How SMBs can use machine learning to do business better

Ever since the SaaS boom (Software as a Service) began, SMBs have evolved. They’re smart enough to understand that whatever their products or services are, they are in the business of technology. New trends in business technology are not to be scoffed at or treated as rich enterprise indulgences anymore. Machine learning, among all the buzzing new and trending tech phrases, deserves more attention than anything else. There’s competitive edge for the taking, there’s automation to be enjoyed, and there’s data-driven insight to be leveraged — all courtesy of machine learning. Let’s tell you how SMBs can use machine learning (ML) to do business better.

Identifying valuable content

If you’re not into marketing, you’re not really into your business. And marketing is about content. Good content builds brands, and bad content demolishes it. Also, user-generated content is a key variable, because SMBs have taken to all prominent social media platforms.

Machine learning’s most prominent applications are right here — identifying relevant user-generated content for your brand, and tagging it (positive/negative/neutral, or high quality vs. low quality). This ML application makes social media listening fully automated.

All an SMB needs to get complete visibility of conversations about their brands on the Internet is a monthly subscription to an ML-powered social media listening and monitoring solution. Flaunt positive comments, act on negative conversations, and treat neutral conversations on merit.

Transforming customer service

machine learning

Another high-potential application of machine learning is in customer service. ML algorithms, in conjugation with natural language programming, can be loaded into instant messenger interfaces, creating smart and self-driven “chatbots.” These bots can then deliver customer service to clients, vendors, as well as internal customers.

Imagine the level of quality of customer service that can be delivered via a bot that keeps on learning from every possible data stream within your organization. And imagine how seamless customer service could become as these bots get better at picking up nuances of the human way of messaging.

Customer service bots can free up hundreds of man-hours daily, which can be used to get more valuable work done by CSRs.

Hiring quicker, better, smarter

Recruiters agree — shortlisting candidates who fit the minimum qualification requirement of a job profile is one of the most difficult, time-consuming, and draining tasks. Ideally, you would want your HR professionals to do more value-adding work, right from building an “employer brand” for your business to making sure that current employees find their employment fulfilling.

ML can help. Machine learning-powered recruitment assistance software can sift through thousands of resumes and help build a priority list of candidates well-suited for the job. The software then keeps on learning from different HRIS-powered datastreams to fine-tune its selections.

A word of caution — watch out for debates about the possibility of human biases being “coded” into such software, and the legal implications they could create for firms that use such software in their recruitment exercises. With adequate human oversight, though, false positives and false negatives can be avoided.

Keeping assembly lines healthy with predictive maintenance

Downtime in the factory is a nightmare for a plant manager and the business owner. Unfortunately, manufacturing units are a mini-universe, where it’s very difficult to predict what will happen in the next hour. That’s until you bring in sophisticated predictive analytics capabilities of machine learning.

Why allow an equipment component to be damaged when machine learning can be leveraged to raise red flags well in time. Based on datasheets, real-life measurements, and information from supplier systems, machine learning can accurately predict when equipment will require maintenance.

This can save SMBs hundreds of hours of downtime per year, the cost-impact of which can go into several thousands of dollars.

Blessing finance with the power of automation

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An unreasonably large number of executives from finance departments within SMBs spend an unreasonably large number of hours of matching payments to invoices. Modern businesses have umpteen number of revenue processes, mapped in a larger number of tools. The result — manual, repetitive, and labor-intensive work in the finance department.

The solution? Yeah, ML again. Machine learning algorithms can quickly learn from your data, as well as from exception handling processes, and become potent enough to identify outliers, match them to appropriate sources, and make the average financial department executive a whole lot easier than ever. This can save SMBs thousands of dollars annually, which otherwise go toward service center expenses.

Keeping organizations fraud-safe

If you lost less than 5 percent of your annual revenue to any kind of fraud in the past few years, you’ve been doing better than the typical organization! Fraud detection has been a much-talked about application of machine learning.

ML algorithms can take in massive data from social media, public domain, and past transactions, and build highly sophisticated models. These models can then accurately point out outliers, exceptions, and anomalies.

For SMBs, such algorithms can help raise red flags related to strange order placement, potential delays in payments, and risks of nonpayment. With time, the models can be expanded to allocate credit ratings to customers, and re-calculate their credit limits based on their risk profile. Even outside the SMB sphere, antifraud algorithms are a hot topic of discussion for financial institutions, including the leading banks of the world.

Getting better at customer engagement and retention

Heard about the famous Walmart case study, explaining how the retailer used trillions of bytes of sales data to find out that American shoppers bought a lot of strawberry Pop-Tarts and beer in times when hurricanes were anticipated!

Well, machine learning can deliver highly deep-rooted insights based on all customer actions (right from a purchase to an aimless web-store browsing session). These insights can then be used to anticipate negative customer actions, such as attrition, and prevent them by making offers they can’t refuse.

Machine learning: You can’t ignore the benefits

Finance, marketing, recruitment, customer service, and manufacturing – you name it, and machine learning has its benefits to offer, in every business function. Now’s the time to speed past your competitors by using machine learning in improving how you do business.

Featured image: Pexels

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