Customers in the digital age have all the information at their fingertips and can find the perfect product for their needs. Hence, organizations must do everything they can to ensure that they take the right products and marketing campaigns to their target audience. To this end, they can use data analytics services to understand their customers better. Customer analytics, or customer data analytics, is the systematic analysis of a company’s customer behavior and information to identify, attract, and retain new and prospective customers. The goal of customer analytics is to accurately recognize an organization’s customer base, which can help them understand how to acquire and retain new customers.
How can Data Analytics improve Customer Service?
- Helps identify future trends
By unearthing and analyzing customer stories, brands can divert their attention towards delivering a more meaningful customer experience. They can understand user needs and wants and tailor their offerings accordingly at different stages of the user journey. They can gain a 360-degree view of their customers based on their needs and past behaviors.
- Provides deep insights to deliver a super-personalized experience
Data translates into insights, which in turn propel customized experiences, ensuring customer satisfaction. Today’s purchasing decisions involve buying into an idea or experience more than the product. This is where high-level data analytics enable brands to maintain a meaningful relationship with their users. Marketers can address questions such as:
- What digital channels are most used by customers?
- What keywords must the brand focus on to encourage content on digital platforms?
- What areas of the website are customers spending their maximum time on?
- Helps determine the pricing
One of the main challenges for brands is determining the pricing for their products and services; data analytics can shed useful insights regarding this. Keeping in mind the ever-changing customer needs, it helps businesses to be agile and flexible with their pricing model and look for opportunities for growth, user satisfaction, and revenue.
- Aids in decision-making
Marketers can make informed decisions regarding pricing, sales, and organizational goals using data analytics and Business Intelligence tools. They can remove the guesswork, help them understand what strategies successfully converted prospects to clients, and change approaches to enhance their ROI. For instance, they can choose sponsored posts over regular PPC campaigns for more conversions, or alter the keyword strategy to get more views and so on.
Categories of Customer Data Analytics
There are four main categories of customer data analytics:
- Descriptive analytics gives insight into customers’ past behavior.
- Diagnostic analytics helps businesses understand the reasons for specific customer behaviors
- Predictive analytics helps marketers predict future customer behavior
- Prescriptive analytics offers suggestions to companies on how to influence customer decisions and increase their ROI.
Five Types of Customer Data Analytics Services
- Customer Journey Analytics
This type of data analytics is used to understand the customer’s interaction with the brand, beginning with their initial product research, leading up to the actual product/service purchase, and beyond. This type of data analytics involves a combination of data points for the various interactions, such as organic and inorganic traffic to the website, shopping cart abandonment rate, and other similar metrics.
- Customer Experience Analytics
Customer experience data analytics can shed light on your customers reactions or experiences with your brand. It can help you understand customer reactions to brand interactions based on certain metrics. Customer onboarding (time taken for users to adopt and value the brand) and customer support (time taken for issue resolution) are some of the metrics used in customer experience analytics.
Another component of customer experience analytics is diagnostic analytics, which includes the data obtained via CSAT scores. CSAT scores are obtained via surveys conducted via email or software after a major event, such as a purchase. CSAT scores can also help marketers determine the quality of customer onboarding. Qualitative data collected from customer feedback and customer effort scores are other useful analytics that can help gauge the customer experience.
- Customer Engagement Analytics
Customer engagement data analytics fall into two categories – engagement analytics for your product or service and engagement analytics for your brand, such as web analytics. Customer teams can track user engagement with the product via usage metrics or via engagement marketing, which includes influencing and analyzing the relationship between your brand and prospective customers.
For instance, you can narrow down the website visitors to understand their interaction with the content and their navigation paths, and then target them with customized ads or emails. Email marketing metrics such as click rates and social media engagement can also give you insight into how to increase customer engagement.
- Customer Loyalty Analytics
This type of data analysis measures the loyalty of customers. It can answer questions like how many buyers are repeat customers, what is the percentage of customer churn, and similar metrics that can tell you if your customers prefer you over competitors. Apart from these, customer retention and churn metrics can also spot any unidentified issues that may crop up in the future.
- Customer Lifetime Analytics
Customer lifetime is a combination of customer lifetime and customer experience; a key metric in this analysis is customer lifetime value. This metric can show marketers how much revenue a single customer can generate from a business relationship. This metric is calculated by multiplying average purchases with the average customer retention rate and then multiplying the product with the average total deal. This metric can be segmented based on customer type to understand the most valuable customers and target audience for your key campaigns.
Customer satisfaction is the driving force behind businesses today, so any customer-driven organization will need to be knowledgeable. Be it customization, catering to customer needs, or helping brands connect with the audience, data analytics play a key role in creating a customer-centric experience. With customer data analytics, it becomes simple to collect and analyze huge amounts of data that you can use to make better business decisions and enhance your product offerings. STL Digital’s data analytics services can help you unlock the potential of your data, derive actionable insights from it, and reach out to your customers at all levels.
- What important questions do customer analytics answer?
Customer data analytics can answer questions such as:
- Who are my loyal customers?
- What is the location of most of my customers?
- How much value does my main customer base bring to my business?
- What are some tools used for customer data analytics?
Some tools used for customer data analytics are Google Analytics, Kissmetrics, Hotjar, CrazyEgg, and Woopa.
- What are the top questions you will ask your customers?
- How can my business better serve you?
- What is your satisfaction level with the use of our products and services?
- What value does our brand provide to you?
- What are your biggest challenges with the use of our product or service?
- What factors made you choose our business over competitors?
- How can you monitor customer behavior?
- You can have polls to get product reviews
- You can analyze all online activity on business websites to understand what customers are looking for.
- You can offer free samples of your products and obtain client feedback
- You can observe your clients during brand interactions.