To begin with, we’re here to emphasize once again that first of all, machine learning is a great tool to boost revenues and, if it happens, a nice side effect of using machine learning development is about to bring down the costs. What’s more, this notion is essential to remember when speaking on principles that make machine learning tick in business realm for the main goal of any company is to sell and do it right being its information, products or services.
But you are probably wondering what the steps to roaring trade are. Here, you need to ask yourself three important questions and decide what to sell, who your target audience is and how to attract their eyes and ears to your brand. Beyond that, you also need to pick the right timing to go all in selling. In a nutshell, machine learning can help to weight all the option when replying to any of these questions.
Still, when you want to have it all at once and get the answers quickly, it might burn a hole in our pocket and affect margins. Therefore, today you cannot do without a human touch when dealing with deep learning technology.
One more thing which is worthy of mention is that machine learning generally is keened on the business effieciency boosting however sometimes it helps not only to generate more revenue but AI can help you to lower your business costs as well.
To orchestrate the targeting process and tap into each of What? Who to? And When? questions, you’ll find a handful of different of tools out there. First off, recommendation systems, sales suites, and marketing instruments come to mind.
Harnessing the power of Neural Deep Learning Networks, these brainy tools can easily guess what a user wants (if any) and tailor the suggestion according to his profile.
For instance, if you’re bored and can’t decide how to spend your evening, it’s raining outside and you’re absolutely not in a mood to head out, movix.ai can bail you out. The engine resembles a smart cinema that helps users with choices in real time.
The idea behind the online service is that a user visits the website, clicks on a few titles he likes (maybe he made up his mind this very moment and usually wouldn’t go for Avatar) and after the system powered by deep learning offers firms he might like. Voila! Time to grab popcorn!
Another great example is a shopping spree. Often times, this type of development pays attention to what users do on an ecommerce website, learns from user experience and based on historical data builds up machine learning affinities to help users choose among a myriad of options. For instance, Strands helps to personalize the shopping experience of every visitor.
By learning behaviors of the online crowd, the engine gathers individual insights and aggregates the data after helping retailers with better sales.
Furthermore, deep learning comes handy when you speak of CRM and predictive lead scoring. Getting rid of guesswork can save a lot of efforts and help pinpoint sales-ready leads quickly. Mintigo, for instance, defines marketing indicators mining data from the web and mergers those with client’s CRM giving a 360-degree profile of every prospect.
The company utilizes machine learning and keeps an eye on a digital footprint of thousands of firms online, refines the data to mine meaningful insights helping with sales decisions.
Computer vision steps into the picture when you’re racking your brain on what exactly and how to sell it to your target audience. The science behind image processing by computers is a complex subject but seasoned deep learning practitioners would agree it’s a revolution. For starters, computer vision eliminates the typical mistakes of humans that get their attention scattered when multitasking and thus, might lose concentration. By doing several things at a time with great precision, companies that bring on board machine learning can significantly speed up their development and lower down the production costs without sacrificing the quality of offerings.
Think of TensorFlow that offers its library for free to those willing to go knee-deep into machine learning models. With a varying level of abstraction, this open-source library is a godsend for numerical computation pros.
You must have heard another story of Japanese farmers automating their farming business partially and outsourcing some tasks to machine learning such as… sorting cucumbers. A tech-savvy son of the local farmers came up with the idea to use TensorFlow to classify the veggies into categories. For three month the engineer was taking photos and grading cucumbers to teach the model sorting to get rid of manual sorting reaching a 95% accuracy over time.
That’s the what to sell problem-solving example in the real world. You can produce thousands of products and offer services in tons of various ways. Now consider the potential of deep learning adding value and help you choose the most effective approach to do so. To brew better beer, the Virginia-based brewery decided to optimize its operations and joined the ties with machine learning firm. The brewing experts gave the tech vendor the data on Great American Beer Festival IPAs so that those can be aligned with top ten country’s best sellers. The result was dynamic recipes that would alter with every new batch after the feedback from beer fans is collected and processed.
Saying “No” to red tape, isn’t it freaking amazing? Machine learning can go an extra mile and help process fewer papers and being able to concentrate on taking your minds off the routine tasks that take way too much time to handle.
Offering automated patent search, the company saves tons of time on document screening and reviews. After experimenting with historical invalidity cases, Amplified created a quick and easy-to-use system powered by machine learning that refines the search singles out three to five results picking the most relevant among hundreds of documents. With such an intelligent development, the necessity for a professional human searcher and its opinion becomes blur in seconds.
No one likes to spend precious funds on things that could be easily passed to deep learning and smart algorithms that take less time to process big data and come up with meaningful insights in a blink of an eye.