If you're like most small business owners, you're always looking for ways to improve your operations and marketing. In fact, a third of SMEs are already using AI tools to streamline business processes.
AI technology is often associated with big companies, but it can also be used by small and medium-sized businesses and startups to improve their operations and marketing. This post will explore 5 ways that machine learning solutions can benefit small businesses and how to use it in a business without burning a hole in your pocket or hiring data scientists.
AI is a branch of computer science that deals with making computers do things that would ordinarily require human intelligence, such as understanding natural language and recognizing patterns.
The way it works is that you provide a dataset with a KPI that you'd like to optimize for, and the algorithms will learn what factors affect that KPI. For instance, you could connect a historical customer dataset to Akkio and select your "churn" KPI. The software will then automatically identify which factors are most predictive of churn and build a model that can predict whether a new customer is likely to churn.
So why should you use AI solutions in your small business? Here are a few key benefits.
Repetitive tasks, like manually scoring leads in order to prioritize them, aren't just time-consuming; they're also tedious and prone to human error. This kind of work detracts from more strategic tasks that can grow your business, and even worsens employee morale.
AI can automate repetitive tasks so that your team can focus on more important work. For instance, you can use AI-powered lead scoring to automatically prioritize your leads, or use AI to automatically classify customer questions so that they can be routed to the right team member, saving a lot of time and boosting customer engagement.
You could use bots to automate marketing campaigns or Customer Relationship Management (CRM) tasks. For example, you could set up chatbot automation to follow up with customers after they purchase something from your website.
When it comes to cybersecurity, AI can be used to automatically detect and block threats, or prevent fraud. And in human resources, AI software can help you keep track of employee data points and performance, reducing attrition.
If you're using tools like SalesForce, LinkedIn, or HubSpot, you're already benefiting from their AI features, but you can also make your own custom predictive analytics models from any data source with Akkio.
This isn't industry- or department-specific, either. Just about any small business has repetitive business needs that could be automated with AI, from customer service to accounting.
Humans are incredibly good at some things, like in-person sales interactions, and not so good at others, like quickly analyzing large amounts of data. This is why it's important to use the right tool for the job.
AI excels at analyzing data. It can quickly identify patterns and correlations that humans would miss, and it can do so at a scale that would be impossible for humans to achieve. This means that AI can help you make better decisions, faster.
For example, you could use AI to analyze customer data in order to identify patterns in customer behavior. This could help you make decisions about pricing, product development, and marketing. Or, you could use AI to monitor your website and social media channels for customer sentiment. This could help you identify problems early on, so that you can address them before they cause too much damage.
Consider just some of the factors that can impact a KPI like conversion: traffic source, referrer, geographic location, promotions, pricing, product availability, shipping options, customer service, and far more. A human could never keep track of how all these factors interrelate, but an AI can.
There are two main ways AI can help you get more customers: by helping you target your marketing more effectively, and by providing a better customer experience.
On the marketing front, AI can help you target your advertising more effectively. For instance, you could use AI to identify the customers who are most likely to convert, and then target your advertising to them specifically. Or, you could use AI to analyze customer data in order to identify patterns in customer behavior. This could help you make decisions about pricing, product development, and marketing.
On the customer experience front, AI can help you provide a more personalized experience. For instance, you could use AI to recommend products to customers based on their purchase history, or you could use AI to provide customer support.
In the 1950s, the term "artificial intelligence" was coined to describe a new field of study aimed at creating machines that could replicate or exceed human intelligence.
For many years, implementing AI was a highly complex and expensive undertaking, reserved for large organizations with deep pockets and teams of highly skilled experts. Everything from building model pipelines to managing data was done manually and with programming languages, and the process was often slow and error-prone.
This only became a greater challenge amidst the advent of big data, when the volume, velocity, and variety of data increased exponentially. At the same time, the demand for AI professionals has skyrocketed, such that it's prohibitively difficult and expensive for most organizations to even staff an AI team, let alone maintain one.
Akkio has emerged as a leading no-code AI platform to help businesses overcome these challenges and implement AI at scale. Our platform is designed for both business and IT users, with the flexibility to connect data from any source and select a column to predict. We then handle all of the data pre-processing, model selection, train/test split, validation, hyperparameter tuning, deployment, scaling, updating, and more.
This means that businesses of all sizes can now harness the power of AI to drive transformative results. Here are five key applications of AI for small businesses.
In the early days of lead scoring, the process was entirely manual. Salespeople would receive lists of leads, and they would check off boxes next to each one to indicate which leads were the most promising.
This process was time-consuming and often inaccurate, as it was difficult to predict how different factors interplayed with one another. Not only that, but such tedious work was often seen as a low-priority task, so it was often left undone.
Thankfully, lead scoring has come a long way since then. These days, AI systems are used to automatically score leads, taking into account a variety of factors such as demographic information, firmographics, engagement data, and more.
This is a much more efficient way of lead scoring, and it allows sales teams to focus their time and energy on the leads that are most likely to convert. In addition, AI-powered lead scoring systems are constantly learning and evolving, so they only get more accurate over time.
If you're looking for a more efficient and accurate way to score your leads, AI is the way to go.
The retail industry is under immense pressure to increase sales and profits. In order to increase sales, retailers are looking for ways to increase order value. One way to do this is to look for up-sell and cross-sell opportunities.
In order to find these opportunities, retailers need to look at data. Data can show what products are being purchased together, what items are being returned, and what items are being left in shopping carts. This information can be used to identify up-sell and cross-sell opportunities.
For example, if data shows that customers who purchase Product A also purchase Product B, then retailers can offer Product B to customers who purchase Product A. This is an up-sell opportunity.
However, reality is far more complicated than this. In order to increase order value, retailers need to take into account a multitude of factors, such as customer lifetime value, margins, inventory levels, and more.
AI can learn directly from historical purchase data to develop a predictive model that finds the best up-sell and cross-sell opportunities for each individual customer in real-time. This predictive model can then be used to dynamically generate recommendations at the point-of-sale.
In today's competitive retail landscape, data-driven up-sell and cross-sell opportunities are a necessity. By leveraging data and AI, retailers can increase order value, sales, and profits.
The "silent killer" of many businesses is churn - the percentage of customers who cancel or don't renew their subscription each month. Churn is a huge problem because it's not just the loss of revenue from that one customer, it's the loss of future revenue (from that customer and their referrals), the increase in costs to acquire new customers to replace the ones who left, the negative impact on morale, and the erosion of your brand.
There are a multitude of factors that can contribute to churn, including poor product quality, slow customer support, high prices, or a change in the customer's needs. So how do you reduce churn?
Instead of trying to manually keep track of every little thing that might be causing customers to churn, you can use data and analytics to get a better understanding of what's causing customers to leave.
Machine learning can be used to automatically identify patterns in customer behavior that lead to churn. This can be things like a decrease in usage, logging in less often, or contacting customer support more frequently.
Once you've identified the patterns that lead to churn, you can take steps to address them. This might mean offering discounts or coupons to customers who are at risk of churning, or it might mean increasing the frequency or quality of your customer support.
Whatever approach you take, the key is to implement predictive modeling and data-driven decision making to reduce churn.
All-time highs in employee attrition have been making headlines the last couple of years. The problem is especially prevalent in sectors like healthcare, retail, and hospitality, where the majority of the workforce is low-wage. High rates of employee attrition can quickly eat into profits and negatively impact customer service.
There are a number of factors that contribute to high rates of employee attrition. For one, the current labor market is tight, with more jobs available than there are workers to fill them. This gives employees more leverage to switch jobs in search of higher wages or better working conditions.
Additionally, many of the sectors with high attrition rates are characterized by low wages, high stress, and demanding work schedules. These conditions can lead to dissatisfaction and turnover.
So what can employers do to reduce employee attrition? It’s important to recognize that there are a number of factors that contribute to high rates of attrition. By understanding the factors that lead to turnover, employers can develop targeted strategies to reduce it.
Employers can use machine learning to identify which employees are most likely to leave. This information can then be used to develop targeted retention strategies. For example, employers might offer more training or development opportunities to employees who are identified as being at risk of leaving.
Most businesses rely on some form of forecasting to make decisions about the future. Whether you're trying to predict how much inventory to carry, how many staff to hire, or what sales to expect, forecasting is an essential tool. Sales forecasting is especially important because it can have a major impact on a company's bottom line.
Machine learning is a powerful forecasting tool that is becoming increasingly accessible to businesses of all sizes. This technology can be used to automatically generate predictions based on past data.
AI is a powerful tool that can be used to improve a variety of business operations, from marketing to customer service to accounting. However, AI can be complex and expensive to implement.
Akkio is a leading no-code AI platform that enables businesses of all sizes to harness the power of AI. Our platform is designed for both business and IT users, with the flexibility to connect data from any source, select a column to predict, and deploy anywhere, all in moments. Sign up for a free trial to get started.