**This post originally appeared on Data Informed**
Customers interact with companies through multiple channels, including CRM, digital ads, email campaigns, website, social media, and mobile applications. Yet most digital and marketing teams continue to analyze customer interactions in silos within each of these systems. To get a deep understanding of customer behavior, data-driven companies are combining all types of customer interaction data across all channels and analyzing it together for insights that are not possible if the data were constrained in silos.
The use cases for analyzing customer behavior are endless as businesses have come to realize that understanding customer interactions to optimize campaigns, create new revenue streams, and build innovative products can yield big results. The potential to win with big data is increasing with the amount of data available. Petabytes of data are generated every minute. As more businesses look to interpret this amassing amount of data to gain a competitive edge, we have witnessed first-hand the benefits they have enjoyed. After working with more than 200 businesses, 48 percent of which use big data for customer analytics, we have real-life examples of how companies are using big data analytics to positively impact their bottom lines.
For example, a credit card company is analyzing large amounts of data as a single set – rather than in separate silos – to uncover significant insights that have resulted in a 25 percent increase in conversion rates and $3.5 million savings in yearly digital ad spend. Likewise, a gaming company is strategically applying knowledge from user demographics and interaction history to introduce game features that have helped grow revenue by $550 million over three years.
With impressive results like that, these real-life use cases provide insight into how to be smart about where you put big data analytics to work.
Consumers interact with companies through multiple channels – mobile, social media, physical stores, e-commerce sites, and more. Each of these interactions produces data that businesses can use to better respond to customer needs, hesitations, and intentions. With the growing mass of customer information, its no surprise that the most prevalent big data use case is customer analytics. Deeper, data-driven customer insights are critical to tackling challenges like improving customer conversion rates, personalizing campaigns to increase revenue, avoiding customer churn, and lowering acquisition costs.
With insights about the customer acquisition journey, marketing departments can design targeted campaigns that improve conversion rates. This data also allows companies to predict and proactively intervene with customers at risk of churn.
One of the most effective ways to put customer data to work is to identify and understand high-value customer behavior beyond simple segmentation. Now, businesses can understand where and how their most valuable customers spend their money, and use that information to deliver more targeted and effective advertisements and offers.
The credit card company mentioned above performed a customer segmentation that combined social media data and transaction data. They found that high-value customers frequently watched the Food Channel and shopped at Whole Foods Market. Armed with those insights, they were able to strategically design advertising campaigns that targeted high-value customers with health food promotions. With this data-driven approach, the company experienced higher conversions and a $3.5 million reduction in advertising spend. The data enabled the company to pinpoint the best opportunity for attracting the most lucrative customers, rather than haphazardly designing ad campaigns and hoping that they worked. With financial service providers spending about $12 billion in direct marketing per year, the savings potential realized by using data analytics to create targeted promotions is huge.
Innovating new products and services is the lifeblood of any business. Unless businesses can develop offerings that closely align with customer needs and desires, how else can they create new revenue streams, gain a competitive advantage, and boost customer loyalty?
Savvy companies are increasingly leveraging big data to optimize products by analyzing customer behavior to understand what features drive product usage and what features drive abandonment. For example, gaming companies strive to encourage users to play longer and stayed engaged. One company used data analytics to analyze Web logs and user profiles to better understand what motivates players to pay and what keeps them engaged in a game. With this knowledge, the company introduced game features that were proven to keep customers engaged. The longer users stayed engaged, the more likely they were to pay for added features. Using data-driven insights for product innovation drove monetization and helped the company grow its revenue from $50 million to $600 million over three years.
Data is knowledge, and knowledge is power. With the access to valuable insights, businesses have the power to better understand their customers and drive greater customer loyalty and product innovation. Learn how some of the most progressive companies are using big data for a competitive edge.