In the era of big data, companies are finding more and more ways to fine-tune product offerings for their customers. There are several layers of analytics to glean insights from, chief among them are: web analytics, enterprise BI, and behavioral analytics.
With web or app analytics, a company will gather data on clicks and page views, as well as a number of other metrics that indicate what kind of traffic the website is attracting. These include the number of unique visitors, downloads, referral sources, and page bounces. Using this data, the company can, for instance, aggregate data on the most popular topics and pages on its website. Such data serves as the blueprint for a website that is easy and pleasant for customers to navigate. And like a judicious accountant, web analytics allows firms to get a clearer view of what is driving sales performance.
But companies nowadays also have the opportunity to go even deeper. With enterprise business intelligence, they can turn the lens back on themselves to reveal any inefficiencies within. There is a dizzying array of internal data at their fingertips, from sales forecasts to inventory levels and revenue or cost data. Slicing and dicing this data with flexible reporting and ETL processes allows businesses to learn valuable things about themselves. Enterprise business intelligence is like the eagle-eyed consultant who swoops in to identify how the firm could improve.
Big data has also given rise to an entirely new dimension of data analytics based on the real-time processing of customer patterns. Companies can observe customer behavior as it happens and respond instantly through dynamic webpages. The company’s own data on past customer journeys (where users go and what they end up doing) can help to create different types of behavioral profiles, called customer segments. Once the website learns to recognize a hesitant buyer, it can respond accordingly, such as directing the customer to a discount offer.
We call this form of analytics Predictive Behavioral Analytics and it allows companies to offer a user experience that is as personal as interacting with an affable customer service representative. It’s like having Don Draper assist every single customer that passes through your website.
Founder and CEO of Syntasa
Jay is the Founder and CEO of Syntasa (a Marketing AI Platform loved by Marketers, Data Scientists, and Data Engineers). For the past 12 years, he has been a successful entrepreneur, having started two high-growth companies. Jay also has over 20 years of professional experience in the field of analytics, data science, performance measurement & management, and strategic planning, having worked at several organizations, including American Express, TARP Inc. and Viant Corporation.