to-action? Or is the content not up to the mark? Or is the user data less important?
Can we really say that the content or structure of a page needs to be changed if the reading time of that page is, say, 9 minutes and the report shows that 90% of the user’s dwell time here is less than 1 minute? Or can we say that users are only looking at the titles and that this action is sufficient to generate leads or sales?
However, these are very simple examples, and we need to use artificial intelligence technology to delve deeper into large data diagnostic analytics. After determining our areas of strength and weakness, we can make precise predictions, in the next step of actionable analytics.
The goal of predictive analytics is to predict what might happen. We can identify and predict likely future scenarios by comparing a large amount of historical data in various batches. Machine learning and AI tools are employed within data science to perform predictive analytics. Companies can gain an advantage over their competitors and make long-term strategic decisions by understanding their users.
With a better understanding of users’ behavioral preferences and purchasing trends, your company can define content strategy, plan advertisement campaigns at the right time, and choose the right channels for the right locations.
“Actionable insights” are those that prompt actions, and we choose meaningful and relevant data based on the purpose. We consider, analytics and data related to important business aspects, as well as KPIs to achieve the right outcome. Data can come from a variety of sources, but it must be analyzed to gain insights and make an informed decision. .