An eCommerce may be doing everything related to email marketing very well, they can develop incredible product texts, manage their RRSS excellently, or even carry out a perfect PPC strategy, however, how do you decide to dedicate your time to an action or other? The answer is in customer analytics ecommerce inspector. In this post, I am going to tell you what it is and how important it is in the world of eCommerce, as well as how it can improve your strategy. Let’s go there.
Customer analytics refers to the collection of data that tells us what customers are interacting with, how, and for how long. Once obtained, this data is interpreted, helping us to understand the different segments of our customers.
Even if you don’t imagine it, many of the marketing actions that you analyze in your day to day have customer analytics behind them. For example:
- Number of daily page views.
- The number of users who click on a featured landing page offer.
- Percentage of users who bounce compared to how many stay to visit another page.
- Average amount of time spent on a web page.
- All of these metrics, in context, can help you better understand how your site is performing, right?
However, these metrics change all the time, and some of the factors may be out of your control. If you have below-average page views on a Saturday, it could be because you just changed your Google Ads keywords, or perhaps the majority of your audience prefers to shop during the week.
To understand it you only need to dig deeper, and thus, if you take a closer look at the data, you can begin to make more accurate decisions based on information.
In an ecommerce it is evident that the reference tool to do customer analytics is Google Analytics, however, it is necessary to carry out a more in-depth analysis related to the shopping experience of your customers with your eCommerce, and for this you will need other tools and techniques such as Machine Learning. Additionally, Google Analytics is limited to the website and does not always provide a complete picture of the performance of broader marketing activities, such as email marketing performance or much more complex transactional information.
In the end, the depth of that analysis will depend on the amount of data you are able to collect, as well as the tools you have for it. But how can customer analytics improve your eCommerce?
How customer analytics can improve your eCommerce strategies.
Experience, hunches or even assumptions have rarely been right. However, collecting, analyzing and making decisions based on that data is usually the best option, clearly going in your favor.
An online business has a tremendous capacity to collect data, and it is a waste that it does not use it to increase sales of new customers, improve retention, optimize the customer experience or even create prescribers, improving engagement. This is accomplished with:
- An understanding of what your customer’s value is.
This is done with a technique called RFM, in which three dimensions are measured: How recent has that customer been buying? How often do they buy? And how much have you spent in total? And based on this, segments are created with said clients, in order to be able to establish different and independent strategies for each one of them.
Thanks to the application of this type of customer analytics, your eCommerce will be able to carry out more personalized and effective marketing, with specific and relevant offers for the right groups of customers, thus increasing engagement.
- Customer retention.
Typically, regular customers are responsible for at least 40% of a brand’s revenue. That is why for an eCommerce, focusing on customer retention is essential and one of the best ways to reduce customer acquisition costs (CAC). Along the same lines, the repeat purchase rate (RPR) is another metric that you should not stop tracking in your online business. This tells you how many customers return to make a second purchase. If this number is too low, you probably want to start putting more effort into a customer retention strategy.
A successful strategy could include creating more educational content to teach customers about the benefits of your product, or it could be creating a rewards program to influence the customer’s next purchase. Regardless of the method you use, customer retention should always be a top priority.
- User commitment.
Just because shoppers visit your website and see your content, it doesn’t mean they’re interacting with it. The difference between a website that provides value and one that just scratches the surface lies in customer analytics. In this case, through bounce rates and time on page.
If you find these numbers are bad (high bounce rate and low average time on page), think about how you can add value to different touch points throughout their customer journey. For example, if you find that site visitors only spend a few seconds on your home page, you could try inserting a video on your home page that tells your brand story.
- In-app purchases.
Conversions are probably the most important result measure for any eCommerce. Therefore, if your online business has a presence or you plan to have it on mobile devices and applications, this is a measure that you must take into account, separating it by device.
Similar to mobile adoption, you can segment your customer analytics by device. To get a better understanding of in-app purchases specifically, you’ll want to look at goals and conversion rates. Once you have these numbers, compare them to what you’re seeing using desktop.
If you see a relatively low number of in-app purchases compared to mobile adoption and number of users, chances are you need to start thinking about how to optimize the mobile experience, either by creating a more convenient payment method fluid or rethinking its design to better fit the mobile user experience.
- Product recommendation systems.
These allow customers to be offered the products that may interest them the most based on factors such as their purchase history or their behavior in the browsing session itself. The way to achieve this is through Machine Learning that is part of customer analytics. In ML, data is its raw material, and both retail and ecommerce are capable of generating a large quantity and variety of it. That is why online commerce is one of the great promoters of this discipline.
- Internal product search engines and chatbots.
This is also achieved through Machine Learning, which helps your eCommerce users to find products more directly by describing some of their features through text. In the case of chatbots, as we all know, it allows customers to experience a conversation as if it were with a human person, when behind it is software that is capable of answering the most frequently asked questions.
- Visual search engines.
Also through Machine Learning, through an image entered in said search engine, it is able to recognize a product that we want to buy or even offer us other products with a similar appearance. These types of tools can be incorporated both into mobile applications, which allow the camera to be used to take a photo on which the product will be searched, and to web platforms, and it is beginning to be used mainly in fashion stores, with results that double and triple the conversion for customers who use them.
- Dynamic price adjustment.
The price is in many cases, one of the main determinants of the sale of a product, and assuming that we have to play at a single price for any profile that visits your online store, is to miss hundreds of conversion opportunities, if that price was adjusted to the personal characteristics of that consumer. With analytics and Machine Learning, you can manage to establish different prices depending on the business situation, the characteristics of the client and even the competition. Here we can find the case of Amazon, which uses information on the demand for the product on the web, the user’s purchase history and the availability both in the competition and in the different providers of the marketplace, to show the most attractive price for the customer. .
- The use of a CDP or Customer Data Platform.
A Customer Data platform will allow your online store to automatically integrate all the data, connecting with external sources that help you enrich the unique customer files that this platform will store. In addition, these platforms connect with other external outbound tools, which draw on the information from your CDP to fine-tune the campaigns you launch.