How Customer Support is quickly becoming a powerfully proactive function

Thomas Lah of TSIA recently gave a really good view of the AI Capabilities Landscape covering over 75 defined AI capabilities relevant to Customer Support,? Managed Services, Professional Services, Customer Success, and Field Services.? It was part of his? Q3 Industry Review webinar, and there’s an accompanying detailed PDF report on their site that’s well worth a read.

The roadmap view for support is really compelling, and there are some converging AI developments that I think are going to bring really powerful data muscle to the Customer Support space.

So let’s dig in- What TSIA sees for future state capabilities in Customer Support

Outside of what’s already broadly in use or coming online, some of the most compelling 3+? year future state capabilities outlined include:?

• Workforce Management (WFM) optimization- AI capabilities to predict demand (using customer trends, sales dynamics, retention/churn modeling, etc), mapped to capacity planning;?
• Complex problem solving with low data inputs- capabilities with transfer learning, domain knowledge incorporation, and simulation modeling;
• Knowledge generation- different than generative AI to compile existing content into curated endpoints, this actually refers to creating net new knowledge by leveraging AI technologies such as machine learning, natural language processing, data mining, and semantic analysis to discover patterns, relationships, and insights in large volumes.
• Proactive incident resolution- predicting incidents (in the ITSM space; issue drivers in the support space) and taking proactive actions to prevent or mitigate their impact.

The last one caught my attention.? Proactive incident resolution- anomaly detection and trend analysis across multiple large data sets-? is evaluated as below the waterline* (already commonly used) for Managed Services and ITSM- makes sense-? but well above the waterline (3-5 years out) for Support.???

(*see below for definitions of the Landscape terms TSIA has adopted)

Are true proactive data capabilities still far in the future for support?? I would argue not, if Support data sources can be matured?

I suspect the capability divergence between proactive resolutions for IT service management and Support is that ITSM already has really robust instrumented data and monitoring tools- think of server CPU and memory load monitoring, network traffic, application response times and error rates monitoring.? Pointing those sources at a data services stack and using anomaly and trend detection to alert events are already well used capabilities.??

But that level of data sourcing and monitoring is a different and bigger challenge in support.? Relevant data? like customer/application issue types, categories and causes driving support contacts still often come from unstructured sources like manual ticket tool flagging or contextual tagging, which vary in consistency, level of detail and specificity. ? It’s messy, unreliable data that’s not well suited as a structured source for automated monitoring and trending.

But here’s the thing- per the TSIA capabilities landscape itself, Support AI capabilities already commonly used or moving to production uses within a year -like case summaries and categorization, assisted troubleshooting (with relevant knowledge linking), contextual issue detection and intelligent ticket routing- are improving the support data capabilities very quickly, because all of these help enable more stable, normalized and reliable datasets. And with that in place, pointing that data at automated monitoring with anomaly and trend alerting is a logical short step.? (On this specifically, I did a Proof of Concept in my article Beyond Generative AI: Using Data AI Solutions to turn Customer Support data into powerful, real-time capabilities)

So, what’s the point here?? The reason I’m so dog-with-a-bone on this support data enablement space is that the capabilities it will unleash (no pun intended) for customer support are enormous.? Think of a use case in which a new SaaS update precedes an unexpected rise in live channel support contact volumes. ? With reliable, proactive support data monitoring above the service layer, that volume change could be seen in real-time -with root cause context - and quickly mitigated before significant impacts materialize over time.? An emerging issue addressed within, say, 1,000 contacts over 1 day rather than 7,000 contacts over 7 days is a highly meaningful and measurable benefit both to the customer experience (for the 6,000 customers who didn’t encounter it, or encountered a mitigated aspect of it) and the business (which didn’t endure the cost and customer angst of those additional contacts).?

Even more, with this kind of capability, customer support is a valuable partner to product and services teams within the release ecosystem - and to the business through significant influence on Customer Experience in terms of Contacts per Customer, Customer Effort and Satisfaction scores, as well as much higher support scale with lower costs.?

Watch this space as this all continues to develop.? As I often say, this is the most exciting time in recent memory to be in Customer Support as these types of capabilities transform the role of Support within the product ecosystem.

The TSIA AI landscape terminology

Within their research, each capability by function is evaluated within a relative “landscape” perspective as:??

“Below the Waterline” AI capabilities that are already common in the industry,?

“At the Waterline” - currently being piloted and optimized,??

“Just above the Waterline”- those that companies are discussing piloting in the next year, and

“Well above the Waterline”- those that companies feel have potential in the next three to five years.

TSIA is also continuing to research and evaluate this industry capability landscape over the next year to refine and add depth to the topics.? As I said, their report is a really good read for anyone in one of these customer facing functions interested in how AI is and will be shaping the technology services industry.

About me:?I’m an experienced Customer Support leader, building, scaling and leading SaaS support and service delivery organizations with a mission to embed customer support into the product value proposition.

[email protected]?|??www.dhirubhai.net/in/robarmstrongpm/ |?robarmstrong.online?