Businesses everywhere are facing serious challenges, and adapting to a new economic reality. Around the world, the impact of Coronavirus (Covid-19) will be felt for years to come. Growth is slowing, and in some cases, countries are entering recession.
Now more than ever, businesses need to be competitive.
Creating new efficiencies and improving existing processes are one of the ways companies can achieve that. Another way is to simultaneously create new, more innovative products and services that are adapted to and benefit from this new reality.
One of the most effective ways to achieve both is through technological innovation. Machine learning (ML) is one area that companies have been investing in to improve operations, increase efficiencies and innovate.
What is Machine Learning (ML)?
Firstly, let’s consider what Machine Learning (ML) is, and importantly, what it isn’t.
ML isn’t, strictly speaking, about companies creating artificial intelligence (AI). ML and AI are phrases often used interchangeably. It might sound as though the only way to advance is to create an AI supercomputer, or bot, or droid, and have that take over various operational areas until humans are redundant. That isn't the case.
We are still a long way off from true AI.
Whereas, with machine learning, this is about designing and using computer algorithms to find patterns in data. Every company that falls into the mid-size and larger categories are sitting on mountains of data. Between data lakes and warehouses, there are millions of terabytes being generated everyday, and vast amounts are in company databases. An awful lot of this data is messy, unstructured, and needs to be organized before it can be made into anything useful. In a lot of cases, data sitting in one silo within a company can’t easily interact with data in another part of the same company.
In order to make data useful and useable, it needs to be cleaned. Known as ‘data janitorial work’, that is often the first step towards getting data ready for ML projects. Once that stage is complete, the algorithms designed to extract value from that data can be put to work. As they process more data, ML algorithms become smarter and they “learn” from the data, producing more useful insights and outputs.
Why businesses need Machine Learning (ML)?
Machine learning helps companies in a wide range of sectors and operational settings.
From predicting traffic patterns to the rates of infection, to the movement of goods through a supply chain, and what consumers are likely to buy or watch next, ML already plays an important role in the lives of millions.
Businesses who've not used ML are starting to experiment in this area. The cost of implementing projects such as these has gone down, and the technology that makes it all work keeps getting better and easier to use. There are more off-the-shelf solutions than ever before, and these can be more easily integrated with custom-made algorithms and systems.
When it comes to implementing machine learning, make sure you’re prepared for it to take time. It won’t solve problems overnight. However, thanks to the advances in recent years, it now takes less time and data to identify patterns, come up with predictions and recommendations, and then learn from those to consistently improve outputs.
Every data source you feed into an ML algorithm can play a role in generating insights that contribute to organizational growth. Here are a few ways ML is already making positive business impacts:
Pricing is one of the most effective ways to increase profits. In B2B environments, a 1% increase in pricing, done the right way, can increase profits between 8 and 11%.
In B2B sales, managers and salespeople, especially in wholesale companies always default to assuming price sensitivities on the part of the buyer. That isn’t always the case, and with the right algorithms, more accurate prices can be generated which has a noticeable positive impact on profitability.
Efficiency is something every business is going to be looking at, more closely now than ever. ML could be the answer to automating processes, reducing workloads, and finding more effective ways to deliver products and services. Companies need to find ways to drive forward operational efficiencies, which is easier when there are systems that can analyze and assess data from every angle. Working with data scientists will help companies ask the right questions. Algorithms then make it possible to generate the answers needed to take steps to improve operational efficiencies.
New products and services. Something businesses should have learned from the previous recession is that they can’t sacrifice growth for making savings. Businesses that innovate in recessions perform better than those that stay stagnant or simply make cuts to survive. Now is the time to help customers. Could you provide new products and services that would serve them better?
Imagine what the data could tell you, if it was analyzed using machine learning?
Would it highlight potential growth areas? Now is the time to explore this and act accordingly. Give customer what they want and need, even if it means iterating existing products and services, and then use this to find other similar customers, that fit the same profile, who would also benefit from these innovations.
Growing during challenging times makes companies far stronger and more resilient than those that don’t. Use machine learning to innovate, improve pricing, generate efficiencies and navigate challenging times.