In today’s ultra-competitive market, a business can’t win by utilizing its products and services. Instead, a holistic approach towards customer analytics can be the solo game player to win the customers’ confidence and leverage end-to-end customer experiences. Additionally, companies need to determine what their customers need to gain a competitive advantage. Customer analytics, which mainly deals with big data analytics, involves synergistic work to get valuable insight.

What is Customer analytics?

Customer analytics refers to the processes and technologies mainly related to big data analytics, which give organizations customer insight. This helps to deliver anticipated and relevant results on time. Like the backbone for all marketing activities, customer analytics comprises techniques such as predictive customer analytics, data visualization, customer journey analytics, information management, and segmentation.

Usually, there are 4 types of customer data businesses can consume for analysis. Most importantly, nowadays, this data belongs to big data rather than traditional data. What is a customer analytics example with each of those types? Here are 4 use cases for them.

1. Transactional data

Retail data is a prime example of such data. Through each purchase, a company can perceive their customer analytics journey. For example, Walmart crunches its historical transactional data to forecast demand for approximately 500 million item-by-store combinations in the United States. This allows them to have the right product in the right place and increase their sales.

2. Service data /product data

Manufacturing data is an example of product data where the data is examined to create a better customer experience and innovation. For instance, Toyota runs several research projects aimed at examining drivers’ behavior. The company applies image analysis to evaluate the drivers’ head pose and hand position, tests drivers’ multiple sitting scenarios and seat belt positions to understand what features will improve drivers’ comfort and safety.

3. Web data

A company can analyze the demographic data of their website visitors. For example, where they come from, which pages they open, how long they stay on the page etc. Utilizing this data, the company can create relevant content to increase conversion rates. Ecommerce data is an example of this where retailers apply this logic to track customer behavior and identify customer preferences to make product recommendations with predictive customer analytics.

4. Customer-created texts data

Online reviews about some product or service and social media data are examples of such data. While customers take the opportunity to share their personal impressions, companies study this content to get an idea about what their customers think about their product, brand, or service by identifying trends, recognizing a positive or negative emotional tone of each piece of text. For example, Samsung uses social media analytics to attract customers from their competitors.

As the above-mentioned, four types of data can be utilized for big data customer analytics; how businesses can retrieve information is the sole concern. It can happen in 4 ways -

- Using a ready-to-use customer analytics tool.

- Implementing a customer analytics solution with the efforts of an in-house team.

- Involving a consulting and implementation vendor.

- Outsourcing customer data analytics.

In the below chart we can see how above four options can meet business expections.

https://www.scnsoft.com/blog/customer-data-analytics

Why Customer analytics is important?

But why is customer analytics becoming critical? In the era of the internet, today’s customers have hooked to the web always. Whether it is buying a simple household item or sophisticated electronics gadget or purchasing a service, they can access enough information through internet access. So, getting aware of customer insight is of utmost necessary to stand in the market. This is necessary to gather enough information about customer trends and respond accordingly and timely. A deeper understanding of customers' buying habits and lifestyle preferences provides more accurate predictions of future buying behaviors.

Customer analytics helps in:

- Increasing response rates, ROI, and customer loyalty by contacting the right customers with relevant offers and information.

- When customers who are most like to respond is identified can automatically reduce the campaign costs.

- Customer analytics gives a clear picture of the probable rate of customers likely to leave. This helps in the right proactive campaigns to retain them and decreases the attrition rate.

- Delivers the right segmentation of customers.

- Utilizing big data analytics, you can personalize selling and marketing strategies for the different customer segments. Thus it can enhance the overall customer experience.

Related post - How Big Data Is Affecting Social Media

Different angles of customer analytics

Customer satisfaction analysis

Customer retention is an important and essential factor for business growth. And this customer retention is directly related to your customer satisfaction and how happy they are with your service or product. Customer satisfaction analysis is a simple yet important process to assess whether your customers are receiving the expected product or service from a business. Additionally, as every business has a target customer base and market, customer satisfaction analysis helps reach that goal.

How is customer satisfaction analysis performed? Well, it is a combination of quantitative and qualitative surveys. More importantly, with the concept of big data analytics, the survey mode has now changed a lot. Now businesses can assess the feedback of customers through social media networking. Every business now has a footprint in social media platforms like Facebook or Twitter.

Customer lifetime value analytics

Who is the most valued customer of a business? How will you get the answer? Customer Lifetime value analytics is the answer to it, and any business must give importance to it. Differentiating between the most valuable and most important customer ones is important for a business, and customer lifetime value analytics as an attribute plays a key role for it. It emphasizes the long-lasting relationship between a business and its customers. Three factors matter here –

- How long a customer sticks to a business

- How often they establish transaction with the business

- How valuable are they over a period.

Besides, Customer Lifetime value analytics increases the tenure of the customer relationship. However, the formula for customer lifetime value analytics does not have a one-size-fits-all formula, which is a challenge.

Customer segmentation analytics

Customer segmentation analytics is a process that helps businesses to find segments or sub-categories of customers within the market. In this process, customers are assessed and split into different segments. In this process, tools like text analysis and data mining are widely used.

Real-time customer analytics

While the customers have more access to the internet, they are more empowered with information about where to buy, what to buy, the cost and the best discount, etc. Thus, customer analytics is gradually becoming critical to businesses. Having a deeper understanding of customers' behavior, how they can interact with a business, and buying habits helps forecast buying trends. So, with real-time analysis, a business can offer customers better products and attractive offers.

How big data customer analytics can impact your business results.

1. Review Analysis

Reviews are the voice of customers. So, they are an excellent source of information related to customer experience. A business can regularly track the reviews from Google, the Facebook page of the app or service, Twitter, Yelp, and many other sources. Through the review, a business can understand how its product or service is performing in the market. Based on this feedback, they can add business value.

Big data analytics play a critical role here. Big data analytics automated tools can go through all these reviews and analyze them to get insights on data. This is often known as sentiment analysis, which analyzes whether a review is positive, negative, or neutral. These types of tools apply a machine learning algorithm to perform data analysis. Here, text analysis is performed, which is nothing but an automated process to analyze a piece of text and extract information.

2. Predictive customer analytics

Predictive customer analytics provides a real-time report on customer behavior. Instead of working on historical data now with AI and machine learning, businesses can predict customer behavior in the future. This is a beneficial method in the e-commerce sector. During online shopping, predictive analytics tools register data about the online shopping behavior of the customers. This includes their shopping habits, shopping frequency, like products, etc. This helps in predicting which product will work in the market and for how long. Here mainly, consumer data is used to predict customer behavior.

3. Real-Time Product Feedback

However, a business needs to concentrate on future improvement and present improvement as well. There comes Real-Time Product Feedback, which gives instant feedback on product purchasing behavior. This is another form of real-time data analysis where a website or product can adapt to its customers’ needs in real-time. Netflix is an example of it that constantly strives to learn more about its customer preferences to improve customer experience. If you skip a certain artist's movie, it is less likely that you will hit another movie of the same artist. Real-Time Product Feedback allows a business to shape its service around its preferences as they use it.

4. Competitor Analysis

Competitor Analysis of a business is about analyzing its competitors' products, strategies, and performance to understand how the business can improve its own products and services and expand its customer base. To improve in a business eying on its own data is not enough. Competitor analysis is another important criterion to understand what competitors of a business are doing. In this highly competitive market, it is also essential to analyze whether your business performs at a per level of your competitors? What are the products and services your competitors are using for the same type of business? What are their marketing strategy and advertising techniques? Their strengths and weaknesses. Many tools are used in the market for competitor analysis, and Alexa is one of them.

Customer analytics tools

Customer analytics tools are specialized tools that provide insight into the customer experience, understand customer behavior and tailor marketing campaigns to specific customer segments. Customer analytics tools can be obtained in two ways – as a part of a CRM suit or as a stand-alone platform. Also, such tools can be integrated into sales and marketing applications, along with content management systems. Several major CRM vendors and software providers offer customer analytics tools; some of them are –Adobe Analytics

Google Analytics 360

IBM Watson Customer Experience Analytics

SAP Hybris Marketing Cloud

SAS Customer Intelligence 360

Below table shows the features of these tools.

Customer analytics best practices

Organizations can create successful customer interactions by measuring and analyzing data using specific metrics. Some customer analytics best practices that can help drive better business decisions include:

- Targeting customers across all channels and analyzing the various ways a product or service can be distributed.

- Assessing and understanding how customers are related to the brand and whether they are satisfied with the brand. This is a measurement of quantitative and qualitative analysis.

- Utilizing right channel to engage with customers.

- Extending a customer's lifetime value.

- Spotting trends in big data analytics and customer online behavior to increase sales.

- Following customer journey analytics and maximize it through personalized selling and market segmentation

- Encouraging fact driven decision making.

- Encouraging Senior Management to involve In Customer Analytics