Do you remember the movie ” Wolf of Wall Street”? This line, “The best way to sell something is to not sell anything at all. Earning the respect, awareness, and trust of people who could purchase,” as said by the protagonist Jordan Belfort, is core to business.
When you put your customers at the core of your business, you maximize your sales. Knowing them, helping them with the right products, and earning their respect can go a long way. Digital transformation can help you be more customer-centric.
Current technologies like Machine Learning, Big Data Analytics, and the Cloud drive this transformation. As a growing business, you can implement these technologies to understand your customer’s preferences better.
According to research by Forrester, several companies will adopt AI by 2025. According to IBM, these are some drivers of AI/ML adoption.
If you are planning digital transformation and want to implement Machine Learning, you must know the two sides. You should know the opportunities available with the technologies.
Additionally, you must be aware of the challenges you will face when implementing it.
This guide will discuss both sides of this technology to help you make an informed decision.
Limitless Opportunities with ML
It was an experiential moment for the viewers when Batman met Superman in the DC world. A similar thing will happen when software development merges with Machine Learning. It will reap limitless opportunities for the business, thus enhancing its capabilities.
We will look at the ocean of solutions ML can offer your business.
#1 Automation to Boost Operational Efficiency
Imagine a situation where you are supposed to ensure all the items on the conveyor belt fall into a single box. Once that is done, the package must be sealed, packed, and labeled. You will need a lot of resources to handle these aspects. This repetitive task can become mundane with time, leading to more operational errors.
You can automate this by adding a Machine Learning algorithm to your system. The machines will learn to seal the box, label it, and pack it. This would save you a significant amount of time. Eventually, you can use the resources to work on core tasks that cannot be automated.
Manufacturing isn’t the only industry that can benefit from automation. You will notice that retail, software and even internal business processes can benefit from this automation.
#2 Precise Predictions for Better Revenue
How would you feel if you could forecast the number of products you sell in the month? It feels spooky. But, with Machine Learning, this could be your reality. You would know the number of products likely sold in that particular month.
The machine will look into the past data for the product. It will identify patterns between the sale and the other factors surrounding it. For instance, the company might notice that they achieved 90% sales for oil during winter. Similarly, their new sweaters are likely to get more customers in winter.
These predictions will help you plan the supply chain with defined distributions. Eventually, you can meet the market demand. You can also strategize your sales team’s goals accordingly, which greatly benefits you.
#3 Personalization to Foster Customer Experiences
Say you want to gift your friend on their birthday. But you aren’t too sure what they would like. You ask their sibling or spouse to help you find the perfect gift. Now, let’s generalize this a little bit. The business wants to provide you with the choicest range. However, they are still determining what you would like.
That’s when Machine Learning steps in. From the first example, they become that sibling or spouse, guiding the business to know more about you. The machine has studied your browsing habits, purchasing frequency, and spending capability. They would have created an algorithm to help businesses carve a more personalized dashboard or product range for you.
Marketing teams can benefit the most from this algorithm. They can personalize the loyalty program, offer more discounts, and even send messages that are relevant to you. This would increase the chances of converting every lead into an SQL (Sales-qualified lead).
If you move to another niche, healthcare, you will notice more personalized care plans. The doctors know that no two patients are similar. Now, they can work on their treatment accordingly.
#4 Insight-backed Recommendations
Machine Learning can offer your business extensive opportunities to build a strong user base. You can cater to their needs by building an apt recommendation engine with Machine Learning technology.
Machine Learning can acknowledge the vast data you derive from Big Data and Analytics technology. It will study the cleansed data and understand how the customers think. They can predict customer behavior and anticipate their purchases better.
You will also know the customer’s unspoken needs and innovate your business to meet them. The technology can easily handle massive data sets and offer crucial insights.
Eventually, businesses can improve decision-making and improve experiences.
Challenges in Adopting Machine Learning Technology
We have seen the opportunities available with Machine Learning. Let’s look at the flip side- the challenges of implementing this technology in your business.
#1 Low-Quality Data Hampering Results
Businesses don’t always possess quality data so you might be dealing with incomplete or inconclusive data. This can impact the algorithm’s outcomes. For instance, if the data related to users’ purchases or spending habits is insufficient, you may not be able to derive solid recommendations.
Similarly, cleaning it may be challenging if you have been looking into data with a lot of noise. This can take up a lot of your time and energy.
#2 Overfitting and Underfitting Issues
If the model is acknowledged as overfitting, it simply means your model needs to be simplified. It also means that there needs to be more biased data that can hamper the decision-making process of the Machine Learning algorithm.
Overfitting also occurs when you have used non-linear models to build the data, which means there are too many trains and chains. This can increase the noise and impact the outcome.
On the other hand, underfitting occurs when you take less data to make predictions. If your model needs to be trained with more data or the chain is too simple, you may view an underfitting scenario. You may notice this owing to inaccurate data sets or noise in the data.
#3 Lack of Skilled Resources
You need skilled and well-trained resources when implementing Machine Learning technology into your application. They must be proficient with data cleaning, noise in the data, and building algorithms.
They should build efficient solutions and be adept with the system’s requirements. They should be bias-free and work within the ethical norms of the technology.
Finding someone with the right experience, proficiency, and understanding takes a lot of work. That’s one of the biggest reasons businesses cannot implement it efficiently.
Conclusion
Digital transformation is complete with Machine Learning. Advanced technologies give you a better purview into your organization’s capabilities. It will translate into improved decision-making, customer experiences, and profits. It is important to partner with the right ML consultant to ensure a smooth process and defined methodology for implementation.
I am a Tech geek and blogger, a seasoned freelancer, and my hobby is to enlighten my views and skills that will helpful for new inductions of the industry.