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11 Ways to Boost Your Ecommerce Sales with Machine Learning

Boost Your Ecommerce Sales with Machine Learning
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Field Experts
Reading Time: 9 minutes

Technology is advancing at breakneck speed. ‘Artificial Intelligence,’ ‘Augmented Reality’ and ‘Machine Learning’ are the buzzwords du jour. It’s mind-boggling how they are able to do what we thought only us mortal beings could.

Not only do they do these things, they do them better. It is amazing to see how much value they are creating for businesses today.

Let’s talk about how machine learning, a type of artificial intelligence (AI) that has been revolutionizing the ecommerce industry. Simply put, machine learning is a method that uses experience to improve performance over a period of time.

Computers automatically improve and adapt their processes without any targeted programming by humans. You can always learn more about this by taking online machine learning courses.

Machine learning is helping ecommerce development companies take the customer experience to a whole new level. It is also making them more agile. Machine learning is helping ecommerce businesses generate revenue in ways that they never could previously. There are a number of ways in which the power of machine learning can unleash the full potential of an ecommerce business.

It feels like ecommerce is in a constant state of reinvention. So much has changed over the years and machine learning appears to be a solid game changer. Just to put things into perspective, here are some statistics that throw light on the current state of the ecommerce industry and how machine learning is impacting it.

  1. Statista predicts that revenue from retail ecommerce sales worldwide will amount to $6.4 trillion dollars. This shows that ecommerce will grow at a rate of 20% every year.
  2. Mobile ecommerce is growing at an even more rapid rate. It was estimated that by the end of 2018, it would account for over 70% of the total ecommerce traffic.
  3. Gartner predicts that by 2020, AI will manage more than 80% of all customer interactions.
  4. By the end of 2020, Augmented Reality will generate over $120 billion in revenue.
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11 Significant Applications of Machine Learning for Ecommerce

Let’s look at the top 11 ways you can apply machine learning to your ecommerce store.

#1 Customer Segmentation, Personalization of Services, and Targeted Campaigning

When a customer walks into a brick-and-mortar store, a salesperson usually approaches the customer and asks them what they are looking for.

He or she also makes further inquiries to understand the customer’s taste and preferences. In addition, the salesperson also observes the customer’s behavior, body language and other such non-verbal cues that help him or her serve the customer better.

When the customer has a doubt, question or concern, the salesperson addresses it immediately and encourages the customer to make the purchase. In other words, the salesperson segments the customer and offers targeted and personalized service.

Ecommerce websites do not have this luxury. Customers usually shop online for convenience rather than an experience. They usually have a specific product in mind. If they find it easily, they may purchase it.

Once they find the product, should they have any doubts about it, there is no one at that point to address those doubts immediately and nudge the customer towards purchase.

Therefore, unlike offline stores, online stores offer limited scope to provide an optimized customer experience that can drive sales and increase revenue.

In order to provide an experience similar to that a customer would have in-store, ecommerce retailers need to collect huge amounts of data and make sense of it. This is where machine learning can help. It can help ecommerce retailers run targeted campaigns that can convert prospective buyers into actual ones.

#2 Optimized Pricing

Online shoppers are usually very price-sensitive. If a product costs as much as it does in-store, customers may feel more comfortable going to the store and assessing it first-hand before purchasing it.

It is also not uncommon for shoppers to compare the prices of products across various ecommerce platforms to find the best deal.

Ecommerce businesses have found much success by implementing dynamic pricing. Machine learning can change and readjust prices by taking into account various factors all at once.

These factors include competitor pricing, product demand, day of the week, time of the day, customer type, etc.

#3 Fraud Protection

Chargebacks are every ecommerce retailer’s nightmare. Most buyers, especially first-time ones, have the impression that ecommerce companies are not secure enough.

Ecommerce companies are vulnerable to fraudulent activities. ecommerce retailers must be very careful. It is not uncommon for businesses, especially online ones, to shut shop owing to a bad reputation.

Businesses must therefore not cut corners when it comes to detecting and preventing any kind of fraud. Machine learning can eliminate the scope of fraudulent activities significantly. It can process reams of exhaustive, repetitive data speedily and can nip fraudulent activities in the bud, by proactively detecting any anomalies.

Not all shoppers are great with keywords. Not all search is intelligent. In order to make a purchase, shoppers must be able to find what they are looking for. Not just that, they must be able to do so easily.

You may have every product under the sun on your ecommerce website. However, that will do you no good if the customer cannot find what he or she is actually looking for in a convenient way.

Search results cannot be based on keywords alone. Machine learning can reveal patterns in search, purchases and preferences that enable optimal search results. Search results based on these factors can show customers exactly what they are looking for and also suggest similar items.

#5 Product Recommendations

Shoppers may walk into a store knowing what they want. However, an excellent salesperson can anticipate customer needs and recommend products even before customers realize that they need them.

Product recommendations can increase revenue substantially. This becomes tricky to achieve on an online platform as it requires identifying patterns in sales and shopping behavior.

Many ecommerce retailers have leveraged machine learning to successfully create a product recommendation engine.

Product recommendation from target
An example of a product recommendation engine

They are able to identify trends in buying behavior to suggest suitable products to a shopper. McKinsey and Company found that 75% of what customers watched on Netflix were based on product recommendations. 35% of purchases made on Amazon were owing to product recommendations.

#6 Customer Support

In this competitive business environment, customers do not just expect a good product. They also assess the quality of customer support.

Most customers dread calling those toll-free helpline numbers, listening to endless menu options and struggling to connect to an actual person who can help them. Nobody looks forward to delayed and impersonal email responses received from customer support IDs.

For most organizations, staying on top of customer service requests can be very challenging. Automating customer support and enabling self-service can help the retailer as well as the customer.

Machine learning can be used in many ways to help customers and enhance customer satisfaction. A great example is the use of chatbots.

Chatbots can identify and resolve issues by conversing with the customer in a natural manner. Machine learning can help businesses offer superior, personalized customer support on a large scale.

#7 Managing Supply and Demand

All businesses resort to forecasting in order to match demand with supply. To forecast well, ecommerce retailers must base their decisions primarily on data, among other things.

To make sound data-backed decisions, businesses must process as much data as possible. It is also important to ensure that the data is accurate and that it is being processed correctly.

Machine learning can process exhaustive amounts of data accurately and much faster. Machine learning can also study data to provide as many insights as possible. This enables not just forecasting but also helps online businesses improve their products and services.

#8 Predictions About Customers

You can use machine learning to learn various things about viewers visiting your website and making purchases. In fact, artificial intelligence can help business owners find out how likely viewers are to purchase from you again or what catches their interest.

An example of Omnisend’s Customer Lifecycle stage mapping

Let’s discuss what machine learning for ecommerce can predict:

Customer Lifetime Value Prediction

In order to enhance your messages and communication, it’s vital to know how much money a customer might spend in your online store over the entire estimated period that they would be your customer.

Artificial intelligence in ecommerce can also help you identify which customers are the most valuable and deserve special attention.

Predicting if a Customer Will Make a Purchase

Picture this: you have an online shop that sells office supplies, and one of your customers orders a certain amount of paper every six months.

While it’s likely that store owners won’t notice their online shopping behaviors, your machine learning algorithms will.

Your machine learning tool can easily calculate when the right time will be to provide a customer with an incentive like a personal discount thanking them for their loyalty. This way, deep learning ecommerce helps boost ecommerce sales by encouraging the customer to increase average order value.

Predicting Customer Return and Purchases

If, based on a customer’s data, they are likely to return to your store, then a different marketing message with dynamic pricing may resonate better with them.

Deep learning in ecommerce can predict whether this is true. In turn, they can initiate enhanced workflows with messages targeted towards reinforcing branding and target loyalty.

Client Size Prediction

Artificial Intelligence uses average order value, purchase frequency, inventory management, number of employees, and company type to estimate the perfect size of the client base for you.

This provides you with deep insights into determining which potential customers to provide special attention to. You may offer personalized offers and deals that are cost-efficient in the long term.

#9 Curating Relevant Marketing Campaigns

As consumer expectations continue to grow for assistive experiences, personalized and relevant artificial intelligence becomes an invaluable tool for marketers.  

With machine language, business owners can create segmentations, enhance search engine optimization, ensure fraud detection, and measure performance effectively. 

In fact, research reveals that 85% of executives believe that artificial intelligence allows them to acquire competitive advantages. Another of the many machine learning use cases in ecommerce is curating marketing campaigns. Here are a couple of ways you can enhance your marketing campaign:

  • Develop better products and services
  • Optimize your content
  • Improve personalization
  • Reduce marketing waste
  • Engage with your customers
  • Improve the customer experience

Here are a few ways machine learning can help marketers boost email campaign effectiveness:

  • Content creation: writing personalized subject lines to boost user engagement
  • Data segmentation: defining a set of guidelines for sending emails to potential customers
  • Timing: using customer data to determine the perfect timing for sending emails 
  • Delivery: improving the reputation of the sending domain in order to ensure all emails are appropriately delivered

#10 Omnichannel Marketing Boosted by Machine Learning

We already know that omnichannel marketing makes for higher customer retention, higher purchase rate, and more engagement. There’s no denying what it can do for ecommerce.

However, given that omnichannel marketing is centered around customer data, more data can only improve the way it works for your online store.

Given that machine learning works based on gathering data and improving algorithms over time as more data is added, your omnichannel marketing strategy can only be made more powerful with this constantly updating data.

Omnichannel vs Multichannel

For example, imagine putting an omnichannel marketing automation workflow in place and having the channels automatically selected based on how the customer engaged with them in the past.

Or perhaps a workflow automatically reordering itself to send the perfect message that will best resonate with your customer based on how they’ve shopped or browsed recently.

Not only that, but your data will automatically update and learn based on how your customer behaves over time, the more data it compiles, the better it becomes.

The fact is that online search and visual search have come a long way. Unfortunately, in most cases, site searches on ecommerce stores don’t work well.

Unless you know the exact item, it can be frustrating to find the product you need.

Intelligent algorithms and deep learning in ecommerce make smart searches super easy to deliver.

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Conclusion

Machine learning in ecommerce is here to stay. As we discussed, it has some powerful applications in ecommerce. If you want to grow the efficiency of your ecommerce operations, you may be interested in checking out the Machine Learning Course.

More and more ecommerce retailers are embracing machine learning and deriving much value from it. For businesses looking to automate tedious, labor-intensive and costly manual processes, machine learning can be a huge asset. It can empower online retailers with meaningful insights about their customers.

They can help online businesses generate more clicks, convert prospects into customers, retain them and even build strong customer relations.

Author Bio:

Himanshu Singh is a Marketing Specialist at SoftwareSuggest. He is well versed in software platforms like ecommerce platforms, project management, document management.

He is also interested in domains like machine learning and semiconductors. In his spare time he enjoys guitar, badminton, and photography.

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