This article was published as a part of the Data Science Blogathon.
Data and Information about a Customer are important for all businesses and companies. For a business to be data-driven, a Company needs to be highly data-driven and focus highly on customer analytics. Information about customers can be collected from many sources. It may be data collected during online purchase transactions, sales at retail stores, online surveys, etc. All this data can give valuable insights into the customer’s demographics, interests, purchasing power, etc. To understand all this, we take the help of customer analytics.
Customer analytics is the process of using analytics to study customer behavior. This is done in order to make better business decisions. As a result, it is a process of studying customer data and information. This leads us to understand and interpret customer behavior in order to make effective business decisions. As a result, many businesses use this information for site selection, direct marketing, and other purposes. Customer analytics can help you attract and retain the most profitable customers.
Source: https://blog.aspiresys.com/digital/big-data-analytics/boost-your-organizations-performance-using-customer-analytics/
Customer Analytics is a very important topic for Data Analyst, Product Analyst, and other data-oriented roles. Let us check some top Customer Analytics Interview Questions.
Customer analytics seeks to create a single, accurate view of an organization’s customer base. This can be used to inform decisions about how to best acquire and retain future customers. It can also recognize high-value customers and recommend proactive ways to engage with them.
For example, analytics that has been properly implemented can aid in many aspects of the business. These include forecasting consumer responses to marketing and advertising campaigns, brand adaptation, and comprehension of the customer experience. Customer experiences, customer satisfaction, marketing campaign success, and other factors can also be evaluated.
There are four types of analytics: descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics.
Source: https://www.scnsoft.com/blog/4-types-of-data-analytics
For example, in Customer Analytics, Customers can be clustered or grouped on some basis. These include their spending habits, their income, profession, and other demographic data.
In a broad sense, a customer’s lifetime overlaps with the customer’s journey and experience. However, the Customer Lifetime Value is valuable in this type of analytics (CLTV). This metric indicates how much revenue you can expect from a single customer throughout the business relationship.The process for calculating this metric may vary depending on your business; in some cases, bringing in a consultant may be more effective in identifying the right formula for your company. However, multiplying the average retention rate by the average number of purchases and then multiplying the product by the average deal total is a simple way to calculate this metric.
Customer analytics is primarily concerned with converting customer knowledge into actions for the organization and thus includes a customer-facing department.Web analytics is primarily concerned with online data and website usage optimization and thus does not include a customer service department.
Customer Analytics works with actual and genuine customers who are interested in a product or service.
On the other hand, Web analytics is concerned with anonymous data traffic rather than individual customers.
Customer service analytics is gathering and analyzing customer feedback to gain useful insights. It can assist you in better understanding your customers’ needs and expectations, leading to better customer experience (CX) strategies and increased customer loyalty and retention.
For example, customer service analytics enable us to identify customer pain points and ways to position our business or product as a solution to those issues. Even the effectiveness of the customer service channels can be evaluated.
Some important Customer analytics best practices are as follows:
Outliers in a dataset are values that considerably deviate from the average of a dataset’s defining characteristics. We can identify either measurement variability or an experimental error with the aid of an outlier.For example, in a retail dataset of 100 customers, 96 customers have purchased in the range of 1000-2000. The other four entries are 9000, 6800, 7000, and 12000. These four data points are very far away numerically from the other data. Hence, they qualify as outliers.
Source: https://www.geeksforgeeks.org/types-of-outliers-in-data-mining/
Collaborative filtering (CF) generates a recommendation system based on user behavioral data. It eliminates information by scrutinizing user behaviors and data from other users. This approach assumes that persons who agree to assess specific goods will probably continue to do so. Users, things, and interests comprise the three main components of collaborative filtering.
Source: https://towardsdatascience.com/from-vague-to-value-data-science-analytics-practitioner-insights-fed92a4bda08
The field of e-commerce offers one of the best instances of collaborative filtering. An e-commerce website that you browse will present you with some suggested products, as you can see. There are some items there that are an exact match to what you were looking for. Now, you might wonder how the website can discern your interests. Collaborative filtering is solely to blame for everything. Amazon has one of the best Collaborative Filtering models, which shows similar products to all their customers.
The Normal Distribution is also called the bell curve or the Gauss distribution. It is a fundamental concept in statistics and the foundation of machine learning. It specifies and quantifies how the means and standard deviations of a variable’s values differ, or how the values are dispersed.
Source: https://www.scribbr.com/statistics/standard-normal-distribution/
The above diagram depicts normal distribution.
When working with trend analysis and time-series data, in particular, time-series analysis, or TSA, is a widely used statistical technique. The presence of the data at specific time intervals or predetermined periods is a feature of time-series data. Time-series analysis is a technique for analyzing data points over time. Time series analysts record data points at consistent intervals over a set period rather than intermittently or randomly.
Numerous fields use time series analysis, including statistics, business, economics, and many others. In Customer Analytics, TSA can be used for Sale Prediction, Web traffic prediction, and so on.
The customer journey can be optimized by businesses that have a thorough understanding of their clients’ purchasing patterns and lifestyle preferences. Large quantities of precise data are necessary for accurate analysis. Without it, analysis insights could be completely off and useless.
To summarise:
The most well-known brands in the world today take their consumer analytics very seriously. The financial performance of the business can greatly be changed by customer analytics.
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Hi Prateek Majumder, Thank you for sharing very informative article! It will useful whoever searching a job in data analytics domian!