s only one great service that 80-90% of heavy users returned to repeatedly. The other sites went unused by these "in-the-know" heavy users. This contrasted to the experience we found for new users (first-time visitors to the services). New users obviously didn't know yet which sport site was going to be useful, so they always selected the first site listed (very few tried other services). If the order of the menu was not based on the quality of the services (or, worst of all, it was alphabetical), then new users usually visited a lousy site had a bad experience, and didn't return to become a repeat user.

In summary, we learned that mobile operators need to shift their focus from "we have the most features" to "we have the best features," where they identify the services that count to consumers and abandon the services that turn consumers away. To learn more about these analytic services, p

Mobile phone operators struggle to find the right approach to designing data services that consumers will pay for and use regularly. Although users are willing to try new data features (like the camera, Internet services, or ringtone downloads), most don't become long-term daily users. In fact, 90% of data services features go unused for weeks or even months on the average consumer's mobile phone. Does having too many features help or hurt consumer adoption of data services?

Over the last eight years, a team of consultants including myself helped large mobile phone operators (e.g., Vodafone, Sprint-Nextel, Orange, and T-Mobile) learn how consumers use their mobile data services. To do this, we collected transaction records from the network components these services pass through--often analyzing hundreds of millions of records for tens of millions of users. We wanted to find out exactly what attracted consumers, how much time consumers spent with each service, and what services got the most repeat visitors.

We soon discovered that operators were failing their consumers in two major areas: operators refused to abandon data services that were failing; and most operators were not investing properly to improve the key services that were succeeding. Mobile phones have too many features, the worst features turn users away before they find the best features, and the best features are not getting the extra attention they need to keep users coming back.

There were bright spots in our work. Virgin Mobile USA, for example, is very good at keeping a small but well-performing selection of data services, only introducing features when it makes sense for their target audience of teens and young adults. This led Virgin Mobile to significantly better "stickiness" performance, which is a measure of how often users return to use services. Another bright spot was Vodafone Italy, who was the best in finding and improving their key services. Vodafone was the first operator in Europe to use business intelligence to discover the stickiness of "Chat" services (they discovered this in 2000), and they were the most consistent at using business intelligence to identify the roadblocks or speed bumps that were causing user frustration. The results for both these operators were strong data services loyalty from their users, along with higher returns.

We kept finding the same patterns: Most consumers (85%) fail to find acceptable value on their first attempt with new data services. People spend only a few minutes (3.4 minutes) looking at only a couple of features (1.2 services) before giving up. Half (45-65%) of the new users give up whenever the product presents a new decision or task. A few (7-15%) of the most determined users--the power users--are able to find the stickiest services (or the value of the service). Only the simplest products and features end up as winners.

Take WAP-based services, for example. Almost every operator in the world offers these services to their users in the form of a hierarchical menu of third-party sites. If a user wants a sports score, he navigates to the "Sports" menu and then selects from 4-5 sports sites. These menus are laid out in an operator-centric approach to ensure the most diverse selection of services. Unfortunately, the goal isn't to solve the user's real need ("What is the current score in the Seahawks game") but instead focuses on arbitrary operator concerns ("Will the user stop using our service if we don't carry ESPN?").

When analyzing these types of menus (i.e., a sports menu with 4-5 sites), we found that there was only one great service that 80-90% of heavy users returned to repeatedly. The other sites went unused by these "in-the-know" heavy users. This contrasted to the experience we found for new users (first-time visitors to the services). New users obviously didn't know yet which sport site was going to be useful, so they always selected the first site listed (very few tried other services). If the order of the menu was not based on the quality of the services (or, worst of all, it was alphabetical), then new users usually visited a lousy site had a bad experience, and didn't return to become a repeat user.

In summary, we learned that mobile operators need to shift their focus from "we have the most features" to "we have the best features," where they identify the services that count to consumers and abandon the services that turn consumers away. To learn more about these analytic services, pl

t attracted consumers, how much time consumers spent with each service, and what services got the most repeat visitors.

We soon discovered that operators were failing their consumers in two major areas: operators refused to abandon data services that were failing; and most operators were not investing properly to improve the key services that were succeeding. Mobile phones have too many features, the worst features turn users away before they find the best features, and the best features are not getting the extra attention they need to keep users coming back.

There were bright spots in our work. Virgin Mobile USA, for example, is very good at keeping a small but well-performing selection of data services, only introducing features when it makes sense for their target audience of teens and young adults. This led Virgin Mobile to significantly better "stickiness" performance, which is a measure of how often users return to use services. Another bright spot was Vodafone Italy, who was the best in finding and improving their key services. Vodafone was the first operator in Europe to use business intelligence to discover the stickiness of "Chat" services (they discovered this in 2000), and they were the most consistent at using business intelligence to identify the roadblocks or speed bumps that were causing user frustration. The results for both these operators were strong data services loyalty from their users, along with higher returns.

We kept finding the same patterns: Most consumers (85%) fail to find acceptable value on their first attempt with new data services. People spend only a few minutes (3.4 minutes) looking at only a couple of features (1.2 services) before giving up. Half (45-65%) of the new users give up whenever the product presents a new decision or task. A few (7-15%) of the most determined users--the power users--are able to find the stickiest services (or the value of the service). Only the simplest products and features end up as winners.

Take WAP-based services, for example. Almost every operator in the world offers these services to their users in the form of a hierarchical menu of third-party sites. If a user wants a sports score, he navigates to the "Sports" menu and then selects from 4-5 sports sites. These menus are laid out in an operator-centric approach to ensure the most diverse selection of services. Unfortunately, the goal isn't to solve the user's real need ("What is the current score in the Seahawks game") but instead focuses on arbitrary operator concerns ("Will the user stop using our service if we don't carry ESPN?").

When analyzing these types of menus (i.e., a sports menu with 4-5 sites), we found that there was only one great service that 80-90% of heavy users returned to repeatedly. The other sites went unused by these "in-the-know" heavy users. This contrasted to the experience we found for new users (first-time visitors to the services). New users obviously didn't know yet which sport site was going to be useful, so they always selected the first site listed (very few tried other services). If the order of the menu was not based on the quality of the services (or, worst of all, it was alphabetical), then new users usually visited a lousy site had a bad experience, and didn't return to become a repeat user.

In summary, we learned that mobile operators need to shift their focus from "we have the most features" to "we have the best features," where they identify the services that count to consumers and abandon the services that turn consumers away. To learn more about these analytic services, p

measure of how often users return to use services. Another bright spot was Vodafone Italy, who was the best in finding and improving their key services. Vodafone was the first operator in Europe to use business intelligence to discover the stickiness of "Chat" services (they discovered this in 2000), and they were the most consistent at using business intelligence to identify the roadblocks or speed bumps that were causing user frustration. The results for both these operators were strong data services loyalty from their users, along with higher returns.

We kept finding the same patterns: Most consumers (85%) fail to find acceptable value on their first attempt with new data services. People spend only a few minutes (3.4 minutes) looking at only a couple of features (1.2 services) before giving up. Half (45-65%) of the new users give up whenever the product presents a new decision or task. A few (7-15%) of the most determined users--the power users--are able to find the stickiest services (or the value of the service). Only the simplest products and features end up as winners.

Take WAP-based services, for example. Almost every operator in the world offers these services to their users in the form of a hierarchical menu of third-party sites. If a user wants a sports score, he navigates to the "Sports" menu and then selects from 4-5 sports sites. These menus are laid out in an operator-centric approach to ensure the most diverse selection of services. Unfortunately, the goal isn't to solve the user's real need ("What is the current score in the Seahawks game") but instead focuses on arbitrary operator concerns ("Will the user stop using our service if we don't carry ESPN?").

When analyzing these types of menus (i.e., a sports menu with 4-5 sites), we found that there was only one great service that 80-90% of heavy users returned to repeatedly. The other sites went unused by these "in-the-know" heavy users. This contrasted to the experience we found for new users (first-time visitors to the services). New users obviously didn't know yet which sport site was going to be useful, so they always selected the first site listed (very few tried other services). If the order of the menu was not based on the quality of the services (or, worst of all, it was alphabetical), then new users usually visited a lousy site had a bad experience, and didn't return to become a repeat user.

In summary, we learned that mobile operators need to shift their focus from "we have the most features" to "we have the best features," where they identify the services that count to consumers and abandon the services that turn consumers away. To learn more about these analytic services, p

. A few (7-15%) of the most determined users--the power users--are able to find the stickiest services (or the value of the service). Only the simplest products and features end up as winners.

Take WAP-based services, for example. Almost every operator in the world offers these services to their users in the form of a hierarchical menu of third-party sites. If a user wants a sports score, he navigates to the "Sports" menu and then selects from 4-5 sports sites. These menus are laid out in an operator-centric approach to ensure the most diverse selection of services. Unfortunately, the goal isn't to solve the user's real need ("What is the current score in the Seahawks game") but instead focuses on arbitrary operator concerns ("Will the user stop using our service if we don't carry ESPN?").

When analyzing these types of menus (i.e., a sports menu with 4-5 sites), we found that there was only one great service that 80-90% of heavy users returned to repeatedly. The other sites went unused by these "in-the-know" heavy users. This contrasted to the experience we found for new users (first-time visitors to the services). New users obviously didn't know yet which sport site was going to be useful, so they always selected the first site listed (very few tried other services). If the order of the menu was not based on the quality of the services (or, worst of all, it was alphabetical), then new users usually visited a lousy site had a bad experience, and didn't return to become a repeat user.

In summary, we learned that mobile operators need to shift their focus from "we have the most features" to "we have the best features," where they identify the services that count to consumers and abandon the services that turn consumers away. To learn more about these analytic services, p

s only one great service that 80-90% of heavy users returned to repeatedly. The other sites went unused by these "in-the-know" heavy users. This contrasted to the experience we found for new users (first-time visitors to the services). New users obviously didn't know yet which sport site was going to be useful, so they always selected the first site listed (very few tried other services). If the order of the menu was not based on the quality of the services (or, worst of all, it was alphabetical), then new users usually visited a lousy site had a bad experience, and didn't return to become a repeat user.

In summary, we learned that mobile operators need to shift their focus from "we have the most features" to "we have the best features," where they identify the services that count to consumers and abandon the services that turn consumers away. To learn more about these analytic services, please visit www.consumerease.com.