B2B sellers need to get on the machine-learning bandwagon

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.

{mosads}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.  

Automation also supports personalization by recommending to customers other products, blogs, demos and related features for exploring web content, videos, case studies and success stories, or even simulations or demos with real customer data, augmented-reality and virtual-reality experiences.

These personalization methods help boost customer engagement and drive experience during the learning and exploration phase. 

There are several building blocks to using machine learning and predictive analytics in B2B for customer data platforms:

First, companies must define the business case for creating an intelligent data platform. All business strategies need to be specific, measurable, attainable, relevant and time-based. They must identify the most promising business scenario and outcomes for the project.

The project should be oriented toward strategic use of data models that can outperform humans when it comes to classifications and predictions in terms of precision and time. The goal is to provide assisted capabilities to the salesforce rather than replacing resources.

Further, companies need to narrowly define the specific problem scenarios. They should identify the customer segment in terms of business impact, a specific touchpoint in terms of relevance of customer experience, the part of the customer journey that requires more understanding and the sales cycle that requires more agility.

When building a model, companies should start with small experiments in order to create a culture of innovation and flexibility. The business case should include expected benefits and costs at the initial scope level.

Trying to estimate those benefits and costs across the full implementation is time-consuming and causes too much uncertainty.

Finally, companies need to decide on valuation criteria to evaluate the hypothesis to be tested.

Once a model is being tested, companies need to revisit, redesign and adjust the operating model to ensure organizational alignment. Artificial intelligence projects require new roles, functions, responsibilities, competencies, skills, technologies, processes, governance and workflows.

This may lead to a cultural change, so the new business model should be introduced as part of the initial scope, followed by expanding the implementation as deployment progresses. 

{mossecondads}The most crucial element is defining who owns predictive analytics and machine learning in an organization and who ensures alignment and interaction with the rest of the organization.

Some companies may have a team of data scientists supporting business units, while others may prefer each business unit to have its own point person to work with the data department.

Using a wide range of open-source tools, it’s less expensive than companies may think to start implementing small machine learning and predictive analytics solutions. Companies that haven’t yet begun exploring their options should get started. This is the direction that B2B is heading, and it’s time for firms to catch up. 

Matias Adam is an alumnus and lecturer at the MIT Sloan School of Management. He is the author of “Improving complex sale cycles and performance by using machine learning and predictive analytics to understand the customer journey.”