Ten Examples of Machine Learning for Small Businesses

The emergence of AI-Tables has democratized access to data science. It’s allowed SMEs to experiment affordably and flexibly with data sets, without needing expert assistance or well-resourced IT teams. Using AI-Tables, any database user should now be able to train and test ML models as if they were conventional data tables.

Below are ten examples of industries where SMEs can train and test data models quickly, affordably and easily.

Retail 

Customer-facing retail brands live or die by the client experience they deliver. ML can identify recurring patterns of consumer behavior, from which times of day/week certain goods are purchased through to how repositioning them affects sales. This helps to optimize factors as diverse as promotional displays, just-in-time stock control, and staffing levels. 

Banking and Finance

Risk mitigation is a critical aspect of financial services, from the probability of loan repayment defaults to pension scheme investment opportunities. ML can scrutinize large volumes of transactional data (both historic and in real time) to provide insights into areas like credit scoring, insurance pricing, fraud detection, and risk management. 

Pharmaceuticals

The breakneck pace of COVID-19 vaccine development has been accelerated by in-silico computations by individual laboratories, while small manufacturing firms have benefited from accurate calculations surrounding demand volumes and production capacity. If ML allows results to be calculated more quickly and accurately, it can save lives.

Marketing 

Marketing campaigns are judged on their effectiveness, but judging effectiveness itself can be difficult. Leveraging AI can optimize expenditure and ROI by improving the efficiency of Pay-Per-Click (PPC) campaigns, refining native advertising to appeal only to relevant demographics, and advancing the functionality of chatbots that guide users toward a decision or end goal.

Food Manufacturing 

Many small businesses produce food and ingredients, but demand can fluctuate, and small product changes may have a big impact on demand. Predictive maintenance can optimize hardware repair, quality assurance helps to predict production-line imperfections, and analysis of historic data may provide advance warning of when demand spikes are likely to occur.

Travel

The travel industry has been decimated this year, and companies will need to exploit every bit of historic data to survive. For example, travel agents might be able to determine which destinations proved popular with residents of certain regions, helping them to target PPC or native ad campaigns. ML could also be used to contact historic customers with curated getaway recommendations. 

Energy

Energy companies have unprecedented volumes of data at their disposal, with numerous power sources generating energy alongside individual premises that feed green power back to the supplier. Firms can use this growing wealth of data to predict demand and outages, as well as calculating likely levels of green energy generation if climate conditions change. 

Sales 

From drop shipping specialists to entrepreneurs selling homemade products, small margins exist across the profit-driven sales sector. The huge volumes of data generated through selling are often poorly exploited, even though they could help companies to make better forecasts, engage with existing customers more effectively, and optimize distribution channels.

Cybersecurity

There is a constant cat-and-mouse game between cybercriminals and IT organizations. Cybersecurity teams can identify emerging zero-day attacks more quickly using ML, learning from previous events to identify suspicious patterns. This can help smaller firms to protect themselves against DDoS attacks and data theft while insulating customers against fraud and account breaches.   

Customer Service 

ML is often used to optimize client-facing functions like contact centers. It can identify recurring themes, predict busy periods and offer granular evidence of individual agent performance. It’s ideal for statistical analysis of call times and volumes, helping companies to determine what – and where – they can do better.

If these examples have inspired you to think about how ML could turn your company’s raw data into refined modeling, MindsDB would be delighted to talk to you. We’ve recently announced integrations with relational databases like PostgreSQL, meaning our AI layer is now compatible with most open source databases. Get in touch or follow us on social media to learn more about how our AI layer dovetails with everything from MariaDB to MySQL.

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