A new generation of retail: innovative uses of AI in retail

AI in Retail

There are many diverse applications within retail, some of which have been quietly utilized under the radar for years. Others are more modern and are helping to change and improve the way retailers operate. 

Whether you’re looking at marketing, customer service or even operations, there are many uses for AI in retail. Below we have included a selection of the most innovative and impactful AI and machine learning-based tools for retailers. 

But how can retailers make the most of these methods and what are the benefits? We’ve set out all that you need to know about about these innovative new methods, with several examples of AI application in retail.

Retargeting

AI retargeting
What is Retargeting?

Retargeting allows retail brands to reconnect with previous customers who have visited their website, maybe even placed something in their basket, but for whatever reason have not completed a purchase. Using this method, retailers can target this audience with ads while browsing other websites. 

One of the benefits of this strategy is that it can be highly personalized. It showcases products and items that the customer has browsed, leading to precision targeting that has a record of converting very well. 

That said, retargeting can be fairly complex – particularly for large retailers who are dealing with massive audience numbers and product assortments. AI and machine learning solutions can help streamline these campaigns and simplify many more complex elements involved in effective retargeting. 

AI and Machine Learning Tools for Retargeting

When building effective remarketing solutions, you need to be able to scale, meaning advanced machine learning techniques are exceptionally useful – they can manage the high quantities and complexities of managing retargeting campaigns in dynamic environments. 

Some of the ways in which AI and Machine Learning are utilized in remarketing include:

  •  Pinpointing locations

    Rather than managing billions of potential ad requests across devices and platforms every day (an endeavour that would quickly become very expensive) – machine learning can predict where targeted users are more likely to appear. 

    This method uses techniques such as look-alike modelling and multivariate regressions to identify key traits of target groups. In doing so, this reduces requests to much more manageable levels while still providing accurate targeting.

  • Timing the engagement 

    AI methodologies can be used to determine the ideal opportunities to target users over the course of a day. Of course, this depends on factors such as their point in the engagement lifecycle and actual physical location – for example, if they are at or near their homes, they may be more likely to complete a purchase.

  • Predicting content performance 

    AI can also be used creatively; methods are being used to test and construct the most effective designs to appeal to target users. Previously, marketing effectiveness would be checked with A/B tests or test campaigns. With an algorithmic approach, it’s now possible to predict how an ad image will perform before it’s gone live, which ultimately helps craft a more impactful design.

  • Managing ad campaigns 

    Digital marketing is an essential part of modern marketing, however, it can be complex. That said, AI-based tools can be used to simplify various aspects. For example, removing and replacing ads that underperform, changing targeting to maximize campaign results, and rebalancing budgets for maximum effectiveness. 

    Read more about AI for remarketing here.

Recommendation Engines

What is a recommendation engine?

A recommendation engine utilizes customer data to suggest specific products and information to them in a retail setting. This data may be the specific historical data of an individual customer, or it may be based on customers’ behavior with similar purchasing behaviors (or other specific segments, as defined internally).

When you consider the vast sea of content and the huge range of options available online today, particularly for large retailers, recommendation engines become a critical way to frame their offering to better appeal to a wide range of customers.

Additionally, a recommendation engine can help support KPIs – this includes increased revenues, click-through rates, conversions, and other essential metrics. By serving up content that is tailored and targeted to specific customers and segments, creating a more positive user, experience helps create better customer satisfaction and retention in the long term.

AI recommendation engine
How to use AI for retail oriented recommendation engines?

The large quantities of data generated by online commerce activities make up the foundation for recommendation engine functionality. These data sets are crucial for understanding customers’ behaviours and preferences and using that to create effective recommendations.

It’s worth noting that data used for recommendation engines can include historical transaction data, ratings, reviews, and other individual-level data such as gender and age. This can also be combined with device-led data such as customer location and time they accessed the retailer’s website. 

While there is a high volume of data involved in building recommendation engines AI and machine learning-based methods are uniquely suited to handle this information and the complexities involved in producing recommendations. 

One of the approaches AI and machine learning methods takes to recommendation generation is content-based filtering, which creates recommendations based on the data collected from a shopper’s past transactions and buying behavior. 

For example, recommendations will be based on looking at established patterns in a user’s choices or behaviours. The content and information that is then pushed to customers can include products or services related to their individual likes or views. 

Another approach to recommendation generation is collaborative filtering, which collects information from the interactions of a segmented group of customers and uses it to create recommendations. This approach makes recommendations based on other users with similar tastes or situations, using their opinion and actions to recommend items to you or to identify how one product may go well with another. 

It is worth noting that the collaborative filtering method tends to have higher accuracy than content-based filtering; however, as it’s relying on segmented data, it sometimes leads to results and outcomes which are more challenging to interpret.

Personalization

What is personalization?

Put simply, personalization is personalized marketing. By segmenting their customer database, retailers can identify and target groups such as high-value customers, locations and marketing channels. 

While customer segmentation is not a new marketing practice, machine learning simplifies and enhances the segmentation process, allowing retailers to more effectively identify their customers and understand their purchase behaviors. Segmentation can be done on a group level, but thanks to today’s technology, it’s possible to personalize down to the individual customer level.

How is AI used in retail personalization?

Both AI and machine learning are being used to create retail experiences tailored to the individual or target customer segments. This means that artificial intelligence can show the right content at the right time for individual site visitors, based on past shopping behaviours and on-site behaviour at the time. 

As a result, applications include: 

  • Creating preference profiles that identify the types of products customers prefer, both by visual affinity (things like style, color, size, etc.) and non-visual affinity (brands, product categories, price). 
  • Understanding shopper intent when they come onto the e-commerce site uses both historical and real-time data. 
  • Using the understanding of the shopper’s intent to provide customized content and personalized recommendations for related products in real-time as they’re browsing on the site. 

AI-based approaches are particularly suited to personalization marketing. Creating personalized content and recommendations for individual customers can take a massive amount of data and operational complexity that teams would not be able to manage otherwise. 

Virtual assistants and chatbots

What are virtual assistants?

Virtual assistants and chatbots are some of the most ubiquitous, AI-enabled website features that most online shoppers are familiar with and regularly encounter. In fact, they already exist in many people’s homes and devices, thanks to Siri and Alexa! They also provide a way to provide always-on, 24/7 engagement and customer support – essentially a whenever, wherever approach to brand engagement.

How is AI used in retail with virtual assistants?

Virtual assistants, which could also be considered ‘conversational AI’, are becoming increasingly sophisticated and can manage a range of automated functions. These include:  

  • Creating an omnichannel experience by providing a level of customer service and support that matches an in-person experience, with easy communication and a seamless time to resolution process 
  • Providing product recommendations elated to the recommendation engines mentioned above, virtual assistants can work as personal shoppers of sorts, providing personalized recommendations based on an individual shoppers purchasing behaviors.
  • Virtual assistants can support order tracking can be integrated with operational functions to give real-time updates on purchases, shipment tracking, order and return status, and more. 
  • Virtual assistants can help to avoid cart abandonment by putting up notifications and prompts to encourage shoppers to carry through to the point of purchase. 

Assortment intelligence

What is assortment intelligence?

Assortment intelligence is a software-based approach used by retailers to understand their competitors’ products and inventory. Using artificial intelligence and crawling techniques, online retailers can gain a comprehensive overview of inventory differences. 

Using assortment intelligence, retailers can determine their competitors’ products and how they’re performing, which can then inform their assortment and catalogue decisions. 

This is also called assortment optimization; it can help retailers decide how many and which products should be offered in a category, thereby meeting the needs of their high-value customers and possibly attracting shoppers from competitors.

What are the benefits of assortment intelligence?

There are many benefits of assortment intelligence; just a few include: 

  • The ability to discover products/brands that competitors are offering and compare them to their own catalogue, identifying gaps and possible areas of improvement. 
  • The ability to determine which unique products and brands the retailer is offering and determine if these products are being priced appropriately based on demand and availability.  
  • Benchmarking assortments across different dimensions and combinations. This can help to determine the retailer’s focus area, as well as that of their competitors. This can be done for full assortments, as well as at the category or brand level.
  • Gauging how competitors are employing discounts and coupons, and understand if there is a sweet spot for customers for certain discounts on particular products or categories. 

Additionally, assortment intelligence can also be very targeted, allowing retailers to monitor specific competitors, brands and sets of products with filters such as colors, variants, sizes and other product features.

How is AI used in assortment intelligence for retail?

Traditionally, assortment intelligence has been determined by the point of sale and syndicated data, which poses limitations. For example, it might not factor in demographic changes, preferences, and other factors which give a comprehensive understanding of customer behavior. 

When applied to assortment management, machine learning takes retailers’ existing data and combines them with external events related to factors such as weather, holidays, demographics, and other criteria. These machine learning algorithms can then identify patterns and trends. 

When considering an AI-based approach to assortment intelligence can thereby provide a current and comprehensive understanding of shoppers’ buying patterns. Using this information, retailers can keep shelves stocked with the right merchandise mix while ensuring that their supply chain is aligned, eliminating expensive out of stock or overstock scenarios. 

AI helps perform these tasks in an automated, predictive way and happens in real-time – rather than relying on historical data that may be out of date and irrelevant to the current market and conditions. Not only does this save time, but it also relieves pressure on human-based calculations. 

Supply chain management

The importance of supply chains is crucial for retail businesses. AI and machine learning are being applied in ways that can powerfully streamline and simplify retail operations. 

Due to the scale of operations in today’s global market, supply chains are hugely complex, and a well-managed supply chain can make a critical difference to the slim margins that retailers work with. 

As the supply chain sector encompasses so many functions and roles, we will briefly touch on some of the most impactful ways AI and machine learning are being deployed to support and facilitate supply chain management.

How is AI being used for supply chain management?

One of the many benefits of using AI within retail is that supply chain planning. Machine Learning can be instrumental in managing and optimizing the complexity of supply chain planning. From production to shipping, logistics to inventory management, all of these can be supported by Machine Learning models that utilize algorithms based on available production data – helping to improve efficiency and reduce waste. 

Predictive analytics can support supply chain visibility and forecasting when using Machine Learning. When working this way, the process aids in supporting processes along the supply chain, transforming the customer experience and making workflows faster.  

Additionally, the automation enabled by Machine Learning can improve response times. These methods can more efficiently manage demand to supply imbalances, creating solutions that work in real-time. Algorithmic learning based on real-time and historical data can also help supply chain managers plan more efficient routes for their supply chains, speeding up service pace. 

This is also true regarding warehouse management. The success of supply chain planning is very reliant on effectively managing warehouses and inventory. Supply flaws, such as overstocking or understocking, can be a disaster for consumer-based retailers. Machine learning uses continuous forecasting methods, enabling continuous monitoring and improvement of matching inventory levels with customer demand, streamlining warehouse management overall.

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