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Artificial Intelligence in the Retail Industry – 5 Common Uses

How Can Artificial Intelligence be Implemented in the Retail Industry? 
 

 

If you ask any business leader or expert in the retail industry what the future of shopping looks like, you’re bound to hear something about “bots” shaking up the way things work. Artificial Intelligence (AI) in retail is being used across the entire service and product life cycle – from manufacturing to post-sale interactions with customers. This means that retailers need to change their strategy and thinking in order to include this technology and remain relevant. 

 

In this blog post we will look at 5 ways in which AI is being used in retail:   

 

1) Product Recommendation through Personalisation 

 

Products are now being recommended to customers based on their personal buying habits. Our online buying habits create data categorised into, for example, products bought, preferred brands, money spent and frequency of buying. Machine Learning algorithms use this data to predict which items a customer is likely to buy. Once this technology anticipates the intent of the customer, it provides a recommendation in real-time with incredible accuracy. These recommendations come in many forms – for example, links to other items (“customers who bought that also liked this….”) or marketing emails that contain the chosen items that are suited to your buying patterns. Whatever the form of the recommendation, customers are more likely to buy products or services that require little extra thinking and align with their already formed self-concept. Machine Learning algorithms and personalised product recommendation allows for just this.   

 

2) Customer Service and Complaints Resolution 

 

Artificial Intelligence software can help build a strong sense of brand trust through communication – by answering questions and resolving issues. Brand trust is earned when customers are satisfied by answers to their questions and by the outcome of their complaints. Artificial Intelligence software manage multiple communications at one time – meaning no customers are kept “waiting in the queue”. In addition to less time wasted, Machine Learning “operators” can answer customer queries more accurately by avoiding human mistakes (that a “mere human” operator would make in fetching answers). Artificial Intelligence software is also able to make decisions to solve customer queries or raise customer complaints with higher management immediately – giving customers a solution without delay. 

 

3) Inventory Management – Demand & Forecasting 

 

One item out of stock can bring a business to its knees. Machine Learning time series prediction models can estimate future demand levels for all items in an inventory. Although this is useful for retailers to know, Machine Learning models are even able to make decisions based on the demand information that they produced. This is a more advanced approach and is done by rewarding and punishing the model for acting incorrectly. For example, a model is punished for letting a particular inventory item run out of stock or for stocking a higher value for too long. A model is rewarded when in-demand inventory items are ordered within a safe window before it’s too late. These reinforcement systems produce outstanding results – more than 30% reduction in inventory operations costs in many cases! 

 

4) Pricing Strategy & Product Bundling 

 

Most of time, sales made depend on the prices of the items or services being sold. Customers will wait for prices to drop for the items they want or for items to be bundled at a better price. This leads to times when sales generate high revenue for short bursts and potentially overall unprofitable sales because of special price deals. Artificial Intelligence models can be used to drive sales by targeting a particular set of customers at their “optimal price” – leading to immediate sales. Different customer sets can be targeted by AI systems by determining which product combinations and at which price will lead to immediate or more likely sales. When it comes to non-urgent items, these methods are proven to make sales more consistent. 

 

5) Strategy Testing 

 

Artificial Intelligence allows retailers to test different strategies against each other without having to implement any of them – saving them potentially lost resources. By using AI and Machine Learning models to monitor customer behaviour (through computer vision and activity maps), individual shopping experiences can be created and resources can be better allocated to areas most needed. Retailers can find answers to their common questions, for example: 

 

Which rack is most explored by customers? 
Is it popular because of location or products? 
Can the most profitable products be placed on the most popular rack? 
Will people buy if prices are increased? 
Should high priced products be shown prominently? 

 

 

These 5 ways in which AI an ML are being used in retail situations are only a few of the possibilities that are available to retailers at the moment; and best believe there will be more to come! Despite these new technologies available in the retail industry, it will continue to remain heavily competitive. Retailers need to be aware of this technological shift and consumer trends that could drastically impact their business and the industry as a whole. As always, the key to success in retail is that the customer needs, wants, and expectations are understood and accounted for. As long as this is kept at the forefront of the retailer’s mind, then a changing shopping landscape is no cause for concern.

 

Informative Use Cases

Natural Language Processing in Business – How Is It Useful?  

What is Natural Language Processing? 

 Natural language processing (NLP) is a branch of artificial intelligence that helps computers understand, interpret, and manipulate human language. NLP draws from many disciplines, including computer science and computational linguistics, in its pursuit to fill the gap between human communication and computer understanding. 

While natural language processing isn’t a new science, the technology is rapidly advancing thanks to an increased interest in human-to-machine communications, plus an availability of big data, powerful computing and enhanced algorithms.  

 

 

While we, as humans, may be able to understand and speak more than one language, most people do not have fluency in “machine code” or “machine language” – millions of ones and zeros that produce logical actions. In the past, early programmers would use punch cards to communicate with computers but now “Siri, what is the temperature today” triggers a real-time response in a human voice. This interaction is made up of multiple sections: device activation from your voice, an understanding of your command, and a response in a fluent human language. This common “conversation” is made possible by NLP thanks to machine learning and deep learning elements. 

 

Common Uses for Natural Language Processing in Business 

Chatbots 

Chatbots are one of the more recent NLP examples. Most business bots are used in Human Resources. Some NLP tools are used to deal with common employee questions, for example, “how many days leave do I have” or even deal with workplace satisfaction requests such as “we need more milk in the breakroom”. Other chatbots are used to improve employee retention and morale – employee chatrooms are monitored by bots and when employees use certain complementary words like “kudos” or “cheers” then staff get rewarded. 

 

Conversational Search 

Other non-bot tools are voice-activated. For example, tools are being used to listen in on company meetings for trigger phrases like “what are” and “I wonder.” When it hears them, a search function activates and retrieves an answer. It would work something like this: “What was the ROI on that last year?” Silently, the tool would scan company financials and display the results on a screen in the room. This can save the 30% of the day that employees spend searching for information and up to US $14,209 per person per year.  

 

Hiring tools 

On the topic of HR, NLP software has long helped hiring managers sort through resumes.  

By using the same techniques as Google search, automated candidate sourcing tools scan applicant CVs to pinpoint people with the required background for a job. This is difficult however, as humans we try to stand out from the crowd and may use creative terms to describe our skills and this can cause CVs to be ignored by NLP scans. More recently, NLP technology has branched to include synonym searching on keywords. In addition to this, as women and minorities use language differently, this synonym-searching keeps qualified candidates from slipping between the cracks.  

 

Call Centres 

Natural Language Processing (NLP) technologies are increasingly active in the labour-intensive world of call centres. Both inbound call centres (usually administer customer and product support or information enquiries) and outbound call centres (used for telemarketing, debt collection, soliciting charitable etc.) are implementing NLP tools. 

The technology used in call centres roughly falls into four categories: real-time speech recognition; intent analysis, which classifies conversations based on context to predict customer intent in real time; conversation management, which ensures simultaneous processing of multi-pass conversations; and conversational analysis, which broadly analyses users’ dialogue. These previously extensively human-intense operations (demanding training, time, and money) are now more efficient and effective – thanks to NLP. 

 

These are only a few examples of ways in which Natural Language Processing can be implemented in business situations every day. While such Artificial Intelligence technology may seem to pose a threat to employee’s jobs; this is not the case. At closer inspection, these tools are simply allowing employees to work more efficiently by having the tedious and time-consuming aspects of their roles sorted out by machines and bots. Thus, ensuring that time spent is well-spent, that employees are satisfied, customers are being heard, and ultimately, businesses save time and money!