Online and digital channels are now where B-to-B marketers spend a vast majority of their time, effort and money. As a result, online marketing has becoming an extremely noisy space, and finding the right prospects poses a major challenge. Lead generation ranks as the top objective for B-to-B companies’ digital marketing programs, and yet, 61 percent of B-to-B marketers say the biggest challenge they face is generating high-quality leads.
However, finding quality leads is only half the battle. With all the noise out there, the greater challenge is actually engaging those prospects. Getting in touch with buyers has never been more difficult. Research from TOPO found that call back rates are below 1 percent, and less than 4 percent of sales emails are opened. B-to-B buyers are inundated with sales emails, to the point where they ignore nearly all of them.
What’s a B-to-B marketer to do? Today, more and more brands are turning to predictive analytics to inform and improve their online marketing campaigns.
Predictive analytics can help B-to-B marketers identify the highest-quality leads and make the conversations and interactions a lot smarter for both sides. The technology uses a wide range of data — e.g., personal and corporate demographics, interests expressed, offers accepted or rejected, products purchased, locations, devices and channels used, and environmental conditions — to forecast future behavior based on how “similar” customers have behaved in the past.
Predictive analytics can create a detailed profile of a company’s ideal customer using data from the structured, open and social web (e.g., content consumption and technologies used). Leveraging this same information, predictive analytics platforms can then use this profile to find net new prospects with similar characteristics to the ideal customer profile.
Each prospect is scored against this ideal customer profile to indicate their likelihood or intent to buy. Armed with this information, sales reps and marketing teams can prioritize the most promising leads, and leave the other leads to lower-contact efforts, like nurture or drip campaigns.
Predictive analytics solutions are also useful for enriching leads and making smart recommendations. They can pull data from structured sources, as well as open and social web sources, to add information to existing customer profiles, such as a buyer’s technology use or a company’s specific expertise. This helps create more context for the salesperson when they reach out to have a conversation.
In addition, predictive analytics can use data — e.g., purchases of individual products or responses to different types of offers — to predict which products a company or individual is most likely to purchase, messages they’re most likely to reply to, or the channels they prefer to communicate through.
Predictive analytics solutions provide lead intelligence as well — i.e., they provide guidance to sales and marketing people about how to treat individual leads. B-to-B brands can use the technology to understand which leads are close to initiating a buying project, making a purchase decision or otherwise responding to a contact. This enables sales reps to apply their time to the most pressing needs.
Once a lead becomes a customer, predictive analytics is a valuable CRM tool. B-to-B marketers can use the technology to anticipate the needs and actions of existing customers, such as how they’ll use the system, what kinds of help they’ll need, what additional products or modules they might buy, and their likelihood of churn.
Finally, predictive analytics can help marketers predict the impact of marketing interactions and estimate customer value. This is increasingly important as marketers move towards automated approaches such as programmatic media buying, which rely on accurate predictions of a marketing interaction’s impact.