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22 September 2023 7 min read


AI AND AUTOMATION IN CUSTOMER SUCCESS

Artificial intelligence

LAURA LAKHWARA

Read More

 1.  What are automation, AI and machine learning?
 2.  My experiences with AI in customer success
 3.  How can AI give a better understanding of our customers’ goals?
 4.  Manual vs. AI-generated health scores
 5.  AI and customer support
 6.  Can AI predict churn and resolve engagement issues?
 7.  Is AI and automation always beneficial?
 8.  How AI can be applied in a CS career
 9.  How to pick the right AI system
 10. Looking for more content like this?

The thoughtful implementation of AI-enabled customer success platforms can
greatly enhance Customer Success Managers’ (CSM) ability to maximize the value
of customer data and ultimately help reduce churn and improve the overall health
of client relationships.

When applied correctly, automation and AI-enabled customer success will open up
additional prospects in the future to completely transform the CS landscape.

In this article, I discuss:

 * What automation, AI and machine learning are,
 * My experiences with AI in customer success,
 * How AI can provide a better understanding of your customer's goals,
 * Manual vs. AI-generated health scores,
 * AI and customer support,
 * If AI can predict churn and resolve engagement issues,
 * Whether AI and machine learning are always beneficial,
 * How AI can be applied in a CS career, and
 * How to pick the right AI system


WHAT ARE AUTOMATION, AI AND MACHINE LEARNING?

If artificial intelligence (AI) is the ability of a computer system to emulate
human thinking and behavior, then automation is a subset of the capabilities of
AI. Put simply, automation is where technology is used to reduce the necessity
for human input or assistance.

Then you have machine learning – a branch of AI that imitates the way human
beings learn. The algorithms that power these computer systems are fed a vast
sum of data to gradually improve their accuracy.

CS + AI = ? Artificial intelligence in customer success
You don’t have to look far in science fiction, be it in books, TV, or film, to
find dystopian depictions of artificial intelligence, commonly abbreviated as
AI.
Customer Success CollectiveGrace Gupta



MY EXPERIENCES WITH AI IN CUSTOMER SUCCESS

For the past eight years, I’ve been at the intersection of artificial
intelligence and customer success. From my time at IBM selling Watson AI
solutions to enterprise customers, to my current role as a Senior Manager of
Customer Success for robotic process automation leader, UiPath.

At each organization, I learned that data (more specifically, understanding
product usage, customer behavior, trends, and predictions) is a key driver of
successful customer outcomes and relationships. Yet, CSMs struggle to keep up
with the dizzying number of data sources across an ever-increasing portfolio of
clients.

While still in their relatively early stages of adoption in the realm of
customer success, AI-powered tools and platforms have already made a positive
impact – providing efficiencies and maximizing value in the data collection and
analysis processes.

AI is the key accelerator for a CSM to manage customers at scale and to identify
growth and risk across a portfolio of customers. As companies increase their
reporting on Net Revenue Retention (NRR) and the market expects NRR growth, the
CSM with AI is positioned to accelerate the up-sell, cross-sell, and renewal of
subscriptions.


HOW CAN AI GIVE A BETTER UNDERSTANDING OF OUR CUSTOMERS’ GOALS?

In the past, it would take hours for CSMs to collect information via their own
sentiment analysis, interactions, and data mining across social networks,
communication channels, and various other platforms.

But collecting customer data was entirely different before the advent of AI
technology. CSMs would face another set of hurdles when it came to organizing
and sharing that data.

Now, not only can CSMs use AI to digest and pull insights from disparate
sources, but they can also leverage AI to build dashboards that make
collaborating easy, turning insights into actionable next steps.

We see AI in other areas of customer success, too, through automation. CS teams
supplement communication with chatbots to answer customer questions in real-time
and to proactively move customers to the next milestone in their product
journey.

The insights from these customer interactions can enhance the accuracy of the
health score and lead to a better understanding of an individual customer. They
also provide executive leadership with a window into trends across industries
and companies of similar sizes and functionality.


MANUAL VS. AI-GENERATED HEALTH SCORES

When it comes to customer health scores, there’s much debate surrounding the
ideal formula and weight of individual components - executive sponsorship,
product adoption, support tickets, NPS, etc. Additionally, the CS industry can
often be found arguing about how much weight should be given to the scores in
predicting customer behavior and evaluating CSM performance.

However, when it comes to debating the merit of manual versus AI-generated
health scores, it boils down to time and scale.

Health scores inputted manually are often limited in scope, demanding continuous
maintenance by the CSM, which becomes unrealistic as the ratio of customer to
CSM increases. Given the growth rate of 24/7 interactions and conversations,
it’s nearly impossible to document and include all the details necessary for an
accurate score.

On the other hand, AI-assisted software makes it easier to tap into a broader
dataset and integrate with fragmented systems of record. Technology can pull
insights from recorded customer calls, email exchanges, support tickets, and
messaging platforms and integrate products, surveys, value engineering, and more
into the collective view.

AI-generated health scores tend to be more accurate because they scale to
capture a larger volume of interactions, can update in near real-time, and
predict risk. These scores free up the CSM’s time, allowing them to take
proactive steps in their engagements and conversations with customers.


AI AND CUSTOMER SUPPORT

Can AI transform reactionary support functions into proactive CS teams? Yes,
100%.

The sheer number of accounts CSMs can have on their plate at any given time
makes a timely "reactionary support function" a best-case scenario. However, as
mentioned above, enabling real-time updates to customer health scores across all
their accounts allows CSMs to be proactive.

To take it a step further, automation software can learn to mimic how CSMs
respond based on their previous responses to calls, general correspondence, and
customer milestones. AI can automatically recommend CSMs to the next steps based
on similar events.

Decoding churn: Find out what your customer is really thinking
In this article, I’m going to walk you through how to decode customer churn.
Before I delve into the crux of the matter, allow me a moment to set the stage
for our topic. Then, I’ll give a quick background, so you understand where my
experience comes from.
Customer Success CollectiveSylvia Yin



CAN AI PREDICT CHURN AND RESOLVE ENGAGEMENT ISSUES?

Without sounding like a broken record, yes, and AI’s impact is again all about
scale and timing.

Anytime you can increase the data and analysis around customer interactions, you
arm your CSM with critical information that enables proactive engagement to
prevent potential churn and other flagged issues.

As a CS leader, predicting risk, getting ahead of renewals, and offering
proactive connections are paramount to helping your organization reach its
growth target.

However, it won’t resolve all engagement issues, and this is where the expertise
of the CSM and/or leadership determines the next best action.


IS AI AND AUTOMATION ALWAYS BENEFICIAL?

Automation and AI in general are in the CSM and customer’s best interest.
However, there are three important caveats to that statement.

 1. AI-powered systems can break down and need to be proactively monitored for
    accuracy. They need to be updated and respond to changes such as process,
    product, software upgrades, etc.
 2. You should always leverage the expertise and relationship management skills
    of your CSMs to give a final recommendation on a customer. At the end of the
    day, AI enables a great CSM to be even more effective, but always includes
    their analysis and review of the data.
 3. Machine learning is only as good as the datasets fed to it. CS leadership
    must continue to think strategically about what data sources make up the
    algorithm and whether those data sources are accurate and continuously
    maintained.


HOW AI CAN BE APPLIED IN A CS CAREER

I’ve always started with the basics, asking myself: “Am I doing anything
manually today that can be automated or solved by AI?” That’s phase one.

Then, I look for the tools to support those efforts. Fortunately, I’ve been at
companies whose products centered around that core question so there has been
progressive thinking about how we could automate our own processes. More
importantly, SaaS tools have added AI features to their platforms that you can
take advantage of today.


HOW TO PICK THE RIGHT AI SYSTEM

Start with the software that is already available at your company. Most software
applications have automation, workflow management, and machine learning
capabilities in their features.

Then, I’d suggest you speak to your organization’s IT and operations teams.
Oftentimes, customer relationship management systems (CRMs), marketing, and
analytics tools can lead to a post-sales experience that you can leverage.

Finally, make sure to bring your ideas and revenue opportunities to the product
team as they’ll be thinking about how these technologies can integrate into the
customer experience.


LOOKING FOR MORE CONTENT LIKE THIS?

If you're looking to implement AI more in your role then we have insights from
Claudie Nicholls (CCO at Simplehealth) on how they use AI for faster and more
efficient customer experiences. Or if you just want to understand more about it
then customer success leaders at Microsoft, Workable, and Alteryx share how you
can lead a human-first CS strategy in the age of AI. All of this is within a
Pro+ membership.

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Written by:

LAURA LAKHWARA

Laura is a Senior Manager of Customer Success at UiPath, building and lead
customer success teams in frontier technology landscapes - including AI,
automation, robotics, and future of work SaaS.

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Customer Success Collective
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AI and automation in customer success


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