Prioritizing customer data analytics for your business strategy has become more important than ever. More and more companies are using big data analytics to refine their growth and marketing plans with customer insights, market trends, & predictions.
Gartner’s recent survey revealed that 84% of customer service & support leaders favor using customer data and analytics for achieving business goals. It also stated that more than 80% of respondents see data insights as ‘very important’ for sales, product development, and quality assurance.
Utilizing big data for customer analytics is rewarding for companies, marketers, & growth strategists. But how can they introduce customer data analysis into their business strategy?
This blog is a guide to customer data analytics & how to make the most out of it.
Table of Contents:
What is Customer Data Analytics?
Customer data analytics is a range of techniques to collect, identify, and analyze valuable data insights for a better understanding of user behavior, likes, & dislikes.
By introducing big data to interpret customer information, companies can determine what their customers need & predict what they may want in the future.
From company owners to sales & marketing experts to customer relationship managers, customer data analytics is the most promising technique used to meet customer demands.
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Besides getting into other branding & marketing practices, hiring data analysts to track and visualize real-time data insights & trends is the first thing to prioritize. Here is why:
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Run more effective customer-centric marketing campaigns to offer what the target audience is searching for. This will also help businesses respond to users’ queries.
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Identify issues in the customer journey in retail or other industries while interacting with a product or service. With this, it will be easier to build strong and smooth customer touchpoints.
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Use individual customer analytics and insights to get familiar with users’ buying patterns, interests, & overall behavior. This way, you can catch the buyers’ persona better.
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Conduct a deep analysis of customer insights to devise your future strategy, plans, and investments. All predictions will prioritize the role of customer lifetime value and roadmap to enhance your existing product or services.
When it comes to customer data analytics, big data experts may discuss some major types of data insights and analytics solutions. Here they are.
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Types of Customer Analytics Solutions
Before getting started, businesses must explore all types of customer analytics solutions to stay in the right direction.
There are six types of data analytics models to leverage for customer analysis and insights:
Segmentation Analytics.
Segmenting your target audience is a great way to broaden your customer reach for marketing campaigns. Obviously, not all customers will be like-minded before finalizing a purchase or service.
Marketers must define regional, behavioral, and cultural differences while targeting customers from distinct groups. Besides that, they can conduct segmentation customer analytics using data insights to:
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Outline personalized messages, content, and promotional materials, catering to specific customer groups.
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Identify varying customer groups and determine their behaviors, interests, and trigger points.
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Increase user satisfaction rate & retention KPIs by finding better ways to communicate your brand.
Customer Engagement Analytics.
Customer experience and customer engagement go hand in hand. If CX is not very satisfying, then the scope for customer engagement is less.
94% of customers with positive buying experiences on your website or app are more likely to return. Organizations focused on improving customer experience can boost their customer engagement rates. In this context, businesses can count on customer engagement analytics for good reasons.
But what does customer engagement analytics mean? Well, it involves a series of practices to evaluate the buyers’ interaction with products or services and their reactions to them.
This type of customer data analytics process will let you:
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Define which ad campaigns are in sync with the needs of the target audience.
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Inspect the challenges customers face while trying your products or services.
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Learn what consumers feel about your offerings & what can be improved.
The process of customer data analytics may involve expert CRM software solutions to gather & analyze feedback for sales & marketing purposes. This will let you touch the pain points of your potential customers and customize your offerings accordingly.
Customer Satisfaction Analytics.
Another aspect of customer data analytics revolves around customer satisfaction. It asks for both qualitative and quantitative practices to chalk out a clear picture of customers’ behavior while interacting with your product or services.
This will take some effort for big data implementation to utilize insights from polls, reviews on social media, support surveys, forums, etc. These platforms are the top sources of insightful data to use for customer satisfaction analysis.
Customer Lifetime Value Analytics.
Not all customers are regular ones. There will be some customers visiting & shopping in your store only once and never coming back.
Customer lifetime value analytics enables businesses to understand customers’ buying patterns & their average spending value. This process refers to analyzing the variations in customer spending value over time. It will help in customer acquisition, retention, & retargeting to the right audience.
Want to understand your customers better? You need to get the best from customer data analytics and insights.
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Benefits of Big Data for Customer Data Analysis
Let’s walk you through the best benefits of customer analytics in your business.
Adds More Efficiency to Marketing Plans
Most of the time, marketers are reliant on Google Analytics for digital marketing strategy. However, digging into customer insights will let them know their customer better & strategize accordingly.
The benefits of big data management and analytics enable companies to record metrics for page views, active sessions, bounce rate, and other marketing trends. This will add value to existing marketing plans aimed at business success.
Great for Higher Sales & Profits
If your business knows your customers better, it’s more opportune to generate high sales revenues. What’s the role of customer data analytics here?
Let’s say, customer analysis for an eCommerce website helps in tracking what users are purchasing and what they’re refraining from. By doing so, owners can visualize and analyze analytics from various parameters and validate their business decisions in the best way possible.
All these tactics will improve customer experience which eventually leads to higher sales.
Reduces Customer Churn Rates
As discussed earlier, keeping up with your existing customers is way more challenging than finding new ones. The benefits of customer analytics empower businesses to keep healthy relationships with the customers.
By deploying custom CRM solutions and big data, it’s possible to reduce churn rates by interacting with customers regarding the non-renewal of subscriptions or incentivizing them to stay.
Cuts Customer Acquisition Costs
Attracting new customers is as important as retaining existing ones. It’s the first step to staying ahead of your competitors & achieving higher conversions. Hence, you need to include a solid customer acquisition strategy in your sales and marketing plan.
However, investing in customer acquisition campaigns is more expensive than imagined. Even if a business is willing to spend some extra bucks on acquiring new users, the quality of leads is not assured. For this reason, the role of customer acquisition analytics comes in.
The benefits of customer analytics can help in:
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Analyzing how effective customer acquisition strategy is.
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Examining & comparing the quality of leads generated from multiple ad campaigns and determining those are working.
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Discovering opportunities to encourage personalization & potential of your go-to marketing plan.
Upon indulging in this version of customer data analytics, you can also determine and reduce the per lead costs.
Improves Customer Support
Lastly, leveraging a customer analytics solution is a good idea to enhance customer support services. Ensuring a great quality of customer support will strengthen your brand credibility and trust in the market.
For instance, online retailers can use customer analytics tools such as Hotjar to determine what their customers feel about their eCommerce customer services, brands, and other key offerings.
Know Your Customers better with Data-driven analytics at BluEnt.
Data analytics is the backbone of a business aiming to gain a competitive advantage over other brands. Implementing customer data analytics is just a step in directing you to a strategic and analytical approach to do so.
The more you keep track of your customers’ needs, the better opportunities you get to engage them. At BluEnt, our business consultants & data engineers are the most reliable helping hands for customer-oriented data analytics and insights.
BluEnt’s expertise in hands-on enterprise data management covers data management, data analytics & insights, data implementation, and predictive analytics.
Besides business and engineering solutions, we specialize in various areas of IT service offerings including mobile and web app development, website design & development, portal development, software development, and product development.
Interested in data analytics or other BI (Business Intelligence) solutions? Get in touch today!
Check out our portfolio section for more insights.
Frequently Asked Questions
What are the key models of customer data analytics?
There are four models of customer data analytics to implement in your business:
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Descriptive customer analytics to gather real-time insights from past and present database.
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Diagnostic customer analytics to identify root problems in internal & external customer data.
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Predictive customer analytics to predict and forecast changes.
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Prescriptive customer analytics to recommend the best plan of action.
What are the best customer data tools to use for analytics?
These are the most preferred customer data analytics tool to try:
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Microsoft Power BI
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Hotjar
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Salesforce Interaction Studio
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Mixpanel
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Kissmetrics
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Crazy
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Woopra