Artificial Intelligence (AI), machine learning (ML), and big data are three technologies that have continued to dominate the technology field and have completely disrupted enterprise operations. Today data-driven decision-making is as crucial to any business as its lifeline. AI and ML, along with others like cloud computing, are the techniques and platforms capable of collecting, storing, and analyzing massive volumes of data generated from various sources for business use.

While it was believed that AI, ML, and big data are most useful for B2C companies, their potential has clearly extended beyond to revolutionize B2B companies. Customers’ ever-evolving expectations, demand for personalization, journey maps, and eventual purchasing experiences are similar in B2C and B2B companies. However, the purchasing cycle for B2B customers is generally more complicated, perhaps lengthy, and involves more players in teams. For this reason, B2B companies stand to benefit more from big data analytics and machine learning.

For this reason, professionals undertaking AI and Machine Learning Courses have broader job prospects. Whichever career path they will choose, it is essential to understand that efficiency is key in customer journey mapping, product design, and delivery. This is where AI intelligence and ML come in to help businesses to anticipate customer needs using predictive ML models and big data. Today’s market demands personalization which has become businesses’ way of aggressive marketing.

How AI, Machine Learning, and Big Data are influencing B2B companies operations

The global machine learning market size was valued at $15.44 billion in 2021, and the market is growing rapidly. It is projected that the AI market will reach $53.06 billion by 2026 as digital transformation continues. Machine learning applications are numerous, including predictive analytics, image recognition, speech recognition, product recommendations, and more. B2B companies are integrating AI, ML, and big data in their operations, including:

   1.Marketing

More companies are integrating machine intelligence into their marketing strategies. B2B marketing refers to businesses targeting and generating demand for their products and services from other businesses. Different from B2C, the B2B buying cycle is longer and more complex involving multiple decision-makers in between.

The marketing funnel involves the following steps:

• Demand generation is a marketing approach that is aimed at creating awareness, driving demand, and building audience interest for a product or service in the B2B space.
• Lead generation is one of the longest time-consuming processes for a sales professional or marketer. Companies are leveraging AI technology to collect and analyze large volumes of unstructured data from search engines, content marketing, and social media to gain insights and leads. Lead generation software like Drift can generate more leads from a company’s existing assets.
• Lead nurturing and managemen AI and ML are the future of lead generation and nurturing and the entire sales cycle. In lead nurturing, data is collected that provides insight into the needs of existing and prospective customers. It also entails designing and communicating solutions that will earn their trust and build a lasting connection.
• Buying committee decision-making. Part of lead management entails identifying members of the buying committees of your customers and understanding each individual’s role in the committee. Also, it is essential to understand the committees’ decision-making processes to be better placed to make an informed sales pitch with a high potential for conversion. AI technology comes in handy when a business has many target audiences, which further complicates marketing and sales.
• Conversion. At the bottom of the sales funnel is conversion. Establishing a sales conversion process involves scoring leads into different categories, including quality leads, sales-ready leads, and more. Machine learning models are used to gather customer data, identify buyer personas, and allocate score values. Scoring leads helps to determine sales-ready leads that you can focus on converting.
• Growing customer relationships. The sales cycle does not end with conversion. Successful businesses thrive on building lasting customer relationships. Beyond the purchase, the company should be interested in following up with their customers after a successful conversion to evaluate their purchase journey and build connections for future conversions.

AI and predictive modeling are useful in developing targeted marketing campaigns and lead generation. Customer data carries invaluable insights that help B2B businesses to understand their customer’s purchasing behavior and anticipate their needs/value. With applied machine learning, it is now possible to have a 365-degree view of consumer accounts and purchasing committees and follow through with very specific leads up to the point of conversion and thereafter.

Marketing automation is often applied to push targeted content to buyers at the right time through their preferred channels, a technique that has proved effective for conversions.

    2. Customer relationship management

Big data and AI have been leveraged widely to create valuable customer engagement and experiences. A B2B company would be interested in its customers’ demographic, firmographic, technographic, chronographic, quantitative, and qualitative data. Gathering this data using big data technologies and using AI and ML techniques like predictive analytics to understand customer behavior and expectations is core to delivering personalized customer experiences.

Unlike in the past, B2B companies have turned to AI-powered personalization to filter through customer big data and intelligently deliver personalized recommendations and engagement to their customers cost-effectively at scale.

    3. Development of new products and services

A 365-degree view of consumer and purchasing committee accounts is possible by having a central data storage accessible and updatable by various departments and teams upon interacting with the customers. Thus having the same CRM system for customer service, sales, and marketing is crucial not only for the mentioned teams but also for product development teams. In an era where collaboration is key to a company’s success, teams ought to have shared goals toward meeting customer needs, and this is enabled by big data, AI, and ML tools and technologies.

Conclusion

This is the time for B2B companies to harness AI, ML, and big data for the immense benefits that these technologies deliver to the business. These revolutionary tools are behind such breakthroughs as recommendation engines, automated email marketing, conversational AI, real-time price analysis, and more, which have transformed processes like customer support, customer engagement, and lead generation.