Is there such a thing as too much data? Modern businesses are finding themselves sitting on top of a treasure trove of business intelligence, from web analytics to internal figures and customer logs. There are several different ways to exploit this data for different outcomes.
As we wrote last week, the three main types of analytics tools available to businesses today are web/app analytics, enterprise business intelligence and digital analytics, and AI Assisted Customer Analytics. With web analytics, businesses can evaluate the success of their websites, computed in page views, clicks, unique visitor numbers and purchase amounts. They can also gauge their best sources of traffic based on referral sources, and verify which pages are the most effective at keeping customers engaged, with bounce page data. All this information provides great insight into how a business is faring online and allows businesses to tweak their websites/apps in order to draw and retain more customers by improving customer experience.
Enterprise business intelligence operates similarly to web analytics. It helps businesses make smart decisions based on the data available to them, but the focus this time is internal. There is an abundant amount of metrics that describe how a company operates (sales forecasts, revenue/cost data, inventory levels, project progress logs, HR data, and much more). All this data, processed through the right analytics system, can help business leaders quickly identify where the inefficiencies lie. A judicious use of enterprise business intelligence will allow business leaders to ensure they run a tight ship.
But both web analytics and enterprise business intelligence are static. That is, they allow the business to exploit the data only after the fact – that is, once the customer has made their purchase online and internal business decisions have been made. In an era where data is an instantaneous, fluid source of essential business information, such a lag is not only inefficient and costly, but also fully preventable.
That’s where dynamic analytics comes in. It analyzes a customer’s behavior and responds by creating a bespoke user experience as-it-happens, thus ensuring an optimal sales outcome each and every time. The data trail left behind by each single customer interaction is also fed back into the program, making the dynamic modeling even sharper the next time around.
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.