Home > Blog > Machine Learning > 6 Creative Ways to Use Machine Learning for Small Business

6 Creative Ways to Use Machine Learning for Small Business

author

Trinh Nguyen

Apr 01, 2024

6 Creative Ways to Use Machine Learning for Small Business

You’ve probably heard a lot about how cutting-edge technologies like artificial intelligence (AI) and machine learning (ML) are reshaping the business landscape. It’s no longer strange to see headlines about big companies such as Amazon, Google, and IBM using AI and ML for incredible tasks ranging from personalized product recommendations to developing self-driving cars and even creating computers that outsmart chess champions.

But here’s something you might not know: machine learning isn’t just for the corporate giants. Even if you’re running a startup or a small business, you can harness the power of ML to improve your chances of success. Predict future customer purchases or streamline tasks that eat up your team’s time; there are numerous ways machine learning can benefit your business. A recent survey by Deloitte found that over 50% of small businesses have already adopted some form of machine learning technology, and another 30% plan to do so soon.

So, if you’re a small business owner or manager, now’s the perfect time to explore how ML can fuel your business growth. In this guide, we’ll walk you through six effective strategies for leveraging machine learning for small businesses.

Why Is Machine Learning Important for Small Businesses?

In the face of competition from big companies, small businesses understand the need to adopt new technologies. And machine learning is just the right tool to help them tackle challenges and stay in the game.

Machine learning is a subset of artificial intelligence that allows computers to learn from extensive data, identify patterns, and make predictions on their own. This is made possible through sophisticated algorithms and statistical models that enable machines to analyze vast datasets and make precise decisions.

The power of machine learning for businesses lies in its unmatched capability to derive valuable insights and facilitate data-driven decision-making.

Now, let’s take a closer look at a few key advantages of machine learning for small companies:

  • Cost-Efficiency: Machine learning-driven automation significantly reduces the time and resources spent on manual tasks. This enhances efficiency, helping small business owners save on labor costs and allocate resources more wisely.
  • Enhanced Decision-Making: Machine learning swiftly and accurately processes large volumes of data, equipping small enterprises with invaluable insights for informed decision-making. It empowers them to spot new opportunities, refine processes, and mitigate risks.
  • Streamlined Operations: Machine learning optimizes various facets of small business operations, including inventory and supply chain management and employee scheduling. By automating these processes, small and medium-sized businesses can work more efficiently and cut operational costs.
  • Sales and Marketing Optimization: Machine learning aids small businesses in identifying user behavior patterns and trends, facilitating targeted marketing campaigns and personalized recommendations. Consequently, leading to higher conversion rates and increased sales.

But how exactly do all these practical applications of machine learning play out for small businesses in real life? Read on to find out!

How Can Machine Learning Help Small Businesses?

How Can Machine Learning Help Small Businesses?

1. Streamline Tailored Marketing Campaigns

Machine learning offers a game-changing advantage in reshaping marketing efforts by automating tasks and tailoring messages for better engagement.

In today’s digital marketing world, especially on social media, engaging with customers is crucial. However, many small business owners struggle with effective marketing on these platforms. This is where machine learning steps in. It assists in crafting personalized content based on user’s preferences and online behaviors, saving time and improving customer engagement by offering what genuinely interests them.

Using ML algorithms, you can analyze customer data from diverse sources like social media profiles, website activities, and purchase history. This deep analysis enables marketers to craft targeted campaigns aimed at specific segments within their audience pool. For example, small business owners can build brand awareness campaigns for new audiences, while sales efforts should be put into loyalty programs for recurring customers.

2. Offer Advanced Sales Insights

Next up is utilizing machine learning’s power to boost sales performance. Thanks to machine learning tools, business owners now have a reliable tool to boost the accuracy of their sales forecasts and catch patterns they might have missed on their own. Then, if they notice a potential decline in sales, they can take necessary actions to address it promptly.

Moreover, as many business leaders skip thorough data analysis due to its time-intensive nature, ML simplifies the process by swiftly examining data far quicker than a human could. It offers immediate insights into whether a sales channel is profitable and evaluates key metrics like conversion rates and customer spending per channel. This helps owners and managers focus their marketing efforts more effectively and improves customer relationship management.

ML algorithms also excel in predicting customer churn rates swiftly and accurately. This proves especially valuable for small businesses in their early stages, where acquiring customers can be costly, and losing even one customer hurts. Here, ML tools might assist by analyzing past interactions and purchase histories to identify common patterns among customers who’ve stopped buying from your business. Understanding historical data allows you to proactively address issues, whether through giving personalized offers, requesting feedback, or simply reaching out to strengthen customer relationships. While success isn’t always guaranteed, this approach can prevent further customer loss and boost retention rates by transforming customer interactions.

3. Improve Products

Another innovative use of machine learning is understanding customer behavior to continuously refine products and services accordingly. A straightforward approach is to employ ML to monitor user feedback and adjust strategies based on prevailing sentiments. Businesses can leverage machine learning to anticipate product issues and address them beforehand, ensuring utmost customer satisfaction. ML also helps eliminate human biases, providing an objective analysis of customer feedback for tangible improvements.

Moreover, ML solutions can gather vast amounts of data faster than humans, offering unbiased insights and comprehensive analysis. Lots of no-code machine learning platforms like Google Cloud AutoML and Amazon SageMaker come with pre-trained models and user-friendly interfaces equipped with NLP sentiment analysis. These tools precisely classify data as positive, negative, or neutral, greatly assisting business owners in making informed decisions.

4. Manage Inventory Effectively

Thanks to continuous advancements, ML-based systems are able to analyze large amounts of sales data to predict future sales so that businesses can automatically adjust stock levels. Thus, they can avoid running out of popular products or overstocking unsold items, significantly streamlining inventory management without constant human oversight.

What’s more, ML’s predictive analytics can help identify which products are likely to sell best during specific times, enabling your company to increase revenue further.

5. Predict Employee Attrition and Improve HR Processes

Machine learning algorithms don’t just forecast customer loss and identify customer dissatisfaction. They’re also able to predict employee turnover or declining performance within your own team.

Machine learning models offer advanced capabilities to analyze employee interactions and consider both quantitative (like sales targets and productivity metrics) and qualitative data (such as employee engagement and peer evaluations). As a result, data scientists can pinpoint reasons for employee departures or predict decreases in productivity. This allows us to take proactive steps to engage and support these employees. Sometimes, acknowledging an employee’s efforts can boost their morale and productivity. Without comprehending their points of view, it’s hard to recognize if they’re struggling and unable to perform as expected. The right data will initiate support based on facts.

6. Expedite Administrative Work

As a small business owner, you’re often bogged down by repetitive tasks that machines can handle easily. With ML tools, your business can streamline operations and administrative work considerably because this technology excels at recognizing patterns and organizing data.

For example, ML automates or simplifies time-consuming duties like data entry, scheduling, and accounting. This not only reduces errors but also frees up time for your team to focus on more important tasks.

Consider Gust – a global SaaS platform for entrepreneurs to find, manage, and fund new ventures, for instance. Facing a surge in spam accounts on their website, Gust turned to ML for a solution. They used Nyckel, an ML platform trained with internal data to detect spam profiles. With a simple API call, it now takes only 20 minutes a week to filter out spam accounts instead of wasting hours.

4 Steps to Get Started with Machine Learning for Small Business

Initiating Machine Learning in Small Businesses: A 4-Step Guide

Now, you might be wondering how to begin incorporating artificial intelligence into your small business. Let’s check out a few simple steps below to get a feel for it.

Step 1: Identify Business Problems You Want to Solve

As a small business owner, you’re likely juggling multiple tasks with limited resources. While you may have tons of work that could benefit from automation, it’s important to prioritize. So, take into consideration some important factors:

  • Development cost
  • Development time
  • Resources needed
  • Data availability (internal/external)
  • Short and long-term returns on investment (ROI)

Here, just make sure you take a close look at your current operations to pinpoint which processes could benefit from integrating an ML model. Once you have a list of these processes, try seeking guidance from a machine learning expert.

Step 2: Seek Expert Advice to Develop a Clear Strategy

Unless you have in-house expertise, we recommend bringing in a professional. These experts will align your team’s objectives with the most suitable technology and craft a development strategy tailored to your goals and resources.

At Neurond AI, we offer machine learning consulting and development services for businesses of all sizes and industries, including finance, manufacturing, e-commerce, and fintech. Whether you’re an established enterprise seeking process automation or a startup in need of scalable AI solutions, our team of machine learning experts is here to assist you!

Step 3: Understand Its Potential and Set Realistic Expectations

Machine learning is a powerful asset, but it’s essential to have realistic expectations. While it can solve many problems, it won’t answer everything. Remember, the tool should be working for you, not the other way around.

Also, look into data privacy and regulation factors. Make sure you understand the rules around using these models and allocate time and resources to address any challenges that may arise. This will help you create a model that aligns with your goals and delivers accurate results.

Step 4: Train the Tool Using Internal Data and Monitor Progress

Next, you need to provide your ML expert with relevant datasets to train the model. Here, thorough data exploration and cleaning is also required, involving:

  • Identifying and addressing biases, outliers, and inconsistencies
  • Labeling the datasets accurately
  • Normalizing the data to ensure consistent scaling
  • Segmenting the data precisely for specific inputs

This process can be handled internally, or you might consider delegating it to the ML expert. Once the data is ready, the model will be trained until it achieves high accuracy and low false positive rates.

You might also need to implement a feedback loop for continuous learning with real-time data. Then, closely monitor the model’s output regularly to ensure it meets your business needs.

Lastly, we highly recommend you check out essential practices for effective machine learning implementation through this process for the best results.

Final Thoughts

As a small business owner, embracing machine learning can be a game-changer for your organization’s success. While it may take some time to integrate into your operations successfully, the impact is so worthwhile.

And yes, finding the right machine learning expert for your business also proves challenging, but you don’t have to navigate it alone. At Neurond AI, we’re here to support you every step of the way. We cater to various industries, offering tailored solutions such as bespoke machine learning algorithms, custom computer vision tools, and personalized forecasting systems. We aim to empower you to work more efficiently, unleash creativity, and propel your business to new heights.

Contact us to start embarking on your exciting ML journey now!

Revolutionize the way you work. Our goal is to help you be more effective, more creative and push your business further than you thought possible. Our solutions have changed the way companies approach working. Whether that be through bespoke machine learning algorithms, a custom computer vision solution or a tailored forecasting tool, you can be confident we will find the right solution to make an impact.

Contact Us Now