Hackernoon logo7 Competition-Killing Ways To Use Machine Learning for Ecommerce Brands by@scalrconsulting

7 Competition-Killing Ways To Use Machine Learning for Ecommerce Brands

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@scalrconsultingScalr.ai

Machine Learning and Data Science Consulting firm focused on increasing revenue for clients.

Leave competitors in your ecommerce niche gasping for air with these machine learning tools that automate costs out and show you where your customers are hiding.

Ecommerce is one of the fastest growing industries in the world, totaling 4.88 trillion dollars in 2019, and an expected gain of 20% each year. With growth this fast and an industry that reinvents itself all the time it's important to keep up with the latest technologies that make your business successful. Companies are using machine learning and deep learning to make all sorts of strides in ecommerce sites that are unimaginable to people unfamiliar.

Many of these machine learning technologies and tools are new and being used by just a few larger companies, which means you could be the first one to implement them in your niche. These technologies have been seen to achieve incredible results that a human could only dream of doing themselves, like an automated backend inventory management system that can eliminate over 20% of an entire workforce. With that being said, lets take a look at how you can start working towards using machine learning for ecommerce. 

Buyer Intent Models To Predict Buyer Interest and Purchasing Preferences

Buyer intent models can help you find groups of customers you didn’t know existed or were profitable

Buyer intent models are one of most powerful and game changing tools the ecommerce space has seen using machine learning. While they come in many different forms, with some made just for predicting what a page viewer will buy, to categorizing potential customers by interest level. These tools allow ecommerce companies to optimize all parts of their funnels, while understanding which products are most popular in the long run. Let’s look at some of the variation of this software and the benefits of each.

1. Advertising Optimization Buyer Intent. Used when predicting buyer interest from an ad or paid keyword, usually involving facebook ads, Google PPC, or other audience advertising. Based on a person's interests, keyword they clicked, or posts they’ve liked you can categorize them by how likely they are to buy from you, and even what products they will buy from you now and in the future. Brands use this to optimize ad spend to the best converting ads and even rebuild their landing pages based on what products are most likely to be bought now and in the future.

2. Social Media Tracked Purchasing Intent. These are models based on a specific social media platforms user and the chance they purchase from you. This model is incredibly useful as it allows you to build an “ideal follower”, someone who has a high chance of buying from you, and allows you to spend your time trying to acquire more of those types of followers. This model takes in information like do they follow you, do they engage with your account, what do they post, how much do they post, and what content do they engage with. This model is becoming increasingly popular because of the ability to create a group that you consider long term ideal customers, and can spend your time targeting new people that fit this group. Here’s a great breakdown of this same model, used on pinterest.

Twitter api allows you to pull millions of lines of text for NLP models

3. Email Model. Adding in machine learning analytics from email campaigns is a great way to optimize your email list and create more buyers from your list. This model can be used to categorize your emailers by how likely they are to buy from you right now, or if they’re not ready yet. This can let you send quick offers to your hot potential customers to convert them faster, without having to path them through a whole funnel where you might lose them, and prime less likely ones into buyers down the road. This machine learning model for ecommerce also lets you do something pretty tricky, if it learns what other competitors an emailer might be interested in, and how interested they are. You can send extremely targeted emails to those people to pull them away from your competition, by giving them an offer they can’t refuse (or something like that). This model integrates well with the others, and lets you build a nice stream of moving potential customers through a funnel where you can predict their intent at all steps.

4. Heatmaps For Model Optimization. A new addition to buyer intent models, and something we’ve seen work very well is adding heatmap data from tools like hotjar. This allows us to understand the flow of customers on pages in a way that correlates to the rest of the model, whether that be social media, email, or advertising. If you’re already using hotjar to track website analytics, this is a must have moving forward.

Automating Inventory Management and Product Listing Tasks

One of the things that slows down ecommerce companies as they scale, especially if they have customized products (like artwork) or custom boxes (delivery services) is how much time it takes to move these out the door. From bundling the right items, to listing them online, and packaging them up this process does not scale well. Building systems that automate most of this process and remove expensive manual labor have begun to increase in popularity, especially as they continue to show tremendous results. Let's take a look at the different ways you can automate parts of the product management process, from raw items to out the door.

1. Automate Product Listing

  • One of the most popular parts of the process to fully automate out, product listing can be done with a collection of models using computer vision. These systems can be shown product images and can tell what items are to be listed through object recognition, and create product titles and descriptions automatically. Adding a price optimization model to make sure you price your product at a great selling point finishes off a new product listing on your store.
  • Once you have the models to recognize your products in images, building a database to store your images in becomes a huge time saver down the road. An automated product listing model can automatically pull the images out it needs, matching whatever product is needed for a new listing, and uploads the best image.

2. Inventory Management Automation With Machine Learning

  • Inventory management and warehousing are another process that can become a handful as you scale in size. Product shortages and untracked inventory can ruin a black friday sale and product surplus can leave you with unwanted inventory that costs you money. Forecasting models for ecommerce can predict which products will reach shortages, as well as predict when exactly they will run out. On the flip, slowing sales trends for products is seen as well which allows you to reduce surplus and keep slower moving inventory down in volume. With these forecasted inventory levels, you can automate product ordering and be hands off while your system takes care of inventory levels.
  • Computer vision for warehousing has become hot in recent years, and while you might not need robots and autonomous carts, there's quite a bunch of powerful models that can automate different tasks that make your ecommerce brand run smoother. OCR label readers and barcode scanners allow you to automatically read in new order right to your backend, and update your website with the new inventory in a matter of seconds. These label readers save you hours cross checking inventory, writing down and updating your backend, as the computer vision does all of that automatically.

Customer Support

People hate calling customer support, clicking through menus, and waiting in a queue for an hour to talk to a real person. On the other hand, vague unhelpful emails from a support email is no better, and you spend more time trying to get in touch with a real person than actually solving the problem! In the ecommerce business, keeping happy customers is just as important as having a quality product.

Chatbots are a great example of using machine learning for ecommerce to reduce costs and increase customer satisfaction, as they are great at resolving issues and answering questions in a natural manner. In some cases Chatbots even improve conversion numbers, seen here in this case study where the chatbot system increased conversions by 11%. The bots help navigate users to helpful pages based on the questions they ask, leading to more converting customers.

Dynamic Pricing In Ecommerce

Also called price optimization, dynamic pricing is a model that adjusts prices in real time according to current demand for the product. The price is set based on a few parameters, including conversion rates, product supply and demand, and sales goals. While the models take in quite a bit of information to make these calculations, the benefits it shows are extremely rewarding, and large companies are already taking advantage of this (Amazon).

Benefits Of Price Optimization for Ecommerce:

  • Fast, real time response to changes in demand
  • Increase ROI for SKU variations
  • Automated price management, giving you more control over price strategy
  • Optimizes relative to competition
  • Allows you to forecast future price levels
  • Adapts to changes in your supply level

Landing Page Optimization For Ecommerce

Landing page optimization is a tool used to test thousands of landing page layouts, to find the best converting ones faster, and with more variables to test with. Old school landing page testing would use a few different layouts like A/B/C, then would pick the best one. The challenge would be that different landing pages have a bunch of variables, how many buttons, where to place buttons, where to put a CTA, how many images etc etc. With a machine learning version of this testing, we can run through thousands of versions of landing pages, with all different variable combinations to make sure we find the absolute best landing page layout. This company Unbounce has seen over 33% increases in conversions using machine learning optimized landing pages. These improvements are game changers for ecommerce. Human based landing page optimization is a lot of guesswork, given the amount of variables you could have. It's so hard to tell if 1 extra button adds an extra .5% to your conversion rate, and it's even harder to tell with an A/B/C split what actually moves the needle. 

Social Media Automation and Deep Analytics

Having a high quality, growing social media presence is one of the best ways to ensure long term success, along with long term customer retention. At times it can be difficult to grow your social media at a quick rate, while still making sure your followers and impressions are high quality, from people who can buy your products. Machine learning companies are building tools to break down the demographics of hashtags and followers to crazy levels, to figure out where social media accounts should target to find the best followers. These targeted models, along with forecasting tools talked about above, allow ecommerce brands to build a focused hashtag and engagement plan with predicted results. In this case study, Scalr.ai used these same models and increased revenue by 36% in just one month.

Fraud Detection

Fraud detection for ecommerce using machine learning algorithms like anomaly detection have become more and more used as ecommerce continues to grow. These tools can detect many different forms of fraud like purchases with stolen credit cards and high probability of retracting payment after purchase. Many ecommerce hosting platforms like shopify are starting to integrate these tools into their tools for use. Without these machine learning algorithms it's nearly impossible to detect such problems given the amount of data required to process.

Why does it happen?

  1. How easy is it to pull off:
    • With tools like the dark web and black market bitcoin sites, its much easier to purchase stolen credit cards and social security numbers today than in the past. In the first half of 2019 alone, there were 23 million credit cards for sale on the dark web
  2. Anonymity:
    • Hackers and frauders understand how easy it is to fake an identity and purchase items. It certainly doesn’t help that for the most part these purchases are small, usually too small to detect by anyone watching for credit card fraud.

Are you an ecommerce brand looking to push past your competition in 2021? Let’s talk about building you so machine learning for ecommerce tools that will kill your competition.

Let’s talk about showing you how you can optimize your ecommerce brand to points you never thought were humanly possible

A little bit about Scalr.ai:

We are a machine learning and data science consulting firm focused completely on building business tools to increase profitability for clients. We specialize in natural language and computer vision systems that allow us to build software solutions for many different industries

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