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top-5-popular-use-cases-in-fashion

Top 5 Popular Use Cases of AI in Retail

Artificial intelligence has already been applied to the fashion industry in so many ways, it’s getting harder to distinguish what’s AI-driven and what’s not anymore. We are just at the start of something much more incredible, but the recent advancements in AI have allowed many fashion retailers to boost their business.

 

From increased customer service to better predictions of changing consumer demand, we look at some of the most popular use cases of AI in fashion. For any company that has not looked into any of the practical applications mentioned below, we highly suggest stepping up your game.

 

Recommendation Engines

If you’ve shopped on Amazon or watched a video on YouTube or Netflix, only to see a list of items that are recommended to you with the tag ‘You Might Also Like,’ you’ve encountered what is known as a recommendation system in the world of AI.

 

As far back as 2013, it was reported that 85% of Amazon’s sales revenue stemmed from personalized recommendations. With whooping numbers like that, it is no wonder this has started to propel many organizations to follow in their footsteps, seeking out ways to make greater recommendations to their customers.

 

In a report by Salesforce, personalized recommendations are said to make up 27-percent of retail site revenue. This is not surprising considering that these AI systems are helping customers shop for what they want and buying things that are specific to their needs. Advanced personalized recommendation systems look to your data and can help suggest items – from color, size, texture, material, fitting – that will be perfect for you.

 

Chatbots

If you’ve shopped online, you probably have come across a chatbot. Usually integrated at the bottom right corner of the page, a chatbot is a form of artificial intelligence that holds a conversation with visitors through audio or textual means.

 

Although the technology has been around for quite some time, through natural language processing algorithms, and greater advancements in AI, chatbots have become stunningly more accurate.

 

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Furthermore, many organizations are seeing the convenience and power that chatbots provide as having one installed in your backend allows for immediate, rapid response. For example, according to a study done by IBM (a leader in the chatbot space with their IBM Watson), the company Autodesk was able to cut resolution time from 38 hours to 5.4 minutes for “most Tier 1 inquiry.

 

No matter how many people you have on the clock responding, there is no way humans can catch up to this speed. To substantiate the efficiency of chatbots provides, another study conducted by Harvard Business Review revealed 24 percent of companies take longer than 24 hours to respond. This is incredibly surprising, as that is a treasure trove of leads and business opportunity thrown out the window.

 

Besides Google IO, most companies do not have a truly powerful auditory chatbot. However, with the way technology is advancing, it is definitely something that we will begin to notice in the foreseeable future.

 

Predictive Analytics

Fashion is all about change. Trends are constantly popping up and consumers – with their lowered attention span – are rapidly moving onto the next, best thing. For that reason, gone are the days of second-guessing what products might be popular. Instead, retailers are finding the power of predictive analytics in their business to do a lot for margins.

 

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In the past, forecasting methods provided nothing more than the ability to predict sales a few weeks ahead, and this was solely based on historical sales data. However, modern AI-driven predictive analytics solutions do so much more, using large amounts of big data from multiple sources.

 

Using advanced machine learning, companies have the ability to predict the best and worst sellers out of their inventory. Not only does this allow companies to make smarter decisions on what items to replenish, but it can also help reduce costs on over-ordering – an issue that many retailers in general face.

 

Inventory Management

As an extension of predictive analytics, one of the most popular use cases of AI in fashion retail is helping manage inventory. Often times, fashion retailers have a lot of their capital linked to their inventory. Finding the right balance of ordering just enough to keep the business moving, but not waste away your cash flow is a difficulty retailer have faced for decades.

 

With the right AI-solution, retailers can be sent recommendations that give information on when and how much to replenish of what product. This will significantly reduce markdowns, minimize a ton of waste, and in turn, help increase margins.

 

Pricing & Promotion Strategy

Another interesting and common way that retailers are applying AI to their fashion brand is to get insights into what their competitors’ pricing is at. Using machine learning, retailers are able to compare between prices of different products, which ultimately give them the ability to align their pricing points competitively.

 

Due to e-commerce and the ease of finding the right price through self-research, consumers are used to being offered the best price for an item. Unless you are a high-end, reputable brand, retailers do not live in a time where they can demand a price for a product without question. There is a lot of risk of doing this and not adjusting prices accordingly – the biggest being losing customers.

 

With advanced analytics solutions, businesses can blend the art of pricing with a bit of science to make the best pricing strategy. AI services don’t necessarily mean an easy ‘go-to’ solution to getting the perfect price. Instead, it acts as a way to get deeper insights. The decisions that are made afterward are then up to you.

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