Big Data refers to the massive amount of digital data we generate every day using digital technologies. This data comes in huge volumes, great speed, and large diversity, making it challenging for conventional data-processing system technologies to handle.
However, companies that manage to draw insights from this pile of unorganized information can gain a critical competitive advantage. Analyzing this data efficiently allows them to identify issues and opportunities quickly, and pilot activities in a data-driven manner.
In the last decade, data scientists have developed powerful algorithms to process Big Data and make it intelligible. They now can find patterns, trends, and relationships that will give them insights into their performance.
With predictive analytics, for instance, they can forecast performance and market changes based on existing and previous data. In this article, we study how this branch of Big Data analysis will impact marketing activities in 2023. Let’s dive in!
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In competitive environments, businesses must always be one move ahead of market trends and preferences. This is made possible by predictive analytics. This method of analyzing Big Data helps marketers evaluate customer trends, identify potential changes in the market, and run marketing campaigns more effectively.
Big Data marketing uses predictive analysis to understand customer behaviors and future trends, allowing them to plan their campaign accordingly and seize opportunities in real-time. Of course, it’s not an exact science, no algorithm can predict future events with 100% accuracy. But it still can give an idea of what might happen in the future with a certain degree of confidence.
Big Data and predictive analytics can be beneficial for marketing as follows:
There are many more use cases for predictive data analysis in marketing, read this article for more practical applications.
Big Data analytics is the challenging process of sifting through enormous amounts of data to find actionable information. It involves applying advanced analysis tools to very large, diversified Big Data sets, which can range in size from terabytes to gigabytes and contain organized, semi-structured, and unstructured data from many sources.
Depending on the industry, marketers can deal with a large amount of very disparate data. The Internet of Things (IoT), handheld devices, media platforms, and artificial intelligence (AI) are driving sources of data to become more complicated than those for traditional data.
Data is usually collected in three forms, which are:
Structured Data — It is a type of data that corresponds to a specific, predefined format. Because of its predictable structure, it requires less processing and is easy to organize, search and analyze.
Examples of structured data: Any relational data that can be stored in a table with rows and columns.
Unstructured Data — This type of data does not have a pre-defined data model. It usually corresponds to factual data and comes in greater volume. Although it’s more difficult to process unstructured data using conventional techniques, it provides better insights and more opportunities.
Examples of unstructured data: Videos, audio, social media posts, etc.
Semi-structured Data — This data cannot be stored in a table but has some properties that make it easier to analyze. Semi-structured data has to be pre-processed before analysis.
Examples of semi-structured data: Emails, dates, credit card numbers, geolocation, etc.
A product or a service that uses behavioral data to guide business choices and development is referred to as a “data product.” Data products are created by turning the data resources (that an organization already owns or that may be obtained) into a product intended to assist a user in solving a particular issue.
Updating and continual feedback are essential to creating exceptional products. Quantitative and qualitative data are gathered and analyzed for more intelligent and strategic judgments. In this way, data is used to improve products. However, there are different degrees of data-oriented product design and development.
| Data-inspired | Data-informed | Data-driven |
| Leverages data that is already implemented to observe general customer behaviors. Data is interpreted, but ultimately decisions are made on intuition using the inputs gathered. | Data is used to understand past performance and develop new strategies. Used to give a contact and understand why actions succeed or fail. Experience guides the decisions. | Customer data is at the core of the process. Experiments are designed to gather behavioral data and validate hypotheses. Experiments and data results drive the decision-making process. |
The influence of data on product development
A data-driven approach leads to very customer-centric products. It required an experimental mindset where the designers generate ideas and experiments, but ultimately the customer behavior drives the orientation to take. The main objective is to generate data and then let this data make decisions and help uncover customer opportunities.
We can say that data is an essential resource in the globe for organisations, and businesses to gain a competitive advantage. With the help of the knowledge acquired by predictive analysis, marketers are better able to anticipate future events and develop effective marketing plans.
Big Data and predictive analytics for marketing in 2023 are more challenging than ever before. Since there is so much data available, predictive analytics cannot be used by marketers without powerful marketing tools and measurement skills. If you’re looking for a partner to assist you on your Big Data journey, reach out to the Mowgli team.
