From Amazon to upcoming reliance JioMart, e-commerce platforms are riding the golden wave by exponentially multiplying their sales and valuations from billions to trillions of dollars, what is the secret that is powering their growth? How are they capitalizing on the market? How are they able to bring a billion dollars in sales? The answer is “You”, curious how? Read on.
Today, we live in the era where Siri and Alexa (virtual assistants/bots on iOS and Amazon that help answer and perform tasks for you) know more about our choices and routine than our friends and family. Siri and Alexa are a very small part of Artificial Intelligence touted as the 4th industrial revolution that we are experiencing.
Artificial Intelligence is one of the major forces driving many industries towards exponential growth including retail. Research done from Gartner indicates that in 2021 AI augmentation to businesses can create $2.9 trillion business value and 6.2 billion hours of workers productivity 
In order to get our head around the trillion-dollar numbers, let’s explore the role of AI in the retail industry.
There are mainly 3 players in the retail industry.
– Retailers via brick & mortar stores / Retailers via e-commerce platforms
In part 1 of this article let’s understand what is artificial intelligence and how is it implemented today through a simple story.
What is Artificial Intelligence?
In very simple terms, AI can be broadly described as the ability to predict the occurrence of certain event/task/sale based on the relevant data gathered in the past over a significant period.
To help you understand this definition better, read a story below that illustrates the impact of AI with help of just one consumer (Seema) who is buying an everyday commodity (Milk) from two different stores (Amaze Store and Dinesh Store)
For ease of understanding, the story is broken down into 5 different scenes:
Scene 1: Observing Seema’s milk buying behavior over a period of 10 days
Carefully, look at the image below and see if you can answer the questions below. Do not scroll down before recording your observation. (No Cheating)!
- What does Seema buy every day?
- How many kinds of milk does Seema buy and what are the store names?
- What time does Seema usually buy milk?
- What price does Seema pay to buy the milk?
The questions were easy enough right? Let’s look at the answers now.
- Seema buys Milk Every day.
- Types of milk: Seema buys either Amul Milk 1L or Goodlife Spl Milk 1L
- Store: Seema buys Milk from one of the 2 stores Dinesh Store or Amaze Store
- Time: Seema buys milk anywhere between 6:30 am to 10:00 am
- Price: Seema pays between Rs 45 – 65/- per day for buying Milk
Got all of them correct?! Good Job!
Now, based on the above data, try the questions below and see if you can predict Seema’s buying behavior for the next 3 days?
- What time would Seema buy milk on 28th Aug Friday?
- What kind of milk Seema would buy on Sunday 30th Aug?
- In which store will Seema buy milk on Saturday 29th Aug?
Although you may have answer popping up in your mind, there still some ambiguity isn’t it? Let me help you here then.
Here is Seema’s buying behavior for the next 3 days.
Now, I am pretty sure you were able to answers all the 3 questions above.
Since we now know Seema’s buying behaviour for 10 days, let’s combine both the data points and see if we can better answer the earlier set of questions.
|Deductions based on 6 Days Data Aug 22-27||Deductions based on 9 days data Aug 22-28|
|What Seema Buys||Seema buys Milk Everyday||Seema buys Milk Everyday|
|Product||Amul Milk 1L or Goodlife Spl Milk 1L||Mon-Sat: Amul Milk 1L Sun: Goodlife Spl Milk 1L|
|Time||Between 6:30 am to 10:00 am||Mon-Sat: Between 6:30 am to 8:45 am Sun : 10:00 am to 10:10 am|
|Store||Dinesh Store or Amaze Store||Mon-Fri: Amaze Store Sat-Sun: Dinesh Store|
|Price||Rs 45 – 65/- per day||Mon-Fri : Rs 45/- Sat : 50/- Sun: 65/-|
As you can see, as we got access to more data, the findings became more precise and we can now find a pattern in Seema’s behaviour. This is nothing but learning patterns in the data.
When machine does this job of learning information out of the data that fed into it without human intervention, it is called “Machine learning”. Pretty Cool! Isn’t it?
Scene 2: Reviewing the Store Operations to understand Seema’s buying pattern
Based on Seema’s buying behaviour, can you answer the questions below?
- Why does Seema buy Milk from Amaze Store from Mon-Fri and Dinesh Store on Sat & Sun?
- Why does Seema pay a higher price for the same milk in the Dinesh store?
- Why does Seema buy Goodlife Spl Milk only from Dinesh Store?
The answer is “No”, we don’t know why!
Now, let’s says we were provided with store information below. The answers are now clear, it is simply because the Amaze store is closed on Sat and Sun & Amaze store does not sell Goodlife Spl milk!
What we did just now, is to combine 2 different sources of information to understand the reason for certain customer behavior. We now personally know Seema’s reason for her choices, don’t we? The insight that we just found out now is invaluable! You will see why.
The more related data you can find to certain data set, the more powerful your insights will be!
Scene 3: Is there any relation to Seema’s buying pattern to Stores sales performance?
Let’s take a quick look Store Owner’s profile and their sales performance.
Remember the most invaluable insight I mention to you earlier? Now, imagine if Store owner Jeff knew Seema paid a higher price for the Amul 1L milk on Sat? or imagine if Jeff knew Seema went to Dinesh store only because Sham sold one additional product than him?
Scene 4: Consultant and Data Scientist come to rescue.
Jeff knew if he did not understand the reasons for the drop in sales, very soon he would be out of business. He hired a consultant and data scientist to uncover the problem.
The consultant looked at Jeff’s store and suggested the below solution.
While bringing up an online store is not a new thing anymore, analyzing customer buying pattern is. This is the job of Data Scientist, the person who can analyze the data to find insights.
If you were to suggest below changes to Jeff based on Seema’s buying pattern, would you agree with the Data Scientist recommendation below?
The recommendation above is worth millions of dollars! No amount of worrying and hard work would have helped Jeff improve sales unless he knows what exactly to improve.
This is exactly what Artificial Intelligence is, predicting exactly what price point would interest customer, what products need to recommend to which customer profile? When is the highest probability of converting recommendation into a sale and much more!
All this is possible because companies like Amazon and Flipkart today can store buying patterns of billions of consumers like Seema and then derive insights to improve sales.
Scene 5: Finally, What Strategy does Jeff take after reviewing the recommendation from the data scientist?
Jeff implemented the below plan combining recommendations and his own business tactics.
What happened to the Amaze store after the changes were implemented did the business sore high or crash down? Did Seema continue to visit Dinesh Stores? How are giants such as Amazon and Flipkart monetizing the billions of users’ Buying Patterns?
Find out more in Part 2 of this series.