February 2014, CNN publishes evidence of a shocking trend sweeping the U.S., culminating in an event never before seen in recorded history:
… Americans used mobile apps more than PCs to access the internet1.
As the capabilities of mobile applications increased, the expectation of mobile apps being used to augment a PC-based experience has long since vanished. Considering the relatively recent proliferation of mobile device types (phones, tablets, phablets, wearables, etc.) and how effective a good app is at streamlining a web-based service, it's easy to see why mobile usage is still on the rise2:
While not an unforeseen event, this shift in web consumption certainly caused a few headaches in the analytics realm. Anyone exposed to the world of cross-platform analytics can attest to the limitations of implementing traditional web analytics software on mobile applications.
Based on that, the question now facing many industries is how much value is there to a robust mobile analytics solution?
As the major players in analytics software adapt their offerings to the transition to a mobile-centric user base, greater value is being realized from the mobile usage data being collected. Arguably, the most important step in deriving value from this data is understanding that interacting with an app is fundamentally different than visiting a website using a laptop or desktop - think about how many times a day you open your favorite app compared to how many times you visit your favorite website. Example: Opening your preferred news site once in the morning, and leaving the tab open all day as opposed to opening the app several times throughout the day.
The key difference: granularity of interaction. Mobile analytics data is typically recorded by session. A session ends once a user closes an app or after a predetermined amount of time has elapsed since the last user action. Typically, default session timeout occurs between 15 and 30 seconds of inactivity, whereas web page visits time out after a 10-15 minute window, if at all. With that being the case, it is easy to see how mobile analytics can provide immense value when used to analyze consumer usage patterns: it inherently describes a more direct and intimate user experience over a given period of time when compared to similar data describing a traditional web page visit.
Business leaders recognize the evolution to a mobile-centric user base and the value to be had from implementing mobile analytics. Companies across various industries are now placing significant investments into analytics solutions with a mobile-focused core to gain a more complete picture of their customer's channel usage:
When Comcast launched an ad with a click to call number, they recorded a 270 percent increase in click through rate on mobile search compared to desktop search, and a corresponding surge in mobile sales3.
Similarly, RadioShack found that 36 percent of clicks on their mobile ad went to a store locator, and then calculated that between 40-60 percent of these were converting to in-store visits3.
With the adoption of Near Field Communication NFC technology (Google Wallet, Apple Pay, etc.). Financial Institutions' mobile capabilities are starting to evolve along with their consumer channel consumption:
As consumers rely on their mobile devices for everyday activities, financial institutions will continue to invest further into the mobile channels. Since many consumers have instant access to their mobile phones, this presents the opportunity for just-in-time information that has changed consumer financial behavior, abstaining the consumer from irrational financial decisions. Many mobile bank consumers will participate in application notifications from their financial institutions, positively influencing their banking behaviors. In the comparing mobile notifications to e-mail marketing methods, it is very apparent on how best to reach the banking consumer4.
Almost 30 percent of mobile banking users indicate that they receive text messages alert from their bank. Among those receiving alerts, 63 percent receive "low-balance alerts," 39 percent receive "fraud alerts" 37 percent receive "payment-due alerts," and 8 percent indicate that they receive "savings reminders". Consumers who receive a low balance alert from their bank nearly all report taking some action in response: transferring money into the account with the low-balance (54 Percent), reducing their spending (36 percent), or depositing additional money into the account (24 percent). Only 14 Percent reported taking no action in response to receiving a low-balance alert5.
Financial Institutions can also utilize predictive analytics in using real-time data to successfully deliver targeted products or value-added services to reach the customer at the right time. For example, a customer could receive a healthy bonus credited to their banking account which would trigger a notification flag. This account event could also trigger an offer to the customer of a high-interest savings account. The offer could be delivered via email or mobile direct message or via online banking website, based on the previous statistic; we can see the power of mobile notifications with banking consumers vs. the outdated email and delayed mailing offer tactics. As consumers continue to use their mobile devices at an increasing rate, it is vital for financial institutions to understand their customer's unique need based on their account activity. Effective Financial institutions strive to lead in many ways using customer satisfaction and mobile banking availability as key value metrics.
Regardless of industry, there is no doubt that utilizing mobile analytics adds tremendous value to an organization. It is evident that adopting a robust mobile analytics solution is critical to creating a complete picture of customer behaviors. Leveraging this wealth of information to create more creative and dynamic ways to reach consumers through apps or mobile marketing is now almost a necessity to remain competitive in today's business environment