Productive, proactive, predictive – three facets of glassbeam’s core value proposition

PUNEET PANDIT
Tuesday, June 17, 2014

Growing a sales organization is a double edge sword ! It brings more prospects in the pipeline, but it also creates demand on marketing and executive management to crystallize the core value proposition very quickly. I am not complaining :-) I think it is great that I have been pulled into some critical pre-sales discussions recently where I had to think on my feet, white board and create new slides to help sales close new deals. So, this blog is is a multi-series blog that highlights our core message and value propositison to the market we sell into.

For a typical sell into support and engineering organization, across any vertical (that we focus on today) like storage, wireless, networking, or medical, I believe Glassbeam offers three core pillars of the value proposition:

Productive: We make our customers more productive in solving their customer issues. We do this by by dramatically improving a support case’s mean time to resolution (MTTR). This is achieved through multiple means, starting with a centralized cloud based log management (LogVault) so that tech support engineers, in any geo, in any group (L1, L2, L3 etc) can pull down raw logs for further analysis, anytime, anywhere, and anyplace. We also provide sophisticated full text and faceted search application (Explorer) that allows a user to perform root cause analysis on an escalation. Finally, we provide an ability to build a centralized knowledge base of “save search” sessions that has complex search expressions on raw logs allowing group sharing of known issues, search criteria, and final results within tech support community.

Proactive: Not only do we make our customer’s support operations more productive in reactive troubleshooting, we also make them more proactive by letting them define rules and alerts on incoming raw data to allow creation of proactive alerts for support, engineering or field organization to take specific actions. Such rules and alerts can also trigger support automation (automatic case opening) through email-to-case alerts into CRM system like SALESFORCE.COM.

Predictive: We can create predictive algorithms through supervised or unsupervised machine learning on both raw machine data (logs) and better still by combining raw logs with case history for that system, customer or a set of customers. This can be initially done through

Data Scientist practice

that we leverage with our partners who specialize in this field of predictive analytics. Once the algorithms are built, they can be translated into sophisticated rules and alerts that can be triggered on incoming machine data or can be run in batch mode against the historical data for the installed base.

 

Above is a simplified view of our value as it pertains to support or engineering organization in a product company. Our value is lot broader to other groups as well, such as product management, marketing, sales and field organization. More on that in a subsequent blog later on, so stay tuned.