Customer analytics is the process of collecting, analyzing, and interpreting data about customers to help companies make better business decisions. It’s a way for businesses to understand their customers in order to improve the products or services they offer. In other words, it helps them get more out of their customers.
If you want to be able to provide your consumers with the best service possible, then you should definitely consider getting some customer data analytics done. There are many ways that you can use customer analytics to benefit your company.
We'll look into it in this article. Furthermore, we'll also explore the main categories of customer analytics, in addition to what's important for omnichannel retailers, pure brick-and-play retailers, and those based on eCommerce platforms such as Shopify.
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The reason why customer analytics matters, is because it allows businesses to gain valuable insights. This will allow them to identify which parts of their business need improvement, as well as how to improve those areas. If you want to know how to improve your business, then customer analytics is an essential tool.
In order to do customer analytics properly, you first have to collect data from your customers. You can do this by using surveys, questionnaires, interviews, etc. The most common technique used today is email marketing. However, you can also use social media platforms such as Facebook, Twitter, Instagram, etc., to gather information.
But this form of data collection is always dependent on the customer's interest to give the required data. Here's where, you can leverage other important customer data such as their purchase, transaction, or loyalty data, which can be easily accessed from your customer data platform (CDP).
After gathering data from your customers, or importing them from your CDP, you need to analyze it. This means looking at the numbers and statistics to see if there are any trends or patterns that emerge. For example, you might find that certain types of customers tend to buy more frequently than others. Or maybe you notice that people who bought something last week tend to purchase it again next week.
Finally, once you've analyzed all the data, you can interpret it. You can do this in different ways depending on what type of business you're running.
There are two main types of data that are commonly used when doing customer analytics: quantitative and qualitative. Quantitative data refers to numerical values, whereas qualitative data refers to non-numerical values.
Quantitative Data: This includes things like demographic data, financial data, behavioral data, and transactional data. These include things like age, gender, income, location, shopping preferences, etc.
Qualitative Data: This includes anything else that isn't numerical. Examples of these would be text, images, audio, videos, etc. Qualitative data is often used when trying to understand the emotional state of your customers.
With that in mind, as far as customer analytics is concerned, these types of data can be bucketed into the following three types of customer analytics techniques.
- Descriptive analytics
- Predictive analytics
- Prescriptive analytics
Leveraging them will help businesses make data-driven decisions.
This category includes things like gaining insight into your current customer base, identifying new opportunities, understanding your market, and developing a product roadmap. It doesn't matter whether you're a B2B or B2C company, descriptive analytics is still relevant for both. It helps you better understand your customers and how they behave. It can help you chart out their buying habits.
For example, if you were selling apparel, you could use descriptive analytics to determine which fashion statement your customers like the most. This way, you can focus your efforts on manufacturing more of those preferred garments.
You can also use descriptive analytics to identify your best customers. This is done through segmentation. Segmenting your customer base is very useful because it lets you target your messages to only those customers who are likely to respond positively to them. This helps you discover not only potential customers but also high-value customers.
In addition, descriptive analytics can help you gain insights into your competitors. By analyzing the buying behavior of your customers, you can learn more about your competition. This will allow you to make smarter decisions regarding your marketing strategies.
This category focuses on predicting future behavior based on past actions. It's often used to predict whether someone will convert to a paying customer or not.
For example, let's say you sell clothing online. If you know that one out of every four visitors to your website converts to a paying customer, then you can use predictive analytics to decide whether you should spend money advertising on Facebook or Twitter.
Another thing that you can predict with predictive analytics is whether an individual user will return to your site. This is especially important for eCommerce sites where repeat purchases are crucial. If you find that people who have visited your site before are much more likely to buy from you than people who haven't, then you can use that information to improve your conversion rates.
The final category of retail analytics deals with making changes to your products and services. For example, if you notice that certain demographics aren't converting at the same rate as others, then you can change your strategy accordingly.
This is where prescriptive analytics comes in. Prescriptive analytics is used to create a plan of action that leads to an increase in revenue.
For example, suppose you want to increase sales by 10%. To do so, you need to first figure out what percentage of your existing customers convert to paying customers each month. Then, you can calculate the number of new customers needed to reach your desired goal.
Next, you'll need to set up a campaign to attract those new customers. Once you've attracted enough new customers, you'll need to analyze their buying behavior to see if they actually become paying customers.
Once you've determined that they did convert, you can then adjust your strategy to get even more conversions.
The wide range of customer analytics report that we measure and monitor typically falls into one of these three aforementioned categories. Here is a list of three useful customer analytics platforms that retailers can use to get marketing analytics to impact their bottom line positively.
Ingage is actually an AI-driven customer engagement platform built exclusively for retailers to give them all customer data they need and execute effective marketing campaigns.
With Ingage, you get a customer data platform wherein you can access all customer data - their activities on your eCommerce site, transaction data, customer loyalty program progress, user engagement, and so much more. Moreover, you get common customer journey templates that you can use in customer attrition, retention campaigns, and others. If you want, you can even design your customized entire customer journeys based on intelligent customer segmentation like RFV.
2. Polar Analytics
Polar Analytics is a multichannel analytics software that helps you aggregate data under a dashboard to view your company performance and monitor issues in real-time. It gives you important metrics such as customer acquisition cost (CAC), customer lifetime value (CLTV), customer cohorts, returns on ad spend (ROAS), among others.
Pricing: Starts at $149 per month and has a fourteen-day free trial.
3. RetentionX Analytics
RetentionX is yet another analytics application built for Shopify retailers who struggle with customer retention. You can use this platform to determine the average order value (AOV), customer churn, CLTV from every angle, create customer segments, predict customer behavior, and keep a clean data house.
Pricing: Starts at $79 per month and has a seven-day free trial.
Customer analytics involves data collection, sorting, and analysis to give you actionable insights and help improve customer satisfaction. While you can have an in-house data science team to engineer predictive models and carry out customer analysis, it's not any good for small and medium-sized Shopify retailers.
Here's where applications like Ingage can give you all the customer analytics tools that anyone from your team can use to determine your valuable customers and keep them close. You can channel all your investment to your business growth instead of your team size and effectively run campaigns with customer-centricity to give the best customer experience possible.
Customer relationship management cannot get any easier. It's your turn to make a wise choice to nail every customer interaction with success.