November 12

The definitive guide to big data collection and analysis in B2B

Discover what big data collection is and how big data analytics can help your B2B company improve sales, marketing and customer service. Here’s what you need to know.


According to McKinsey, 47% of businesses today say that big data analytics has significantly changed the nature of competition in their industries. If you want to stay competitive in the B2B market, you need to employ big data collection and analysis in your company. Read on and learn how.

Key takeaways

  • Big data analytics collects large volumes of data from multiple sources and uses it to improve B2B operations and the customer experience 

  • Big data analytics is used in B2B sales to improve lead generation, customer experience and customer retention

  • Big data analytics is used in B2B marketing to provide campaign optimization, predictive analytics and account-based marketing

  • Big data analytics is also useful in improving customer service

What is big data collection and analysis?

Big data is a term used to describe large, often difficult-to-manage amounts of data. Big data can come from a variety of channels and sources. It can be either structured (as in a database) or unstructured. Businesses collect big data about their sales, their customers, their competitors, and their markets – as well as their own internal operations and activities. 

Analyzing all this data is challenging. Big data analytics must take data in varying formats and types and somehow make sense of it all. Big data analytics platforms employ a variety of advanced technologies to integrate data from multiple sources, extract key insights and identify important trends. These technologies include:

  • Artificial intelligence (AI)

  • Machine learning (ML)

  • Natural language processing (NLP) 

This collection and analysis of big data are essential to the success of B2B companies going forward. Companies that can use big data analytics to predict market trends and create personalized customer experiences have a major advantage over competitors who lack this sophisticated insight. When you employ big data analytics, you'll not only uncover important information, but you’ll be able to make faster and more informed business decisions. 

Big data analytics in B2B sales

Big data collection and analysis are particularly useful for B2B sales. Your sales team will find many beneficial uses for big data analysis, from generating more leads to retaining more customers.

Lead generation

Big data is particularly useful in generating more and higher-quality sales leads. Lead generation has typically been a haphazard and costly affair, but that changes when big data is brought into the picture. 

Big data analysis turns lead generation into a more structured and predictable process requiring less guesswork and generating more revenue per sales rep. The ability to easily collect big data sets provides more potential leads, while the use of AI and big data analytics helps identify those leads that are your most likely prospects. 

Personalized customer experience

B2C customers have long demanded a more personalized customer experience, and B2B customers are becoming equally demanding. By collecting more data about customer behavior, expectations and needs, you can fine-tune the experience you provide to your B2B customers. Big data analytics enables you to:

  • Improve customer communication

  • Improve customer service

  • Create products, packages, and pricing specific to each customer

Big data analytics is particularly important in providing a personalized B2B ecommerce experience. Ecommerce personalization is complex, requiring knowledge of who's visiting your site, whether they've visited before, what they're looking for, and why – answers that analyzing big data can provide.

Image Source: https://www.cosoit.com/must-have-data-science-tools-for-b2b-communications 

Customer retention and CLV

Offering a more personalized customer experience can dramatically improve customer satisfaction. The more satisfied your customers are – and the more they feel they have a direct connection to your business – the more loyal they will be. B2B customer acquisition costs have risen more than 60% over the past five years, which means that it is much more profitable to retain existing customers than to acquire new ones. 

Retaining loyal customers also increases the Customer Lifetime Value (CLV), which makes your business more profitable over time. Using big data analytics to keep your existing customers satisfied is essential to the continued success of your business. 

Big data analytics in B2B marketing

Big data collection and analysis helps you fine-tune your B2B marketing activities and better analyze their performance. The more effective your B2B marketing, the more effective your sales team will be.

Campaign optimization

Big data analytics is particularly useful for evaluating the performance of your marketing activities. You can now track the effectiveness of a given campaign in real-time, so you know which activities to continue and which to change or eliminate. You can optimize your campaigns for specific:

  • Markets

  • Channels

  • Customers

Predictive analytics

Until recently, data analysis consisted solely of the examination of past performance. While looking back is useful, it's even better if you can look forward to anticipate what might happen in the future. Collecting and evaluating big data with AI and other technologies enables truly predictive analysis. This helps you identify market opportunities, customer needs, and potential customers, as well as create timely content and products for your customers. You no longer have to guess what's going to happen; big data and predictive analysis let you forecast real-world results and proceed proactively.

Image Source:
https://www.predictiveanalyticstoday.com/big-data-analytics-and-predictive-analytics/ 

Account-based marketing

Big data analytics enables your organization to employ account-based marketing (ABM) and better personalize your marketing and sales to specific accounts. ABM is a strategy that focuses your sales and marketing resources on a group of target accounts within a given market. This enables you to personalize your marketing activities and fine-tune your marketing message to the specific needs of each account. 

Customer service

By using big data systems to collect and analyze a variety of customer data, you can provide more responsive customer service. Big data analytics provides more relevant and actionable information about customer questions, problems, and behavior so you can adjust your customer service accordingly. Predictive analytics helps to forecast both future support load and the questions that customers are likely to ask – so your staff can be better prepared to handle them.

Let Optimizely help you get the most out of B2B big data collection and analysis 

Optimizely can help you get the most out of your B2B company's biggest asset: its data. We use big data analytics to help you collect customer data from a variety of sources and use that data to better understand your customers and deliver a more personalized user experience. We will help to better predict customer behavior and optimize your company's B2B digital experience.

Contact Optimizely today to learn how you can use big data analytics in your B2B company!