There is no question that machine learning is at the top of the hype curve. And, of course, the backlash is already in full force: I’ve heard that old joke “Machine learning is like teenage sex; everyone is talking about it, no one is actually doing it” about 20 times in the past week alone.
After having #echodot and trying to build some skills on it, also using Philips Hue and see the possibilities of machine learning and its impact on simplifying life, it’s clear that machine learning is already forcing massive changes in the way companies operate. It’s not just futuristic-looking products like Siri and Amazon Echo. And it’s not just being done by companies that we normally think of as having huge R&D budgets like Google and Microsoft. In reality, I would bet that nearly every Fortune 500 company is already running more efficiently — and making more money — because of machine learning.
It’s not just Google that needs smart search results.
There are other famous areas that machine learning has significantly improved it such as:
- Forums and all those businesses who have high dependency on User Generated Contents. It can be rife with misspellings, vulgarity or flat-out wrong information.
- Spam Emails, ESPs are avoiding to accept sending spams or even your mail providers are detecting spams easily to avoid spamming on your mailbox. Machine learning helped identify spam and, basically, eradicate it.
- Pinterest uses machine learning to show you more interesting content.
- Yelp uses machine learning to sort through user-uploaded photos.
- NextDoor uses machine learning to sort through content on their message boards.
- Disqus uses machine learning to weed out spammy comments.
- Google recently put an artificial intelligence expert in charge of search. But the ability to index a huge database and pull up results that match a keyword has existed since the 1970s.
- Successful e-commerce startups from Lyst to Trunk Archive employ machine learning to show high-quality content to their users. Other startups, like Rich Relevance and Edgecase, employ machine-learning strategies to give their commerce customers the benefits of machine learning when their users are browsing for products.
- Machine learning also excels at sentiment analysis. For example, say a movie studio puts out a trailer for a summer blockbuster. They can monitor social chatter to see what’s resonating with their target audience, then tweak their ads immediately to surface what people are actually responding to. That puts people in theaters.
Big companies are investing in machine learning … because they’ve seen positive ROI. And that’s why innovation will continue.
If this year’s "The State of Machine Intelligence" shows anything, it’s that the impact of machine intelligence is already here. Almost every industry is already being affected, from agriculture to transportation. Every employee can use machine intelligence to become more productive with tools that exist today. Companies have at their disposal, for the first time, the full set of building blocks to begin embedding machine intelligence in their businesses.
So in my short experience in eCommerce, machine learning in following operation side of eCommerce can be very helpful:
- Assuming you keep locations of each SKU, then a machine learning algorithm can help you to improve your item location
- Assuming you keep locations of each SKU, then you can get a recommendation on where to keep the stocked-in items, which location in the warehouse
- A machine intelligence can recommend you how to prioritize what sales orders, in order to improve your Click-to-Delivery with respect to cut-off time of each 3PL, traffic, lean time and pick rate.
- A machine intelligence can suggest a pick path in order to reduce the pick time and improves the accuracy of picking
- Also machine can learn from each pick associate over each recommended pick path based on pick time and number of items in order to: a. personalize the pick path for each pick associate and b. improve the sales order prioritization to packing
- Machine can also predict a better delivery time and it can ensure predicted Click-to-Delivery is being followed.
- Machine Intelligence can also prioritize the tickets from customers so the most important customers or the issues that have a tendency to be a big risk, will be served first.
- Machine Intelligence can also be a assistant for each buyer and help them to save more money by spending wisely.
I am certain the possibilities are not limited to what I shared in this post. Your company will still need to decide how much to trust these models and how much power to grant them in making business decisions. In some cases the risk of an error will be too great to justify the speed and new capabilities. Your company will also need to decide how often and with how much oversight to revise your models. But the companies that decide to invest in the right models and successfully embed machine intelligence in their organization will improve by default as their models learn from experience.
Economists have long wondered why the so-called computing revolution has failed to deliver productivity gains. Machine intelligence will finally realize computing’s promise. The C-suites and boardrooms that recognize that fact first — and transform their ways of working accordingly — will outrun and outlast their competitors.