How will using AI tools as CSM's change our day-to-day work?

Meg Valentine

I was given permission by the author of this post, Daphne Costa Lopes, Director of Customer Success at Hubspot to re-purpose the LinkedIn post below here. Daphne posted this thread on LInkedIn just a few days ago. My hope that we can all have an interesting discussion about AI tools that we are all using or at least learning how to use in our daily work as CS leaders and CSM's.

https://www.linkedin.com/in/daphnecostalopes/

"AI is going to transform the day-to-day of CSMs.

No more:

- Google sheets

- Manual forecasting

- Manual prioritisation

- Copy & Paste Account Plans

- Building Business Reviews from scratch

- Scrambling to find the right playbook to use

- Deciding which customer has growth potential

- Dozens of reports to understand usage and value

- Reviewing all accounts to identify risks and opportunities

All the soul-crushing tasks a CSM has to perform before they engage with customers to drive value will go away. ☁️

CSMs will get nearly 50% of their time back. 😲

How?

🤖 AI co-pilots.

These are bots, smart workspaces and alerts that live in the systems CSMs already use to get work done (ie. the CRM, Slack, the CS platform etc).

And it's not a far-fetched future. It's already happening.

- Propensity to grow scores

- Behavioural alerts for customers at risk

- Automated call summaries and follow-up emails

- Product recommendations based on the use case

- Top customers to connect with plus attached playbooks

- Auto-generated content (ie. emails, presentations, landing pages etc)

UpdateAI, Gainsight, HubSpot, Gong, Hook and Slack are just some of the tools that put AI at the centre of their strategy, to help CSMs spend more time doing the stuff that drives the most value.

💡 And CSMs are already reaping the value...

In a recent survey, Gainisight revealed that 47% of CSMs report that AI is helping them save time and drive efficiency by automating redundant tasks.

So what can CSMs do with 50% of their time back?

💡 Spend more time driving value.

They should focus on:

- Unlocking use-cases

- Nurturing growth opportunities

- Helping customers manage change

- Multi-threading with key stakeholders

- Consulting on strategies to increase value

- Holding customers accountable to Success Plans

- Communicating the value and ROI to decision-makers

These are high-value activities that yield better NRR. 🤩

Here is where humans can play to their advantage.

The catch is...

🤷🏻‍♀️ There are a bunch of CSMs that resist AI tools.

Beware of falling into that category, or you will quickly fall behind your peers.

You will get less done, your insights might be of poorer quality and you'll spend too much time on low-value admin tasks.

Eventually, it will catch up with your performance.

So if you are a CS Leader, it's time to unlock the full potential of the tools you have in your stack to help supercharge CSMs.

And if you are a CSM, it's time for you to embed AI into your workflow."

Thank you, Daphne, for kicking starting what I hope is an engaging and passionate discussion about AI's role in Customer Success!

Meg Valentine

Community Volunteer

Carmit Proper

Thanks for Sharing, I've been working as a customer success Director in AI and ML companies in the last 6 years and have constantly thinking about how to leverage AI better in customer success.

A few weeks ago I posted something on that on Linkedin:

Meg Valentine

Thank you, Carmit for contributing.

It sounds like a lot of agreement here. I've asked another CS leader to post his comments here as well, since he's been experimenting with various AI tools for different uses in CS.

I heard yesterday in AI webinar that most CS professionals are using AI tools 50% of the time in their daily work. I ask though - which tools, for what purpose, and how much time and LOE are being saved by using them? Which tools will likely end up being the standard for CS?

Anyone want to take a stab at determining how these tools will impact CS teams? I like Daphne's suggestion that we should put more time into Value Capture and delivery. What do you think?

parth

@Meg Valentine Bringing our convo here- While I don’t consider myself an AI expert I do work on building AI functionality into CS workflows using Airtable and Make. Some of my inspiration for these use cases come from off the shelf tools where I see an opportunity to deliver 80% of the value at 20% of the cost. 

One of the challenges in deciding which tool to go with is not knowing which ones will continue innovating and be worth it 12 months from now and which ones will be commoditized.

With that said, you can’t sit on the sidelines as there are several use cases with tools that can make an impact today.

1: Support - 

Chatbots- Tools like Gleen (4.9 stars on g2) are interesting to me as they seem easy to set up and allow for automatic escalation to human support personnel when the bot gets stuck. Intercom is another more established player in the space. 

Self service documentation- Orgs often struggle to keep their knowledge base up to date. Ariglad is a tool that promises to help create and update knowledge bases based on past support tickets. It also matches the language used by customers to the language in your documentation. This can make it easier for them to self service in the future. 

2: Forecasting- 

Long before ChatGPT orgs have used Machine Learning to identify patterns using historic data. This includes- churn risks pre sales, post sales, identifying cross sell patterns, etc. One tool that I’ve been playing around with is Obviously.ai. Solutions like Reef.ai seem to be a much more robust and complete offering. Many CSPs are also implementing these types of capabilities (with varying degrees of success). @Carmit Proper I'm going to take a deeper look into the work you all are doing at Casual seems interesting!

Caution: While reef.ai is very focused on CS use cases more open ended solutions like obviously.ai require a stronger grasp on statistics/data science. They actually only recommend using the tool alongside a Data Scientist as part of the paid package. The free option is there if you would like to run some tests or you have sufficient background in stats and data science. If a larger strategy is based on your findings you want to make sure you’re tracking towards what actually matters. @Ed Powers is an expert on the topic and has pointed out several of the shortcomings of new tools and features implemented in larger CSPs in his latest post

Extra Caution: Using GenAI based tools like ChatGPT Plus to do data analysis is a mixed bag (as of today). So although you can do regressions and classifications I wouldn't trust the results.

Obviously.ai screenshot:

3: Call Summarization

Being able to generate call summaries is low hanging fruit and every org should be doing this by now. If you can eliminate the need to take notes that is a huge win but the bigger opportunity here is being able to use this data to better gauge customer sentiment, develop acct headlines, aggregate product feedback, customer marketing stories, etc. From a pricing and features standpoint @Josh Schachter's UpdateAI (4.9 stars on G2) has a clear advantage over rivals like Gong and Chorus as it relates to post sales teams. Image from their website.

CSMs spend anywhere between 1-2 hrs/day on follow ups. I made a vid on LinkedIn on how you can take these summaries, turn them into action items, and automate the execution of those tasks. To direct tickets to Jira, Zendesk, and Marketing. I have a few others on automating QBR decks and Acct Summaries using Airtable and Make that you can find on my page as well.