If you are someone who owns or manages a small or medium ECommerce company, then you know how coveted data science is for you. You have no background of data science, machine learning, or artificial intelligence, but the ECommerce industry is leveraging data science big time. You haven’t started using data science to make your ECommerce business decisions as yet because of any of these reasons,
- You don’t know how to begin with data science in your business, or
- You feel you are not ready and you don’t know when will you be prepared, or
- You think you don’t have money and time to do this right now
If so far, we are aligned, then this article is for you. We are going to give away three specific steps to help you begin your data science journey.
Making ECommerce Decision without Data
Think about a day to day situation when you have to make any of the following decisions in your business,
- Which products to show? Whether you have to decide on your product placement, or showing recommended products to each visitor or customer. This decision has a direct impact on getting customer’s interest to add the item to cart.
- What discounts to offer? Discount is what people love, but you know you have to run a profitable ECommerce company. It is essential for you to always be in control of your margins. Discounting decisions are often taken emotionally to address every visitor, but everyone doesn’t buy.
- Which customers to engage? You can call up or send email/ SMS to the customers every day, but that’s when resources do not constrain you. Deciding upon which customers to engage first whom to engage later, is a difficult one because you never know who might buy from you.
- How much inventory to keep ready? Deciding on the stock could be tricky, especially when you can’t forecast demand. A wrong guess can leave you with much of the money stuck in inventory with very few sales or many prospects who can’t buy due to being stocked out too soon.
The chances are that you are taking most of these decisions based on gut feel or your implicit domain knowledge. To run a sustainable ECommerce business, you need to put your data to work. Let your data make the decisions so that you are in full control of the outcomes of your choices.
What is Data Science for ECommerce?
Data Science is the scientific way of an algorithm learning the patterns of cause and effect emerging from your business data. The learned patterns help you see which actions are likely to lead you to a favorable result. Data science is an application of complex statistical models and technologies to your data. However, you don’t have to worry. Unlike in the past, you can move to data science way of ECommerce operations without getting into these complexities. In ECommerce, the data science helps you acquire an advanced understanding of your customer behavior based on your past transaction data, data about other external factors. You may not have to have all the data points available right away to begin applying data science to your business. Data science should help ECommerce businesses in multiple ways. You can come up with product recommendations. You can also predict pricing that will fetch more buyers. You could now identify customers who may not buy in the future or know the sales numbers in advance.
Adopting Data Science in ECommerce
There are three simple steps you can follow to adopt data science. You can do this yourself or take help from one of your software developers. A preparatory step for following steps is to know which use case you think if addressed will reap the best returns for you immediately. Yes, to start with this, it is got to be based on your gut feeling. Your use case could be anything out these:
- We want to get product recommendations so that we drive better upsell, or
- We want to know what will be the demand for each product in the next month, or
- Which of my customers are not likely to buy from us.
So get clarity on a single goal to start with and follow the steps.
Step 1: Bring Together Your Transaction Data
Assimilate all the data points that you have, and you feel that these are important to decide on your chosen use case. Here is an example of what your dataset could include:
- Last month/ year sales
- How many people added a product to cart
- How many bought it
- How much time per purchase
- What was the price of the item
- What was the amount of the transaction
- Buyer gender, age, mode of payment.
Bring them together in the form of a simple excel sheet to begin.
Step 2: Train the Data Science Model
Here is one of the basics that you must know while getting into data science. Whether you are using an automated machine learning platform or a data scientist, you have to teach them about your data. The data model in nothing but an algorithm that can learn your data provided you train it so that it can do the forecasting or classification for you. Choose an automated ML platform where you find ready to use data models for your first data science use case. The ready to use data models are an application of self-learning AI. What it means is that they do all the intricate data science work while you focus on providing the right data input. Choose the automated machine learning platform that has a self-learning data model for your specific use case (such as, find recommended products) and uploads the data to train the data model. An automated AI platform will help you know when the data model training is complete.
Step 3: Experiment with Ongoing Data
On completing the training of the data model, build an app for your use case connecting it to the trained data model. Building an app or API will help you make a forecasting or classification decision on an ongoing basis for the live datasets. When you get an outcome, try it out to judge its effectiveness in bringing additional revenue to your business. For example, if your AI app is helping forecast demand, then you can plan inventory accordingly. See percentage sales from the inventory and how many sales are lost due to the item being stocked-out. Compare these outcomes to the previous period when you did not use data science or AI to make these decisions and assess whether you have derived any tangible benefits.
The most important aspect of adopting data science in ECommerce is to experiment on your data continuously. The more you test the data, the more accurate the data science predictions become. More precise the data science predictions become, more direct returns you get from your ECommerce business by using data science and machine learning.
Datoin is an automated machine learning and AI platform for ECommerce companies that are small and medium-size. Datoin has more than 30 use cases covered with ready to use data models embedded within. You can try Datoin to use data science in your ECommerce business.