These Digital Transformation Trends Are Transforming Online Retail

The retail landscape is more competitive than ever — and that doesn’t just mean cost. While pricing remains very important for many customers, it’s not the only consideration that people weigh up when they’re interacting with a business and considering becoming repeat users. Both speed and convenience are also very important metrics in the customer journey, since these dictate another type of cost involved with being a customer: not just the financial cost, but the time and energy cost on the part of the buyer.

Even if a product is cheap, a customer may not become a regular customer if the process is unnecessarily laborious and unpleasant. This is the calculus Steve Jobs and Apple took when it launched iTunes back in the early 2000s. While it was possible to illegally download music for free over the internet, this typically came fraught with problems. By offering music at a reasonable price, but via a much more intuitive and safe user interface, Apple carved out a market that helped propel it to the stratospheric highs it enjoys today.

Furthermore, by focusing on metrics like speed and convenience, businesses get to build up customer loyalty in a way that they won’t if they focus only on price. If businesses are competitive only on price, they risk losing customers to rivals unless they push those prices lower and lower all the time. However, if they can make the buying experience enjoyable, and make sure it remains this way, customers will stick around, even if they could theoretically get a similar product for less money elsewhere.

With retail taking place online to a greater extent than ever, technology such as customer journey analytics can help businesses navigate this changing marketplace. Here are some of the key digital transformation trends currently helping businesses in the online retail space.

#1. The world of hyper personalization

In the real world, a store looks the same regardless of who enters it. The clerk behind the counter can offer personalized recommendations to customers who ask, but this is not an automatic service every customer receives. Nor is it one that scales particularly well for larger stores. On the internet, things are different.

Through the gathering of customer data, it’s possible to carry out various types of customer segmentation so as to offer intelligent recommendations. These can be based on past purchase information, online behavior, demographics, platforms and preferred channels, and myriad other approaches to segmentation. The results are that customers will experience more seamless journeys to discover what they’re looking for, along with relevant information that focuses on what they are likely to be interested in.

#2. Demand forecasting

Recommendations are great, but they’re only good for a business when that recommended product is in stock. A bookstore recommending a novel they don’t have on the shelves (or, at least, digital shelves) does nothing more than drive that customer into the arms of another retailer. Sure, they might appreciate your honesty in recommending a product you don’t have to sell them, but this will also reflect badly on you as a business. After all, if your recommended product is so good, and you know it’s likely to be in-demand, why aren’t you stocking it at that moment?

E-commerce demand forecasting can help predict future demand for products by using historical data to model this demand. There are assorted different types of demand forecasting, but the key here is that machine learning and various data analytics tools can help comb through the morass of data available to you about stock levels, fluctuating demand, and customer profiles. It can then help you manage your stock so that the right product is available when and where you need it.

#3. Fraud detection

The opposite of speed and convenience in the customer journey is fraud. Fraud can affect both customers and retailers, although both will ultimately negatively impact the overall experience of using a particular service. Fraud could take place in multiple ways, ranging from malicious code planted by hackers that steals credit card information for identity theft to affiliate fraud that manipulates sign-ups and traffic to make merchants think they’re receiving customer attention they actually are not.

Using machine learning fraud detection systems can help cut down on online fraud through means such as seeking out suspicious behavior and taking action. By incorporating these tools, retails can protect themselves and their customers against attack.

#4. Reducing churn through AI

Customer churn rate refers to the number of customers who will stop doing business with a company over a certain period of time. A high churn rate can indicate dissatisfaction with a business, potentially highlighting fundamental structural failures to be addressed. Thanks to data-driven tools such as the so-called Journey Excellence Score (JES), it’s now possible to use AI and machine learning technology to delve into customer behavioral data and predict which actions could result in a complaint or churn, among other valuable insights. Uncovering this information gives businesses the opportunity to step in and make proactive moves to address customer concerns before they become a major (potentially relationship-ending) issue.

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