The rich variety of data that enterprises generate contains valuable insights, and data analytics is the way to unlock them. Data analytics can help an organization with everything from personalizing a marketing pitch for an individual customer to identifying and mitigating risks to its business. Let's take a look at five of the benefits of using data analytics.
Businesses collect customer data from many different channels, including physical retail, e-commerce, and social media. By using data analytics to create comprehensive customer profiles from this data, businesses can gain insights into customer behavior to provide a more personalized experience.
Take a retail clothing business that has an online and physical presence. The company could analyze its sales data together with data from its social media pages and then create targeted social media campaigns to promote their e-commerce sales for product categories that the customers are already interested in.
Organizations can run behavioral analytics models on customer data to optimize the customer experience further. For example, a business could run a predictive model on e-commerce transaction data to determine products to recommend at checkout to increase sales.
Enterprises can use data analytics to guide business decisions and minimize financial losses. Predictive analytics can suggest what could happen in response to changes to the business, and prescriptive analytics can indicate how the business should react to these changes.
For instance, a business can model changes to pricing or product offerings to determine how those changes would affect customer demand. Changes to product offerings can be A/B tested to validate the hypotheses produced by such models. After collecting sales data on the changed products, enterprises can use data analytics tools to determine the success of the changes and visualize the results to help decision-makers choose whether to roll the changes out across the business.
Organizations can improve operational efficiency through data analytics. Gathering and analyzing data about the supply chain can show where production delays or bottlenecks originate and help predict where future problems may arise. If a demand forecast shows that a specific vendor won't be able to handle the volume required for the holiday season, an enterprise could supplement or replace this vendor to avoid production delays.
In addition, many businesses — particularly in retail — struggle to optimize their inventory levels. Data analytics can help determine optimal supply for all of an enterprise's products based on factors such as seasonality, holidays, and secular trends.
Risks are everywhere in business. They include customer or employee theft, uncollected receivables, employee safety, and legal liability. Data analytics can help an organization understand risks and take preventive measures. For instance, a retail chain could run a propensity model — a statistical model that can predict future actions or events — to determine which stores are at the highest risk for theft. The business could then use this data to determine the amount of security necessary at the stores, or even whether it should divest from any locations.
Businesses can also use data analytics to limit losses after a setback occurs. If a business overestimates demand for a product, it can use data analytics to determine the optimal price for a clearance sale to reduce inventory. An enterprise can even create statistical models to automatically make recommendations on how to resolve recurrent problems.
All businesses face data security threats. Organizations can use data analytics to diagnose the causes of past data breaches by processing and visualizing relevant data. For instance, the IT department can use data analytics applications to parse, process, and visualize their audit logs to determine the course and origins of an attack. This information can help IT locate vulnerabilities and patch them.
IT departments can also use statistical models to prevent future attacks. Attacks often involve abnormal access behavior, particularly for load-based assaults such as a distributed denial-of-service (DDoS) attack. Organizations can set up these models to run continuously, with monitoring and alerting systems layered on top to detect and flag anomalies so that security pros can take action immediately.
To obtain the best results from data analytics, an enterprise needs to centralize its data for easy access in a data warehouse. Stitch is a simple data pipeline that can replicate all of your organization's data to your warehouse of choice. Try it for free today.