6 AI for Customer Success Use Cases

With the goal of efficiency looming large, the past year has compelled organizations to re-evaluate their tech stacks. If a particular tool doesn’t offer enough value to improve ROI, it’s eliminated from the tech stack.

The difference between sticking to a tool and letting go becomes significant for sellers. And the key to resolving it?

Having a meaningful relationship with customers.

On average, loyal customers place order values that are 67% higher than new customers. Plus, you have a 60-70% chance of upselling to existing customers than a 5-20% chance of converting new ones.

With so many growth benefits on offer, businesses are turning to artificial intelligence (AI). 

AI can help improve your net retention rate (NRR) and customer success processes.

Unfortunately, most companies are late to the tech party. And they don’t know how to deploy AI for customer success. It’s 2023, but over 66% of customer success reps still don’t use AI in their current role. It’s not optimal for business at all.

Because AI isn’t coming. It’s already here.

This blog aims to understand why AI for customer success is critical and how you can build use cases for it.

How Has Customer Success Changed Today?

In the past 12 months, here’s how customer success has evolved for organizations.

1. A shift in customer behavior

Most notably, customer preferences, pain points, and interests have evolved since 2020. As a result, customer success needs to be agile and quick to adapt to these changes.

2. Remote operations

More and more customer success roles are going remote. Alongside this, companies are incorporating tools and processes that are completely hybrid.

3. Digital-led operations

Customer onboarding is now being automated. Generative AI in chatbots quickly resolves customer complaints based on account intelligence.

4. Evolving growth outlook

Businesses have moved from a “growth at all costs” outlook to “resilient growth.”

During tough economic situations, capturing new customers is difficult. On the other hand, retention assumes more importance.

Subsequently, companies have learned to find ways to drive more ROI from existing tools without incurring extra costs.

Ep #1: Hubspot Customer Retention With RevOps ft. Sid Kumar

5. Churn prediction

Importance is increasingly placed on monitoring customer health to identify potential churn. So that customer success managers (CSMs) can step in early and change the account’s sentiment.

All of the above changes show how important it is to use AI for customer success correctly.

Why You Need AI for Customer Success Now

As pointed out earlier, retaining customers is more important than ever today. Plus, it’s important to continually drive value from your solution for customers and do it efficiently.

But driving value becomes an insurmountable challenge when so much data is up for grabs. As per recent estimates, the human populace generates nearly 330 million terabytes of data daily. That’s not just huge; it’s MASSIVE.

Imagine searching for customer behavioral data in this mammoth pile.

Even if you somehow gather the data, your CSMs may need to learn how to use this information to build unforgettable customer experiences.

And so, clients feel forgotten. They are clueless about how to make the most of your solution or simply resolve their day-to-day queries.

Coupled with the business shift to driving more value from existing tools while keeping costs low, customers are left in a lurch.

As a result, they derive no value from associating with you.

So, how do you grow by optimizing retention?

You leverage data for building AI-based customer success models and prevent churn. Even a 5% increase in customer retention increases revenue by 25-95%.

Here are some more reasons to consider deploying AI for customer success:

• AI automates workflows. It doesn’t replace humans but drops over-reliance on manual intervention.
• Consequently, customer success reps have more time to build relationships. It boosts your productivity and efficiency.
• It can dive into that massive pile of data and surface more accurate, complete, and reliable data. The cleaner the data, the better your insights.
• AI alerts you when customer sentiment changes from positive to neutral to negative. It also suggests the next best steps to course correct immediately.
• You can use AI to show customers new ways of exploring your product. This constantly drives additional value.
• With AI for customer success, your actions are more proactive than reactive.
• When you know everything about your customer, you can tailor your communication approach. You may also recommend upgrades or use cases at the right time.

Overall, AI boosts customer satisfaction. Let’s dive straight in if this is reason enough to consider AI for customer success.

6 Use Cases of AI for Customer Success To Prevent Churn

To truly prevent churn, it’s best to encourage your CSMs to help customers drive value from the time they first use your solution.

Here are 6 ways how AI for customer success improves NRR:

1. Quick onboarding

a. Avoid high operational costs

AI ensures a seamless customer experience, starting with onboarding. Instead of assigning reps, you can use digital means to avoid high operational costs and lengthy processes.

For example, a chatbot embedded in your product can guide the customer through initial onboarding. This makes it less time-consuming for everyone involved (who would otherwise spend hours on the task).

b. Accelerate TTV

Secondly, with quick onboarding, you can accelerate your customers’ time to value (TTV).

You can determine the use cases that encouraged their purchase of your solution. And get right down to helping them deploy the use cases for maximum value.

AI also optimizes the time between educating customers about new features or releases. Plus it optimizes the business value they achieve from your solution.

2. Monitor renewal group activity

a. Concentrate on building relationships

AI lets you automatically capture critical data instead of manually logging in each customer success activity. Your team need not worry about losing insights.

This activity data includes information from emails, meetings, virtual messaging apps, and more. such as the integration of a message forwarding app to streamline communication across multiple devices.

b. Attribute data to source

Not only can AI capture this data, but it can also attribute each data point to its source. And it can harvest this information in only a few seconds or minutes.

c. Consistent engagement

In due course, you consistently engage with each customer account as well as with leaders.

This lets you provide value. Plus, you can show continuing benefits for buyers via regular engagement through:

• Emails
• Meetings
• Virtual calls, and
• Other interactions.

d. Complete visibility with insights

You get complete visibility and deep insights on stakeholder interactions. These are available at contact and activity levels. By monitoring the renewal group’s activity, you never let the relationship lose momentum.

e. Identify coaching opportunities

AI for customer success can set up alerts for inactive customers. It happens when CSMs haven’t engaged with an account for a long time. At this point, you may also identify coaching opportunities to improve the quality of interactions.

Source

When customers are up for renewal, you don’t rush the process by engaging only closer to the renewal date.

3. Prioritize and personalize

a. Integrated customer journeys

Integrate with CRM-adjacent tools to get insights on the entire customer journey.

These include tools like:

• Conversational intelligence
• Sales enablement
• Support platforms
• Messaging platforms
• Surveys

b. Real-time recommendations

With contact-level insights, AI for customer success shares real-time recommendations for each account. These recommendations let you personalize the customer success experience.

You can get insights into:

• Customer interests
• Preferences
• Needs
• Purchase behavior
• Transactional history
• And even changes in the buyer organization.

c. Scale and adapt

Using AI, you can rapidly scale and adjust your solution to the increasing amounts of data as you grow.

d. Data-driven insights 24*7

AI for customer success provides data-driven insights rather than gut feelings. Your customer success reps don’t need to input information continuously.

Plus, digital interactions with the customer could take place 24*7. It’s rather impossible to maintain a manual log at this rate.

e. Health scores

You can set rules for health scores and automatically assign them using AI. In addition, you receive recommended playbooks.

There’s minimal to zero rep adoption or intervention needed.

f. Prioritize the right accounts at the right time

Using contact-level activity data and health scores, CSMs prioritize the right accounts at the right time. They can use inputs on client engagement, building individual and solid strategies for each account.

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4. Spot single-threaded accounts

a. Spot single-threaded accounts

Today, the buyer is a group rather than an individual. Effective customer success understands this and harvests each stakeholder’s data to engage with them.

With AI for customer success, you can easily spot single-threaded accounts.

b. Multithread better

It could be that multiple stakeholders engage in conversations with your rep. However, their contact details may not be added to your CRM or customer success platform. AI alerts you on such accounts.

You can then move from engaging with a lone champion to multithreaded interactions with all key stakeholders.

c. Discover hidden contacts

AI for customer success can skim through historical data and discover hidden contacts. Plus, you can dive into granular insights on the industry, audience segments, and strategic accounts.

d. Account mapping

As you shift to multithreading, auto-capture maps each contact to the respective account with their role, email ID, and phone number.

You can monitor each account to see if CSMs are building relationships with new stakeholders in these accounts.

Contact-level data also presents opportunities for CSMs to personalize communication and content for each stakeholder and account.

e. Account expansion

AI for customer success throws light on accounts with potential expansion opportunities.

For instance, a customer has purchased your CRM solution. But recently, they may have expanded their sales team and could be in the race to add a sales enablement or training feature.

Source

You can get in on this expansion opportunity early if you provide these features.

f. Relevant suggestions

With personalized recommendations, you can suggest highly relevant products, services, features, or use cases. These recommendations may vary for each business and industry.

g. Agile collaboration

Multithreading isn’t a single function’s responsibility. You may need to collaborate with marketing and sales teams to expand an active account.

Working with other RevOps teams helps you provide overall business value rather than concentrating on revenue alone.

5. Develop strategic interventions

a. Predict churn

The global benchmark for acceptable customer churn is 5%. Yet, 30% of organizations have unacceptably higher churn rates.

Using predictive analytics, AI lets you predict churn so you can intervene at the right time and steer at-risk customers toward renewal.

You can also include leadership in the conversation and plan strategic interventions at the executive level.

b. Spot and analyze evolving trends

AI dives into multiple data points including customer preferences, demographics, transaction history, economic factors, competitor activities, customer journeys, and sentiment.

It also scours multiple channels to mine these signals.

You get notified of changes, trends, or patterns that may be otherwise missed. Plus, you can choose to receive these alerts via several mediums–email, messaging apps, and tool dashboards.

c. Identify alternative champions

For over 81% of reps, a critical stakeholder leaving the client’s company leads to lost or stalled deals.

AI for customer success shares insights on who could be your backup champions early on. So, when the current champion leaves, you can immediately contact the next one.

d. Track champion movement

AI sends real-time alerts when a champion quits the client conversation.

With relationship insights, you can track their movement. You can engage with them strategically if they’ve moved to another customer account in your portfolio or a potential target account.

Instead of losing an account, you can take advantage of the change and turn it into a new revenue opportunity.

e. Track executive engagement

You can determine which executives attend each quarterly business review (QBR) and track their engagement levels.

Simultaneously, you’ll know who doesn’t attend the meeting and improve engagement with them.

Stronger executive engagement presents access to key stakeholders. You can then leverage leadership buy-in to present solid use cases for retention and expansion.

6. Automate support

a. Uncomplicate repetitive tasks

Use AI for customer success to automate routine tasks like customer queries, creation of tickets, and other uncomplicated conversations.

b. Chatbots

You can develop AI-powered chatbots and voice bots. These chatbots use conversation flows with suggested responses, content, and product recommendations for prompt communication.

They emulate human conversational style and learn from negative feedback to continuously improve. Most chatbots these days also offer multilingual support.

They’re also versatile and can be used on the website, support page, product dashboard, and more.

Overall, Chatbots help reduce the response and query-handling time. It eventually boosts customer success metrics.

c. Leverage self-service or filter queries

AI can retrieve real-time customer data from your systems. It gives you the advantage of leveraging self-service support by directing customers to relevant resources.

Or you can intelligently filter and route queries to a CSM or rep with the right skill set to solve the particular problem.

Deploy AI for Customer Success With Clean Data

The topmost challenge to effectively using AI for customer success is having clean data.

Data (more importantly, accurate, reliable, and consistent data) is the key driver to understanding your customers. AI dives deep into this data to retrieve insights and run more reliable models.

Yet, several organizations still rely heavily on manual intervention for customer success. Only 38% of customer success processes today are automated.

For CSMs, collecting and analyzing mammoth amounts of data is a monumental task. Instead, give the power back to your reps by leveraging AI for customer success.

Nektar’s AI uses graph machine learning tech to deploy a superior “capture-enrich-map” method. It works effectively even in complex renewal groups.

Our platform has applications for common, advanced, and edge use cases. So you can capture data based on your preferences.

Nektar integrates seamlessly with the rest of your GTM stack or CRM-adjacent tools.

With Nektar, you get:

✔ Automated capture of CSM activities

✔ Spotting single-threaded accounts for early churn warnings

✔ Instantaneous alerts for champion movement

✔ Contact-level activity alerts

✔ Quick implementation

✔ Zero-rep adoption

Share the right insights for the right people at the right time to increase retention, expansion and overall customer satisfaction. 

See how you can reduce churn and power customer success with Nektar.