How to leverage big data to make better marketing decisions 


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Organizations regularly rely on big data to make decisions, keep the business running and strategize for the future. They have come to adapt an ever-growing set of data sources – both internal and external – and an increasing range of tools to put the data to use.  

Modern businesses use big data on a day-to-day basis to understand, drive and continue developing all aspects of the organization’s goals. But stakeholders need to understand how and why the quality of the data is directly linked to the quality of decision-making. Big data, by definition, refers to vast amounts of information collected at high velocity. If not analyzed objectively, it can create analysis paralysis. However, the same data, when dissected thoughtfully, can help organizations gain the right insight.

The place to start this analysis is understanding customer buyer needs and challenges, and this in turn will help successfully develop strategy and understand performance as the business progresses. To scale business, leaders need to understand the nuances involved in locating and collecting relevant data, deriving the most valuable insights from it and putting it into action.  

Of course, pattern recognition is key. It should funnel up from multiple sources and merge toward a single point. Data from finance, partner businesses, multimedia performances, systems and applications need to converge toward a pattern to help make informed business decisions.  

Utilizing data for decision-making 

The applications of data for strategic decision-making are broad – reporting, analytics, data mining, process mining, predictive and prescriptive analysis, developing performance metrics, reporting, sharing with trusted partners, regulatory compliance and more. These functions can be used to locate and develop new business opportunities. The data informing these functions should combine information from both the business’s proprietary internal sources and from the market.  

Often, internal data is stored in structured systems. Unstructured and semi-structured data can be much more of a challenge to gather and process as it’s stored in disparate locations by companies that don’t share common nomenclature. It is common to find there’s far more unstructured or semi-structured data in the picture than there is structured data. Organizing this in a meaningful way will be a good first step toward business decision-making.  

Understanding types of data 

Data from campaigns help marketers identify patterns and enable them to learn more about the customer buying process: what resonates with the prospect, what is helping them learn more about the business. Also, what regional and cultural preferences do prospects prefer: a short-form ad for learning or a more detailed document, and much more. It is all about identifying patterns and the goal is to use these patterns to optimize business practices. This is about what will make our customers successful. 

Data from any marketing or advertising can contain insights into customer and target audience demographics, intent, behavior and more. Sales data should also be part of this equation for a complete view of the entire marketing funnel and path to purchase. Stakeholders need to know the right metrics and key performance indicators (KPIs) therein that can help inform future business strategy.  

Data collection, analysis and application to business decisions is complex, especially since data is varied (and frequently siloed). This is what makes it challenging and interesting at the same time. Again, it is about pattern recognition.  

Because of how varied and frequently siloed it is, enterprise data poses challenges to consolidation and analysis. Quality and accuracy of enterprise data are crucial to its value and effectiveness. Datasets demand attention and quality assurance before being put to use. 

Data analysis as a form of pattern recognition

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

Until now, we talked about leveraging customer data for the analysis. Layer this with the insights we gather on competitors in the market and now the analysis starts getting stronger with additional context bringing together learnings from the company plus competitive companies in the market. 

An additional point here is it does not have to be just competitors, this is about the ecosystem. Data collected from the company, competitors and the ecosystem at large will take us to that pattern recognition with elements that are common and different. This balance is needed for the right business decision-making where you consider the relative information and not just absolute data. 

All the data that is meaningful and relevant to the business’s objectives, from all its sources, must be integrated before it can be made actionable. The data needs to be unified in one warehouse, where stakeholders across the organization may access it when they need to. Once unified, it must be processed to remove redundancies, structured, made legally compliant and private, run through quality assurance, cleaned, and reassessed at intervals to remove outdated or irrelevant data.  

Why do big data analytics matter? 

Big data analytics allow stakeholders to uncover signals and trends meaningful to business goals. It also enables modeling of unstructured or semi-structured data, including from social platforms, apps, emails or forms. Big data analytics handle the processing and modeling of data, as well as predictive analytics, visualization, AI (artificial intelligence), ad targeting and other functions. It can also be used internally, for optimizing marketplace performance and customer relations.

Big data analytics must be used with an eye on any potential security issues, and on the overall quality of the data, as new data continues to stream into the data warehouse.  

Stakeholders should start with the overall area of focus and goals. Then work toward collecting and analyzing data that adds up to the focus area. As mentioned above, this will help with the pattern recognition from multiple sources of data, thereby enabling their capture of insights in order to choose the right analytics tools and uphold quality control. 

How businesses are leveraging data 

Businesses in any conceivable industry vertical leverage big data, but one specific use case we can explore is gaming. Video games have deep user engagement, involve a social or communications aspect among gamers and require substantial technological investment to develop. Commerce occurs within games – players can buy, trade or earn access to game features, bonuses and merchandise. Also, gaming is an incredibly competitive industry, with countless gaming companies investing in advertising, marketing and development.  

Gaming businesses can use the data they gather here to gain insights on how to advertise and market their games, incentivize gamers to pay for premium versions, deepen user engagement and draw inferences for use in modeling or finding new business opportunities. They can also draw insights that can be used in customizing experiences within the game for niche audiences or subgroups. It’s possible to slice up the data at hand and create smaller audience segments relevant to the individual brand or product line’s goals. Plenty of other industries use big data for the same reasons – consider how retailers use similar insights to recommend products to consumers.  

How to qualify data 

Qualifying data is a challenging process, but key to making warehoused data actionable. Qualifying data is a separate process from cleaning it. It is the process of addressing any vagueness or over-generalizations in the data that need qualification to specify what the data is supposed to communicate for the benefit of the business. Qualification is also important to resolve discrepancies and resolve inconsistencies in nomenclature that occur when datasets are combined from disparate sources and businesses. The way a business qualifies data depends on its own objectives, which must be clarified prior to the qualification process.  

Any conversation about gathering and processing data in 2022 must highlight the drastic changes underway in that realm. Data providers that businesses partner with to supplement their own proprietary data need to comply with GDPR (General Data Protection Regulation), CCPA and other regulations that require the user’s consent before their data is to be collected. Businesses must understand how their external data partners are managing compliance, identity and personalization in this environment.

Many leading data providers are looking to contextual data to help cover any gaps they will be seeing in the absence of voluminous third-party data. In addition to providing insights into online and in-app consumer behavior, contextual data can help datasets be more searchable, because it can be used to analyze content consumers are engaged with and to layer in metadata from the digital environments where consumers are spending time. 

The applications and nuances of big data are myriad and continue to multiply and evolve over time. A business’s approach to big data cannot be static. For the sake of competitiveness and compliance, any business should continually reassess its warehoused data and any applicable business partners’ practices for managing data. An up-to-date, comprehensive data strategy is key to the progress of any modern business.  

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

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