Your customer service department is at their wits’ end.
They’re small and not capable of providing the same proactive customer service as larger teams. It feels like they’re losing a battle against their own software – firefighting technical issues, but never able to see past the smoke.
You know there’s got to be a better approach, even as a small business that can’t afford to hire more in-house staff. You don’t want to outsource. Your SaaS product has the potential to be great, but at this stage there’s a lot of bumps in the road that customers aren’t pleased about.
What if you could address customer concerns before they even arose? You’ve heard of “predictive” customer service before, but brushed it off because of its associations with AI – which you’ve always figured was too costly or complicated to implement for your team.
While it is true that predictive customer service often employs AI-based software to anticipate customer need, it doesn’t have to be costly or difficult to integrate into pre-existing workflows.
How Does “Predictive” Customer Service Differ from “Proactive” Customer Service?
We’ve written about proactive customer support on our blog before.
So, what’s the difference between “predictive” and “proactive customer service?” Is there one? While both approaches to support look for ways to eliminate negative feedback loops and anticipate needs, the main difference is how tools are leveraged in each approach:
- Predictive customer service uses data analytics, machine learning models, natural language processing and understanding (NLP + NLU), and AI software to predict needs. In any of these cases, your goal is to use software to identify the problem (i.e., pain point, friction, etc. ) before the customer reaches out.
- Proactive customer service relies on things like automation rules to send out canned responses to common queries. Or, it involves leveraging a Knowledge Base to provide 24/7 support to customers. In both cases, your goal is to resolve the concern before it escalates.
What Role Does AI Play in Predictive Customer Service?
I want to be transparent that AI will certainly make the entire process a whole lot easier to navigate and way less time invasive.
The initial added cost of software may deter budget-conscious businesses, but the trade-off in increased productivity (without having to allocate your already stretched-thin staff) is definitely worth it.
AI can analyze large, complex data sets and simultaneously improve over time as it’s fed more data. This is especially useful in areas that require NLP (Natural Language Processing) to predict future trends based on qualitative data like customer feedback.
For example, you can employ (either manually or through software):
- Statistical models: which include regression analysis and decision trees. These models use historical data to identify trends over time and predict future outcomes.
- Rule-based models: which are based on algorithms that use ‘rules’ (if X, then Y) to make predictions on future outcomes.
In customer service, AI is often used to predict feature uptake, churn, or areas of friction. It does so by analyzing large chunks of data. Doing this job manually on a regular basis isn’t quite as efficient.
Think about if you’ve ever applied for a loan. There was likely some sort of scoring system in place to predict your ‘risk’ level based on credit score, debt, and income. This is an example of a rule-based model.
Make Predictive Customer Service Easier for Your Small Business (3 High-Impact Use Cases)
In each of the quick-win strategies below, I’ll provide some concrete examples of affordable software or free tools – and walk through how to use each one to fulfill the desired goal:
Predicting X using Y will result in Z.
There’s tons of options to focus on, but I’ll just focus on a select handful of the most cost-effective.
We’ll also get into what to do with the data sourced from these tools – how to actually implement it for predictive customer service.
Tip 1. Leverage Advanced Data Analytics To Process Large Sets of Customer Data
As your customer base increases, data grows. And analyzing this data becomes more difficult, time-consuming, and ineffectual.
AI software can use different technologies to derive customer insight from large data sets:
- You can predict/anticipate future customer actions or needs.
- Process and analyze data relating to customer interactions, purchase history, feature adoption, customer feedback, browsing/usage habits, etc.
- These predictions can be used to alert other departments of issues before they snowball (for example high-risk customers stuck at a pivotal moment in the customer journey).
- It also allows you to address problems faster, so other customers don’t encounter the same difficulty.
There are a number of affordable options out there for small businesses, although we’d recommend Google Analytics as the perfect starting point (since it’s free and provides most of the basic data you’ll need to monitor the customer journey).
- Google Analytics 4 (GA4): Enables businesses to track website traffic, performance, clicks, impressions, and various ecommerce KPIs like conversions. There are a number of in-depth tutorials on how to get the most out of this free tool as a small business.
And there are plenty of great, in-depth guides on getting yourself set up on the platform like this one from Reflective Data specific to SaaS. If your business is experiencing some growth and you’d like to invest more resources into monitoring social media or ad data (clicks, conversions, brand sentiment, etc.) we’d give the following two options a look:
- Amplitude: Is a digital analytics platform that leverages AI to provide you with insight into conversions. It allows you to predict trends and improve user experience. They offer a ‘free’ plan option.
- Sprout Social: Allows you to analyze your social media performance. It provides concrete data on engagement, growth, trends, audience demographics, value perception and brand sentiment.
Grammarly and Sprout Social
An example of a business that leverages data analytics is Grammarly.
Grammarly is obviously now a very successful business, providing millions of users with writing help. But 14 years ago they started out as a freemium product & simple browser extension. And it took a long time for them to see success across social media.
Sprout Social enabled them to automate otherwise time-intensive processes. Their “social listening” tools gave a concrete overview of brand health and their “share of voice” (SOV) in the marketplace. With Sprout Social, you don’t need a ton of technical knowledge to start leveraging social media insight and customer data.
For example, you can use their AI tools to build automated queries that track conversations about your brand across every imaginable social platform – from forums like Reddit, to X (formally known as Twitter), to Instagram and Facebook. Granular sentiment analysis can then be employed by Sprout Social’s AI to zero in customer feeling toward a specific feature or product.
Tip 2. Leverage Generative AI To Create Personalized CX
Based upon the above, you can use generative AI to improve the customer experience with personalized service, in-app messaging, recommendations, email campaigns, content, or promotional messages. And there are plenty of success stories big and small to be found online from Wayfair to Expedia. Both of our independent software tools leverage generative AI:
- Helply AI: Uses natural language processing and machine learning to interpret customer questions and provide a response via generative technology. It’s an “advanced” AI chatbot that can be deployed on your website or app to field questions you receive from customers.
- Groove: Was designed as an easy-to-use support tool. It’s a help desk with a number of handy AI features that aren’t cost prohibitive. These features include sentiment analysis and AI text modifications.
Helply, our AI agent, can dig into previous responses your human team has sent off to customers. A specific/relevant data set ensures that every interaction is both personalized and accurate. Most people have had negative experiences with AI chatbots because they’re used to robotic text (that doesn’t feel authentic). Most of these chatbots aren’t true AI and depend upon pre-scripted workflows. Thankfully, Helply is a lot different because of its unique data sources.
Seacharrones and PartyRock
An example of a business that leverages generative AI is Seacharrones (Blue Dot Kitchen).
Blue Dot Kitchen manufactures harvested kelp snacks that don’t require land space, pesticides, or freshwater to grow. A large segment of their audience are interested in sustainability and trends within the health food niche.
As a bootstrapped business, there wasn’t a lot of time to research scientific papers or exposés on conventional farming practices. At the same time, they still wanted to leverage this research to demonstrate why they’re a healthier alternative and drive brand loyalty.
The business used PartyRock to analyze and summarize research content in seconds, allowing them to consistently stay up to date on the latest industry trends. Now, this information can finally be used to communicate more effectively and proactively with customers about the benefits of their product.
Tip 3. Leverage Machine Learning Models To Predict At-Risk Customers and Prevent Churn
We’ve spoken at length before about the importance of preventing customer churn and regularly conducting a customer churn analysis.
Any number of predictive AI tools can help your business assess churn risk more easily than manual analysis, and more specifically, machine learning models (as employed by software like Churnly.AI) can help your business to identify risk factors. Namely, by looking at historical data and user behaviour. A couple of the more popular tools include:
- Churnly.AI: Leverages AI to predict when and where customers are likely to churn within the customer journey.
- Usermaven: Allows for fast and efficient data analysis. Support teams no longer need to rely on IT departments or web developers. You can closely monitor the onboarding process, keep tabs on inactive users, track feature adoption, or look at user engagement.
- Userpilot: Is an intuitive growth platform that allows businesses to engage with their users in-app, providing contextual guidance as they navigate software. “Feature tagging” tracks when customers drop off by looking at where users are struggling.
If you can predict potential churn from in-app engagement or customer feedback, you can easily target your high-risk customers with re-engagement strategies that include:
- Customer segments based specifically upon needs and expectations.
- Personalized communication in-app (contextual guidance targeted at feature adoption or low uptake) or via email (generative AI content based upon your own internal Knowledge Base and self-service resources).
- Proactive support that is focused on customer success and business growth.
Hydrant and Pecan
An example of a business that leverages machine learning models to predict churn is Hydrant.
Hydrant is a wellness company that manufactures electrolyte drink mixes – free from added sugars, preservatives, and artificial flavours.
While email marketing was always a large component of their overall strategy, customers were initially not segmented properly. They noticed high-value customers were churning, too. None of their promotional materials were personalized.
They decided to deploy Pecan’s Predictive Gen AI. Pecan created a predictive model based upon Hydrant’s data so churn was more easily assessable. In this case, it found that a particular low-value customer segment had a high probability of churning. This was based upon:
- Customers who were highly likely to make a repeat online purchase.
- Customers who were likely to transition from one-time purchase to monthly subscription.
- Previously churned customers who were likely to be persuaded back to Hydrant.
Hydrant decided not to waste additional resources trying to win-back these customers, and instead focused their efforts on their high-value segment. They grouped customers on a more granular level based upon CLTV (customer lifetime value) and created a targeted campaign for their biggest spenders.
Empower Your Team To Implement Predictive Customer Service Today
Predictive customer service involves leveraging customer analytics to gain rich insight into customer needs. These insights can then be used to predict future actions.
In this sense, it differs from proactive customer service, as your team will need to leverage analytical software to make preventive improvements to the CX, or tailor service to particular customer segments.
Groove and Helply can both integrate easily into your predictive customer service workflow.
Groove enables your small business to leverage the power of AI through an all-in-one customer service platform. It’s affordable, easy to navigate, and feels familiar. Sign up for a free trial today to enable essential AI features your business can quickly adopt without a costly investment or steep learning curve.
Helply enables your small business to deploy an advanced AI chatbot across pivotal customer touchpoints. Based upon analytical insight, you can determine where and when conversational AI is needed most. Request a free demo today for automated support that never sleeps; easily embed it on your website or in-app.
