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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.

 

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Winner of the Differentiation Partner of the Year Award 2019

It happened! Intellify is officially the winner of the APN Differentiation Partner of the Year Award 2019.
It is a great honour to have received this award – being up against so many innovative industry leaders in Australia.

We confidently attribute our success thus far to the hard work of our employees who thrive on providing the most innovative and effective AI and Machine Learning solutions to businesses across multiple industries. We would like to thank our clients and partners for the business and support as well as AWS for this exciting opportunity.

Being a member of the global AWS Partner Network (APN) means we were up against tens of thousands of successful businesses who also use AWS Cloud solutions. The APN awards recognise industry leaders across the local channel that are playing a key role in helping their own customers drive innovation using the vendor’s cloud platform. The event also recognises partners whose business models continue to evolve and constantly drive costs down, while modernising for customers’ benefits. Our award, the Differentiation Partner Award, recognises partners that have developed an innovative capability on or integrated with AWS and this year Intellify has been recognised as the best in this category.

We look forward to keeping up this level of innovation and hard work in the future and hope to be able to participate in many more opportunities like this one going forward!

Take this as an opportunity to check out more about what we do and book a consultation with the official leaders in the industry – http://www.intellify.com.au/contact/.