VMware Customer Connect has recently rebranded and updated My VMware with some cool new features. It integrates digital engagements for VMware Connect Communities™, VMware Connect Knowledge™, VMware Connect Learning™ and VMware Connect Support™ with VMware Connect Success™ coming soon. Check out Mike Olingy’s blog on VMware Customer Connect to learn more.
What is Connect AI?
Essentially Customer Connect AI is what I call the “brainpower” within VMware Customer Connect. It uses artificial intelligence and Machine Learning (ML) technologies in several ways to help customers find resolutions faster by anticipating their needs and providing automated virtual assistance that seamlessly guides them to the help and content they need.
How does Customer Connect AI help customers get resolutions faster?
In Virtual Assistant, yes, we have a chatbot (I know, I know the stigma around chatbots…). Still, this one helps resolve your non-technical issues, or at least that is what our data analysis and customer feedback are telling us so far. Customers can ask questions, not the meaning of life or predicting winning lottery ticket numbers, but about non-technical issues. Our developed and trained ML’s Natural Language Processing (NLP) can understand the asks and suggest answers. It helps in various forms such as automated task operations (e.g., add user, remove user) performed within Virtual Assistant, it offers an exact Knowledge Base article for resolving an issue and helps customers navigate to a correct page in the VMware Customer Connect ecosystem as well as other tasks.
Another example is the integration we have done with the Support Request (SR) filing process. It provides a guided experience that prompts the customer to efficiently gather all the information they need to provide our Support teams to ensure they will have everything they need to resolve the customer issue faster. Once the SR is submitted, Customer Connect AI routes it to the right engineer.
Coming in a near-term release
Another example of really cool intelligence we are exploring is to proactively suggest troubleshooting steps or resolutions for technical issues while it is being described by the customer with a Support Request filing. In this case, we’re building deep learning through the use of Natural Language Classifier (NLC), a service available in our current ML technologies stack. NLC combines various advanced ML techniques to provide the highest accuracy possible without requiring a lot of training data. NLC utilizes an ensemble of classification models, along with unsupervised and supervised learning techniques, to achieve its accuracy levels. Long story short, we focus on common problems that are relatively straightforward for customers to self-solve rather than take the long route of engaging with a Technical Support Agent. We are spending a lot of energy on making the ML accurate, and we think the benefit to our customers is going to be huge.
Features under consideration for future releases
For the future of Customer Connect AI, we have some ambitious goals. At VMware, we are looking at solutions and technologies that could use predictability and personalization to send relevant information and suggestions to the customer’s fingertips.
First, we assess the ability to combine advanced analytics and ML technologies to enable predictive tech support resolution recommendations. The concept we are researching is to take a customer journey (e.g., search entries, portal navigation and click-throughs, Virtual Assistant interaction) and gather and analyze that data in real-time. It integrates with ML technologies to be able to recommend a specific Knowledge Base article or content that helps solve the customer’s technical issue. One of the challenges we are working to overcome is calibrating the amount of data collected from a customer session and accurately classifying that data and running it thru the ML model and technologies to establish a match that is highly accurate and will solve the problem.
Regarding personalization, we are researching the ability to channel relevant content like a direct feed to topics and information that the customer cares about. For example, we need to establish a unified profile for a given customer, including their Entitlement Accounts, downloaded products and licensed products. And then, based on this profile, we would provide a personalized information feed of new relevant KBs, community posts, product documentation and even hot trending topics. This enables customers to get the relevant information they care about readily available at their fingertips rather than spending precious time seeking it out.
Again, these topics are still longer-term initiatives to evaluate and assess. Nevertheless, we hope it gives an interesting sneak peek in the direction of where Customer Connect AI is going and how it intertwines throughout VMware Customer Connect.
Customer Feedback is the Foundation of VMware Customer Connect
Do these future features sound helpful? Do you have other ideas for what you would like to see? VMware Customer Connect is built from the input of our customers. If you would like to share your thoughts, join our VMware Customer Connect Pacesetter Program. It allows you an opportunity to engage with our design teams and tell us what you find most valuable. To participate, you will need to register as a Pacesetter Program member. Once you are a member, you can participate in our design events. You can learn more about our design events here. Because space is limited, availability to participate is on a first-come, first-served basis.
It just so happens I will be leading our next Pacesetter event on February 25th at 11:30 am ET, and it is focused on Customer Connect AI. Register and join, or feel free to leave a comment here on what would make your VMware Customer Connect experience better.