Following the Behavioral Analytics Meetup, Daphna Gal, Marketing Manager, CoolaData, shares her insights to how marketing efforts can be informed by behavioral analytics. Read her guest blog post below.
Online marketing efforts for eCommerce businesses are often dominated with the need to acquire new customers. While acquisition is an important goal in selling online, it is unfortunately also expensive. It’s true that shoppers can easily pass from one website to the next, but over the past few years a highly loyal segment of online shoppers has emerged, and tools such as internet bookmarks have led to usage patterns that are almost addictive. Therefore, marketing to first-time buyers should not come at the expense of returning purchasing users, a highly effective segment.
Surprisingly enough, these users are a true asset as they are more likely to buy additional products from the same seller. A Bain & Co study shows that the longer their relationship with an online retailer, the more customers spent in a given period of time. In apparel, for example, the average repeat customer spent 67 percent more in months 31-36 of his or her shopping relationship than in months 0-6.
So now the given question is how can we act on these facts?
To measure loyalty, we need to start by understanding which of our users are returning shoppers and which of our users are one-time shoppers. Then, with the help of behavioral analysis, identify the factors that are influencing your customers’ behavior and measure satisfaction accordingly. What were their purchasing patterns? What is causing repeat customers to come back?
The Cohort is a great tool to measure user retention. This tool breaks data into related groups of people, rather than only looking at everybody as one unit. These related groups share common characteristics or experiences within a defined time span. Cohort analysis allows companies to pick up on patterns and trends so that necessary changes can be customized for relevant consumers only.
Analysis will help us determine how to bring back returning purchasing users to buy additional products and services in the future. It enables eCommerce companies to learn about the habits of their user base, and provides an effective way to discover and improve ROI.
The Art of Behavioral Analytics and Marketing
December 16, 2014
Farrah Fidler
Following the Behavioral Analytics Meetup, Daphna Gal, Marketing Manager, CoolaData, shares her insights to how marketing efforts can be informed by behavioral analytics. Read her guest blog post below.
Online marketing efforts for eCommerce businesses are often dominated with the need to acquire new customers. While acquisition is an important goal in selling online, it is unfortunately also expensive. It’s true that shoppers can easily pass from one website to the next, but over the past few years a highly loyal segment of online shoppers has emerged, and tools such as internet bookmarks have led to usage patterns that are almost addictive. Therefore, marketing to first-time buyers should not come at the expense of returning purchasing users, a highly effective segment.
Surprisingly enough, these users are a true asset as they are more likely to buy additional products from the same seller. A Bain & Co study shows that the longer their relationship with an online retailer, the more customers spent in a given period of time. In apparel, for example, the average repeat customer spent 67 percent more in months 31-36 of his or her shopping relationship than in months 0-6.
So now the given question is how can we act on these facts?
To measure loyalty, we need to start by understanding which of our users are returning shoppers and which of our users are one-time shoppers. Then, with the help of behavioral analysis, identify the factors that are influencing your customers’ behavior and measure satisfaction accordingly. What were their purchasing patterns? What is causing repeat customers to come back?
The Cohort is a great tool to measure user retention. This tool breaks data into related groups of people, rather than only looking at everybody as one unit. These related groups share common characteristics or experiences within a defined time span. Cohort analysis allows companies to pick up on patterns and trends so that necessary changes can be customized for relevant consumers only.
Analysis will help us determine how to bring back returning purchasing users to buy additional products and services in the future. It enables eCommerce companies to learn about the habits of their user base, and provides an effective way to discover and improve ROI.