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Deep Learning Reshaping eCommerce: 10 Ways Web and Mobile Will Never Be The Same
Offer Optimisation
Gonca Gulser
Co-founder
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Deep Learning – it sounds like something you’ll need a wetsuit and dive tank for. Luckily, if you prefer dry land, you can keep your snorkel safely stashed away. Deep Learning has nothing to do with sea legs or underwater exploration. It’s a method in artificial intelligence which teaches computers to process data in ways and patterns inspired by the function of the human brain. That may sound like something out of a sci-fi movie, but that’s far from being the case.
Deep Learning (DL) is already a reality and, far from inspiring robot uprisings, it’s proving extremely beneficial to ecommerce and digital retail.[1]
From automated product recommendations to enhanced customer segmentation we’ve pulled together a few examples of AI & DL in e-commerce (10 in fact). We’ve also shared how they’re changing the face of the digital retail landscape so you know exactly how they could give your business a competitive edge.
1. Improved Product Recommendations
If you work in retail, you’ll probably have wished that you could read a customer’s mind more than once. Deep learning does not make it possible to read minds, obviously, but it comes close enough. That’s thanks to the accuracy with which it can predict which products customers are likely to buy.
Improving product recommendations by analysing customer data - things like the customer's browsing history, purchase history, and even social media activity - is one of the most significant ways Deep Learning algorithms have revolutionised ecommerce.
Crunching this data means those algorithms have a keen understanding of what a customer is looking for. That makes it easier for websites and mobile applications to suggest suitable products tailored to their needs.
A smarter recommendation engine for e-commerce doesn’t just make your life easier. It makes the whole shopping experience better for your visitors. They take some of the research out of product research and put the shopper on the fast track to making a purchase. It’s a win-win.
2. Dynamic Pricing
Your prices aren’t chiselled in stone. Demand, competition, and customer behaviour all play a part in adjusting what you charge and whether that price is higher or lower for any product on any given day. To stay a step ahead of the competition, dynamic pricing adjustments are essential. If your site or app offers a product at a noticeably higher price point than a competitor, it’s easy to see how that’s going to hurt your bottom line.
Deep learning is already being put to work across the retail landscape to implement dynamic pricing strategies. Amazon for example uses dynamic pricing to optimise product prices as frequently as every 10 minutes. The addition of this tactic led to a 143% increase in profits between 2016 and 2019. DL analyses relevant factors in nanoseconds and adjusts pricing in real-time to maximise sales opportunities across the web and mobile shopping apps. Handling this task manually would be a herculean effort and leave you wide open to over or under charging.
3. Offer Optimisation
We just saw that thanks to sophisticated algorithms and neural networks, Deep Learning can predict customer behaviour and preferences with incredible accuracy. This insight means you can effortlessly tailor your offers to each individual customer, increasing the chances of recording a sale and making it much less challenging to build customer loyalty. That last point is especially important right now because brand loyalty recently hit an eight-year low[2].
Let’s put this into context. Growing sales from an existing customer is cheaper than winning a new customer. But it’s like the Wild West out there. Your customers are being bombarded with tempting deals from rivals desperate to make their own sales.
DL analysis of a customer's past purchases, browsing history, and social media activity can determine what products they are likely to buy in the future, so you don’t have to navigate that battleground alone. Your audience engine* means you’ll know what your customer wants even before they do. No matter who they are and what they are shopping for, you can serve tailored, hard-to-pass-up suggestions and offers, at any time.
Starbucks is one example of a brand doing offer optimisation well. Users of its rewards app are often sent offers, tailored to each person’s buying habits, to encourage them to return to store. Offers could be anything from buy one get one free, free in-store refills and extra reward points on orders placed before a certain period of time.
*Not sure how an audience engine works? We’ve got you covered here.
4. Chatbots and Conversational AIs
Providing 24/7 customer service isn’t always possible and customers don’t often like having to discuss their grievances with a machine. So, what’s the solution? Easy - a machine so advanced it comes across as human![3]
Deep learning-powered chatbots and virtual assistants can understand natural language and respond to customer inquiries in a conversational manner. They can provide personalised recommendations and resolve issues quickly and efficiently. The good thing about DL is that it’s continually learning so your chatbots will only get better.
There are plenty of examples of brands using chatbots well. The food box delivery service Hello Fresh is one such example. Its chatbot makes it easy to make account-based changes - you can ask it when your delivery is expected, report a missing delivery, make changes to your account, ask payment related questions or flag up a poor quality item.
5. Fraud Detection
Retailers have had to deal with fraud since the dawn of ecommerce and its only getting worse. As ecommerce continues to grow, so too does the risk of fraudulent activity. Fortunately, Deep Learning has emerged as a powerful tool for detecting and preventing fraudulent transactions on websites and mobile apps.
While your favourite TV detectives may solve cases on gut feeling alone, DL doesn’t rely on hunches. Advanced algorithms analyse vast amounts of data to identify patterns and anomalies that may indicate fraudulent activity. Those red flags could be anything from an unusual purchase pattern to a suspicious IP address. This technology can not only improve security for retailers but also protect customers from fraud and identity theft.[4]
6. Driving Deep Insights About Customers and Products
You’ve got this far so it should be obvious that Deep Learning is changing the game for ecommerce and digital retailers. It provides businesses of all sizes with useful tools to stay ahead of the competition and deliver an exceptional customer experience.
Deep Learning can also help retailers extract valuable insights from customer data collected from websites and mobile applications to continue that cycle. This data is invaluable to understand behaviours, preferences, and the needs of customers. It also makes it much easier to identify significant patterns and trends in customer data which can be fed into product design and pricing strategies.
7. Journey Analytics
One of the most significant ways that Deep Learning is changing ecommerce and digital retail is through journey analytics. By analysing the path to purchase, retailers can gain valuable insights into behaviour and preferences. Which products or pages are most frequently visited? How long do customers stay on each page? Which pages lead to the most conversions? Deep Learning algorithms can identify these patterns, and then use that knowledge to optimise the shopping experience for each customer.
8. Improved Product Search Results
None of us like to spend hours searching for that one product we need. Deep Learning removes that frustration by improving the product search functionality offered to shoppers on mobile apps and ecommerce websites.
This brings us back to understanding customer intent. The Deep Learning algorithm understands what a customer is really on the hunt for. It then uses this understanding to provide more accurate search results. The shopper isn’t forced to scroll through endless not-quite-right products. Instead, they are quickly and efficiently presented with items that fit the bill. If only real life were so easy.
9. Personalised Shopping Experiences
A good e-commerce recommendation system is a bit like having a quiet, discreet personal shopper waiting in the wings ready to help choose the perfect gift or outfit.
If we had to narrow down the benefits of DL to just one thing, it would be its ability to analyse behaviour and preferences and then create personalised shopping experiences for each customer.[5]
Powerful, real time data analysis capability means DL is that personal shopper. Its algorithm dynamically personalises searches, offers up suitable product recommendations, and customised results. It gives customers not only the product they want, but products they are likely to be interested in, even if they haven’t tried them before.
10. Supply Chain Optimisation
Supply chain optimisation is one of the most significant factors in determining whether a retail business remains profitable. More accurately forecasting demand, improving logistics, and reducing waste all have real bottom line consequences.
Deep learning can help retailers predict which products will be in high demand and adjust their inventory accordingly. It can reduce the likelihood of stockouts and overstocking. It can also take shipping times, delivery routes, and other factors into account to streamline logistics.
Brands like DHL and Amazon use deep learning across their logistics network to improve fulfilment, route planning and avoid delays. Amazon’s warehouse-based robot army is also powered by deep learning technology. They have been used to maximise storage space in Amazon warehouses and speed up the process of locating misplaced inventory.
Learn How Quin's No Code Deep Learning Can Boost Your Growth
It’s clear. You simply cannot afford to ignore the impact of Deep learning. As an audience engine for ecommerce, QUIN sits right at the heart of your tech stack. It effortlessly integrates with other tools to trigger appropriate actions to help your business grow. Schedule a discovery call with us to find out more about using QUIN’s no code deep learning to boost your growth.
Sources:
[1] https://aimarketingspot.com/ecommerce-ai/
[3] https://venturebeat.com/ai/how-ai-powered-conversational-commerce-transform-shopping-2023/
[4] www.databricks.com/blog/2021/07/08/four-e-commerce-challenges-that-can-be-addressed-with-data-ai
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