Customer Analytics in Retail
There are multiple ways to improve your customer analytics when working with retail. However, retail can also be blamed for being the source of digital disruption on multiple levels. In this article we’ll discuss five typical methods of using customer analytics in retail business.
The Definition of Customer Analytics
Сustomer analytics is the process of both collection and analyzing of customer data by using multiple systems, including IT infrastructures, Machine Learning, Artificial Intelligence (AI), etc.
To alleviate your worries – both Machine Learning and Artificial Intelligence aren’t the same as they are described in modern pop-culture where everything is controlled by intelligent robots. It’s not that scary – using AI for easier customer data collection and analysis is quite normal for a lot of companies.
Some of the data that is collected by customer analytics in eCommerce includes: the type of device you’re using to browse the selected online store, the name and version of browser you’re using to do that, some demographic data, approximate customer location, and a number of others.
Reasons for using Customer Analytics
One of the arguments is that through collecting customer data you’ll have better understanding of the channels your customers are using to get to you. This, in turn, means you’ll have the ability to make customers’ experience with your channel better based on the data collected through their past interactions with that channel. It is quite simple.
The existence of a platform that collects and analyses your customers’ data would give you a number of benefits, fr om quite detailed metrics and less divided data sets to creation of customer intelligence based on collected customer data and general optimization of both strategies and products for different levels of retail.
1. Targeting and Segmentation
One of the ways to use customer analytics to your benefit is the audience segmentation based on the collected data. This process allows you to see the purchase numbers of people in different countries, each of them with different interests and/or values. With that data you'll be able to correct both individual products and overall selling campaigns to match each of the groups better than before.
This leads us to the definition of targeting. Targeting is the process of concentrating on specific customer segments based on data you've collected with customer analytics. That way you can target one or several of those segments with anything from specific ads to the entire campaigns designed to attract more customers of that same segment. All that process is quite fast, too, real-time fast.
The other way to use customer analytics is to figuring out when and why some customers might leave the funnel, the exact products they abandon in carts, and wh ere they spend most of the time. And all of that is still just the tip of the iceberg, so to speak.
There's also the ability of customer information to make it easier to work with more complicated metrics, like measuring Rate Of Interest, predicting lifetime values, and others.
2. Inside a Store
One of the huge advantages of using Artificial Intelligence and/or Machine Learning for backend is their appliance both in the digital sales and in the actual stores.
For instance, if you’re seeing that some of the products are bought more frequently than others – you can physically place them near one another to boost the conversion numbers.
You can also modify your actual physical inventory in store with the accordance to the behavior of your digital visitors and customers to provide a smoother customer experience. This helps with saving money, time, and also more often than not leads to the sales increase.
The gist of it is this: you can quite easily anticipate customer behavior with the usage of the obtained customer data in analytics and prediction. On the other hand, that same analytics can be used for both management and measuring of Key PerformanceIndicators to better plan and support long-time goals of your company.
We all know that managing a proper customer experience for different countries is a difficult task, because when one country’s clients see something as a proper CX – the other country’s customers deem that inappropriate, at best. That’s why it’s highly advised for all of the international companies to localize their services as widely as they can. Localizing is one way (though, not the only one) of gaining the reputation of a multilingual retailer – it tends to drastically increase your market share.
Usually, the localization process consists of three main steps:
The 1st step is the general intelligence collection, it means using the best analytics solution available to better understand the analyzed market and its’ needs.
The 2nd step is the search for specific data for that market, like cultural nuances, demographics, language nuances, and others.
The 3rd step is the localization with all of the collected data and nuances in mind.
For example, Dutch prefer more price-sensitive info, and Germans might “work” better with innovative tech. All those nuances can be quite easily found by using AI and/or ML to be, in turn, analyzed by your own experts to choose the proper way of acting based on that data
Some eCommerce solutions are providing translation services with admirable quality, but the matter of all kinds of nuances (steps 1 and 2) is still for you and your company to find out and act accordingly.
The easiest way to elevate a product’s prominent features is to use the product attributes. They make it easier for you to inform your customers about the exact nature of the product they’re buying, with tags like “high-quality leather”, “zero chemicals”,“durable fabric” and such.
This also helps people with faster finding out the product they need.
It’s not really necessary for attributes to be only one or two words long – they can easily be in the form of “bullet-points” (Amazon) or almost anything else, the possibilities are numerous.
The important part is this: a quality analytics solution that’s used to optimize your products decreases the number of returns and allows for much easier centralization and organization of the product info.
5. Retargeting of Ads
One of the most prominent long-standing issues with customer analytics is that most companies have no idea how to make a good use of it. And one way of fixing it is using ad retargeting.
Ad retargeting uses that data to improve the personalization and understanding of the shopping experience for each and every customer separately. All of that is done by showing targeted ads to customers who’s been on your site at least once. This method is also used to increase conversion rates and improve CX in general.
It is quite natural that the process of creating customer analytics and performing it at different levels and channels will result in improvements with both CX and engagement since improved customer experience is the biggest ROI for most companies.
Photo credits: Kobu Agency / Unsplash