If you could travel back in time, it would be so easy to get rich. Simply scan the financial news of today, pop back a decade or so, and make a smart investment. Alternatively, if you could travel into the future, you could take a peek at the trending products of tomorrow, investing in them now to get ahead of the competition.
While this isn’t an option, you can use tools like predictive customer analytics to anticipate future customer behavior—the next best thing to time travel. Here’s how predictive analytics works and how your business can use it to retain existing customers, attract new ones, and plan for business growth.
What is predictive customer analytics?
Predictive customer analytics uses customers’ behavioral data to predict how they’re likely to act in the future. It’s one application of predictive analysis, a data analysis technique that uses current and historical data to forecast future outcomes.
Predictive analytics helps businesses anticipate future events and make proactive, data-driven decisions. Supply chain optimization is one common application. Businesses can view the probable effects of major geopolitical events or changing costs and adjust their strategies accordingly.
How predictive customer analytics works
Predictive customer analytics is a subtype of predictive analytics. It can provide valuable insights and make it easier to anticipate customer actions. This helps you increase customer satisfaction and boost sales. Here’s how predictive customer analytics typically works:
1. Your business gathers and organizes customer data such as demographic information, browsing behavior, purchase history, and customer engagement history.
2. You then use statistical modeling and machine learning to analyze the customer data, incorporating additional relevant historical data like market conditions and political climate.
3. From there, you construct predictive models (algorithms projecting trends) based on these findings.
4. You can then apply the predictive analytics model to current conditions and existing customer data. The model projects future outcomes based on these data points.
Businesses rely on suites of tools like IBM SPSS Predictive Analytics Enterprise, Microsoft Azure Machine Learning, and SAP Analytics Tools to design and deploy predictive customer analytics models.
How businesses use predictive customer analytics
According to Neeti Singhal Mahajan, vice president of strategy and insights at the plant-based meal subscription service Daily Harvest, better forecasting accuracy is one of the main benefits of predictive customer analytics.
“Many traditional business functions like operations, demand planning, and corporate finance wind up doing some kind of predictive tasks that rely heavily on assumptions and rules of thumb,” Neeti says.
When predicting customer behavior, you might easily fall into the same trap—making broad intuitive assumptions about customer actions that don’t pan out in reality. Predictive customer analytics helps minimize the effect of human error and bias.
“Letting the data drive and temper our own assumptions [made us] dramatically more accurate on average with our predictions,” says Neeti.
Here’s how predictive customer analytics helps businesses increase sales, cut costs, and improve customer satisfaction and brand loyalty:
Predictive pricing
Businesses can use sales records, customer behavior, and market and competitor information to set prices. Predictive pricing models forecast the optimal price for a product or service based on a desired outcome, such as maximizing revenue or hitting a specific sales target. Predictive models can also support dynamic pricing strategies, which adjust prices in real time based on purchase behavior and market conditions.
Lead scoring
Predictive customer analytics can also help businesses anticipate a customer’s likelihood of converting into a buyer, a process known as lead scoring. You’ll analyze demographic and behavioral data, identify patterns linked to conversion, and use that information to build a predictive model. You can then apply the model to relevant data about a prospect, and the model will automatically score your new lead.
Marketing personalization
Predictive analytics helps businesses develop personalized marketing strategies by providing insight into how customers are likely to respond to specific campaigns. Personalized recommendations are one common approach. You can use predictive analytics to identify the products a customer is likely to purchase and target them with upselling or cross-selling messages.
You can also use personalization to nurture high-value customer relationships.
“We’ve used predictive modeling and machine learning to estimate customer lifetime value,” Neeti says. “Being able to predict and understand individual customer expected revenues helps us segment and target marketing, optimize discounts and offers, and deeply understand customer behaviors.”
Customer experience
Predictive analytics can help you improve the customer experience, which can increase customer satisfaction and loyalty. You might use sentiment analysis tools to extract sentiment data from customer reviews and address pain points in the customer journey. If you identify a negative sentiment trend around your checkout process, for example, you can review the process and fix the issue.
Businesses can also extend the value of experience data with predictive data analytics. A customer attrition model uses behaviors, sentiment, and demographic factors to identify at-risk customers, allowing you to reduce customer churn by proactively focusing on accounts you might lose.
Business operations
Predictive customer analytics can also help you anticipate demand fluctuations, which simplifies inventory management and prepares you for variations in order volume.
“We primarily use predictive analytics for demand forecasting within our DTC [direct to consumer] business here at Daily Harvest,” Neeti says, adding that the company relies on unique historical data models to predict total orders and new customer orders each week.
“These models help us understand our DTC demand in a given week,” he says, noting that the models also help inform operations planning, so they can accurately and efficiently meet demand.
Limitations of predictive customer analytics
Predictive analytics is a powerful tool, but it takes time and experience to build a program that works for your business. Here are three limitations to keep in mind:
Requires a lot of data
Accurate predictions require a large volume of customer data, which can be difficult for small companies to acquire. If your data set is too small, your model can give inaccurate predictions.
Relies on specialized knowledge
A successful predictive analytics program requires expertise.
“Business leaders can make huge investments in teams with really talented data scientists, machine learning and AI researchers, and machine learning engineers, but not see dividends, because they’re often not exactly solving the right problem,” says Nico Van de Bovenkamp, data platform lead at Daily Harvest.
Additionally, you need clarity around your goals, business strategy, and specific business situation. Solid predictions alone aren’t enough without clear direction.
Results take time
Predictive analytics requires an upfront investment of money, and it can take time for the program to pay off.
“Building out advanced predictive analytics programs within a company is difficult,” Nico says. “They take a lot of patience to build out and maintain, and even more time to ensure you’re correctly identifying the problem you want to solve. Once you’ve identified a clear, salient use case for predictive modeling and analytics, the team has a defined benchmark for success as a north star. Give it time, then you’ll see the payoff.”
Predictive customer analytics FAQ
What is predictive analytics in customer analytics?
Predictive customer analytics uses information about past customer behavior to provide actionable insights into probable future customer behavior. Predictive analytics can help businesses predict future trends, anticipate demand fluctuations, score leads, and reduce customer churn.
How are predictive analytics different from prescriptive analytics?
Both predictive analytics and prescriptive analytics can analyze historical data and forecast customer behavior, but while predictive models only predict the future, prescriptive ones can recommend a course of action. Prescriptive models run multiple predictive scenarios to identify the strategies most likely to achieve desired business outcomes, such as lowering costs to increase sales.
How is AI being used with predictive customer analytics?
AI and machine learning technologies help businesses gather and process larger volumes of customer data. They can also build more advanced predictive customer analytics algorithms by using multiple data transformations to uncover patterns invisible to human users.
