January 2019
SaaS Customer Journeys

Welcome to The Cirrostratus Group January newsletter. It's hard to believe that it is now 2019, several years since SaaS and Cloud applications became mainstream.

In this month's newsletter I've included my recent blog post on Your SaaS Customer's Journey. I think you'll find it informative.

If you are going to have artificial intelligence and machine learning as part of your SaaS offering one of the biggest challenges is getting the right staff. Daniel Faggella of Emerj has some excellent thoughts on that topic.

Gartner has recently released their Magic Quadrant for Cloud Management Platforms and Forrester has just released their Wave report on Cloud Integration Platforms. These reports are available free at these vendor links, Cloud Management Platforms and Cloud Integration Platforms.

I'm always interested in hearing about your experiences with delivering cloud applications.

Paul Ressler
The Cirrostratus Group  

    Your SaaS Customer's Journey 
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A customer journey is typically defined as the sum of all the experiences that your customer has with your brand and product. In the case of your SaaS product, it includes interactions prior to buying, the actual buying process, on-boarding, continued learning about how to use the product including best practices, up-selling, technical problem resolution, invoicing, other problem resolution and learning about the future direction of the product.
Your customer's journey and their perception of that journey is key for a number of important SaaS financial metrics including customer retention/churn percentage and up-sell percentage. It can also have substantial influence on new customer acquisition cost in the form of how easy it is for the customer to buy and the amount of customer referrals.
Read my full blog post for 5 key best practices for your customer's journey. 

 Staffing for your Artificial Intelligence Application  
Daniel Fagella of Emerj has written an insightful article about the challenges of hiring, training and retaining artificial intelligence technical staff. He has divided the needs into three categories:
  • Academic use
  • Internal use
  • External/customer use
Application providers fall into the latter category which is the most challenging from a risk perspective. He indicates that it will be difficult to train resources or to recruit them outside of Silicon Valley. Taken to it's conclusion it appears that providing early and innovative artificial intelligence applications outside of Silicon Valley will be very challenging. Although he may be painting an extreme picture this is a well thought out article on the staffing challenges for all phases of artificial intelligence applications.

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