How to Increase Sales Using Customer Value Prediction

How much will consumers spend on Product A next quarter? On Product B? Understanding these patterns can help you make better-informed business decisions and significantly boost revenue. You can gain a better perspective on future sales through customer behavior prediction — and this guide explains how.

Predicting Customer Behavior: The Basics

Customer behavior reflects the journey a prospective buyer takes as they become aware of brands, research, select, and buy a product or service. Many factors influence customer experience, including:

• ✦Interactions with a brand
• ✦Previous experiences as a customer
• ✦Purchase history
• ✦Age, income, and lifestyle
• ✦Culture and media habits
• ✦Education and beliefs

Using big data and predictive analytics to analyse past behaviour and personal characteristics, you can predict future customer activities — then leverage the results to improve your marketing and business operations.

To find out what your customers want, you'll need to combine your operational data (O-data: sales, finance, marketing) with experience data (X-data: CSAT and NPS). Merging these provides a complete understanding of the connections between revenue, growth, and human behaviour.

Benefits of Predicting Customer Behavior

When you are able to predict how customers are likely to act, your business will be able to:

• ✦Identify the people most likely to purchase
• ✦Lower customer churn rates
• ✦Increase customer loyalty
• ✦Efficiently meet product demand
• ✦Optimise marketing spend
• ✦Personalise your customer experience
• ✦Improve customer service

Businesses that leverage predictive analytics to anticipate customer behaviour typically earn a high return on their investment in it.

Customer Behaviors that Businesses Can Predict

The most useful customer behaviours to understand are:

The Value of Predicting Customer Behavior

Human behaviour is not static. It can be impacted at any time by many things, such as evolutions in social media or economic changes. You may already use tools to monitor past customer behaviour — this is valuable, but it doesn't reveal how they will behave in the future.

Behaviour prediction lets you better understand factors that might influence customers to change or maintain patterns. There are several benefits, all of which can help improve sales.

Enhance Segmentation Strategies

Most segmentation is based on historical behaviour from your CRM. Predictive analysis adds another layer by accessing dynamic data showing where current behaviour will likely go next.

Adding the predictive angle to buyer personas and segments indicates what products those in each segment will likely be interested in and purchase. It can also help identify people likely to leave positive reviews and become brand ambassadors.

Improve Personalisation

Consumers are no longer happy with "personalisation lite." They demand fully customised interactions, or they will move on to competitors who seem to get them better.

Predictive models use AI-based deep learning to better understand customer needs, tastes, and motivations. Personalised experiences, coupled with high-quality products and superior service, drive higher sales, build loyalty, and improve retention — all resulting in higher average customer lifetime value (CLV).

Focus Marketing Efforts

Monitoring and analysing customer behaviour helps you focus your marketing efforts on top-performing tactics, messages, and offers customers are most receptive to.

Predictive analytics also helps with people who have not yet converted — perhaps they've shown interest in content or abandoned a basket. It allows you to determine where people are likely to leave your funnel, so you can adjust by inserting pop-ups, offers, or messages that address their pain points and move them forward.

Boost Sales by Forecasting Customer Behavior

Here's what you need to do to leverage predictive analytics to increase sales.

Access Quality Customer Data

The data you use for predictive analysis must be accurate and relate to the customer behaviour you want to understand. You may leverage data from your website, CRM, mobile apps, and prospecting tools. Your predictive analytics data falls into two categories:

Develop Predictive Models for Analysing Customer Data

Follow these six steps to transform your data into predictive models:

1. 01Gather relevant customer data.
2. 02Organise and combine the data into a single dataset.
3. 03Clean the data to ensure predictions will be as accurate as possible.
4. 04Add any variables needed for machine learning to understand your data.
5. 05Select the methodology or algorithm that meets your needs and fits your dataset.
6. 06Build your model.

The right algorithms, coupled with machine learning, can identify potential patterns including high or low purchase intent, churn, and preferred products. View predictive analytics results like lead scoring — assign a numerical value to each metric to more easily assess purchase likelihood.

How Predictive Analytics Can Influence Marketing

Use factors like purchase likelihood and product preferences to recommend tailored options to your customer segments or feature their wants in content marketing campaigns.

For instance, if you know a particular segment is likely to buy pink dresses, you could feature them on your website when they visit, include them in social posts, and offer style content about them. Repeat exposure will eventually compel them to purchase.

Reduce Churn Using Predictive Analytics

Customers are more likely to continue buying from you if you target your marketing to meet their needs. If they see themselves reflected in your marketing, they are more likely to buy more and less likely to check out competitors — and they may even become brand advocates.

Reducing churn is critical because selling more to existing customers costs far less than acquiring new ones. Targeted email marketing is also less likely to irritate people, helping reduce unsubscribe rates.

Use Predictive Analytics to Cross-sell and Upsell

Predictive analytics provides the opportunity to boost revenue by upselling or cross-selling. It allows you to identify high-value customers who may be interested in increasing their average spend.

An analysis could reveal that customers who purchased a pink dress might be interested in buying a second, or want accessories. Featuring these things in your marketing will likely generate additional purchases.

Use Predictive Analytics to Enhance Customer Service

Predictive analytics combined with CRM data can indicate what people expect from service interactions and where your business may be falling short. More importantly, it can show you what good service means to different segments of your customer base.

Excellent customer service may not directly generate revenue, but it reduces attrition, improves loyalty, and results in future purchases and referrals from loyal customers.

Key Takeaways