What is the meaning of customer analytics?
Customer analytics refers to the use of analytics to study customer behavior and analyze customer data to understand customers’ needs for effective business decisions.
The primary goal of customer analytics is to improve the overall customer experience and journey. Organizations use software and analytics tools, to understand and collect customer insight and their behavioral patterns, based on customer segments and groups given their shared characteristics and values to provide them with personalized interactions with the brand. The process of measuring and analyzing the different customer behavior and experience, their awareness and need for the product, helps the organization to formulate successful marketing and sales campaigns that resonate with the expectations of these customers or customer segments.
Companies use customer analytics and insights in their sales, marketing, and product development efforts. Through this, they can offer a more relevant experience to their customers in all of their interactions. A personalized experience is what makes a customer stick to a brand and eventually become a loyal customer.
Benefits of customer analytics and why does it matter?
Customer analytics & data are crucial elements while framing product-related plans & campaigns, any major or minor efforts towards enhancing the customer experience. It becomes the base of multiple strategies from sales to marketing, pricing to packaging, and most importantly for product development. Customer analytics ultimately focuses on customer retention, loyalty, and success.
Following are a few key benefits of customer data analytics.
1. Customer acquisition
Gaining new customers is equally important as retaining old customers. Customer insight helps create effective and focused marketing & sales strategies that can attract new customers.
2. Customer retention & satisfaction
If the brand successfully engages the customer at different touchpoints throughout the entire journey, the customer definitely would feel connected with the brand, which is really important for customer retention and satisfaction.
3. Customer engagement
Communication is the key to customer success. Effective customer engagement helps brands create a rapport with their customers. And with the help of analytics, brands can perform predictive analysis and make the customer journey more satisfactory and engaging.
4. Effective customer service
Knowing who your customers are can make meeting their needs simpler. And you can streamline your customer service operations, enhance efficiency, and improve overall effectiveness to increase customer loyalty and retention.
What are the 4 main categories of customer analytics?
1. Descriptive analytics
Descriptive analytics includes demographic data like gender, age, geography, and income. And, gives you insight into past customer behavior & trends.
2. Diagnostic analytics
These analytics helps brands to understand the reason or "why" behind customer behavior.
3. Predictive analytics
Predictive analytics talks about and predicts the future behavior & pattern of customers based on their past purchases and interactions with the brand.
4. Prescriptive analytics
These analytics helps brand take measures to influence customer behavior or enhance customer experience.
What are the types/techniques of Customer Analytics?
1. Customer experience analytics
Customer experience analytics gives insight into how your customers feel when they interact or engage with your brand. Analytics takes into consideration things like customer satisfaction, their interactions on the website, time spent on each touchpoint, etc.
2. Customer interaction & engagement analytics
Customer interaction analytics is often considered the most important analytics as it shows the relation of brands with their customers. Since the journey is complex with various stages and multiple touchpoints, the focus should always be on knowing the complete customer’s interactions with the business.
3. Sales channel analytics
Understanding the sales funnel and knowing what works and what doesn't? can save money and other resources. Sales channel analytics looks at all the various ways that you distribute your products to your market to see which channels are the most effective, allowing you to make the best use of your resources.
4. Customer loyalty & retention analytics
You can always turn to customer loyalty analytics to develop customer-centric marketing strategies that can ensure customer loyalty and retention. As it covers the customers' feedback and their shared experiences, why they buy products, and why they feel an emotional connection with your brand.
5. Customer churn analytics
Customer churn analytics is the process of assessing how many customers you are losing over a year or a specific period. It also allows you to predict customer churn in the future and take evasive action before you lose those customers.
6. Customer lifetime analytics
CLA is a metric that shows the lifetime value of a customer or what could be the expected benefits that the customer might bring during their lifetime association. It helps you understand how much revenue to expect from a single customer throughout depending on their current behavior and buying habits.
7. Web and Social media analytics
Social media analytics is the process of gathering and analyzing data from social media to see what people are saying about your product, service, brand, or company. And how they interact with the website, how often they come back to the website, average spent time, and talk about the navigation journey or flow, to enhance the overall experience.
8. Customer thought analytics
The idea behind tracking customer opinion is to know their feedback and expectations so that you can always adjust your marketing and target them with the right offers. Anything that gives you a peek into the customer’s thoughts is always a great piece of data, so you should always need focus on listening to that.
Customer analytics tools
"The right analytics will allow you to see a trend, and in today's world where artificial intelligence is starting to analyze data for us, it will not only spot trends but will make predictions with uncanny accuracy. If all you do is put in the big numbers, it will be able to predict the big trends. But if you start putting in analytics and data from individual customers, it will start to predict individual customer behavior with uncanny accuracy."
Following is the list of the best customer analytics tools and software that you can use.
What is the example of Customer analytics? And, use cases of Customer analytics.
- Featuring material that matches customer interest.
- It uses big data analytics to draw patterns from people’s search and viewing history and offers suggestions for their next watch basis.
- Predictive analysis to make a new season on the basis of how many people are watching a movie or series and finishing the entire show.
- Processing and analyzing customer data combined with machine learning algorithms to pitch promotional spending.
- Keeping a track of all transactional records to identify deviations such as incorrect or fraudulent charges.
- A strong recommendation engine, that feeds on browsing data from users.
- Amazon helps its users make convenient buying decisions instead of getting lost in the huge variety it offers.
- It also gathers data about what you explore on the site, time spent browsing each page, and much more, to build a 360-degree view of the customer.