If you want to thrive in this competitive SaaS world, then the importance of understanding customer behavior can hardly be overemphasized. Customers are getting more empowered in today’s marketplace. Companies are investing heavily on understanding their likes and dislikes so that they can make informed business decisions. Hence, customer analytics is a useful process towards that end.

What is Customer Analytics?

Customer analytics is the process of capturing and leveraging customer data analysis to identify, attract and retain customers. These customer data can be collected from various sources through which a customer interacts with the brand. An intelligent software gathers all these data and converts them into useful insights that business can use to create better strategies.

Companies use these valuable insights in their sales, marketing and product development efforts. Through this, they are able to offer more relevant experience to their customers in all of their interactions. A personalized experience is what makes a customer stick to a brand and eventually become a loyal customer.

Why is Customer Analytics important?

There are many cases when a company needs to use customer behavior analytics properly. It helps them in processes like pricing, promotion and management. Without the understanding of customer behavior, their strategies would be far from being effective.

Using customer data gathered from all communication channels, companies use predictive analytics to anticipate customer behavior. A 360-degree customer view helps companies gain a holistic view of the customer. It helps them in creating strategies for customer acquisition, customer retention as well as proactive engagement with the customers. This is how it works:

• Customer acquisition: By studying customer behavior, you can design effective marketing and sales strategies that fit the right customer. By targeting the right audience, your marketing cost can be reduced considerably. Creating personalized marketing campaigns would also help in increasing the conversion ratio.

• Customer retention: By studying the customer behavior of those who got churned, you can develop predictive customer analytics. Then proper strategies can be formed to engage proactively and effectively with at-risk customers to prevent further churn.

• Customer engagement: When it comes to effective engagement with customers, personalized experience is must. Customers love it when they get bespoke solutions for their needs. Studying their support queries, niche and challenges, you can engage with them in a more relevant manner. Customer engagement analytics help a great deal in product adoption and nurturing customer relationships.

How Customer Analytics work?

When it comes to putting customer analytics into work, there are three steps that you need to look into. These are:

1. Data collection
2. Validating Data
3. Data analysis

Data collection

There are multiple touchpoints that a customer makes with your brand. Data collection is the process where you gather data from all the channels of customer interaction. These channels include:

• Website analytics: There is a lot of useful information you can glean from your website visitors. Tools like Google Analytics, Mixpanel can help you gather these data. There are other tools as well like CrazyEgg, Optimizely which can give you advanced information like heat maps, session recordings, etc.

• Customer Service Calls: Through customer service calls, you can learn the kind of queries a customer is raising. This would help you in identifying their challenges. These are the qualitative data that you can use for in-depth behavioral analysis.

• Social media: This is another channel to study customer behavior. Through social media, you can understand customers as how they are as individuals. They engage with you on different posts on your business page through which you can know their opinions, likes and dislikes.

• Customer feedback: This is the most direct form of information you can get from customers. Companies perform regular surveys to understand customer’s sentiments about their brands which can be used later to measure various metrics like customer satisfaction, net promoter score, etc.

Validating Data

All the data you collect has to go through proper validation. Without validation, all your efforts towards customer analytics can go waste. Hence, a proper validation mechanism has to be designed to ensure the accuracy of your data. Few pointers for validation are:

• Have a dedicated resource in your Data operations team whose sole job is to validate data.
• Make sure that the data is complete and no fields are left blank for later analysis.
• Use a customer data analytics platform that gives a complete 360-degree view of a customer. It can be a customer success platform as well.

Data analysis

Before you begin consumer analytics through data, you must have your customer personas in place. By cross-validating the gathered data with customer use cases, you can create a predictive model for your business. This helps in identifying customer’s choices that have a direct impact on your business. These choices may be something like, how customers discover your product, which features they like most, what value means to them, and why they leave.

Apart from the above business-related data, you can also analyze the customer’s personal information like their job profile, their age, sex, marital status, location, etc. After gathering all this data, you would have to use data mining through technologies like AI, Machine learning, to identify the underlying patterns within such a huge amount of data.

There are many tools like Google Cloud ML Engine or BigML, that can help you create a predictive model. These tools help you create models through which you can forecast your business outcomes and improvise in advance as needed.

Customer Analytics best practices

Customer interactions can become way more effective when companies start leveraging customer analytics. Few of the best practices that you may include in these lines are:

• Analysis of all the omnichannel customer interactions and understanding various ways your product is catering to different customers.
• Assessing customer dynamics with the brand to understand their satisfaction level. This can be understood through various relevant customer feedback metrics.
• Choosing the most appropriate channel and time to engage with a customer in a most fruitful way.
• Identifying the at-risk customers and taking proactive measures to increase customer lifetime value.
• Identifying patterns through AI/ML and leveraging them to run successful marketing campaigns and increase sales.
• Using customer segmentation to provide personalized customer experience across all channels.

10 Types of Customer Analytics

Customer analytics is collecting and analyzing customer data across devices, channels, and stakeholder interactions. You get an insight on how customers engage with products, services and more. In a report with McKinsey, companies who are using customer analytics can generate 2.6 times higher ROI than others who analyze their data irregularly. There are categories of customer analytics and types that will help improve success and growth.

Descriptive Analytics

This type of customer analytics is one that involves data greatly. The data that organizations collect is in various formats- structured, unstructured, or semi-structured. This data may be tough for many to understand. This needs to be made easy to understand for customer success analysts. Descriptive analytics will help understand the past, whatever happened, and also enables better strategies for the future. You can get information such as –

• Customer browsing details
• Customer information
• Purchase information
• CSAT information
• Transactional behaviors and more

With this, you can get a grasp on the organizations’ internal structure.

Diagnostic Analytics

In a diagnostic analysis, you can understand the ‘why’ behind customer behavior. You can identify anomalies and fetch other valuable insights. You can do a deep dive research into the company to uncover the reasoning of why certain thing happens. Diagnostic analysis includes-

• Data discovery
• Data mining
• Drilling down of data
• Creating correlations

With diagnostic analytics, you can get answers to some specific questions and find answers. This means you can reduce many challenges and discover new opportunities.

Predictive Analytics

Predictive analytics will help predict the performance of any business. Though the predictions may not be 100% perfect, you can rely on them to forecast many outcomes. You can involve deep learing that includes neural frameworks. Some of the key aspects in this include-

• Statistical modeling based on Moore’s Law
• Regression

You can extract customer attributes that influence an outcome. You can build models and run sets on that so that you can measure the revenue generated. You can also maximize the ROI on advertising and inventory spend.

Prescriptive Analytics

Prescriptive analytics is for short term predictions that can assist an individual user. Prescriptive analytics can help chart the next course of action for every user. This will help personalize customer experience and point out what products work at what time and place. This will help optimize team productivity and increase the prediction of the likelihood of churning or converting. Customers who look like they might churn can be found and receive attention for problems. You can know-

• What customers are searching for
• What category of products they prefer
• What is the images they look for
• How to convert customers
• Who to sell to

Customer Satisfaction Analytics

Customers who are happy with your product are vocal about it. Even those who are not happy resort to customer forum sites to talk about their reviews. Customer satisfaction analysis is the process of charting down what your customers are receiving versus what they are getting with respect to the product or service. You need to assess customer satisfaction with a blend of qualitative and quantitative techniques. You can interact via your social media handles and emails to improve customer satisfaction.

Customer Lifetime value Analytics

You need to attribute a lifetime value to each customer. This will help find out the ones who are more valuable and important to you. Customer lifetime value analytics will help understand how valuable a customer is to the business. You need to see how long the customer might stay with the company. This will help comprehend them better, improve marketing attention, and identify ways to extend or increase the length and value of the relationship. Having your KPIs set will help find the right method to do that.

Segmentation Analytics

Customer segmentation analytics is finding all the segments in the market. With these segments, you can improve communication, targeted marketing efforts, and content. There are many benefits of segmenting customers as you know. You can use various data mining tools to get that information. You can also create sub-segments that can help narrow down customers to deeper levels. Having smaller sub-groups will help increase the stickiness and understand the behavior better.

Customer Churn Analytics

Keeping your customers from churning is important as it is tough to retain them. Customer churn analytics is assessing how many customers you may lose over one year. With this analytics, you can predict customer churn and why it happens. You can take preventive measures that will help you fight churn before it happens. You can assess customer turnover rate and pay attention to how customers are counted.

Customer Acquisition Analytics

If you spend a lot of money on acquiring customers, you might be having the wrong strategy. Customer acquisition analytics establishes how effective the company is with customers. This has a number of metrics and KPIs that will help acquire customers better. You can calculate the cost per lead and find out what makes a campaign or initiative work as compared to others.

Customer Engagement Analytics

Customer engagement analytics is a changing rapidly as businesses are looking to map the entire customer journey. Analyzing customer engagement will help map the points of interaction, touchpoints, and communication. It can be surveys, social media analytics, direct messages, phone calls, video chats, or more. Customer engagement analytics will help identify aspects of your product or service that provide maximum value to customers.

Final Thoughts

Whether you want to run a targeted marketing campaign, increase ROI on your customer acquisition cost, or to leverage brand loyalty, customer analytics can help you fulfill all of it. You must have heard about the importance of right product-market fit while launching a product. But equally important is the match between customer and brand too.29

Through customer journey analytics, you can not only offer them relevant experience but also turn them into your loyal customers. And that is the only way towards long-term sustainability of your business.

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