To ensure the best outcomes and profitability, businesses that sell online must ensure the highest cart values for their customers. Cart value is the value of products in a shopping cart when they check out. Two strategies businesses have used extensively in the past and that continue working well are upselling and cross-selling. Start using machine learning yo drive cross-selling and upselling in online retail following this strategies guide and best tips.
Understanding Cross-Selling and Upselling
Cross-selling is recommending complementary or related products depending on the items a customer is buying. Let’s say you are a web developer who needs a new laptop and wants to buy it from Amazon. You will likely see a mouse, monitor, external hard drive, and other complementary products somewhere on the product page. This is cross-selling.
Upselling, on the other hand, is suggesting premium or more expensive versions of a product to increase the order value. Say you are buying a monitor for your web development agency. You might see a 4K option of the same or another model advertised on the page.
Businesses need to know what products a customer has added to their cart and work out what products to cross or upsell. You can do the same in your business using machine learning for cross-selling and upselling on online retail to boost profits.
The Role of Machine Learning in E-commerce
Machine learning is such a powerful tool in ecommerce because it can analyze vast amounts of customer data. It can use past purchases, current customer behavior, previous order values, the items currently in a shopping cart, and other data to identify patterns and make correlations that humans might otherwise overlook.
Once a business decides to use machine learning algorithms for cross-selling and upselling, it has several algorithm options. Examples include unsupervised or supervised learning and reinforcement learning.
Once they choose a machine learning algorithm that would serve their use cases best, businesses can turn to cloud computing providers to access the GPUs they need for these computational tasks. GPU servers are perfectly suited for this because of their parallel computing features and mathematical calculation abilities.
Understanding the above, where can your online retail business leverage machine learning when cross-selling and upselling?
Using Machine Learning for Personalized Recommendations: Online Retail
Upselling and cross-selling only work when the recommendations are things that interest the customer at the current time. The recommendations also have to be highly personalized and targeted to be effective.
Creating such recommendations starts with customer segmentation. Online retailers already have the data needed to group their customers based on purchase history, demographics, browsing behavior, and other factors. Machine learning algorithms can sift through all this data, segmenting customers as it goes.
The next step is product association. Recommending a kitchen appliance after a customer adds a monitor to their cart is a great example of where a business or developer did not create great product associations.
When creating these associations, a business or developer must identify:
- Products customers purchase frequently together
- Products bought by similar customers
- Complementary products a customer might need but not know it yet or have not considered
Businesses can also use machine learning for real-time recommendations. Although this is more computationally demanding, studies show it is very effective when cross-selling and upselling and it drives billions of dollars in additional revenue for companies like Amazon.
Leveraging Machine Learning for Predictive Analysis in Ecommerce
Predictive analytics uses data analysis, artificial intelligence, machine learning, and statistical models to predict future events. Businesses already use predictive analysis to determine things like product demand and required inventory levels. However, they can also use it in cross-selling and upselling.
Predictive analysis is a potent tool for churn prediction. Churn is the number of customers who leave a retailer and never return. Businesses can identify these customers and offer targeted cross-selling and upselling opportunities. By doing this, they can increase the likelihood of retaining these customers.
Also, these customers become more likely to return because they will feel the retailer “understands” them and can find whatever they need when they need it, even if it’s products they might not have thought about yet.
Businesses can also leverage machine learning for purchase predictions. Predicting the likelihood of a customer purchasing a specific product or product in certain categories is a great way of knowing which products to cross-sell or upsell to them.
Dynamic Pricing: Machine Learning ForOnline Retail
There has been a lot of discussion about dynamic pricing in the last few months as more retailers have admitted to using it as a marketing technique. Many people do not understand that businesses have been using this tactic to increase revenues since the dawn of commerce. The only difference is that they are now doing it using machine learning algorithms.
With the data they have, businesses can determine how sensitive customers are to price changes and how likely they are to purchase a product depending on its price.
They can then set a price that ensures profits for them while also increasing the likelihood of a customer adding that extra product to their cart. All of this is possible with the right type of data analysis, an area machine learning algorithms excel at.
Many businesses are also now offering personalized pricing. This entails offering different prices to different customers depending on their perceived value. For example, a customer purchasing a monitor for their web development agency is more likely to pay more for a 4K monitor upsell than one purchasing it for gaming.
The former sees it as a business expense or investment so they do not mind paying more. The latter likely sees the same monitor as an entertainment expense, so they might not be swayed to pay as much as the web developer.
Challenges In Using Machine Learning for Cross-Selling and Upselling
Even though machine learning is great at these tasks, businesses must be aware of the challenges that can arise when using it like this. The main challenge is data quality. Accurate and up-to-date customer data will produce the best results.
They should also consider algorithm complexity. The algorithm should not be so complicated that it takes too long to make product recommendations and should also be robust enough to produce the results the business is looking for.
Online retailers can leverage the power of machine learning to create personalized shopping experiences. By doing so, they can increase overall order and cart values, increase customer satisfaction, and drive business growth supported by smart cross-selling and upselling.
The post Using Machine Learning to Drive Cross-Selling and Upselling in Online Retail appeared first on Visualmodo.
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