Frost & Sullivan, projects that U.S. B2B e-commerce sales will reach $1.9 trillion by 2020, as global B2B online sales will reach $6.7 trillion.

Imagine the amount of data that’ll be generated through so many digital customer interactions every day across the world. The sheer size of catalog information that B2B companies need to maintain is mind-boggling. With so much happening in the B2B online segment, Machine Learning can be the logical solution to help leverage the hidden strength in your customer data.

Machine Learning for Content Creation

B2B eCommerce Marketers can leverage Machine Learning to create content that most of the store visitors look out for. For example, Machine Learning toolsets can combe through millions of chat logs to detect the most frequently asked customer inquiries and responses. These FAQs can be added to the Product Detail pages to make it more informative, as well as reduce the load on support teams.

Machine Learning for Content Extraction

Delivering the right content to the right contacts at the right time can do the trick for B2B stores in improving ROI. As B2B websites have chunks of data like lengthy product descriptions and technical specification, it could be challenging for users to place eyes on what they want to know first. Machine Learning can help here.

Machine Learning algorithms can be created to identify and pull out snippets of information. For instance, a B2B store selling machinery parts has ‘wheel bearing’ as a category, ML can be used to extract information based on features like ‘measurements’, ‘material’ etc.

An application like this can save a huge amount of time for marketers who would to manually perform the activities.

Machine Learning for customer segmentation

The effectiveness of B2B lead generation depends largely on focusing on the right prospects. Customer segmentation forms the key here. B2B buyers expect companies to anticipate their specific needs and act accordingly. Only with segmentation, B2B marketers can improve personalization contributing to better conversions.

In the US, 70% of retailers see personalization of the customer experience as their top priority – eMarketer.

With growing digital transactions, customer segmentation criteria can get even broader over time needing more than the human effort to unfold the complexity. Here’s where machine learning can chip in.

For instance, a machine learning algorithm can be programmed to segment users as an “engaged customer” based on multiple criteria like ‘abandoned carts’, ‘page views’, ‘heatmap’, ‘time spent on site’, ‘replies to email campaign’ etc.

Knowing user segmentation data can help marketers to come up with customized web page contents and discounts to improve conversion.

Machine Learning for B2B sales enablement

Though B2B business prepares to be digital-ready, sales pitches and briefing calls happening over a phone is going to be an integral part of its B2B sales and lead nurturing process.

Mining crucial data out of sales calls that can help B2B sales executives prepare better for followups is an uphill task when done manually.

Machine learning can help here as well through what is called ‘conversational intelligence’ solution. Machine Learning solutions using NLP can, record and analyze calls. Based on the analysis, the algorithm can transcribe and highlight areas where customers have mentioned pain points, competitor’s name, pricing and so on.

Apart from reducing human effort, ML can help B2B sales executive understand their sales calls better and focus on the most important areas where a prospect expects them to provide solutions. Large companies that rely on phone calls for B2B lead generation and nurturing, can leverage such ML innovation to prepare better for positive outcomes.

Machine Learning for localization

Your B2B store can have buyers hailing from different geographical location. Localization is the key here. Geo-location based personalization is a good example of broader level localization.

A B2B store selling home construction equipment has to show different equipment types customized for users based on their geographical locations and seasonal changes.

For example, the site should display hurricane-proof equipments for a user accessing from a state that’s hurricane-prone. Hence, as the buying season and location-specific needs differ, the product requirements of the specific user will differ as well.

Developing a geo-location based product personalization model can guide marketers to show appropriate product suggestions customized for specific users.

Key takeaways for B2B businesses

B2B businesses have to respond to change in online customer behavior quickly by creating systems that detect change almost real-time without having to go through reports days later.

Your existing analytics system and B2B eCommerce platform might churn data but that’s just not enough. Consider Machine learning and predictive analytics irrespective of your business size. As an early adopter, you can easily outrun those who hesitate.

Read Our CIO’s Quick Guide To Taking The Machine Learning Dive.

Closing lines

As B2B buyers have started embracing online buying, data generated through every customer interaction is growing rapidly. On the other side, computing systems are now available at reasonable costs making machine learning adoption, not a distant dream.

This clearly indicates that B2B store owners can now start adopting Machine Learning at a fairly low cost to derive intelligence out of customer interactions. This will act as the fulcrum of how your business operates adopting a customer-centric approach.