Modern businesses operate on a data-driven approach to develop and market relatable products. Unlike the traditional hit and trial methods, current organizations leverage data to make informed decisions that have a high probability of success. Companies, collect data related to their customers using various channels such as surveys, tracking purchase journeys, recording complaints, etc. Afterward, the Analytics Teams use various techniques to search for patterns in the vast sea of collected data and provide reports to the management. One such technique is called Customer Intelligence Analytics.
Customer Intelligence Analytics is the continuous process of gathering and analyzing customer interaction data to extract valuable insights regarding customer behavior. This article will introduce you to Customer intelligence and explain its importance. It will further discuss the difference between Customer Data, Customer Intelligence, and Customer Insights and will provide you with the popular use cases of Customer Intelligence. Furthermore, it will dive into the steps required to set up Customer Intelligence Analytics and list the benefits of using this methodology. Read along to learn more!
Table of Contents
What is Customer Intelligence Analytics?
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Customer Intelligence Analytics refers to the collection of insights gathered from raw customer data. Businesses obtain customer data to understand the changing patterns and trends in customer behavior. This allows various Business Teams to plan ahead for future marketing and production plans and customize their product optimally. The process of extracting such insights operates using advanced real-time techniques like Feedback Management, Monitoring Social Media Activities, and even Natural Language Processing (NLP).
The information classified as Customer Intelligence Data includes behavioral data, web browsing activities, demographic data, results of sentiment analysis, support team interactions, survey and research data, social media tractions, daily transactions, customer feedback, and sales team records. The ultimate objective of going to all this length by collecting and analyzing vast datasets is to enhance company-customer interactions and create relatable products and ads.
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Why does Customer Intelligence Analytics Matter?
Today organizations work on a customer-driven model and Customer Intelligence plays a key role in sustaining this work approach. The insights collected as part of the Customer Intelligence empower the organization to customize its products and make them more consumer-oriented. This also allows your Marketing Teams to understand the mindset of their target customers. Furthermore, you will be able to identify the challenges that your consumer may face by using your products or services and you can then seek ways to overcome those bottlenecks.
Customer Intelligence is a definite means of prioritizing your customers’ needs. Moreover, it allows you to build customer profiles and have fruitful interactions with them. This mechanism coupled with Sales Analysis allows you to obtain a clearer bigger picture of your business.
Difference between Customer Data, Customer Intelligence, and Customer Insights
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The following 3 terms may sound similar but contain different meaning and use as explained below:
Customer Data
Customer data is the raw form of information about an organization’s customers. For instance, answers to survey questions, call center records, etc. fall under Customer Data. Many organizations consider this data as the initial point of their analysis. However, without any additional context, this form of data can offer only weak results and can not provide you with predictions and understanding s of customer behavior.
For example, the amount of credit card usage of a particular consumer will come under Customer Data for a bank. However, this information is only beneficial when either compared with past usage patterns or mapped to the demographic data of that consumer.
Customer Intelligence
Customer Intelligence is the output of comprehensive analysis in which Customer Data is put into some context to produce a meaningful result. Unlike Customer Data which could be in the form of an individual record, Customer Intelligence is vast in scale and is generated by the analysis of data gathered from multiple sources. This form of information is essential in classifying target customers, analyzing product popularity, and understanding customer grievances.
Customer Insights
Customer Insights take data collection one step further by performing a deep analysis of Customer Intelligence Data. This results in valuable patterns and actionable insights which can facilitate data-driven decision-making.
The objective of generating Customer Insights is to discover the reason and mentality of consumers on which they base their purchase decisions. This can help your business in developing optimal marketing campaigns and boating your sales performance.
Use Cases of Customer Intelligence Analytics
Customer Intelligence finds applications in the following 4 use cases:
- Behavioral Segmentation: The most popular application of Customer Intelligence, Behavioural Segmentation classifies users into separate clusters on the basis of behavioral patterns. Factors like being in the same purchase lifecycle stage, similarity among previous purchases, etc., dictates this classification.
- Modeling User Flows: User Flow refers to the path that a consumer takes while performing a task on your website or mobile application. Customer intelligence empowers you to monitor consumer journeys and identify the bottlenecks faced by users in their tasks. This will allow you to simplify your in-app features so that your customers can avail of your services in a hassle-free manner.
- Geo-targeting: Geo-targeting using Customer Intelligence simplifies the process of customizing messages and offers for customers. A popular example of this application is the current online food delivery systems. They rely on Customer Intelligence data to offer popular restaurants near users’ locations based on their demographic data.
- Personalized Emails: Based on behavioral segments, businesses can send specific messages or offers that are customized to known preferences or buying patterns of these customer segments. Personalized emails are also a use case of account-based marketing (ABM) in B2B.
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How to Build Successful Customer Intelligence Analytics
The following steps will help you in setting up Customer Intelligence Analytics for your business:
Step 1: Data Collection
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The first step in Customer Intelligence is the data gathering process. This requires you to collect raw data from a multitude of channels including emails, feedback, websites, calls, and social media platforms. This huge set of input data can be better understood by classifying it into the following 3 types:
- Direct Feedback: This include surveys, questionnaire, complaints, and other means of data collection in which the users willingly submit their feedback relating to their experience with your company. Analysis of this data can not provide great insights but is still important to get a clear idea of the issues and expectations of your customers. Alternatively, customers can also provide their direct feedback to a third party that can share its results with your Business Teams.
- Indirect Feedback: This form of data is present in social media platforms or customer service complaints where users reveal their thoughts regarding your company.
- Inferred Feedback: Inferred Feedback consists of data collected using Process Mining, cookies, purchase history, and other such mediums from all enterprise processes associated with customers.
Step 2: Setup Analytics Infrastructure
Once you have assembled the required information in step 1, it’s time to implement analytics on your Customer Data. Your Engineering Team needs to either build an analysis setup from scratch or use online platforms to extract valuable insights from the raw data. Using the results of your analysis, divide your customers into various segments depending on their behavioral patterns. This will allow you to analyze each segment thoroughly and gather more detailed insights.
Step 3: Work on Actionable Insights
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The last step of the Customer Intelligence process involves taking action on the insights extracted in step 2. Dashboards, Customer Journey Maps, Reports, etc are some formats in which you can present the insights to your teams. This will allow your Marketing Team to cater to different segments separately and create more personalized ad campaigns.
Benefits of Customer Intelligence Analytics
Customer Intelligence Analytics facilitates behavior-induced segmentation of customers and enables you to provide personalized services for various consumer types. Implementing this methodology can provide you with the following benefits:
- The results of Customer Intelligence Analytics and you in delivering higher sales. This occurs as a consequence of efficient marketing and personalized product development.
- You can expand your loyal customer base by providing efficient solutions to their challenges by using the data reports of Customer Intelligence Analytics. Understanding the customers’ mentality better will also allow you to share their viewpoints and create customer-oriented plans in the future. Furthermore, customized products and fast services will increase your odds of customer retention.
- Since the current market is flooded with data, making data-driven decisions can give your business an edge over its competitors. Therefore, using the results of Customer Intelligence Analytics as input for further analysis can provide you with actionable insights for the future.
Conclusion
The article introduced you to Customer Intelligence Analytics and explained its importance. It also explained the difference between Customer Data, Intelligence, and Insights. Moreover, the article elaborated on the various popular use cases of Customer Intelligence Analytics and listed the steps required to set up this process. It also mentioned the various benefits that this process can provide to your business.
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Now, to run queries or perform Data Analytics on your raw data, you first need to export this data to a Data Warehouse. This will require you to custom code complex scripts to develop the ETL processes. Hevo Data can automate your data transfer process, hence allowing you to focus on other aspects of your business like Analytics, Customer Management, etc. This platform allows you to transfer data from 100+sources to Cloud-based Data Warehouses like Amazon Redshift, Snowflake, Google BigQuery, etc. It will provide you with a hassle-free experience and make your work life much easier.
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