Many business owners shy away from machine learning (ML) for the fear of cost or lack of usefulness. And while large enterprises used to have the edge on ML technologies, small businesses are stepping up their game by integrating affordable AI solutions.
Let’s start with the basics: machine learning is an AI technology that finds patterns in data and uses those patterns to predict the future.
Big businesses like Google and Amazon use machine learning all the time--most often for search algorithms and product suggestions--but small businesses are integrating machine learning too, for many reasons:
Of the businesses that are currently investing, 84% believe that adopting AI will lead to greater competitive advantages. Another 75% believe that AI and ML will provide opportunities for new businesses and new ways to enter their market.
Additionally, because machine learning has been in development for a couple of decades now, you don’t have to start from scratch with your AI integration. Open source ML algorithms are more widely available now as well as pre-trained ML models. These kinds of affordable, informed ML options are a great asset to small businesses.
Even though we’re a startup, we quickly opted to integrate machine learning in order to keep up with our competitors. As a cloud-based accounting software, we compete with products like Quickbooks 2019 and alternatives.
The repetitive nature of bookkeeping means that our data lends itself well to pattern observation. As ZipBookers enter transactions, our software automatically categorizes it based off of historical trends. The more you categorize, the smarter our ML software becomes, meaning that your bookkeeping becomes more efficient and automated.
Additionally, we use data-driven intelligence to help users improve their invoicing processes and business health. We provide smart scores based on accounting best practices. This allows ZipBookers to see what’s working and what’s not and then use our observations to improve their processes.
This series by Growth Tribe tries to take the buzz out of AI and focus on the practical applications instead.
Consider your business—machine learning is all about making predictions. What kind of predictions should your small business be making? What is a mission critical function that you’d like to simplify? In what ways could AI and machine learning be affordable and helpful to you?
If you can’t think of realistic answers to these questions, then maybe wait before implementing machine learning. There are some substantial upfront costs and if you don’t have the kind of data that would benefit from pattern observation, that’s okay. You will likely find these areas of need later and when you do, you shouldn’t hesitate to implement AI solutions.
If you have been able to identify some areas of need, here are some good places to start.
These 7 commonplace ML applications can keep your business from falling behind and will help you on how to apply machine learning to business problems.
Google’s Search Engine algorithms are largely informed by machine learning. Since there are billions of searches performed every day, you need to keep up with SEO in order to stay relevant.
Analytic tools like SEMrush and Ahrefs use AI technology to improve their algorithms and offer fresh feedback to their customers. This helps you to target keywords that are within your reach and compete for your spot at the top of the page.
Machine learning can help you to automate your marketing services. By identifying patterns in user behavior, ML allows you to determine which individual advertisements are most likely to be relevant to specific users. ML can also help with real-time bidding and successful retargeting.
Create smart algorithms using machine learning technologies to improve your advertising. Companies like Facebook do this all the time. Self-learning algorithms measure the success of previous campaigns in order to guide the direction of subsequent campaigns. Tools like the RocketFuel project use AI solutions to optimize video ad length for potential customers.
Remember that machine learning is about monitoring patterns in order to adapt to change, so there needs to be some room for error. There are certain tasks that do not allow for any error (like entering or submitting payments), so ML may not be the right tool for those jobs.
However, there are aspects of online payments that can be simplified with AI technologies. Automatic order tracking and invoicing is a great place to implement AI. We’ve used machine learning to track invoicing data and make recommendations to clients about how to improve their invoice score.
Many companies also use machine learning to track credit card purchases and detect fraud. Sift Science is an example of one of these companies; they specialize in applying machine learning to protect your business from user and payment fraud.
While some elements of design require the human touch, there are other routine tasks that can be automated, such as basic HTML and CSS coding.
Companies like the uKit Group, train machines to evaluate and update the look of old websites using big data and web archives. In today’s world, it doesn’t take long for your website to become out-of-date or irrelevant. Using AI solutions will allow you to identify problem areas and quickly implement solutions.
In the age of machine learning, your clients are expecting extreme personalization, whether they’re aware of it or not. They want to see their name in your welcome email and personalized recommendations for products and services.
North Face is one company that uses AI technology (Watson) to help personalize their customer’s shopping experience by asking a series of questions and then refining product selections based on the answers. Netflix does this automatically by monitoring your viewing history and making new recommendations based on genre, gender or artwork.
Another way to take the mundane out of your workday email categorization. E-commerce sites with hundreds of customer support emails use AI technology to instantly label incoming emails and identify what type of ticket they’ve received (return, complaint, review, etc).
Knowmail is one solution that “fixes” email for you, though Google has been doing it for free for years. Gmail’s AI has even gone so far as to automatically answer your email with suggested responses.
As a small business, you regularly monitor market conditions and competitive trends. You can use AI-powered products to simplify the process for you. Regression models can help you make your pricing and sales forecasting more dynamic.
For example, Crayon is an intelligence tool that alerts you to changes in industry pricing and helps you stay ahead of the curve. Sales platforms like DOMO use AI to detect changes in data and predict ROI.
The truth is, if you’ve already started implementing SaaS products, cloud-based software and other digital solutions, you are ready for machine learning. Machine learning is affecting your small business and there are many non-intimidating ways to start transitioning towards full integration.
If you want to avoid being outsmarted by digital businesses, you need to seek out machine learning solutions.