If you are running a SaaS business, then it is paramount that you know your customers really well. Only by getting their information completely can you rely on your product offering. You would be able to place your product better to the relevant customers. Hence, customer data is essential to proceed towards this end.

Customer’s information is widely available if you look for it keenly. But most of the companies don’t know how to put it towards its best usage. There are a lot of insights you can glean from those which can help you drive growth to your business. So, let’s look into this concept in more detail.

What is Customer Data?

It is defined as the information your customers provide while interacting with your brand. This information includes their interests, behavior, demography as well as personal information. They share this information on various channels, e.g. in your website, mobile apps, feedback, surveys, social media, etc.

Data-driven organizations leverage the power of data that their customers provide in many ways. It helps them provide a better customer experience and implement growth strategies. This information is helpful in both B2C or B2B scenarios. Although, the strategies that businesses implement vary to a vast extent in both of these sectors.

Types of Customer Data

The amount of data you can extract from your customer interactions is huge. They vary according to different stages of a customer journey. The channels through which they interact with your brand also play a crucial role in deciding the type of data. Let’s look at each of those types individually.

Personal data

This information lets you identify a customer as a person. When it comes to the identification of a person there are two types – the physical self and the digital self.

The physical self can be identified through basic details of a person. These include:

• Person’s name
• Date of birth
• Contact details – Email address, Phone number, House address
• Gender
• Age
• Job details – Job title, Company’s name
• Login details
• Driver’s license number
• Social security number
• Passport number
• Bank account details

These details are enough for you to identify a person and authenticate them as genuine. There are many ways companies use this information that we will look upon in a moment.

With the rising use of technology, the digital self is also becoming a thing to identify someone. The information used towards this end are:

• IP address
• Device ID
• Cookies

Interaction data

Sometimes referred to as “engagement data”, this is the information which is derived from customer’s interaction with your brand. This information is very useful for companies to study user behavior and draw valuable insights from them. Then later companies use them towards modifying their approach of customer engagement to drive better outcomes.

Few examples of customer data of this type include:

• User queries
• Customer’s downloads from the website
• Demo request
• Social shares
• Pageviews
• Backlink clicks

By studying this information, companies get to know where the customers are in their buyer’s journey. Based on that, they modify (if needed) their customer interaction to help them proceed further on that journey.

Behavioral data

This is the information a customer reveals while interacting with your core product or service. This is a clear reflection of the usage of your product by the end-users. The data that you can extract in this type include:

• Which features customers are using
• How they are using
• Their login patterns
• What are the most active hours on your app?
• Deactivations
• Customer churn
• Upgrades
• Heatmaps

Behavioral data is one of the most useful data when it comes to knowing your product performance. Is your product usage easy to understand? Does your product drive enough engagement? Do customers find value from your product? These are few of the questions that can be answered through behavioral data.

Attitudinal data

Attitudinal data helps you understand your customer’s emotions and overall attitude towards your brand. These are called qualitative data as well because it is often difficult to quantify this information. Yet, there are few ways you can quantify this data. This data is revealed through the following sources:

• Customer satisfaction
• Net promoter score
• Customer feedback
• Success stories
• Customer grievances
• Purchase criteria
• Challenges and motivation

This information is mostly subjective and helps you understand the customer’s views about your brand in more light.

Customer Data Management

Having understood the different types of data, let us now understand how its management works. Customer data management comprises of three steps:

1. Data Collection
2. Data Validation
3. Data Analysis

Data Collection

Companies use various avenues for collecting data from the customers. Customers interact with a brand at different stages and hence, they reveal different information based on the current context. Few ways to collect data are:

• Website analytics: Tools like Google Analytics, Mixpanel, etc., can be used to extract information. This information is mainly about website interaction. However, you can use other tools like Optimizely, Crazy egg for more qualitative details like heatmaps, session recordings, and conversion funnel visualization.
• Social media: You can collect various engagement data of your customers e.g. likes, shares, comments when they interact with your page on social media
• Tracking pixels: This is a small piece of HTML or JavaScript code that you can insert in your emailers or website and it reveals details like the visitor’s IP address, device, operating systems, etc.
• Customer feedback: This is the timely exercise companies conduct with their customers where they share their feedback on surveys.
• Customer Service: Your support staff software could also be a good source of information to get the customer’s more technical details. These include their support queries, problems in your product, etc.

Data Validation

Validation is an important part of Data management. It’s a sheer waste of time if you have invested efforts in data collection only to find out later that your data is incomplete. You need a proper validation process in place to keep your data intact for further usage. Few ways you can do validation is:

1. Have a plan for validation. Dedicate resources who would validate all the important data points like customer name, email address, contact number, etc.
2. Check for the accuracy of the data along with its size. Make sure the data is available in its entirety.
3. Make use of data enrichment which allows you to merge data with an authoritative entity having more accurate data. It reduces redundancies and enhances the accuracy of your data.
4. Use a customer data platform that gives you a 360-degree view of customers along with their technical environment. The data from this tool is completely trustworthy.

Data Analysis

Collection and validation are only useful if you finally use them for analyzing customer data. Through this analysis, you must be able to draw valuable insights to better strategize your business. A process like data mining is much useful here to drive better results.

Through data mining, you can identify the underlying patterns from your raw data. It uses technologies like AI, ML, and statistics to identify those patterns. It classifies the data into various categories and makes associations with results to create a prediction mechanism.

Companies also use tools like BigML or Google Cloud ML Engine to identify the patterns of a particular phenomenon. Based on the insights they provide, companies forecast their business performance and improvise as needed.

Final Thoughts

This blog intends to just give you highlights of what you can achieve from customer data along with giving you a basic understanding of it. There’s a whole lot of science on each topic if you want to look further. Customer information is one of the most valuable assets of any business. It gives them the power to manipulate consumer behavior in their favor.

When combined with next-generation technologies like BigData, Machine Learning, or AI, the power of data can be leveraged up to a vast extent. Companies operating on the principle of “economies of scale” can use customer information extensively to drive massive business growth.

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