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In this blog, We will give you practical ways to use Predictive Analytics using the E-commerce Data you have from your own websites, social media and even the data from third party Marketplaces like Amazon, Walmart etc.
You will see this is the future Game Changer and using Artificial Intelligence and Machine Learning will not only be simplified by absolute must. We are going to combine these power of Data Analytics with User Experiences and Journeys to identify the and eventually solve the pain points of the customers to find the deep hidden value in ways.
We will cover
Here I will be exploring with you the phases and stages to generate predictive analytics that can deliver useful insights into the customer behaviours.
Predicting Analysis using the data will allow you to answer core responses, on your Customers, Product and Services.
Note – It is not a fool proof magical system which will 100% accurately predict the future but can give you the likely and unlikely of some events and predictions
Here is some drill down questions for E-commerce brands –
There are three Core Process –
Results from previous exercise then will be used to build the deep insights and actions for the business team to identify the process and areas of growth.
As I said earlier, it has to start with finding the right questions. This requires you to be –
Let’s look at the example from a fictional company in Health and Supplement business for Men and women say Brand is NutriKaSupplements
What is the business problem of NutriKaSupplements? Of course, generating more revenue
NutriKaSupplements like other Brands is doing okay handcrafted but Brand owners always have in mind that they could do better. Now, unlike a lot of businesses, Brand owners are smart enough to realize that they need to take a look at themselves at what they’re doing, how their customers are behaving before they start implementing the changes make a radical change. They know they’re
products are great but their sales seem to be tied to certain times like New Year when many customers think of health. But they need to unlock a way to get more consistent revenue for the rest of the year.
To increase sales, we will need to dig further down. Profits are related to costs and expenses plus sales and any other legal liabilities etc. E-commerce is a margin business so lower your spending will increase your revenue as well.
They are not able to spend on sales and marketing and ad spending on the actual sales that are happening
We’re going to explore how to find the relevant data sources for predictive analytics for your E-commerce Brand (in this case NutriKaSupplements). So how to identify the data that we need? Simple, ask questions, which then leads to a question – what kind of questions to ask?
Let’s look at NutriKaSupplements (our fictional company) and for a small E-commerce Brand, we will be limiting ourselves to data that directly relates to the buyer’s journey.
There is core five W’s + 1: Who, What, When, Where, Why, and How
Segregating your questions into different groups will help you start to identify what’s important.
For example, the main question when it comes to buyers is Who are your Buyers.
Check out more details on 5W+1H:
In the Who group, questions are mainly demographic information like:
And if your E-commerce Brand is for B to B (Business to Business), you’d probably tend to focus a lot more in business data and information points for example about their jobs, etc
In what group, we find questions like,
Here we have time-specific questions.
This are all location-oriented questions.
This is the most important aspect where we can really find the behaviours of our buyer’s actions and motivations.
Once we have identified the questions, we need answers for, we need to identify the places where we can get the relevant data.
This exercise of identifying the data sources can be bit overwhelmed. Too much data is as bad as too little data. We need the right inputs.
We will be focussing here for a small to medium E-commerce brands, like our NutriKaSupplements health supplements
Let’s look at few data sources –
Now that we’ve identified the information sources, next step is how to start gathering the data, refine it, remove the noises and start making sense of the analytics.
Summary – So far we have discussed about what are the main goals, what questions for which we need answer for and then applying the data aggregation and models to predict.
There are lot of various Models and Tools and Programming libraries which can be used to train the model using the E-commerce data. However idea of this blog is to give an idea from the business perspective.
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