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How to leverage big data to make better marketing decisions


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Organizations frequently rely on big data to make decisions, stay in business, and strategize for the future. They have adapted to an ever-evolving set of data sources – both internal and external – and a growing range of tools to put the data to use.

Modern businesses use big data every day to understand, drive, and further develop all aspects of their organization’s goals. But stakeholders need to understand how and why the quality of data is directly linked to the quality of decision making. Big data, by definition, refers to large amounts of information collected at high speed. Without objective analysis, it can create analysis paralysis. However, the same data, when analyzed thoroughly, can help organizations gain insight.

The place to start this analysis is to understand the customer’s needs and challenges, and this will help to successfully develop strategy and understand performance as the business evolves. To scale a business, leaders need to understand the nuances involved in locating and gathering relevant data, extracting the most valuable insights from it, and put it into action.

Of course, pattern recognition is key. It will generate from multiple sources and merge to a single point. Data from finance, business partners, multimedia performance, systems and applications need to converge in a pattern to help make informed business decisions.

Using data to make decisions

The applications of data for strategic decision-making are extensive – reporting, analytics, data mining, process mining, predictive and descriptive analytics, development of performance indicators, reporting , share with trusted partners, regulatory compliance and more. These functions can be used to locate and develop new business opportunities. The data that informs these functions should incorporate information from both the company’s proprietary internal sources and from the market.

Typically, internal data is stored in structured systems. No structure and semi-structured data can be much more difficult to collect and process because it is stored in different locations by companies that do not share the same nomenclature. It is common to see more unstructured or semi-structured data in the figure than structured data. Organizing this in a meaningful way would be a good first step to business decision making.

Understanding data types

Data from campaigns helps marketers identify patterns and allows them to learn more about a customer’s buying process: what resonates with potential customers, what helps them learn more about business. Also, what regional and cultural preferences do potential customers prefer: short-form ads for learning or more detailed material, etc. It’s all about identifying patterns and the goal is to use patterns. this to optimize business operations. This is about what will make our customers successful.

Data from any marketing or advertising activities may contain detailed information about demographics, intentions, behavior of customers and target audience etc. Sales data should also be part of the data. this equation to get a complete view of the entire marketing funnel and path to purchase. Stakeholders need to know the right metrics and key performance indicators (KPIs) within them that can help inform future business strategy.

Collecting, analyzing, and applying data to business decisions is complicated, especially because the data is so diverse (and often obscured). This is what makes it challenging and exciting at the same time. Again, it’s about pattern recognition.

Due to its diversity and frequent obfuscation, enterprise data poses challenges for consolidation and analysis. The quality and accuracy of business data is critical to its value and effectiveness. The datasets need to be taken care of and quality assured before being put into use.

Data analysis as a form of pattern recognition

Market analysis itself is of great importance, as it can help a business understand its products, competitor performance, and inform its marketing and product development strategies. Karma.

Up until now, we’ve talked about leveraging customer data for analytics. This class with the insights we gathered about the competitors in the market and now the analysis is starting to get stronger with the added context of a set of lessons from the company and companies competing in the market.

An additional point here is that it’s not just about the competition, this is about the ecosystem. The data collected from the company, its competitors, and the ecosystem at large will lead us to that pattern identification with common and differentiating factors. This balance is needed for sound business decision making when you consider relative information rather than just absolute data.

All data that is meaningful and relevant to the goals of the business, from all its sources, must be integrated before it can be done. Data needs to be unified in one repository, where stakeholders across the organization can access it when they need it. Once unified, it must be processed for redundancy, structured, legally compliant and private, run through quality assurance, cleaned and periodically reevaluated to remove data. whether outdated or irrelevant.

Why is big data analytics important?

Big data analytics enables stakeholders to uncover signals and trends that have implications for business goals. It also allows modeling of unstructured or semi-structured data, including from social platforms, apps, email or forms. Big data analytics deals with data processing and modeling, as well as predictive analytics, visualization, AI (artificial intelligence), ad targeting and other functions. It can also be used internally to optimize market performance and customer relations.

Big data analytics must be used to look at any potential security issues and for the overall quality of the data, as new data continues to flow into the data warehouse.

Stakeholders should start with the focus area and the overall goal. Then work towards collecting and analyzing additional data for the focus area. As mentioned above, this will help recognize patterns from multiple data sources, thus allowing them to capture the insights to choose the right analysis tools and maintain quality control.

How businesses leverage data

Businesses in any conceivable industry take advantage of big data, but one specific use case we can explore is gaming. Video games have a deep level of user interaction, involve a social or communication aspect between gamers, and require substantial technological investment to develop. Trade happens within the game – players can buy, trade, or earn access to game features, bonuses, and merchandise. In addition, gaming is an extremely competitive industry, with countless game companies investing in advertising, marketing, and development.

Game businesses can use the data they collect here to better understand how their games are advertised and marketed, encouraging players to pay for enhanced, premium versions user engagement and draw inferences for use in modeling or finding new business opportunities. They can also derive insights that can be used to customize the in-game experience for the appropriate audience or subgroup. It is possible to break down data and create smaller audience segments that are relevant to individual brand or product line goals. Many other industries use big data for similar reasons – consider how retailers use similar insights to recommend products to consumers.

How to qualify data

Data quality checking is a challenging process, but is key to making inventory data actionable. Data evaluation is a separate process from its cleaning. This is the process of resolving any ambiguity or over-generalization in the data that requires expertise to identify the data being communicated for the benefit of the business. Competency certificates are also important to resolve discrepancies and resolve nomenclature inconsistencies that occur when data sets are combined from different sources and businesses. How an enterprise audits data depends on its own objectives, which must be clarified prior to the QA process.

Any conversation about data collection and processing in 2022 should highlight the dramatic changes taking place in that area. Data providers that businesses work with to add their own proprietary data need to comply with GDPR (General Data Protection Regulation), CCPA, and other regulations that require user consent used before their data was collected. Enterprises must understand how their external data partners are managing compliance, identity, and personalization in this environment.

Many of the top data providers are looking to contextual data to help fill in any gaps they would see in the absence of massive third-party data. In addition to providing insights into online and in-app consumer behavior, contextual data can make datasets easier to search, as it can be used to analyze content without Consumers are engaging and layered in metadata from the digital environment in which consumers are spending time.

The applications and nuances of big data are countless and continue to multiply and evolve over time. An enterprise’s big data approach cannot be static. For the sake of competitiveness and compliance, any business should continually re-evaluate its warehoused data and any business partner practices for data management. A comprehensive, up-to-date data strategy is key to the growth of any modern business.

Gita Rao-Prasad, senior director of growth marketing at Agora.io

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