There was a time, not too long ago, when big data evangelists were eager to debunk a dangerous myth: The (dead-wrong) claim that B2B marketers didn't need to put the same emphasis on big data as their B2C counterparts. Instead, they (rightly) argued that these marketers needed to embrace big data, or suffer the consequences. Unfortunately, those same evangelists also advised B2B marketers to put the cart before the horse through a one-size-fits-all approach modeled after B2C.
The trouble with adopting B2C methodologies wholesale -- something a growing number of B2B marketers say they're doing -- is that not all data is collected equally. Put simply: B2B and B2C aren't the same animal, and the abundant data sets that define B2C just aren't as widely available in the B2B context.
While there is plenty that B2B marketers can learn from their B2C counterparts when it comes to deploying big data, they need to develop their own quality-first, scale-second strategy.
B2B buyers wear two hats
It always starts with audience, but a B2B audience wears two hats at the same time because they lead lives as both consumers and professionals. Naturally, B2B marketers want to engage their audiences in a professional context. Although, given the size of a particular industry relative to the larger online population, marketers aren't likely to find the same quantities of third-party data B2C marketers rely on to slice and dice audience segments.
To overcome that challenge, B2B marketers may need to blend many types of data. For example, online behavioral data like work-related searches and website visits are good indicators of a prospect's interests. But without the addition of firmographic data such as a company IP or industry, that behavior lacks context. By combining these two types of data, B2B marketers can make sure they are not only targeting the right people, but that those people will be receptive to their message because they're wearing their work hats during the engagement.
Scale isn't the same
Scale is something of a misnomer in B2B because the total market is rarely as large as the market for a consumer product. By way of example, if you sell dental chairs, your idea of scale isn't in the same ballpark as a B2C marketer selling cars. But the fact that the overall pool of potential prospects is smaller leads to two very different outcomes for B2B marketers.
First, a small pool of leads means that B2B marketers must adopt a strategy that's the opposite of their B2C counterparts when it comes to reach. A B2C marketer will look to maximize their impressions to the broadest possible audience. In other words, they want their message to go as wide as possible. But B2B marketers play a narrow game. Instead of maximizing impressions to the broadest possible audience, they need to maximize impressions to the narrowest possible audience.
Second, given the different approach to reach, B2B marketers face higher stakes. Each engagement, after all, is worth more because good prospects are always scarce. Typically, B2B marketers see this issue in terms of price; they pay higher CPMS than their B2C counterparts, because scarcity makes each opportunity to impress more valuable. But those costs should have huge consequences for creative. If an impression is worth more, it certainly merits custom creative. Here, data can make a critical difference in outcome.
For example, knowing that the prospect searching for a dental chair or visiting a site that covers dentistry trends is actually an orthodontist is a tremendous advantage. But to seize that advantage, the marketer must be able to generate custom creative that's tailored to that specific audience segment. An orthodontist may respond to an ad aimed at dentists, but they're much more likely to respond to creative that references orthodontists, even if it's the same product.
B2B purchasing is more complex
In B2C, marketers like to think of a purchase funnel. But a better metaphor for B2B is a maze with multiple entrances. Rather than focusing on a single transaction at the bottom of the funnel, B2B marketers work in a maze that is orders of magnitude more complex because B2B buyers are rarely a single person. Their path to purchase is often a product of the buyer's unique, and at times, cumbersome buying process.
A procurement officer might enter that maze at one point, while the person (or people) who will ultimately use the product the company buys might enter from somewhere else. One of those prospects might have a relationship with your sales team because they met at a trade show, but the procurement or finance departments at that same company might not have any relationship at all. And just when you think the buyer is ready to pull the trigger, the boss chimes in with a better idea.
From a targeting perspective, you want to be able to influence everyone in that maze, but in order to do that, you need to use data to place those people within a specific context. By way of example, if the prospect is the orthodontist, you'll want to use data to serve ads that speak to the functionality of the chair. But if the orthodontist works for a larger provider and the prospect works in procurement, that ad should speak to the chair's durability and cost.
The danger of running a standard B2C big data playbook is that it puts B2B marketers in a mindset that equates quantity with quality. Ultimately, the more data you can deploy, the better. But if that data doesn't address the unique challenges of marketing in the B2B space through quality, adding more of it will only increase the level of difficulty on your end.