Why AI takes user experience to the next level

Many industries have been swept away by the fast-growing developments around artificial intelligence (AI), which is revolutionizing businesses with rich, in-depth data about consumers and the market that our human brain might not be able to analyze. The initial reaction to AI was slightly hesitant for the fashion and apparel industry, and who can blame them. The past has shown that this industry has been built on human creativity, so bringing in facts and figures assembled by technology can be seen as an understandable concern.

Let us start by saying that artificial intelligence uses human input as a reasoning model and does not have the end goal to replace it. It is merely a tool to automate and optimize specific data analyses, establishing thinking beyond our human capabilities.

We live in a world driven by digitization, and this does not seem to change any time soon. Hand in hand with this trend goes the constant change of customer expectations, something you as a brand need to keep an eye out on constantly. Otherwise, you might fall behind with your organizational workflow, which can lead to missing sales potential. So how can you best use additional information and knowledge gained by AI?

Expert Xin Zhang from Chainbalance and the founders of the AI Design Competence Collective, Melenie Hecker and Professor Dr. Ingo Rollwagen, share their visions and expertise on using artificial intelligence and algorithmic innovation.

What is artificial intelligence?

Before we dive into the experts’ insights, let us first answer the question 'What is artificial intelligence?'. Unlike human and animal intelligence, which is natural, artificial intelligence is intelligence demonstrated by machines. AI provides software that can reason on input and explain on output. Can you feel your headache starting already? AI can seem a little scary to some because they do not 'understand' it, but in the end, it is all about logic. AI brings together rich data and can point you and your company in the right direction when it comes down to decision-making. Still, it seems scary, doesn’t it? Well, AI is already widely used in daily life. Think about chatbots, autocorrect, autonomous vehicles, facial recognition, AI searching engines, and recommendation algorithms. Every day we are confronted with some form of artificial intelligence.

AI within the fashion and apparel industry

Have you ever shopped online for a new pair of jeans and ended up buying not only jeans but also new shoes, a new bag, and four new shirts to finish the look? Ever thought about how you ended up buying all these items which you did not intend to purchase, but did anyway? Yep, that's intelligent selling, again a form of AI. The 'complete your look' method shows you displays recommendations of items that would look amazing in combination with the items in your shopping cart. These recommendations are made based on products that are frequently sold together. The software can detect this and recommend it to others because if one consumer likes it, why wouldn't a similar customer? Customers are feeding the software with factual input, and an explainable output is given. But still, research shows that globally in the fashion and retail industry, the investment in AI and machine learning (ML) is only one percent, according to the latest McKinsey & Company analysis, "Pitchbook August 2021". Despite enormous potential, the fashion industry is struggling to make significant investments in this area. In addition to the obstacles in the field, which lie in still a scarcity of adequate software - both in quantity and quality, there are challenges for the use and the practicable application area for the individual company.

So let’s ask ourselves the question: “What are the biggest challenges in the fashion industry, and how can AI help to battle these challenges?”

More time for creativity

One of the biggest challenges is overproduction and merchandise management. AI can help battle these issues by analyzing consumers' buying behavior and making production and purchase prediction decisions. At the end of the day, this cannot be done without you, the consumer!

If you do this with the help of technology, you can save up to 80% of manual work. In addition to saving time, you can also make decisions that you hadn't thought of before, such as assortment optimization or consumption-based production forecasting.

In assortment optimization, cluster analyses can help identify sales patterns within specific products and use these for similar POS. This way, you can identify hidden sales potential for products or product groups in areas where they were previously not or not sufficiently placed. Searching for patterns across hundreds of POS and even more SKUs is impossible to manage manually, so to do this successfully, you need to get some software in place to help you out along the way.

Daily data-driven software decisions can also be helpful when deciding on production quantities, e.g., for size curves per POS: What used to be done by A B C clusters can be adjusted to the future demand, individually for the store and the product, by forecasting the behavior of the product lifecycle in a reference period.

The possibilities of ML&AI are unlimited and enable people to achieve better and faster results. So, why not just put AI into everything we do? Xin Zhang, Business Intelligence Consultant at Chainbalance has the answer: It's not just about using AI just because you can. It's about serving and satisfying today's costumer with the help of AI.

Expert on board for consumption-driven decision making

Business Intelligence Consultant Xin Zhang, who graduated from Tilburg University with a master's degree in Cognitive Science and AI, has gained a few years of experience in the AI field and now applies this expertise at Chainbalance. Chainbalance helps fashion, sports, and footwear brands to optimize operational processes in merchandise management as well as production processes, using technologies such as AI and algorithm innovation.

Organizations like adidas, Esprit, and Triumph have trusted the Dutch company and its technologies for over 12 years now with massive success. Check out our case studies on our website for more in-depth information about our clients!

"In a digital world where consumers can buy anything instantly through hundreds of different channels, there is no place for manual micro decision making anymore. AI and algorithm innovation help us at Chainbalance to keep our clients fast, cost-focused, and most importantly, fully aligned with consumers and their resulting needs." says Zhang.

Smart Supply has several replenishment strategies for various stores. It would be easier and faster for our solution to figure out which approach is the most appropriate for a specific store in a certain period with the assistance of ML. Additionally, ML helps us to implement outlier detection to engage in customer performance monitoring efficiently.

The attitude-behavior gap

Consumer behavior is constantly changing, and so is the entire market. So, what does this mean for the future of AI? We asked the founders of the AI Design Competence Collective, Melenie Hecker and Professor Dr. Ingo Rollwagen:

"Major online retailers face the digital frontier of bridging the gap between their customers' sustainability attitudes and behavior. This means there is no data-based entity between consumers' opinions on ethical and sustainable issues and their purchasing behavior. Suppose in the future a quantifiable reflexive connection is found. In that case, we can use the new customer data to design completely new products and services based on algorithmic innovation, which can be produced in a resource-saving way. However, to use algorithmic innovation appropriately, the company must first clarify which problem, such as overconsumption, should be solved to define measurable sustainability goals and the algorithmic tools needed to achieve them."

What is next?

If you let your mind wander, the opportunities for AI and fashion are endless. As analysis opportunities grow, the rich data that can be extracted grows too. So, what is next for Chainbalance? Which AI solutions can we expect?

"AI needs to be fed with data, and as we have learned, data can be about anything. Not only POS data from our clients regarding sales numbers or on-hand, but we can also collect and process external data such as weather forecasts, consumer reviews, and so on used for data mining to enhance the product allocation and replenishment operations. If the forecast shows that September will not be a warm month, you replenish your autumn season sooner, for example. All this data will help you to sell accurate items battling the problem of overproduction and waste. " says Zhang.