ENG | 日本語
ENG | 日本語

Born

Machine Learning for the e-Commerce Industry

1 year ago

By Ashwin Kumar Ramamurthy

Statistical methods and data-driven decisions help in accelerating the performance of the e-Commerce industry. The scientific breakthroughs in Machine Learning have created a broad range of e-Commerce applications that can leverage a vast amount of data into real business value.

opt-Machine_Learning-v3-b

opt-Machine_Learning-v3-a

The most notable machine learning breakthroughs in the e-Commerce industry include:

Enhanced Customer Experience

The world of online businesses is rapidly changing and it is often challenging to improve the customer experience. Technological advancements and newer platforms rise in a myriad of ways making people’s online interaction different each time.

Here is a powerful and a new way to creatively improve e-Commerce customer experience that will eventually increase customer loyalty and ultimately sales – A process via Machine Learning.

1) Search

In most e-Commerce businesses, the total number of products in the catalog tends to increase. A rising product catalog stresses the importance in providing efficient search algorithms, because regardless of quality or price point, the application cannot generate sales if customers are not able to find the exact product they are looking for within a click or two.

As per recent studies, it is said that over 80% of the online customers use search to shop the products.

Machine learning in e-Commerce can assist with features like,

  • Search query predictions(search term completions that are auto-suggested while the customer is still typing)
  • Sorting search suggestions (sorting a list of dynamic suggestions based on the search input)
  • Image and voice-based search (based on the customer’s interests and profile information)

Since this is a data-driven process, Machine Learning can dynamically predict things based on what exactly the customer might be looking for.

2) Search Results Page

Using ML, we can get the search results to default sorting (generally ‘By Relevance’ or ‘By Featured’) based on search text as well as customer’s profile and analysis (Ex: Gender, Age, Product views, etc.). A search utilizing a customer’s profile has a higher probability of its listed products converting to sales, as the system predicts what the customer wants to view, rather than listing down all products that match a given sequence of letters.

3) Product Recommendation / Up-sells / Cross-sells

One of the great benefits of applying a product recommendation to a product page is to generate individual web experiences for each web browser. Typically, online shoppers have more affinity and trust in online businesses that give them more attention and personalization.

The product recommendation displayed is based on customers who had already purchased the bundle or the combo products along with the parent product.

The products for Product Recommendation/ Up-sells/ Cross-sells are set up in the backend of any e-Commerce application. With the help of ML,

  • An e-Commerce application can dynamically predict the mapping products based on Attribute Filtering (Recommendations that try to match the content of customer profile to the content of products) and Conversion Filtering(Recommendations for a given customer are based on what similar customers have chosen in the past)
  • An e-Commerce application takes farless time to filter and map the products in backend
  • An e-Commerce application will result in less effort seamless updates and an increased probability of sales

Through these features, ML can increase revenue, create customer satisfaction, personalize the individual’s interests, and help customers find the best products with minimal effort.

Less Maintenance cost

An important step for any e-Commerce business is a well-designed website with professional layouts and products as it represents the business’s status on the Worldwide Web. Just designing and having a website noted by the SEO is not enough. Updating and maintaining the company website with all the required latest information that eases customer experience plays an essential role in a successful value-adding website.

Hence, one of the main focus for any e-Commerce businesses is to lower maintenance costs and provide the most efficient shopping experience.

4) Catalog deviations

The most challenging part in e-Commerce is to maintain a catalog. The more products, the more complex this task becomes

ML algorithms can seamlessly reduce the deviations in the products and catalog that comes via feed. They can also reduce the overall maintenance cost and provide the following benefits:

  • Reveal product names when there is non-friendly product name. For example, from the feed, a product can have the name as the SKU of the product. Using the ML algorithm, it can predict the user-friendly name of the product from the SKU that is provided
  • Predict product name from the incomplete names automatically. For example, from the feed, a product can have a name as “Samsun Galaxy” instead of ‘Samsung Galaxy S9’. ML can predict all of these deviations using the efficient algorithm
  • Correct products mapped to an inappropriate category through automatic suggestions (Improved customer layered navigation search). For example, from the feed, an ‘iphone 7’ can have the category as ‘TV’ instead of ‘Mobile phones’. Since ML processes the huge sets of data, it can easily predict the appropriate category of a product.

Catalog maintenance provides many benefits like providing customers with better information about products, their description, and their usage. It also enhances simplified and centralized purchasing.

Catalog maintenance also helps the online business holders to yield better productivity of their products by reducing or altogether removing the deviation from the product feed.

5) Predictive Inventory Replenishment Analysis

Inventory replenishment is a standard e-Commerce business practice which ensures that the right products are in the right place at the right quantity. Replenishment is essential to avoid stock-outs and it ensures that the customer is delivered the product that they demand, in accelerated timescales. Efficient inventory control is vital for any business and even more so for businesses that function online.

Replenishment can be automated using intelligent ML algorithms based on historical and current sales analysis to forecast more accurately with complete visibility. It can be achieved for any type of product configuration such as ‘Deliver to Customer address or ‘Pick-up from store’.

6) Trend Analysis of a product

It is important to analyze the trend of a product, whether the product is a best-selling one, a normal seller or an outdated one. Trend analysis, in turn, has an impact on the procurement, be it a vendor fulfillment or fulfilled by the own business. If trends are not analyzed, then the return of goods will play a role in procurement. Hence, it is very important to analyze the trend of the product as it can reduce the overall size of the catalog, the maintenance cost of the product and increase effective space utilization if it is stored in a warehouse or a store.

Using ML, this can be easily achieved based on the Product Reviews and Ratings and other social media inputs. The respective products can be disabled at any point in time.

Improved Strategic Approach

A strategic approach is important to an e-Commerce business because it provides a sense of direction and outlines measurable goals. Strategic planning and approach is useful for guiding day-to-day decisions, evaluating progress and changing approaches to move forward.

7) Predictive Dynamic Pricing

It was initially the airlines industry to embrace the concept of automatic dynamic prices or adapting prices. On the basic level, this can simply mean to increase prices when the demand is high and decrease them when the demand is low.

In e-Commerce, there are plenty of other variables that can be used to estimate optimal prices, such as prices of competitors, time of day, warehouse stock and season. However, pricing algorithms need to be adapted for specific products to accommodate factors like marketing strategies.

ML can seamlessly bring in all of the above variables and predict accurate dynamic pricing for specific products to boost the sales.

ML can also bring in customer group dynamic pricing (focussed only for a specific group of customers) by analyzing the spending behavior of the customers apart from demand factor.

8) Improved Marketing Strategy

Marketing strategy always provides an edge over its competitors to its organization. It helps develop the best profit-making potential.

With the implementation of ML,
  • The system can predict efficient demanding promotions to wider customers or to a specific customer group
  • The system can predict suitable promotions based on product views and analytics
  • The system can help an organization make the best optimum utilization of resources
Less Operational cost

Reducing operational costs is a proven practice to gain better margins. Profit margin is a simple mathematical equation that depends on two factors: revenue and expense. To achieve a higher profit margin, we have two choices – either increase the revenue or reduce the expenses.

9) Customer Support

There are many reasons why a customer might have a bad experience with their purchase and many reasons why they might need to engage with customer support. It’s important to think about enhancing customer support just as much as we think about all other marketing efforts as it plays a key role in maintaining customer loyalty.

From one of the recent surveys, it is said that 65% of customers would remain loyal to certain applications because of good customer support.

ML provides a round the clock customer support 24/7 with no manpower necessary and an efficient help control mechanism. It can enable the following assistance.

  • Speech recognition and Natural Language Processing which eliminates the human dependency for any on-call assistance
  • Chatbots
  • Automated email assistant
  • Creation of Service Tickets
  • Appropriate priority flagging
10) Fraud detection mechanism

The volume of electronic transactions has increased significantly in the past years due to the rise of e-Commerce. Therefore, it is necessary to develop and apply techniques that can assist in fraud detection and prevention. In most of online purchases, the credit card operations are being performed by Web payment gateways.

Using ML, the pattern of data is identified and since we have more data during the study, it is easier to spot anomalies

Here are the advantages of using ML:

  • Automatic detection of possible fraud scenarios and eliminates manual work
  • Helps in finding hidden cases and implicit correlations in data apart from obvious fraudulent scenarios
  • Real-time processing

Detecting the fraudulent cases with haste and efficiency can save time, money and effort for the business.

Advances in Machine Learning have added a new layer of depth to the e-Commerce industry. For organizations to get the most value out of machine learning, they should define strategic priorities with utmost consideration. The future of the e-Commerce industry will be further shaped by the continued advancement of ML, and it behooves all e-Commerce businesses to keep pace with its innovations.

Other Blogs

This site uses cookies. To see how cookies are used, please review our cookie policy. If you agree to our use of cookies, please continue to use our site. more information