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 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.
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
2. Inventory Management Automation With Machine Learning
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.
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:
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.
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 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?
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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