Since Google Analytics launched in 2005, it has quickly become the most widely used analytics platform for online businesses. What made it so popular? Probably that it’s free, known to be the standard and it’s easy to implement.

All you need to do is quickly register and implement a single line of code and voila – businesses have web analytics at their fingertips. But at some point, organizations need to move beyond the standard web metrics – the number of pageviews, new vs. returning visitors, average session duration, or the number of goal completions to a deeper analysis based on raw data of customer events, which include but aren’t limited to path analysis, cohort analysis and advanced funnel analysis.

These types of analysis, or advanced analytics, provide answers to more complex business questions, such as:

  • What is the comparison of conversion rates between different customer segments?
  • How do the steps in the user funnel compare among different customer segments?
  • Which series of steps was the most effective in driving conversions?
  • Which product placement was the most effective in driving purchases?
  • Which group of customers are more likely to buy a particular product or line of products in the next 30 days?

These are advanced analytics capabilities that go beyond the standard visitor tracking.

A quick migration to the next generation of analytics

Digital intelligence, also known as advanced web analytics, is the evolution of business intelligence. Built to track user actions from different data points and process masses of data, it offers powerful querying and analysis capabilities, as well as smart user identification across sessions, devices and touchpoints, to discover patterns of customer behavior over time.

While BI is often wrongly associated with heavy implementation, complex infrastructure and HR costs, this new generation of digital intelligence is as easy to implement as Google Analytics. Businesses that have already implemented Google Tag Manager can use their event data as their foundation as they move towards more advanced analytics.

Here are the stories of two businesses who decided to amplify their analytics and moved to advanced analytics.

Fast insights that lead to data driven product optimization

Fibiz, a fast-growing trading company that was successful in quickly acquiring customers that generated masses of user events from day one of their beta launch, decided to move to the next stage of high growth. As the company readied itself to move from virtual money trades to real money trades, they realized they needed to quickly and easily implement advanced analytics to optimize their product for this important launch. That meant not waiting another minute to start understanding how traders act and interact with their fun trading app.

They produced a tutorial video to teach the new users how to trade. Although their standard analytics measured how many users watched each video, it couldn’t compare video views of different types of users. For that they needed advanced analytics.

The company needed to compare the behavior of users who completed two different conversion funnels: those that completed the trading tutorial video before trading on the app, and those that skipped the tutorial and immediately started trading.

Source: The behavioral analytics blog
http://www.cooladata.com/wp-content/uploads/2017/02/Funnel_Tutorial-Side-by-Side.jpg

They found that conversion rates were over 30 percent higher with traders who watched the tutorial!

By implementing analytics quickly before their real money launch, they were able to optimize their app to better engage new traders with the tutorial, which led to higher overall conversions.

Their ability to quickly implement advanced analytics insights also helped the company optimize its marketing campaigns and allocate its budget for the geographic regions, channels and campaigns that were the most successful.

Another company, a leading online travel booking agency needed to answer two complex business questions:

  • What is the optimal number of emails to send to a list member within a three-month period?
  • How many emails per week would result in maximal revenue with minimal attrition of the email list?

With the implementation of advanced analytics, the travel company pulled all the raw data from their Hubspot email campaigns into a data warehouse, adding the purchase and revenue data from their backend system. It then connected the Hubspot email activity for each user with their purchases and the events each user triggered in the backend.

At this point the business had unified session data for each user, including their email, website, and purchase behavior. By querying these different sets of data, the marketing team came up with answers to those two questions. they then determined that the optimal number of emails each user should receive to maximize the open rate is between 36 and 40.

The number of emails that cause an uplift in the open rate
Source: Cooladata http://www.cooladata.com/wp-content/uploads/2017/06/How-many-emails.png

These are also the same number of emails that result in the most revenue per user, until another peak forms at 50+ emails per user.

The number of emails that cause an uplift in revenue

Source: The Behavioral Analytics blog http://www.cooladata.com/wp-content/uploads/2017/04/Impact-of-Monthly-eMails_openClickRate.jpg

Know the path customers take towards purchase

An online eCommerce business in the staged fashion industry wanted to move beyond measuring the number of impressions of products on their website, which they were doing with Google Analytics. But they wanted to explore their customer behavior further, drilling down to specific user behavior:

  • Which types of customers clicked on which items?
  • How did they react to different types of product placement?
  • What items did each of these customers purchase?
  • What types of users are most likely to click on which items?

For this type of raw user level data analysis, this eCommerce business needed advanced analytics. With advanced user path analysis, businesses can understand the path or journey users take towards a particular action. They can then can optimize the conversion path that results in a major uplift in conversion.

For example, a business saw that male shoppers first browsed the sporting goods department before adding an item to their cart and then, after browsing the hardware department, added another item to their cart. Advanced analytics would show the business that these customers’ behavior was optimized through Next-Best-Offers (NBOs). This business could then easily implement NBOs for other customer segments as well, like returning shoe buyers.

Advanced analytics can also compare the conversion rates of different paths of these buyer segments. For instance, do returning shoe buyers react differently to displaying the latest shoe line on the top of the page?

Source: Cooladata http://www.cooladata.com/wp-content/uploads/2017/03/CoolaData-Path-Analysis-eCommerce.jpg

The ability to gather raw user level data to understand each action customers take allows businesses to have a more holistic view of customer behavior. By receiving continuous reports on user behavior over periods of time, the business can monitor any sudden changes and dig deeper to see what might be the cause, such as:

  • A faulty button on the conversion page
  • A problem in the functionality of the product itself
  • The effect of a successful marketing campaign

Two Sides of a Coin: Standard Analytics and Advanced Analytics

It’s no surprise that Google Analytics, with its ease of use and insights into visitor interaction with web pages is the most popular entry level web analytics tool. But now, businesses are finding that they must move beyond event tracking to advanced analytics. Not only that, but they want to do it with the same ease, even moving their data from GTM to a more advanced analytics platform in order to create deeper insights and act on them.

With a fully managed advanced analytics solution, businesses don’t need a staff of BI analysts or data scientists. There’s no down time to wait for implementation. Businesses are using the same resources as before, but simply having the freedom to ask any question and receive deeper insights. So stop thinking of the migration to advanced analytics as risky, like flipping a coin.

Start thinking of it as the other side of the same coin.

Register or login for access to this item and much more

All Information Management content is archived after seven days.

Community members receive:
  • All recent and archived articles
  • Conference offers and updates
  • A full menu of enewsletter options
  • Web seminars, white papers, ebooks

Don't have an account? Register for Free Unlimited Access