Data Analytics is the new imperative for business growth and a lot can be learned from e-commerce and media industries.
Big data is the fuel that powers artificial intelligence (AI) and machine learning (ML) applications. But there is growing unease among consumers and governments around the world about how personal data is being gathered, stored, used, and shared. Businesses need to get out in front of consumers’ growing data privacy concerns to allay their fears.
The vast majority of companies that use AI and ML-algorithms in their data analytics abide by strict self-imposed standards for protecting user data. Consumer data is essential to creating and improving personalization and customer experience, so companies must be both diligent and transparent about their data collection, storage, and application processes.
When it comes to data mining, most consumers have a general idea of how things work. In the online media and retail sectors, for instance, brands analyze what sites we visit and use sophisticated applications to serve up what they think is the most relevant content. For example, when we visit an e-commerce site, the home page is typically pre-loaded with products or news items we might find interesting because we have recently searched for them. While these applications are intended to improve our online experience, we sometimes see different outcomes. In some instances, a user might continue to receive information about a product long after they have purchased it. This is because the company’s data analytics didn’t find the transaction in its data search. While relatively benign, this is a case of sub-optimal data analytics that can be improved over time.
This is why it’s imperative that companies use data analytics to examine the data sets and analyze the information contained in the data to draw inferences and make decisions. AI- and ML-based data analytics are widely used to help companies make informed and timely decisions. This is because the algorithms learn and improve as the amount and diversity of data increases over time. Errors and bad decisions occur as a consequence of too little data.
Optimized data analytics allows companies to create sophisticated customer profiles that will help them develop better product campaigns, personalize messaging, and fine-tune their target audiences. Data analytics vendors provide software development kits (SDKs) and dashboards to their customers so they can conduct ongoing research to improve their results. Different vendors provide different types of SDKs. For example, one may have expertise in video tracking while another may have expertise in tracking consumer actions. Typically, a company will integrate more than one SDK in its code to generate more meaningful data.
The leading users of data analytics are media and entertainment companies and e-commerce retailers. An e-commerce company will look for a completely different kind of data than a media or entertainment company.
E-commerce companies are most interested in:
- Product categories most frequently purchased
- Products that get the most clicks
- Products kept in the buyer’s cart but not purchased
- Profiles of customers (for example, gender and age)
- Spending power
- Interests of the buyer
Media and entertainment companies are most interested in:
- Most viewed content
- Most viewed videos
- What type of ads a user looks at
- What type of ads a user generally skips
- Customers interested in buying special subscriptions
- Total time customers spent watching videos
- The sections of an application that a user is most and least interested in
Once a data analytics application is operational, the dashboard tool provides a convenient, visual way to understand the data. They help analytics-research and data-science teams monitor changes in the day-to-day data and monitor increases or decreases. They are in constant communication with sales and ad teams to review the data. Based on input from these teams, the analytics code is updated to achieve more optimal results.
The combination of data analytics and visualization of code updates allows companies to refine their offers, create more personalized sales campaigns and push specific ads. For example, if an e-commerce company finds that a user has put a product in their shopping cart but has not purchased it, they may send the user a discount promo code to encourage the user to make the purchase.
Likewise, if a media organization knows the kind of videos or shows the user is interested in, they will not only show similar kinds of content but will embed revenue-generating ads in the content the user is currently watching. At the same time, data analytics help companies decide to discontinue content that has low viewing numbers.
AI- and ML-based data analytics is the most effective way to understand consumer digital behavior and to increase revenue, bar none. The constant challenge for all companies is to be transparent with their customers to ensure continued trust in the efficacy of their analytics.
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