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How is Machine Learning Impacting B2B Businesses?

How is Machine Learning Impacting B2B Businesses?

B2B businesses love efficiency. They throw their hat at anything that makes them better, smarter, and more efficient. In business parlance, they refer to better efficiency as ‘throughput.’ Businesses are always on the lookout for better technologies and efficient processes. Adopting new technologies that raise their output by 10x or 20x (the ‘x’ here is vanity) is the holy grail for most B2B businesses. 71% of executives say machine learning (ML) and artificial intelligence (AI) are game-changers for their business. If you are doubtful about the abilities of AI and ML in your B2B business, here are ten reasons that might change your mind.

Here is how machine learning is impacting B2B businesses deeply:

1. Predictive account management:

ML analyzes your sales and marketing efforts to figure out the prospects who are more likely to buy. It helps you choose the prospects who deserve more attention so that you can offer them the right kind of messaging, products and services. It will also help you understand what separates prospects who are more likely to buy from you than those who are not. This helps with lead scoring which prioritizes your sales efforts.

Functions like predictive sales and marketing, forecasting, lead scoring, extracting data from different sources, and being able to provide dynamic pricing based on a predefined set of values, etc., are what makes the contribution of ML unparalleled for B2B businesses. Any B2B business that does not take a serious interest in technology, especially like AI and ML, would be found wanting because scaling becomes a tad too difficult. Example: The airline industry is a heavy user of dynamic pricing strategies to maximize their revenue from each seat.

2. Increases the quality of life

The healthcare sector relies heavily on advanced technologies for reasons as varied as increasing life expectancy to reducing overhead costs. Healthcare is an industry that generates trillion gigabytes of data that certainly cannot be analyzed by human beings. Following are some of the ways in which ML affects the healthcare industry:

  1. It can identify patients who are at the risk of developing certain conditions or illnesses.
  2. Helps in the early detection of tumors that increase the chances of survival.
  3. Machine learning helps in creating customized treatments. With bio-sensors and other sophisticated medical devices becoming mainstream, ML will be able to produce remarkable results, thanks to the data that they will bring about.
  4. It has a huge role in clinical treatment diagnosis and documentation.
  5. There are algorithms that can find the difference between cancerous and healthy tissue, thereby improving the results of the radiation treatment.

3. Helps offer better customer service:

Customers these days want instant responses, no matter which channel or what time they communicate. A 24-hour delay in response will not make you look good in their eyes. Before a few years ago, we were introduced to software where customers could chat with you, Live Chat was one of the most popular tools, they still are. With live chat tools, prospects/customers could directly talk to a customer service agent with their queries, whenever the agents were available. Chatbots changed everything.

We were introduced to Chatbots recently, a phenomenon, when coupled with ML and AI, changes customer service forever. Chatbot allows you to respond to your customers 24*7. The best part? The ML system will learn the various responses based on its chats, previous chats, and other materials to keep improving its responses. It will not only be able to answer routine questions but even come up with ingenious solutions since the machine keeps learning.

4. Improves the buying experience:

Did you know that Netflix saves $1 billion every year with its recommendation engine? They say that the combined effect of personalization and recommendations does the trick for them. Considering that Netflix spends $5 billion and more every year to churn out content, $1 billion is a big number. According to them, consumers lose interest after 60 to 90 seconds if they cannot find something of interest. In such a case, the risk of a user leaving their service becomes high if this happens often.

Recommendation engines that are powered by ML help customers make the right choices by providing them options that are exclusive to them, thereby creating a personalized experience. In the new age of things, customers will appreciate, even deem such a level of personalization necessary.

Here’s an interesting note on the kind of information that Netflix uses to recommend the right titles for users. It collects data on each moment that is spent on their service, right from what we add to our queues, how much time we watch continuously, when do we stop a show, etc. Even YouTube has one of the most powerful recommendation engines on Earth. We are designed to binge-watch on these platforms. That’s what ML can do.

5. Hyper segmentation:

One of the most essential terms that marketers have to be familiar with is segmentation. Grouping unconnected customers based on their demographic factors, behavior patterns, buying patterns, and other parameters are called segmentation. For businesses that want to understand their customers, segmentation is a must, otherwise, your targeting will be off the charts.

Remember that each and every prospect is different. They have unique needs, specific objectives, and goals that might be completely in contrast to another prospect. So if you keep shoving the same kind of communication strategy to everyone in your lead lists, it will only be for a lost cause.

B2B customers expect personalization and one-to-one attention. The account value is going to be high and they want to be sure where they put their money in. Since there are massive amounts of data, it becomes almost impossible to do segmentation manually. There needs to be a granular level of personalization for your messaging and strategies to be highly effective. Hyper-segmentation powered by ML converts prospects into segments based on a set of attributes.

6. Increases sales, reduce costs, and optimizes retail operations:

Machine Learning has affected the retail industry in a myriad of ways. It helps with demand prediction, local optimization, churn prediction, fraud detection, sentiment analysis, and even ensuring that the right price is calculated based on preset conditions. ML algorithms enhance the user experience and better the website content based on customer behavior and interactions.

Retail giants like Walmart, Amazon, Target, etc., are heavily dependent on machine learning algorithms to power their day-to-day operations. Every single improvement big retailers make is usually powered by artificial intelligence algorithms. Customer engagement, supply chain logistics, inventory management, etc., are some of the most critical applications of ML in the retail industry.

7. Automation abilities:

Mind-numbingly tedious and repetitive tasks like inputting data, documenting a task, sending a series of emails, etc., can be totally eliminated with the help of ML. Machines can identify patterns and create processes to efficiently do something that will do away with the need for humans to work on it. The ML algorithm will process data, do repetitive tasks automatically, alert humans if something requires intervention- all the while allowing them to focus on their core tasks.

8. Optimizes HR efforts:

The algorithms search for relevant credentials and terms on resumes to shortlist candidates for the positions that are open. It saves countless hours for the HR department as they cannot be expected to scan through each one of them. ML can also help with analyzing feedback from employees which it converts into actionable tasks. It will also help with understanding employee referrals.

ML and AI help build employee experience in a variety of ways. It personalizes the learning journey based on someone’s role, existing skill sets, a roadmap for their future, and so on. ML assigns tasks from cross-functional projects based on the employee’s skillsets helps them with broadening their knowledge. Gartner’s Artificial Intelligence survey says that more than 17% of organizations use AI-based HR solutions and they expect another 30% to join the bandwagon by 2022.

9. Deliver the right content:

Sending irrelevant content to a prospect is akin to prospecting an Eskimo to sell them ice. Do not make the mistake of sending content to customers that are not in line with what they want from you. B2B customers expect personalization, thanks to ML, that is possible.

If you are a B2B business, you could use ML to analyze each content on your website. The visitors on your website and their data could be analyzed to personalize the content they would receive. You will be able to send relevant content at the right time and that too based on their stage in the buyer’s journey.

10. Changes the way we do manufacturing by ushering the era of smart manufacturing:

Companies are increasingly using ML and the Internet of Things, thereby ushering in an era of smart manufacturing. It is estimated that by 2025, the smart manufacturing market will be worth $384.8 billion. From increasing productivity, reducing labor costs, to reducing downtime and increasing productivity, the results from ML in the manufacturing sector is impressive.

ML is extremely capable of taking care of daily processes in the manufacturing units. It takes care of an entire gamut of departments like logistics, inventory assets, supply chain management, and more. With respect to product development, ML is an asset because it analyzes consumer data from different sources to detect business opportunities, create better processes, and prevent mistakes that others made.

One of the most popular uses of AI in the manufacturing sector is the use of robots. Robots are the new assembly line in the manufacturing sector, they are capable of performing tasks that can be construed as dangerous for human beings. They are also adept at handling repetitive tasks. It is no surprise that manufacturing industries are open to adopting new technologies as it has delivered brilliant results so far.

 

Conclusion:

The best thing about technology is that it has actionable solutions for most problems that businesses face. It aims to understand the psychology of prospects and the business objectives to bring solutions that give the maximum bang for your buck. The ML algorithms help businesses make smarter decisions that will fetch results. There is no guesswork involved, with ML and AI, every piece of insight that you receive is based on data.

Adopting technologies like AI and ML is not a luxury anymore, if you want to be an undisputed leader in your market, then it is a necessity. The benefits that these technologies bring into each department- marketing, sales, operations, finance, etc., can be liberating. It might turn into a game-changer for businesses that embrace them wisely.

Lini Susan John

Chatty & gregarious, you can find her with her baby plants when not with her marketing team.