How to predict customer behavior
What if a business could fulfil customers’ wants and needs before they’ve even asked for them? Welcome to the world of predicting consumer behavior.
What is customer behavior prediction?
- Previous experience as a customer
- Previous purchases
- Social characteristics
You can accurately predict how your customers will behave by using big data and predictive analytics to analyze their behavior, and use this to inform your business decisions.
To find out what your customers want, you’ll need to combine your
- operational data (O-data) such as sales, finance, and HR and
- experience data (X-data) - like CSAT and NPS
Merging X and O data gives you a holistic understanding of your company. It provides a rounded understanding of your brand by showing the connections between revenue, growth, and human behavior.
Why is predicting customer behavior important?
When you predict how your customers are going to behave, you’ll be able to:
- Reduce customer churn
- Identify and target high-value customers
- Encourage loyalty
- Meet customer demand
- Get products to market quickly
- Reduce marketing campaign spend
- Personalize the customer experience
- Improve customer experience
Customer behaviors that you’ll want to predict
To understand future wants, needs and spending, you need to understand customers’ past interactions. You do this by collecting information from previous customer journeys and use it to predict future ones. The most useful customer behaviors are:
- Churn - Also known as attrition, it’s when a customer stops doing business with you, or buying your products or services. It’s one of the easier metrics to calculate, predicting the probability that someone cancels or fails to renew.
- Retention - This involves engaging your customers so they keep coming back for more. Ideally, you nurture a relationship sufficiently for your customers to become brand loyal, and even advocates. It’s easier (and less expensive) to keep an existing customer rather than win a new one. It is also easier to stop a customer leaving than it is to convince them to come back once they’ve left. Like churn, it’s fairly a straightforward metric, calculating the probability of someone remaining a customer.
- Satisfaction - You’ll want to know how happy your customers are with your product or service. When they are satisfied, they’ll be more likely to buy from you again, recommend you, and remain loyal to you.
- Engagement - This monitors the ongoing relationship and interactions between a customer and your company. You’ll need to explore the most effective ways of keeping your customers engaged, then you’ll be able to predict how people with similar attributes will also engage. You can dial up what works, and stop what doesn’t.
How to predict customer behavior
Leading organizations - including Uber, Chobani and Finder - are embracing Experience Management (XM) not only to understand customer behavior, but predict it and solve problems before they even occur.
Here are the ways that companies unlock the insights needed to win more customers, increase their share of wallet, and boost workplace performance:
1. Reduce churn
Combining data from experience insights along the customer journey with operational insights such as declining repeat purchases, reduced purchase amounts, decreased purchase frequency etc, predictive software combines these to help you predict individual customer behavior, and take action before it is too late.
2. Increase retention
Customer retention software helps you measure and understand the customer’s journey and their experiences, both relational and transactional such as purchase or post-support follow-up. By combining X data with O data, you can identify which customers are likely to stay and which will churn. With closed-loop and action planning tools, you can follow up with at-risk customers to resolve their problems and in turn encourage them to stay and reduce the need to acquire new customers.
3. Improve customer satisfaction
Knowing, and understanding that your customers are satisfied with your brand is essential to success. It will predict their likelihood to return, purchasing and recommendation to others. Improved customer satisfaction will result in a higher return rate, more customer loyalty and higher spending. CSAT is the key performance indicator for customer satisfaction.
4. Increase customer engagement
Explore what motivates customers to engage with you by collecting experience data from them via surveys, questionnaires and other feedback channels. After exploring the types of engagement happening in your business, the next step is to map them against business outcomes such as sales, NPS, CSAT and customer effort scores (CES).
5. Triangulate CX data
Data triangulation is when you validate your data using two or more sources. This approach ensures the insights captured are accurate and relevant. It’s therefore critical when understanding and anticipating your customers’ needs.
Data triangulation relies on multiple methods to collect, analyze and act on CX data to predict behavior before it occurs. For example, episodic NPS can unpack brand perception using customer journey episodes (e.g. sales, billing and support) alongside focus groups to build out regression models. This process helps eliminate bias by cultivating empathetic research to analyze customers on their terms.
6. Use product feedback
Making great products means creating great product experiences for customers, listening to feedback and quickly acting upon it. If you create a great product early on then you won’t need to react to the ongoing problems that occur from a bad product. As the maxim goes, “buy nice or buy twice.” Getting it right may be costlier to begin with, but this approach will pay dividends in the future. However, it’s also likely that your products will evolve with time, so when the need arises make sure that you’re incorporating customer feedback into those changes
7. Track brand health
The task of understanding your customer is never really over: expectations, platforms, and needs are constantly changing. This is why measuring brand health is vital when creating a sustainable CX program. Tracking your brand provides a consistent benchmark for you to measure yourself against in fast-changing economies, as well as mitigating future incidents before they occur. This will allow you to track the impacts of the customer experience on the brand.
8. Embrace AI and machine learning
This turns ‘big data’ into ‘useful data’. Predictive CX needs to tell a simple story if you want results. However, to take action, your brand needs to focus on critical and creative thinking rather than drown in a sea of data. AI and machine learning is your life raft. By automating research, you’ll be able to make faster operational decisions to solve problems before they spread, simply because you’ll be able to see what actions you need to take.
Meeting challenges head on
The challenges your company faces around high churn, reputation management, website bounce rates, trust and inconsistent brand messaging (to name just a few) all relate to customer behavior. You can’t afford to wait until customers leave to begin addressing their needs. Qualtrics Predict iQ uses deep learning neural networks to identify customers and accounts likely to churn, and gives you insight into what is driving that behavior.
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