Artificial Intelligence: Secret ways for Amazon Seller E-commerce Success

Artificial Intelligence for Amazon Seller

Introduction

How to use Artificial Intelligence for your benefit as an Amazon Seller?

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. There is a growing use of Artificial Intelligence (AI) in Amazon Seller and other E-commerce businesses and Predictive Analysis is one of the core components to start with.

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 this power of Data Analytics with User Experiences and Journeys to identify and eventually solve the pain points of the customers to find the deeply hidden value in ways.

We will cover

  1. The phases and paths necessary to start gathering data and generating predictability
  2. How to gather and extract relevant data
  3. Identifying user behaviors using data insights. Basically, know as much about your customers like what they really want, why they buy and when, etc.
  4. Identify the critical paths which are actually impacting your sales.

Here I will be exploring with you the phases and stages to generate predictive analytics that can deliver useful insights into customer behaviors.

Predicting Analysis using the data will allow you to answer core responses, on your Customers, Product, and Services.

NoteIt is not a foolproof magical system which will 100% accurately predict the future but can give you the likely and unlikely of some events and predictions

What is Predictive Analytics for Amazon Seller?

It all starts with finding the main problem you are trying to solve

Here is some drill-down questions for Amazon Seller and other E-commerce brands –

  1. Need More Sales
  2. Reduce the costs
  3. More New Customers
  4. More repeat buyers
  5. Keeping only those products which will sell more etc

There are three Core Process –

    1. Identifying the Information
    2. Analyze the Information using the models to validate the accuracy
    3. Implement the results based on the insights predicted.

Predictive Analytics - Stages

1. Identifying the information:

  • Define the E-commerce business problem which is required to be solved.
  • What data is required to answers those questions/problems?
  • Look at the quality of Data. Are we capturing sufficient enough data?

2. Analyze the Information and Selecting the Model:

  • Choose the right data modeling algorithm. Start with a small sample of your E-commerce data and validate the results and model.
  • Making sense of the results.

3. Visualize and Implement:

Results from the previous exercise then will be used to build 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 –

  1. Understanding your Brand
  2. Have a curiosity and open to all sort of questions
  3. And good skills in understanding the process

E-commerce Applications like Fulon allows giving the deep customer insights and actions using the actual sales data from Marketplaces like Amazon.


Grab a Demo with Fulon Data Expert

How to identify the real problems – Our Fictional Company NutriKaSupplements

Problem Analysis

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 are 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.

What are the core problems:

  • NutriKaSupplements doesn’t have enough buyers. More buyers mean more sales.
  • They are not getting enough revenue out of our existing customers. Few repeat buyers.
  • They are not efficient about what they manufacture, which means launching products with less demand.

They are not able to spend on sales and marketing and ad spending on the actual sales that are happening

Note – You can adapt the same process for your E-commerce Brand to find the legit problems. Plus, you can further drill down to the core of each and identify the areas to focus on. You need to combine intuition with observation.
You may use all the latest data tools and mining techniques. But still, the best way of finding stuff out is the oldest one of all; that is, talking to customers, listening to what they say, developing empathy for what these customers are dealing with.

How to find the right Data for Predictive Analytics

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.

Core Questions:

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 (using AI in eCommerce):

https://marketingland.com/5w1h-marketing-analytics-5w1h-framework-86043

https://www.smartinsights.com/managing-digital-marketing/planning-budgeting/5ws-h-business-planning/

Who:

In the Who group, questions are mainly demographic information like:

  • age,
  • gender,
  • education,
  • marital status,
  • income level,
  • job title, hobbies,
  • interests,
  • who they rely on for information?

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

What:

In what group, we find questions like,

  • What products buyers are buying?
  • What would buyers want to buy that our Brand cannot fulfill? (This will help to find new product fit in your catalog)
  • What other products did they buy?
  • What is their most preferred way of buying?
  • What don’t they like (Lookout for Negative reviews on Products and Brand), that turned them off to not buy anything?

When

Here we have time-specific questions.

  • When are they buying or not buying?
  • When did they spend the most time on our marketing messages and ad campaigns?
  • When they preferred to be contacted and when they do not want us to contact them?

Where:

These are all location-oriented questions.

  • Where are they when they’re buying from our online store?
  • Where they buy from, is it our online store, or Amazon, Walmart, etc
  • Where they make the most purchase from (Home, Office, Mobile device, etc)
  • Where they reside? In certain geographic locations, states, or close to some business centers?
  • Where are they buying from when they’re not buying from us?
  • Where their products are getting delivered?

Why:

This is the most important aspect where we can really find the behaviors of our buyer’s actions and motivations.

  • So why are people buying, say in case of NutriKaSupplements – why they are buying supplements
  • Why are they buying the amount that they’re buying?
  • Why don’t more people buy from us
  • Why they only respond to some of the communications and why the rest of it fails?
  • Why does our stock sell well sometimes while it won’t sell and in the storage?

How:

    • Here we are thinking more from the process side
    • How are they paying us?
    • How do they like hearing from us?
    • How much really buying at a time?
    • How much are they paying?

To figure out questions related to your own e-commerce brand, you can check below websites –

http://customerjourneymarketer.com/

https://customerthink.com/

https://www.xminstitute.com/

It is really critical as Brand Owner to deeply understand and get the answers to the questions because without it, your actions will be based on wrong hunches and not validated by the real data

What Data Sources and information you need?

PREDICTIVE ANALYTICS - DATA SOURCES

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 a 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 a few data sources –

    1. Email Analytics – Emails may seem outdated but still it is one of the best mediums to grow sales. You will look at open rate, click-through rate, time to purchase. With tools like Fulon and Curvv (you can drill down further to each buyer level on when they buy, how much they buy from Marketplaces like Amazon). However, not all your buyers will be in your email list so still will be missing out on your potential buyers who are in different mediums.
    2. Pinterest Analytics – It is especially important for sectors like food, health, and fashion, and if you have marketing campaigns there, as our Supplement store does. You can bring in other kinds of data, such as what kinds of pinboards are getting attractions? What’s bringing them to our store? What kind of ad are they responding to? What other things are customers interested in? Are they just health freaks, or is there something else that’s driving them?
    3. Facebook/Instagram Analytics – This is where you get insight into your customers, personal lives, desires, likes, and dislikes and fears. You will get demographic information, behavioral information, and relationship status. It might be relevant when your campaigns are based on certain life events of your buyers like Marriages etc
    4. Google Analytics – which is almost an industry standard on websites. Email and other social media are just marketing channels, that drives the customer to your store or marketplace store. Now there is a big brother Google Analytics. We can get all the data on how they’re visiting our store. How many visitors came to the store? How many of those then visited the products and how may read the reviews and buy from us? plus, much more you can find all these things out in Google analytics. We can also find demographic data from Google Analytics. You can build a segment that focuses in on how many people who visited the testimonials page went on to buy, or a segment that looks at who gave the bulk order versus just a single purchase.
    5. Order analytics – One of the most important from various sources like your own stores and marketplaces like Amazon, Walmart, E-bay, etc. Eventually, these are your real buyers who are converted and can tell you all kinds of stores based on their purchase behaviors. With Analytics of Fulon, you will be able to deep dive into the customer minds. Actual buyers will give you the outputs on the customers which will then fee to the inputs to your marketing campaigns, products, and new launches.

Now that we’ve identified the information sources, the 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 what are the main goals, what questions for which we need answers for, and then applying the data aggregation and models to predict.

There are a lot of various Models and Tools and Programming libraries that can be used to train the model using the E-commerce data. However, the idea of this blog is to give an idea from a business perspective.

Fulon - AI ML E-commerce Growth

One way of doing it by using the Export functionality which most of the data sources will provide and export to the Excel sheet. Luckily, all of these data sources also have their own analytics, but they work in silos and not giving you a complete picture of what’s happening in your E-commerce business in totality


Also read, Everything you need to know to create a listing that ranks high on Amazon

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