Monthly Archives: October, 2016

dynamic analytics

Dynamic Analytics Are the Way of the Future

October 12th, 2016 Posted by Analytics 0 thoughts on “Dynamic Analytics Are the Way of the Future”

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 Predictive Behavioral 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.

Three Types of Analytics to Leverage Big Data

October 6th, 2016 Posted by Analytics 0 thoughts on “Three Types of Analytics to Leverage Big Data”

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.

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