#35: 3 Practical Ways To Start Using AI to scale your CS

AI is the word in everyone's mouth, including mine.

Recently, I got to chat about the potential of AI in the Video Voyagers Podcast where I share my predictions on how Customer Success will be transformed by AI.

But let's face it, talking the talk is different than walking the walk.

And right now, a lot of the AI content is just talk.

To quote Matthew McConaughey in Wolf of Wall Street:

"Fugayzi, fugazi. It's a whazy. It's a woozie. It's fairy dust."

If you are feeling excited but don't know where to start, you are not alone.

Many CS leaders are still unclear on how to integrate AI into their processes in a way that helps their teams scale better.

But don't worry, I've got you!

This week's newsletter is here to help you by giving you 3 practical ideas on how to use AI to deliver better results for your customers and your business.

📈 Use Case 1: Forecasting

Every Customer Success team that owns a revenue number is likely forecasting where they will land against their targets.

If you are not forecasting yet, I wrote this newsletter on how to get started.

But if you are a veteran at forecasting you know how many hours go into getting accurate forecasts for your team. You need:

• CSM Sentiment input
• Health Data
• Usage Data
• Contract Data 
• Product Mix

Sometimes the process of forecasting can become so laborious that CSMs lose sight of why we are doing it in the first place.

Here is where AI can *really* help take all the mindless data crunch out of CSM's hands.

Not only by automating the inputs but, also by creating real-time predictions of whether customers are likely to renew based on how other customers like them performed in the past. 

And your CSM input can still be taken into account. Using a CRM object for "CSM Success Prediction", AI can take the sentiment input into the calculation. 

This solution can help:

1. CSMs to focus their time on mitigating risk and adding value 
2. CS Leaders can focus on understanding the trends and solving for the customer at scale. 

And guess what? You don't have to go and build this yourself.

Tools like Clari are already incorporating AI into their forecasting technology (this is not an ad).

🚨 Use Case 2: Risk and Growth Identification

The number one complaint that CSMs have is that it's hard to prioritise their work.

Even when you have a customer journey and a good process mapped in your CS platform, it's still hard to be responsive to everything that is happening with customers.

CSMs can't know everything, and be everywhere, all at once.

But AI can. 

In fact, AI or ML models can continuously monitor your customers' behaviours and their outcomes. Then it can compare these to the top-performing customers to tell you whether customers are displaying risk signals or have growth potential.

It can even take external information into account if you are enriching data in the CRM.

That means CSMs can spend more time acting on insights than trying to find them. 

At HubSpot, we've built our own ML engine.

But you don't have to go build this from scratch if that's unrealistic for your business.

CS Platforms like Gainsight have been shipping hot and exciting AI features to help optimise adoption (also not an ad).

🚨 Use Case 3: Tailored Customer Enablement

The key to scaling Customer Success is to:

Deliver the right intervention to the right customer, at the right time.

So simple, yet so complex!

It's no longer good enough to take your long-tail customers, put them in a "digital segment" and personalise your messages with [first name] tokens and a slightly modified copy depending on the product mix they use.

The old marketing automation methods will not work for CS at scale.

The reason why Customer Success works is because we meet the customer where they are, understand their goals deeply, and help drive change in their behaviour to help them get to those goals.

So we can't simply scale the messaging if we want to scale CS.

We need to tailor the customer experience for each unique persona, and proactively drive them to their desired outcomes. Just like a human would.

Realistically, achieving that at scale is only possible when we use AI. 

There are too many things to do at once:

• Monitoring behaviours and correlating them to outcomes
• Comparing them to other high-performing users of the same persona in the network 
• Flagging risky or opportunistic behaviour
• Deciding what intervention to trigger based on what works for the network and for this user and persona specifically
• Crafting messages that resonate to drive the right behaviour 

I know I promised you 3 practical tips, however, I will be honest with you... this is holy grail stuff.

I still haven't seen an organisation that is delivering CS at scale in this way. Or a platform that can make this happen (if you have seen one, send it on to me so I can feature it).

But.. now that you have the vision, maybe you can be the first!

TL'DR:

Translate the AI hype into these 3 practical use cases to scale your Customer Success Team

1. Forecasting
2. Risk and Growth Identification
3. Tailored Customer Enablement

See you next Friday!