Want to increase your sales? Here’s a detailed case study on how ecommerce companies can leverage profits with machine learning algorithms. Here you can find a more about Data Science for Ecommerce - https://www.romexsoft.com/blog/ecommerce-conversions/

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 !
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