Most SaaS firms see Customer Success as a necessary evil. However, it’s a growth strategy! Customers buy your product to achieve their goals. If they do, you’ll have more revenue in the form of renewals, upsells, etc. It is the responsibility of the customer success team to ensure that a customer makes a repeat purchase. So, customer success is the ‘growth hack’ that your SaaS firm is looking for! Let’s see how Artificial Intelligence (AI) in customer success has lifted Customer Success to a whole other level!
As customer success space advances, the related tech has also seen a huge uptick. Artificial intelligence (AI) and Machine Learning (ML) for Customer Success are not cutting-edge concepts anymore. But rather a tried-and-tested best practice. Customer success has transformed as a result of AI, with a digital-first approach.
But, first, let us answer a couple of important questions.
- What’s the issue with customer success tech right now?
- Are the customer success teams struggling to get the right data at the right time?
- Why do we need AI and ML in Customer Success?
Problems with the current Customer Success platforms
- CS Ops dependency– Ultimately, the Customer Success Ops team is a group of people. That to only a limited number of people with enough experience to set the rules right. This requires hours of calibration and recalibration. You can’t expect people to solve a problem as efficiently and quickly as the tech. Your CS tech should embrace technology adoption and save hours to make customer success more efficient.
- All data but no insights- There’s just too much data to analyze! Research indicates most CS executives only are able to use just 1% of the data. Even if you succeed in analyzing all these data, without meaningful insights there’s just no use of the information.
- Customer 360 view is good, but NOT enough- Yes, you read that right! User behavior is tracked with product telemetry and all the metadata from tickets to all the engagements. Millions of data points are there, yet you’re only guessing customer retention drivers. Hence, only the view will not drive net retention!
- False positives and negatives- Customer Success tools are based on certain rules, set up manually. They have a certain threshold beyond which they can’t be trusted. So then how can a customer success manager trust these signals?
- CS platform is NOT CRM + Analytics- Presenting the same old wine in a new bottle won’t work! Dedicated CS tech is the only option to drive growth. The risk criteria stop working when there is a new product update.
The current customer success tools that you use are good but not enough! Your customer success team is dependent on them. If you’re wondering what sort of CS tech is actually great, read on further.
Challenges of the current Customer Success team
- Hiring qualified CSMs- A qualified customer success manager (CSM) is an asset. But that’s been a struggle for many SaaS firms. Delivering value consistently is not an easy task. For that, you need CSMs who can hit the ground running every time.
- Scaling the team- If hiring a CSM was tough, onboarding him/her is tougher! Training sessions are not enough. It’s learning and knowing which feature of your product appeals to your customer the most is crucial. Scaling the team with this kind of effort hinders the best customer experience that you intend to provide.
- The learning curve for CSMs- Real struggle is the duration that CSMs take to learn. Because it takes time to know what works for the customer and how to deliver value. Taking 6-9 months may not be the efficient way of onboarding a CSM.
- Getting risk signals, but what next?- Prediction is good, but what is better is the prescription. CSMs get the risk signals with your CS tech. However, they usually keep guessing what exactly needs to be done next. Guesswork can bring them so far. You need a tech that tells you the next best course of action.
- It’s too late: Renewal is due soon- Too late to jump in! Start thinking about renewals and expansion the moment they start adopting your product. This is about 60-90 days prior to the renewal date. It gives you enough room to think and strategize the customer success plans you have in place. Check out our 30-60-90 day plan template to know more.
It’s pretty sure that you’re able to relate to these pointers. Why, because this is the real struggle of modern-day customer success teams. Now, let’s figure out what are the problems that you might be facing with your CS tech.
Why do you need AI and ML in Customer Success?
Technological advancements ensure that all of the customer’s actions are recorded as data. This information is then used to examine the customer’s purchasing trends, inclinations, and behavior.
While data is a crucial component of customer success, most customer success initiatives do not fully utilize technology or data science to maximize the value of their data. There’s no denying that customer success managers (CSMs) must be able to interpret client data. And it’s at this point that AL and ML come into the picture and help in-
- Gaining a deeper understanding of the reasons for customer churn;
- Constructing predictive and prescriptive models of customer churn;
- Improving the overall health of your client relationship;
- Increasing customer retention and up/cross-selling chances.
Now that we know why we need AI and ML, the next step is to understand issues that plague our modern customer success teams across the globe.
What is Customer Success Intelligence (CSI)?
Customer Success managers are looking for answers in the midst of a sea of segregated data buried in systems. So what happens as a result?
- As soon as someone else in your organization speaks with your customer, the Customer Health Score becomes an outdated number.
- Due to a lack of information preparing for an EBR with a customer takes at least a week.
Every interaction with your clients should provide you with data about them. Give your team digital powers so they can make the most of every customer interaction using AI and ML across the customer journey.
Your business strategy should be able to provide predictive and prescriptive information for better decision-making. It should not be a ‘game of luck’ to get to a 125 percent + NDR (Net Dollar Retention). Rather, it should be the ‘game of information’ to make intelligence a core tenet of Customer Success.
Difference between traditional Customer Success and AI-enabled Customer Success.
Traditional Customer Success, which we’ve seen and used over the years was based on certain rules. Similarly, the Customer Success tools developed were limited to the configured rules. This arrangement impacted (read decreased) the probability of success. With the advent of AI, the traditional CS tools have been remodeled as Customer Success Intelligence tools. They provide intelligence that was missing from their traditional counterparts.
|Rule-based Customer Success Tool||AI-enabled Customer Success Tool|
|Based on Rules set by humans||Based on User Behaviour|
|Limited to the rules configured||Learns and get smarter continuously.|
|Customer Success OPs dependency||No such dependency beyond connecting data sources|
|Works for only experienced CSMs||Works even for first-time CSMs as well.|
Final thoughts on AI and ML in Customer Success
Artificial intelligence (AI) and Machine Learning (ML) allow your firm to process large amounts of client data in a more automatic manner. Machine learning is unquestionably a fantastic opportunity for firms looking to extend their client base. Because let’s face it, if you’re a larger company, you’ll have to deal with a lot of data in order to implement a solid customer success plan.
AI-enabled Customer Success will open up additional prospects for customer success in the future. It has the potential to completely transform the customer success landscape and open up new opportunities. Hence, undoubtedly, this is the era of Customer Success Intelligence!
P.S. – The main image has been taken from pexels.com