Digital Customer Experience: 6 Customer Analytics to Pay Attention To
In the race to optimize business success in the internet era, there’s fierce competition to deliver a more appealing digital customer experience. After all, positive experiences generate more sales, which in turn drive growth. Furthermore, the use of customer analytics is central to understanding buyer behavior and preferences.
Key performance indicators (KPIs) provide companies with vital insights into their customer base. These metrics can be leveraged to refine your eCommerce recommendation system. Moreover, customers will appreciate the more personalized information, and revenues will grow accordingly. It’s a solution that benefits everyone—so why stay in the past when you can provide a better digital customer experience right now?
Understanding Digital Customer Experience
The digital customer experience can be defined as the total set of interactions between a company and individual customers, evoking emotions that lead to sales. In today’s business landscape, this is an essential component of gaining—and retaining—buyers. Most people are accustomed to using digital tools in every aspect of their lives. As such, they expect the companies they support to deliver an engaging online journey.
Once a prospect feels you’re treating them like a unique individual, navigating the sales journey becomes much easier. And better sales figures are just the motivation you need to develop a top-notch digital customer experience.
Customer Analytics and Key Performance Indicators (KPIs)
The crux of the digital customer experience is how best to engage your audience. Therefore, you need to understand how they act and why. Customer analytics does just this to precisely measure the core dimensions of interest—key performance indicators (KPIs).
KPIs give you powerful insights into human behavior. You can use these insights to offer customers more of what they want and less of what they don’t. A digital customer experience that builds on your KPIs will thus generate a higher return on investment.
Here are the top six KPIs you need to pay attention to:
1. Purchase History and Frequency
Past actions are often the best guide to future behavior. In other words, knowing purchase history is vital. The types of items that have sold before are more likely to sell again. Likewise, purchase frequency matters—the more popular products are also liable to sell well in the future too.
One approach is to focus on the finer purchase details of an individual prospect. Another is to study the broader behavior of demographics similar to that prospect. For instance, a vehicle that sells well among young adults in a certain state or area will more likely appeal to another young adult shopper in that same area.
By identifying purchase patterns, you can tailor offerings accordingly, refining the digital customer experience in a way that boosts sales.
2. Customer Lifetime Value (CLV)
Customer lifetime value (CLV) is the sum of expected earnings from a given customer over the term of your business relationship. It’s a practical method of assessing where to spend marketing money, i.e., on customers providing the biggest returns.
You can calculate CLV in several ways. Essentially, it’s achieved by totaling up revenues or profits. For example, multiply an average $100 spend per week by 52 weeks per year by 10 years, and your hypothetical CLV is $100 x 52 x 10 = $52,000. Compare CLVs to decide where and how to market.
3. Customer Segmentation and Persona Analysis
A common practice in marketing is customer segmentation. This involves categorizing shoppers into several groupings on the basis of relevant characteristics, such as geography or demography. Segmentation allows you to target marketing efforts more precisely.
Let’s say you have one customer segment shopping for sports and outdoor items and another looking at homeware and décor. By creating separate marketing programs for each segment, it’s possible to target certain products to shoppers based on which segment they belong to.
A related practice is persona analysis, where you develop a qualitative character for specific shoppers. Persona analysis helps you deliver a more personalized digital customer experience.
4. Click-Through Rate (CTR) and Conversion Rate
The click-through rate (CTR) and conversion rate are techniques for calculating sales performance. These KPIs measure the empirical efficiency of your marketing efforts. Specifically, the CTR calculates what percentage of people seeing a promotion click through to the target page. Meanwhile, the conversion rate measures what percentage of consumers considering your product actually finalize the deal.
These rates are significant in customer analytics because they attach meaningful data to the performance of each asset. The result is greater insight into the effectiveness of your digital customer experience—and the opportunity to create more of what works.
5. Average Order Value (AOV)
In addition to knowing the frequency of transactions, it’s also important to learn the average order value (AOV). This metric tells you how much customers spend on an average order, which helps steer decisions on pricing and product selection.
Analyzing AOV helps companies find opportunities to increase sales and profitability by encouraging customers to spend more per transaction. When you have your finger on the pulse of order value, you can make adjustments that tend to yield bigger purchases.
6. Churn Rate and Customer Retention
Finally, it’s useful to monitor churn rate—the speed at which customers leave your site or app. This metric tells you about customer attrition and the extent to which you have regular shoppers versus one-off sales. You can use customer analytics to identify and address the forces contributing to the loss of customers. Furthermore, this approach improves customer retention and loyalty over time.
Enhancing eCommerce Recommendation Software with Customer Data
More and more businesses are enjoying the revolutionary benefits of personalization for eCommerce. Recommender systems like AutoIntel, powered by S44, analyze KPIs to determine the optimal products to display. Leveraging this vital customer data can be a game-changer for enhancing the digital customer experience.
Let’s find out more about how recommendation software can help you.
Importance of Personalized Recommendations
Personalized recommendations have the power to improve the customer experience, boost engagement, and maximize conversion rates. Recommender software, such as AutoIntel, enables the intelligent selection of products for specific customers. Tailored suggestions make the digital customer experience much more manageable and fun. When prospects see that the vendor “gets” them and that each choice is tantalizing, they’re more likely to close the deal.
Leveraging Customer Data to Improve Recommendation Algorithms
Recommender software relies on data science to analyze and improve product suggestions strategically. AutoIntel’s technology executes algorithms that weigh various factors in determining the ideal match for each customer’s mindset and preferences.
When you implement a solution, AutoIntel will work with you to leverage customer data and hone the recommendation algorithms for your organization. The right combination of data sources, analytics, and machine learning can capture valuable insights on customer behavior to drive sales.
Optimize Your Digital Customer Experience
Each customer embarks on a journey, starting from awareness of your brand, through to product selection, and finally to completing a sale. The quality of this digital customer experience varies in terms of what you offer—and how you present your offering. A product recommender analyzes key performance indicators to select the right products for each individual.
Effective use of customer analytics will increase customer satisfaction and conversion rates while fostering long-term loyalty, ultimately achieving success in the competitive eCommerce field. Get in touch to learn more and how we can help you deliver an optimized digital experience through custom software.
