Customer Analytics: Empowering Revenue Teams

In every other Mad Men episode the Don Draper’s team of superstars gather a focus groups for market research. Those scenes are where the “magic” happens, where they listen to customers and come up with an incredibly creative idea. What was true in the 80s, is true to this very day. Companies have to listen to customers carefully to create new business and promote growth. Fortunately, we can do this better today than in the 80s by using the capabilities of customer analytics. 

What is customer analytics?

Customer analytics is a data-driven method of collecting and analyzing inputs from customers to understand them better, with the goal of attracting and serving them in the best way possible. When it comes to analyzing customers behavior, an end-to-end approach that taps into the voice of the customer is needed.

Today’s financial climate, combined with the fact that customers have become more demanding, makes accommodating customers a multi-channel and multi-threaded practice. But manually tapping into all data from emails, chats, tickets and other customer signals is simply unrealistic today, and frankly, is a total waste of time. 

Using modern customer analytics solutions can help automate customer data collection, data analysis, and generate insights to share across the board. With this customer-centric philosophy, marketers can attract the right leads, sales pros can meet their targets, product teams can serve users with the right features and flows, and CS teams can minimize churn and maximize growth. 

This is part of an extensive series of guides about machine learning

Sounds like utopia, right? 

But it’s real. The technology is out there, and the pain is not going away. A good customer analytics solution should cover all customers signals: 

• Text-based communications – emails, instant messaging (e.g. Slack, Microsoft Teams etc.), support tickets, chat bots. 
• Live communication – meetings via phone calls or video. 
• Customer changes signals – role transitions, promotions or leave, companies go through reorgs, and more.
• Product/service engagements and signals – product engagement, specific usage (or misuse), trends and more.

While it’s hard to argue with the benefits of having a strong customer analytics solution on board today, many businesses are still lagging behind. Traditional methods like manually collecting partial chunks of data for processing and summarizing calls or chats is a hit or miss strategy that can’t possibly allow sustainable growth anymore.

The main benefits of an customer analytics solution

CRMs and product analysis tools are great to have in the grand scheme of things, but they cannot replace a modern customer analytics solution. An automated and comprehensive approach is needed to get optimal results. 

Let’s dive into the main benefits of having a customer analytics solution in place:

1. It shows the truth, and nothing but the truth. Unlike old methods that hinge upon the limited data that employees collect, customer analytics leaves nothing to the imagination. Data is 100% accurate and reliable.
2. Hands-free CRM – Teams don’t have to worry about inputting data and lose their day-to-day to manual CRM updates. They can focus on what really matters – engaging with customers and doing their work.
3. Automation is bliss – Opportunities and risks are discovered automatically, unlike waiting for someone’s feedback or rule-based CRM workflows that break in “special” use cases or internal changes that were never updated. 
4. Cross-department collab – Customer analytics platforms connect all dots and make all teams well synced.
5. Cost reduction – An end-to-end customer analytics solution won’t require you to invest in expensive third-party tools that come with all kinds of integration, onboarding, training, and maintenance requirements. 
   

How to choose a customer analytics platform?

Here are some things you should look out for in your customer analytics platform.

1. Embrace Artificial Intelligence 

AI and Machine Learning (ML) have evolved exponentially, in part due to the pandemic. These technologies are evolving and becoming increasingly human-centric, which helps capture the voice of the customer. 

Besides automating all cumbersome data collection and summarizing tasks accurately, AI-powered customer analytics solutions can now leverage the booming generative-AI tech to put all of this data into work in real-time. From generating insights to suggesting the right content, research shows generative-AI can save 40% of your time and free you up for other pressing tasks.

Furthermore, artificial intelligence allows you to steer clear of rule-based tools, which come with a wide range of maintenance and configuration requirements. AI-powered solutions analyze and learn customers automatically.

2. Integration with the source, not the CRM

CRM solutions can never give you all of the information you need since they need someone to feed them constantly. You rely on employees to memorize (and forget) to input meetings, call summaries, update a field here and there, and that is simply an imaginative request from most. Customer Analytics tools that rely mostly on CRM data provide limited analytics capabilities that are also probably inaccurate.

So which sources do you need to check? Any communication source with your customers has to be integrated directly with your customer analytics solution. For example, if you wish to analyze emails, you can integrate straight with the email provider and not via your CRM, which also doesn’t “catch” all of the emails.

3. Analysis of ALL signals, not just product usage data

Data harvesting and processing is a big factor in your customer analytics platform’s performance. Calls, tickets, emails, product usage, business data, and more are crucial when you are trying to better know your customers. Your customer analytics platform should be able to collect, digest, and break down all of these signals, ideally in real-time. 

4. Ongoing customer sentiment analysis 

Timing and context are big drivers of good customer analytics platforms. You probably know the feeling of running after customers for survey responses, getting feedback from the wrong people, and finally getting inputs only from your biggest fans or the entire opposite (and often only from them). 

A good customer analytics solution will help you gain visibility into your customer sentiment in real time and get close to 100% coverage. If a customer is disappointed with something, sentiment analysis will pick it up in one of the emails or tickets, without waiting for an NPS. On the positive side of customers, it will automatically create and put a spotlight on new advocates. 

5. Discover trends: Learn from the past to save your future 

Last but not the least, your customer analytics platform should be one that understands your historic trends and usage patterns to guide you on planning ahead. You must get your historical analysis right because these insights and learnings from customer analytics help marketing, sales, and product executives make better decisions.

For example, you can use customer analytics to track user trends, understand pain points for better damage control, recognize new upselling opportunities, and trigger optimized  playbooks to boost engagement.

Customer analytics with Staircase AI

Understanding customers is important as it helps serve them better and leverages the data to create new business. Product managers can now use customer analysis to optimize and create new features, marketing executives can better learn their ICPs, sales professionals can identify opportunities for growth, and CS teams can track customer needs better with this customer-centric approach.

It’s also important to understand that the old way of doing things is no longer going to yield results. AI-based customer analytics is a true gamechanger, not just with the automation side of things. These next-gen solutions offer data harvesting, analysis, and processing capabilities that simply can’t be matched by humans anymore. These abilities are now crucial to be ahead of the curve.

AI-powered customer analysis platforms are helping businesses identify churn risks, spot growth opps, all in real time. It’s really that great.

See Additional Guides on Key Machine Learning Topics

Together with our content partners, we have authored in-depth guides on several other topics that can also be useful as you explore the world of machine learning.

Customer Intelligence

Authored by Staircase

• Customer Intelligence: Benefits, Challenges, and Best Practices
• The Top 9 Product Analytics Tools 
• Customer Intelligence Platform: What Is It and How to Leverage It

Generative Models

Authored by Kolena

• Generative Models: Types, Concepts, and Popular Applications
• Generative AI vs. Predictive AI: 4 Key Differences
• LLM vs. NLP: 6 Key Differences and Using Them Together

Large Language Models

Authored by Swimm

• Large Language Models (LLMs): Technology, use cases, and challenges
• Transformer Neural Networks: Ultimate 2023 Guide 
• Zero Shot Learning: Use Cases, Techniques & Impact on LLMs