5 B2B Tech Trends That Will Pay Off in 2018


Featuring RK Maniyani, CTO, True Influence

Marketing technology has exploded in the last few years, and today’s B2B Marketers are faced with a daunting array of new tech trends and solutions which promise everything from automatically writing marketing copy to predicting future customer behavior.

To get an idea of how complex the landscape has become, check out this infographic from Martech Today, which lists more than 5,000 providers across various verticals and categories for 2017.

That’s up from 150, just six years ago.

All these options can be overwhelming. And B2B Marketers’ choices are made even more difficult by a trade press that so often focuses on technologies promising revolutionary change – sometime in the future. But as our VP of Sales Ken Stout noted recently, even many large, successful companies are still early in their learning curve when it comes to data-driven Account-Based Marketing.

In 2018, successful B2B Marketers will focus on optimizing their ABM processes by integrating and analyzing Big Data to drive more precise prospect targeting and content customization. You’ll want to keep an eye on other emerging technologies, such as advanced voice recognition and hyper-local mobile search; however, the coming year will be a time for most businesses to strengthen and expand their mastery of data-driven marketing.

Here’s a quick overview of five areas of B2B Marketing technology that will yield real benefits in 2018.

Integrating Big Data Sources

“Big Data” was cited as the top priority for 2018 by 14 percent of respondents in a recent Smart Insights survey. As you might expect, the survey’s definition of “Big Data” is extremely broad, encompassing both data acquisition and analysis. But the message is clear – marketers know Big Data is essential, and they also know that have a lot of work to do in 2018 to get the most from this vital asset.

I have to note the top priority in the Smart Insights survey is Content Marketing, at 20 percent. Our CMO, Janet Rubio, recently shared some great insights how marketing teams should invest in high-value content in 2018. Remember, successful ABM efforts are based on both tech and content development.

In addition to behavioral data collected on your own sites and email, your team should invest in Intent Signal Monitoring data from third-party services such as True Influence’s InsightBASE. Intent data gives you millions of additional data points about what target accounts are researching across the Internet, allowing you to target them at times of peak interest.

Other data sources that should be integrated for marketing analysis include:

  • Social media mentions and activity;
  • Digital marketing / advertising performance;
  • Reputation / mentions in trade press and other sources; and,
  • Competitive market intelligence.

Building a B2B Marketing Tech Ecosystem

With more than 5,000 providers in the market, it’s not uncommon for a company to have a dozen or more home-grown and third-party tools employed for various niche functions, such as news feed monitoring. Integrating key data sources gathered by these tools is essential, but the tools also must be able to work together to execute automated campaigns and other key tasks.

And this B2B Marketing Ecosystem, as it’s called, extends beyond your own systems to those of key partners. For example, inventory data from a supplier’s ERP or Supply Chain Management system can be used to quickly fire off an e-mail campaign from your Marketing Automation System (MAS) notifying current customers about great end-of-quarter discounts.

In an ideal world, all your disparate systems would communicate seamlessly. But key systems to include in an extended B2B marketing system include:

  • CRM
  • MAS
  • ERP
  • Product Information Management
  • Order Management

Enhancing Personalized Content Creation

The more data you have to drive your ABM campaigns, the more precise you can be in not only targeting prospects’ expressed interest but also creating highly personalized content based on numerous factors.

Traditional methods of personalization have relied on A/B tests to evaluate broad response and effectiveness, and segmentation of audiences into profiles based on that testing.

But Big Data, when coupled with machine learning and other advance analytics tools, can define audience profiles at a far more discrete level, based on a wide range of factors that simply is not possible with manual testing and segmentation. These segments can be just a handful of users. And they can be updated constantly, which again is not plausible with traditional methods.

If you can identify key influencers for major spending decisions, an investment is high-value content for even a dozen or so prospects can be readily justified.

The buzz in the trade press often takes this one step further, focusing on emerging Artificial Intelligence (AI) tools which can interact with prospects on a one-to-one basis and create content for them on the fly. Forbes, for one, is high on AI-generated content, and some analysts suggest that within five years half of all marketing content will be machine generated.

That may be true in the future, but for now I think machine learning’s big payoff will be in more discrete audience profiling and the smart investment of content creation resources it supports.

More Advanced Segmentation of Email and Other Channels

There may be no better example of the gap between market buzz and reality than in segmentation for email.

The Smart Insights article I referenced earlier reports that only 50 percent of companies employ data-driven email targeting at all. Only 15 percent of surveyed companies use segmentation and personalization rules to deliver highly-tailored, automated email messaging to audiences, as defined by attributes captured in their own database.

This despite most of these companies having invested in MAS or other technologies designed specifically for that purpose.

So, there’s work to be done before implemented technology is meeting its full potential. As I wrote earlier this year, the real current value for AI and machine learning analysis is finding meaningful patterns is Big Data intelligence and using those patterns to create segmentation and automation rules to optimize email campaigns and other channels. This effort should include PPC and Digital Marketing campaigns, where machine learning can help your team identify not only the best platform but also Geo and campaign timing patterns that will greatly increase response and ROI.

2018 should be the year your team fully recognizes the power of advanced segmentation and targeting.

Extend Content Personalization Throughout the Customer Journey

With data-driven ABM segmentation in place, you can extend content personalization to every touch point with your prospects and customers.

Most Web-based Content Management Systems (CMSs) support some level of personalization, based on obvious site interaction (e.g.: since you are reading this blog post, you might well be interested in this offer). With data-driven segmentation in place, this capacity can be expanded to present CTAs and additional content that is far more likely to drive high-value conversions.

Additionally, with key systems incorporated into a Marketing Tecnhology Ecosystem, you can extend content personalization to all critical points of customer contact. For example, your Order Management System can present upsell offers based on Intent Signal data, not just past order history. Effectively, your Marketing team can present messaging, which you might have otherwise planned for a major email push, while a customer is already engaged with your company and pulling the trigger on a spending decision.

That’s powerful.

Realizing the Real Benefits of Tech

2018 will be a year when smart B2B marketing teams realize the full potential of technologies which are reaching maturity, and may well already be in place in their organizations. You always need to be aware of emerging technologies, of course, but optimizing your ABM systems will yield the most payoff in the coming year.

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