B2B sellers need to get on the machine-learning bandwagon – Matias Adam

Matias Adam, Lecturer at MIT Sloan School of Management

From The Hill

As companies collect increasing amounts of data about customers, a key challenge is connecting that information to customize the customer experience and boost sales. The customer journey begins long before the actual sale.

It starts with online searches, store visits, conversations and emails. Companies need to connect all of these touch points to identify potential customers and turn research and exploration into sales.

While business-to-consumer (B2C) markets have been deploying customer data platforms to consolidate the customer experience and improve marketing personalization, this has been a bigger challenge in the business-to-business (B2B) markets.

This is due to the complexity of B2B, where each customer has multiple decision-makers and users that are not always identified in the early stages, and the entire sales cycle is longer and relies on fewer leads, prospects, opportunities and sales than in B2C space.

Additionally, the content required in each stage could be different and may depend on the needed solution and design, where the value-added selling approach is more relevant than the product differentiators. The benefit of personalization is to deliver the proper message to each customer, and that isn’t straightforward in B2B.

Further challenges include resistance to change, shifting priorities and strategies, organizational capabilities, talent retention, as well as technology obstacles. Creating a customer data platform for B2B isn’t a fast or inexpensive undertaking. In fact, only 30 percent of companies decide to continue a rollout due to shifting priorities or resistance to change.

However, this process could be much smoother if companies used intelligent data-supported decisions systems to aggregate, augment, classify, segment and sort data on their platforms.

Artificial intelligence models are tools that can help companies discover topics for specific audiences and define content marketing initiatives based on customer, market and competitor information during the strategy and planning stage.

In the next phase, artificial intelligence models support the creation of documents, emails, advertisement, web pages and blogs, etc. Intelligent models can run controlled experiments, such as A/B Tests, to measure which variation of a website is the most effective at turning them into customers.

By combining the machine-learning and controlled-experiment tests, it enables evaluation and optimization of performance by measuring the impact on customers’ experience at account-based engagement (ABM) level across all channels and programs.

Read the full post at The Hill.

Matias Adam is a Lecturer at the MIT Sloan School of Management.

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