Fast fashion retail industry: challenges and AI solution

High amount of unsold products

A big number of unsold items is one of the most important issues, that fast fashion industry faces today. The point is, that it is very hard to decline costs in order to increase profits due to the big amount of changes, that are needed to be implemented into the production, logistics, operations etc. But sales can easily be increase by reducing the amount of unsold product. If we take into consideration, the fact, that traditional retailer loses up to 25% [1]on their sales only on the items, that did not satisfy the needs of consumer, it is obvious, how big this problem is, and that it is definitely needed to be solved.

As an example, the fashion giant ZARA was hit hard in mid-2018, after accumulating more than $4 billion in unsold inventory, [2] forcing significant discounting to clear out the goods. The effect of this resulted in unexpected reductions in profits for the sixth straight quarter.

Low responsiveness to consumer needs

One of the biggest marketing strategy drawbacks of every fast fashion retailer is a low responsiveness of the brand to the consumer needs. Usually it is caused by weak learning of consumer behavior and, especially, of their propensities. Because of very intense rivalry, potential customers are switching to their competitors, because they find what they need there. Thus, the number of products, that was produced do not find their customers and leads to the high level of overproduction and loss of potential sales. So, as soon as fast fashion retailers will improve its customer learning process, it will be able to meet more client’s expectations and thus will gain more competitive advantage on the market.

Low consumer loyalty

Fast fashion retail industry is not the market with highly loyal customers. However, the loyalty here still plays the very important role. In this market, consumers search for the latest trends and first of all they go to the retailer, where it is more likely to find the peace they need. So basically, the retailer who produce clothes with high responsiveness to consumer need, wins this customer to the next potential purchases. Due to the fact, that in that market the classic and traditional loyalty programs do not play the crucial role to hold consumers, the retailer should be very responsive to the clients wants and be well informed about all the fashion trends at the moment. Basically, this problem is directly connected with the previous one about low responsiveness. So, if the previous problem will be successfully solved, it will lead to the improving of the customer loyalty issue.

Fast changing fashion trends

Today big high fashion houses create a numerous collection during the year. Further, celebrities and influencers have a large impact on creating the trends and influencing the minds of people in order to stimulate the purchases. So, fast fashion retailers should always follow up.

The fast fashion business model is based on reducing the time cycles from production to consumption such that consumers engage in more cycles in any time period. For example, the traditional fashion seasons followed the annual cycle of summer, autumn, winter and spring, but in fast fashion cycles have compressed into shorter periods of 4–6 weeks and in some cases less than this [1]. Marketers have thus created more buying seasons in the same time-space.

The problem here consists of two main issues: time spent on total production and accuracy of analysis.

How AI can be used in fashion retail?

Let us consider one of the the crucial problems, which were discussed above of fast fashion industry- high amount of unsold inventory. On the example of this problem the decision tree was built in order to illustrate, how AI can solve the issue.

On the figure 2 it can be seen, that the problem of overproduction in fast fashion industry is caused by 4 possible factors: poor quality, price higher, than expected by consumer, mismatch with consumer demand and inaccurate targeting policies. Every sub factor is leading us to an understanding, that a solution of these problem is lying in the segment of pricing strategy, consumer behavior and decision-making analysis and analysis of external factors, such as competitive environment and overall global industrial trends fast changing. All these factors are connected with a process of data collection. It can be collected in two ways: by people and by digital technologies. Today most companies are still using people for data collection, analysis and forecasting. Some companies have started to introduce innovative solutions.

Introduction of AI solution into fast fashion retail industry will improve the process of data collection and will have several advantages:

Figure 1. Decision tree of AI solution for unsold inventory problem

Source: created by an author

• Faster data collection
• More accurate analysis
• More accurate forecasting
• Faster decision-making process within the company
• Better targeting
• More personalized offer

Overall, it will lead to the solution of current problems, which occur in front of the companies, which operate in fast fashion industry.