The world of customer success is growing. As demand for customer-centric operations shifts the balance of power in virtually every industry, artificial intelligence (AI) is starting to play a significant role. Not only can AI consume and organize data, but it is also the key to unlocking insights that help prevent churn, stopping loss before it starts.
In sequence, here is how CX data can be used by AI to generate actionable insights: AI unifies disparate messy data without tedious field mapping or coding required of users. An AI model automatically measures and analyzes key indicators for customer experience. These signals go through python algorithms to predict top churn risks, revenue expansion opportunities, and tasks to improve the overall customer experience.
As customer experience quickly becomes a critical focus for every company, this kind of technology is increasingly used to ensure great CX by providing deep customer intelligence to drive positive churn and growth factors.
Which CX Metrics to Measure
The organization of data is a crucial exercise of any AI-powered system. Analysts have identified the nine most important metrics for measuring customer experience, especially as it relates to churn risk and customer growth:
- Product usage, or the frequency at which a customer purchases or uses a product
- Net promoter score/customer satisfaction score (NPS/CSAT)
- Interaction frequency, measured in units that can include calls, emails, or visits
- Support tickets
- The severity and tone of support tickets
- Upsells or downsells that relate to increased or decreased revenue from each customer
- Customer Life Cycle
- Renewal sentiment
- Customer owner pulse
Customer success teams play a powerful role in ensuring the data collected manually is accurate, but much of it is generated through software. Companies already collect data from platforms like Salesforce, Snowflake, Zendesk, Gmail, Zoom, JIRA, and the like.
How SaaS Companies Handle an Overabundance of CX Data
The fact that so much data is available, and can be tied to key performance metrics for customer experience, is good news for companies with streamlined operations. But this can hamstring less prepared or smaller companies. When companies face limited resources, all of the collected data that could yield valuable insights to mitigate churn risk and growth is out of reach.
Gaurav Bhattacharya and Saumya Bhatnagar are SaaS experts and co-founders of involve.ai who believe that, in the right system, CX data can foretell whether customers are prone to leave and then be used to prevent that attrition before it happens.
All of the above-mentioned backend processes have plenty of inherent insights if the unstructured data can be used effectively. That is what AI does best, and it is what Bhattacharya and Bhatnagar’s rigorously precise 9Factor algorithm can analyze.
Bhattacharya explains it this way, “Numbers don’t lie. Tracking information isn’t the problem for many SaaS companies since they are data-focused, but analyzing data and interpreting it into meaningful insights that drive change is what they struggle with the most.”
He continues, “An overabundance of data becomes a data nightmare for companies because they get overwhelmed and often overlook critical data. They may prioritize numerical data (quantitative) over qualitative to analyze, and as a result, many companies are unable to paint the complete picture of customer profiles. This leads to inaccurate decision-making for management on down.”
Having a holistic picture is mission-critical for every SaaS company, many of which are operating on smaller budgets and lean teams. However, not taking advantage of deep CX data insights to flip the script on churn and growth will impair their ability to scale at some point.
That’s a dilemma that AI and machine learning (ML) can easily and effectively solve. Without sacrificing quality and handling far more quantity than can be achieved by manual processes, AI/ML systems actually get more accurate over time by training the model with additional data points. The infinite feedback loop and continuous learning cycle make using AI for customer intelligence the best (and most scalable solution) available.
“Acquiring a new customer can be 25 times more expensive than selling to an existing one, so it’s no wonder that we are seeing companies heavily invest in their customer success teams. These teams need tools that are purpose-built for them,” according to Cathy Gao, Partner at Sapphire Ventures. It’s clear that purpose-built tools for CX are more than a hype cycle: they are here to stay.
Early Warning Systems and Preventing Customer Loss
Important warning signs relate to all sorts of data sets, including subscription types, inactivity, support tickets, customer service activities, and product understanding or usage.
Once the system is in place, the gold standard outcome of any AI system for CX is its ability to prevent customer loss and drive revenue growth. Knowing which moves will hurt CX and how to prevent those from occurring is transformational for businesses of any size.
Many SaaS companies focus on annual recurring revenue, but preventing churn helps companies focus on retention and growth, increasing the critical measurement of net dollar retention (NDR). By analyzing the difference of upgrades, downgrades, and churn from existing customers, companies can prioritize operational decisions to maximize customer loyalty and growth.
AI for Customer Happiness
When AI works at the highest level, its intelligence rivals humans and can capably support the effort of businesses to secure and retain customers. From chatbots to personalization to automation and analysis, AI that drives true customer intelligence is a powerful tool that can help startups and established businesses alike.
The smarter the systems become, the more accurately they will predict what will happen before it is too late.