What Is Customer Analytics, Why It Matters, and How It Works

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.

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.

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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