How can data science help you retain or onboard your clients? Understanding what your customer experience metrics say about your business is exactly what you need to improve both onboarding and retention. This guide will cover customer onboarding basics, customer retention basics, and four ways to use data science to improve customer onboarding and retention.
“I don’t need data science. I’m not a character from Terminator.”
Well, people in the 1990s also thought that a wall phone, fax, and pager were enough to communicate. But nobody’s talking about those in 2021. Businesses that haven’t adopted data science might face the same scenario.
Data science is a method to segment customers and get rid of tons of spreadsheets and manual work. Also, it provides an overview of how customers are interacting with your website, software, and even predict their behavior.
Customer Onboarding: The Basics
Customer onboarding combines activities to help a new customer use a SaaS product. Basically, it’s the process that customers go through to get acquainted with the new product and is the first step in customer life cycle management.
Examples of customer onboarding:
- A series of emails welcoming a new user and explaining how to use a product
- Built-in product usage tips, instructions, and best practices
- Knowledge base articles containing helpful guidelines
- Short videos that explain how to use specific product features.
Customer onboarding is essential for improving user retention, satisfaction and revenue per customer.
Customer retention is the tactics businesses use to increase the number of repeat customers. The tactics engage customers to turn them into loyal buyers.
Customer retention examples:
- Educational content like blogs to show how to benefit from an app
- Discounts and promotions to loyal customers
- Knowledge base to help customers stay updated
- Rewards for referrals
- Occasional surprises like thank-you emails, birthday discounts, holiday offers, etc.
Customer retention is an essential strategy for the long-term survival of any business and provides the basis for why customer satisfaction is important in business. If you have data indicating how satisfied your customers are, then you have an indicator of how many will continue to use your software.
Four Ways to Use Data Science in Customer Onboarding and Retention
Here’s how data science can be useful to onboard new customers and retain them for the long-term as satisfied customers.
Emails are a popular way to introduce a customer to new software. When someone creates an account, companies send emails to encourage them to explore the product.
This welcome email could encourage new user to create social media posts about their experience to organically grow your social media marketing. The email has an encouraging text, too, to reduce hesitation and inspire the reader to log in and start creating.
Welcome emails are essential to improve both onboarding and retention, and data science can help. Businesses need to define which messages work best to engage new users. Sounds like testing is needed, right? It’s a perfect match – data science is all about testing!
Businesses can use AI-powered onboarding email workflows and analyze their performance with data analysis models. Here’s how this process goes:
- Create several versions of email onboarding campaigns with different messages for testing
- Set up an AI-powered email workflow with an AI email marketing tool
- Share the campaigns with new users
- Machine algorithm will analyze the open rates, click-through rates, projects/billing created, and many other engagement indicators
- The algorithm defines the best email variations to reach your retention goals.
A machine learning (ML) algorithm analysis can become a major tool for user onboarding and how you engage with customers. In fact, many companies also rely on it to make better hiring decisions – the algorithm can process data scientist resumes and determine the best candidates.
SaaS churn means lost revenues, potential problems with software, and poor retention performance. Reducing churn is a goal of any company in any industry – especially in the software business where customers need help learning complex apps.
That’s why SaaS companies have been continuously collecting information about user satisfaction with user analytics, customer survey answers, and historical churn rates. In many cases, businesses managed to gather tons of data to improve their SaaS onboarding practices.
Now, there’s data science to take advantage of that data. An AI can analyze historical data on user behavior to determine behaviors suggesting potential churn. As a result, data scientists build a predictive churn model, which defines the steps and stages of user churn. The model is essentially a statistical model that relates churn predictors to potential responses. Data scientists analyze the results to make predictions.
The list of churn predictors includes various business cases, periods of inactivity, specific actions (plan downgrade, etc.), and many other examples. The so-called “high-level process” describes the steps to generate insights.
The model will help determine which customers might be close to churning. Businesses using these insights will engage those specific users to ensure that they stay. A proactive response can help reduce customer churn and learn more about user needs.
Excellent customer service is a major part of a positive user experience. Customers invest in diverse support solutions to provide timely help and prevent user frustration. In the case of complex SaaS, this couldn’t be more true. In fact, poor customer experience is the top reason why customers leave. It means long waiting times, multiple transfers, unavailability, and many other problems.
Data science can help to resolve a major share of customer requests. The answer: AI chatbots. They are computer programs that mimic conversations with a human and generate answers in seconds.
Chatbots rely on AI to understand user questions and give relevant and meaningful replies. Chatbots are among the top customer service software solutions in both B2B and B2C industries. Thanks to different personalization options, these programs are perfect for taking care of repetitive queries that often take over 50% of all support requests.
A great thing about chatbots and customer retention:
- App dashboard page, website page, you name it – you can add a chatbot to any page on your website or online software.
- AI allows chatbots to learn about customer needs by analyzing the questions.
- Chatbots can ask software users if they need help when they log in or after a period of inactivity
- A chatbot can share promotions, news, and links with users
- A chatbot can transfer a user to a human support agent in case there’s a complex issue.
The ultimate goal of AI chatbots would be to keep users updated and offer help where needed. This tactic will help to establish measurable customer service goals, reduce churn and engage more users.
Customer segmentation is a common strategy for personalizing marketing communication and increasing retention. A major benefit that AI and machine learning bring to the table is the automation of this process.
An AI algorithm can process customer behavior data to find:
- Users who have the highest customer lifetime value – and should be prioritized
- Users who are likely to buy a premium plan
- Users who are likely to churn, so need incentives.
The number of such customer segments depends on how many criteria you set in the analysis. Businesses often create about 10 segments ranging from the highest-paying users to users who are extremely likely to churn soon. With an automatic segmentation, personalizing marketing communication and understanding customer needs will become easier.
Does your business need a data scientist on your team? In a word — Yes. Customer data is a goldmine of insights that can help onboard and retain users, especially SaaS customers more effectively. For one, predicting customer behavior better than competitors can put your business ahead of them.
Once again, data science can be used in these ways:
- Optimize onboarding email campaigns
- Reduce SaaS user churn with predictive analytics
- Engage more users with AI chatbots
- Classify SaaS Users for better service personalization.
Keep in mind that we live in a world where every business soon will be data-driven. Data science is the first step towards such decisions, so consider learning your options to stay competitive and solve more SaaS growth challenges.
Author bio: Estelle Liotard is a business communication specialist and a freelance writer with years of experience. She’s passionate about topics such as professional advancement, career opportunities, and business strategies. She currently works as an editor at an academic writing service Get Good Grade and also reviews best academic and tutoring services online.