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2008 Financial Alerts Forecast: On-the-Fly Alerts Transform Account Relationships To Profitable and Primary vs. Static and Secondary
Javelin Strategy & Research, Sep 2008, Pages: 43
The financial industry is setting the stage for a day when consumers will be able to sign up for a full menu of e-mail and text alerts beamed to their personal computers, smartphones and mobile phones. This increasingly will give Americans real-time control of their finances – enabling them to manage, monitor and move their money around the globe anytime, anywhere they can log on or get cell-phone reception.
Such alerts hold the promise of helping consumers and small businesses to avoid bounced checks, make mobile payments, transfer cash to students at college, stop crooks before they can hijack or set up fake accounts – and much, much more. This Javelin report forecasts rapid adoption of such alerts, examines which alerts consumers value most and advises financial institutions how to build and market alerts that will put consumers in control.
Primary Questions
- What is the business case for offering financial alerts?
- How can financial institutions make it easy for consumers to adjust settings “on the fly”?
- What features do technology specialists need to prioritize?
- How do alerts affect customer relationships?
- What marketing opportunities do alerts bring?
- Who gets financial alerts today – and how often?
- Which consumers value financial alerts the most?
- What actions do consumers take when they receive a financial alert?
- What will the next generation of alerts look like?
Methodology
The consumer data in this report is based on data collected online from several different surveys:
-A random-sample panel of 2,350 respondents in March 2008. The overall margin of sampling error is ±2.86 percentage points at the 95% confidence level.
-A random-sample panel of 3,367 respondents from August 2008. The overall margin of sampling error is ±1.70 percentage points at the 95% confidence level.
The surveys targeted respondents based on representative proportions of gender, age and income compared to the overall U.S. online population. Secondary data from public sources such as the U.S. Census Bureau and the Bureau of Labour Statistics was incorporated into the forecast.
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