Costs skyrocket in the battle against spam

By
Follow google news

The average company could be wasting over US$180,000 a year in lost productivity because of unsolicited email, according to security giant McAfee.

The vendor's March 2009 Spam Report (PDF) said that the average annual productivity hit for companies with 1,000 employees works out to around US$40,000 for every per cent of the total email volume that is spam.

The figure does not include the cost of storing and recording spam, the bandwidth it consumes, or any clean-up that might be needed on corporate networks and PCs if the spam contains malware or malicious links, advised the report.

"IT administrators often say 'we've got an anti-spam solution' and that's it, but they maybe don't realise it's costing the business a lot of money," said McAfee European product marketing manager Mike Smart.

"If you have to reduce the workforce but do the same amount of work, for example, this stuff becomes more important. A saving of $40,000 is not insignificant and something that business leaders would recognise."

Smart explained that spammers are optimising their targeting with more accurate email lists, and are seeing a reduction in the number of bouncebacks after recent mass mailing campaigns.

Spam could increase by as much as 15 per cent in March, traditionally one of the worst for unwanted email, compared to February, according to the report.

Costs skyrocket in the battle against spam

Add iTnews as your trusted source

Got a news tip for our journalists? Share it with us anonymously here.
Copyright ©v3.co.uk
Tags:

Most Read Articles

Poor WA gov M365 security led to $71k theft and children's data breached

Poor WA gov M365 security led to $71k theft and children's data breached

US medical device maker Stryker's Microsoft environment attacked

US medical device maker Stryker's Microsoft environment attacked

CBA chief impersonated in global investment fraud on Facebook

CBA chief impersonated in global investment fraud on Facebook

Services Australia describes fraud, debt-related machine learning use cases

Services Australia describes fraud, debt-related machine learning use cases

Log In

  |  Forgot your password?