How Data Science Can Increase E-commerce Profits

Want to increase your sales? Here’s a detailed case study on how e-commerce companies can leverage profits with machine learning algorithms.
You can find a detailed guide here - https://www.romexsoft.com/blog/ecommerce-conversions/

Transcript

1. How Data Science Can Increase Ecommerce Profits
2. PRESENTED BY TARAS HISHCHAK Data Science Specialist, Romexsoft
3. E-commerce is becoming a very crowded space. Competing businesses can sell their products all over the planet, and getting a good piece of the marketplace is harder and harder to accomplish.
4. Most e-commerce entrepreneurs have mastered content marketing. They understand the concepts of building relationships with customers, of keeping each content marketing platform engaging and up-to-date. They are even moving into geo- location and personalization with their content outreach. And still, they are not able to increase sales performance for all of their efforts.
5. THE ANSWER LIES IN ECOMMERCE ANALYTICS AND DATA SCIENCE
6. Why Data Science? Data science has been used to group you with customers who may be of the same age range, the same sex, and with the same interests that you have. Data science is tracking your behavior and offering other potential purchases to you, based upon all of these factors. Chances are you will look at those other products, may purchase one or two, or at least be aware that they exist so that you may return and purchase them.
7. Did you know? Big data analysis allowed Amazon to customize its website in real time, just for you. And it can do much more.
8. WHAT DATA SCIENCE CAN FIX FOR YOUR BUSINESS?
9. The problems ecommerce businesses face are pretty typical: Low conversion rates High bounce rates Cart abandonment Lack of customer loyalty, etc. Sounds familiar? Then find out how your business can increase its revenue, user by user, customer by customer.
10. CASE STUDY: BOOSTING CUSTOMER LOYALTY AND THE AVERAGE CHECK WITH BIG DATA
11. Online retailer came to Romexsoft with a problem: He has a large line of casual and sports clothing and shoes for people of all ages, for both genders, and for style preferences.
12. What he was discovering was: He could get a customer “in the door,” and often get a purchase. But most customers were not “coming back for more” and/or purchasing other products that would suit them.
13. What he wanted from Romexsoft was: A full analysis of what he could do to change his customers’ behaviors and move them to purchase more. So what we did?
14. First stage: Problem: Big number of pages which were obviously least popular, those pages that resulted in the most bounce rates, most and least popular products, based upon the correlation between views and actual purchases. Analysis of the Site Structure Itself
15. Analysis of the Site Structure Itself Example: Several shoe products that the retailer was considering discarding. While there were many views, the proportion of purchases was quite low.
16. What we discovered through our analytics: The problem was not the product – the problem was the pricing. Analysis of the Site Structure Itself
17. Going deeper: To prepare for deep analysis, we had to first organize products based upon type (e.g., shirt, shoes) sex, age groups, their purpose (casual or sport), brands/pricing, and a full history of the numbers of views of each product page and the information that was provided on that page. We generated more than 150,000 records of data to test. Generating The Test Data
18. Statistical Analysis and Machine Learning Using data science with Java and Apache Spark, we applied an item- to-item correlation filtering system recommended by Amazon. What this means is as follows: Each product was described by its type, sex, age, brand and purpose. We filtered by three variants – the item code, the product code, and the “rate” which we defined as click-throughs to that product.
19. Statistical Analysis and Machine Learning We were then able to generate data on actual customer taste. Here is a sampling of that data:
20. Establishing Predictions for Customer Rates Based Upon Actual Rates Next, we wanted to generate data that would tell us the predicted rate (click throughs) of customers who looked at more than one product, if they were shown similar products. This is a sampling of that data: This first chart shows a customer looking at a specific product and the actual product rate (number of times the customer actually clicked-through).
21. Establishing Predictions for Customer Rates Based Upon Actual Rates This next chart shows the same customer and the predicted product rate if shown similar items: What this data science machine learning tells the business owner is that he should be showing individual customers similar products, which customer might not even heard about but which will suit him the most.
22. Predictions of Product Presentations/Ratings Based Upon Customer Groups Now that the retailer knows he will be presenting similar products to his customers, the next data science challenge is to determine the products to present. The following chart is an example of what this data report will show, based upon six additional products that should be shown to each customer, along with predicted ratings.
23. Predictions of Product Presentations/Ratings Based Upon Customer Groups Based on the existing data, we can also determine the potential buyers for a certain group of products or a certain brand even if they did not express any prior interest in some particular brand. As a result, we can narrow down the potential buyer segment that will feel interested in a certain group of products:
24. Predictions of Product Presentations/Ratings Based Upon Customer Groups The concept is simple: Customers’ who have completed specific purchases in the past, and those purchases have been similar to those of a group of customers, then future purchases can be predicted. Using real data of these purchases, and applying machine learning for data science, the business owner can customize and personalize (and direct) each customer’s experience and journey on his site.
25. The Benefits of This Model 1. Increase of the potential for purchases by displaying a larger assortment of similar products to each customer – products the customer didn’t even realize were on the site and products that will suit customer’s needs the most.
26. The Benefits of This Model 2. Sales can be more accurately. The business owner can then better manage his inventory – something that will certainly help to grow business profits. The predictions can be as accurate as claiming that your company will sell 100- 120 Nike Air Max Model shoes with a 90% probability in the next week.
27. The Benefits of This Model 3. You will have an opportunity to determine the exact factors that may (or may not) impact the sales volumes. For instance, in most cases the frequency of visiting your website has no direct impact on the sales. Users may spend a lot of time browsing and comparing goods without committing to a purchase. While factors like age, seasonality and past record of purchases have a significant impact on the probability of a purchase.
28. You may have the insight to know that you are not growing as you should. Knowing why is another matter. And that is where business analytics comes in. It is a complex matter, but data science case studies continue to show that big data and machine learning can provide the answers. Romexsoft is ready to build a model for you, based upon your unique circumstances. Let’s discuss your problem today. So What Are Your Problems?
29. T H A N K Y O U F O R Y O U R T I M E ! W a n t t o k n o w m o r e ? C o n t a c t u s ! i n f o @ r o m e x s o f t . c o m r o m e x s o f t . c o m