Harry Djanogly

As a company engaging in B2B e-commerce, you’re probably looking for ways to make good use of your customers’ online behaviors to drive sales. You can increase your conversion rates drastically by using big data to make predictions about your customers and personalize your marketing and sales efforts.

Getting personal

Your typical website visitors bring with them a plethora of data. Right off the bat, you have access to information about what products they’re interested in, if they’re looking to use a discount, and more.

While it’s hard to use all that data in a meaningful way, analytics platforms can help you gain insights from customer data that you never could before. Based on these insights, you can build and serve personalized banners, pop-ups, and even page content.

As part of your data collection strategy, make sure you’re collecting, storing, and analyzing data from all platforms. This means you’ll want data from social networks, CRM applications, marketing tools and inventory records, just to name a few.

Using your data, you’ll be able to find key identifying features like the ages, demographics, communication behaviors and media preferences of your various customers. You can then segment your audience into customer types. While sorting through your data to segment your customers sounds like a daunting process, data management platforms like Piwik PRO, Lotame, and V12 Data are designed to do most of the work for you.

Once you’ve segmented your customers, you can tailor your marketing channels to specific demographics, interests, and more. For example, you’ll probably find that some types of customers are most accessible through email, while other types are most accessible through Facebook. Once you decide which marketing channels are best suited for which customers, personalize your messages to better fit the unique needs of each customer type.

Gathering sales intelligence

For a huge impact on your B2B e-commerce conversion rate, you can also harness big data as part of your sales intelligence strategy.

Sales intelligence is the gathering, organization and analysis of customer data. With it, you can achieve powerful insights about customer preferences and make even more powerful predictions about their next purchases.

You can use predictive analytics to anticipate what product a customer is likely to purchase. You can then use that information to create targeted ads, personalized offers and workflows that help push the right products.

To implement this, use a platform that gathers and organizes all of your customer data. With a little research, you’ll find platforms available with tracking and predictive capabilities. Pepperi, for example, gathers customer data and organizes it in a way that helps you predict your customers’ interests and preferences. It also allows you to generate unique price lists for different customers, as well as discounts and cross-sell and upsell promotions for those customers.

Sales intelligence can also take the form of tools like Optimove, which uses micro-segmentation to organize and analyze customer data. By micro-segmenting customers, the platform is able to predict the behaviors of multiple customer types so that companies can create targeted marketing and sales campaigns. Another tool, Datanyze, specializes in technographic segmentation. Also known as technographics, this technique segments potential customers not only based on demographics, but also based on those customers’ preferred means of technological engagement—whether it be through smartphones, desktop computers or other devices.

Overall, sales intelligence tools analyze as much customer data as possible to help companies better adjust prices and marketing efforts for individual prospects. Effective use of customer data not only lets you convert visitors into customers, but also helps you increase your customers’ average order sizes.

Enhance lead scoring with AI

You’re probably already using lead scoring to help you market to the most qualified customers. Lead scoring is the methodology of ranking prospective customers based on their estimated value to your company. The process is useful because it can help your company determine which prospects to prioritize in your marketing and sales efforts.

Accurately ranking prospects is key to converting them into customers. You can use artificial intelligence platforms to analyze 50 to 100 times more data than standard lead scoring. This results in dramatically more accurate predictions as to who might become a customer and what products or services they’d be interested in. Artificial intelligence, or AI, is a term used to describe software that learns from the data it compiles to improve on its own such functions as recommending products or sources of products.

B2B predictive analytics is providing double-digit increases in leads, opportunities, and sales because of its power to create accurate predictions.

As with customer segmentation and sales intelligence, a wide array of artificial intelligence platforms are available to help you use AI for predictive lead scoring. Lattice, for example, scores leads in real time to ensure that marketing and sales teams are prioritizing the most likely buyers. Mintigo and New Relic similarly monitor customer data, intelligently identifying prospective buyers so that you can market relevant products to the most qualified customers.

Harnessing big data to improve B2B e-commerce conversion rates isn’t as hard as it sounds. Whether you’re stepping up your personalization efforts, gathering sales intelligence, or improving lead scoring with artificial intelligence, there’s already an abundance of software to begin implementing your new strategy. With such simple implementation and the possibility of high double-digit returns, there’s no reason to hold back.

Harry Djanogly, manager of inside sales at software developer Alpharetta Software, writes about marketing and related technology systems from his base in Atlanta. His articles also appear on his website.