If you’ve ever shopped online, then you’re familiar with triggered emails alerting you of items left in your shopping cart or products similar to those you recently purchased. These types of messages are simple examples of the prevalent use of behavioral information in B2C marketing.
Today, you would be hard-pressed to find an ecommerce site that didn’t use some form of behavioral data to enhance user experience and boost conversions. For a long time, the same couldn’t be said of B2B sites, but that is starting to change.
As it often happens, B2B marketers have been slower to deeply integrate behavioral data, though, as it becomes more available and widely adopted, they have found inventive ways to make use of this valuable information.
Now, in several areas of B2B marketing, behavioral data has become major factor. Here are four ways behavioral data is changing B2B marketing.
1. Omnichannel
The basic idea of the onmichannel and omnichannel marketing is that users should have a seamless experience between channels and devices. Communications, regardless of where they take place, should take into account past actions in order to avoid redundancy or conflicting information.
Thanks in large part to the prevalence of omnichannel in B2C, customers have come to expect the same quality of service from their B2B vendors. And it’s not just a matter of customer support. There’s also a lower tolerance among buyers for sales reps who restate information they’ve already researched. Self-educated buyers want intelligent, customized pitches, which require omnichannel support.
To enable omnichannel, companies need a clear, consistent, up-to-date, and universally accessible stream of behavioral information. To use the earlier example, a rep only knows about a lead’s level of self-education if they have been fed a record of behaviors collected by a marketing automation system and served to them through their CRM system.
2. Predictive Lead Scoring and Analytics
Past is prologue, as the famous Shakespeare quote goes. It’s a sentiment shared by many in the marketing community. Predictive lead scoring and analytics are the realization of this idea. Where a traditional lead score assigns points based on actions that have already been completed, a predictive score assigns points based on the actions that are likely to be completed.
Historical behavior is the basis for predictive analytics. Using a sizable body of historical data, analysts can identify behavioral patterns in past purchasers and look for similarities among current leads. As a result, marketers and salespeople are able to more effectively segment and prioritize leads.
Along the same lines, predictive lead scoring seeks to find individual behaviors in a body of historical data that are closely tied to buying. The lead scoring values of these behaviors can then be waited accordingly, helping to jumpstart the best leads through nurture.
3. Micro-Segmentation and Personalization
Thankfully, the days of ‘batch and blast’ emails are largely behind us. Segmentation has allowed marketers to send a smaller number of more relevant and ultimately more effective emails. Traditional segmentation is based largely on demographics. It uses only the most basic behavioral information (purchases, newsletter opt-ins, etc.). Though segmented email is a big improvement over loosely targeted blasts, it can still leave much to be desired.
Micro-segmentation represents a step deeper into segmentation. It uses more in-depth behavioral information – like type/frequency of content consumption and device/channel preferences – to further distinguish contacts. The key value of micro-segmentation is that, once segmented, communications can be personalized to align with highly-specific behaviors, interests, and preferences.
4. Enhanced Remarketing
A marketing campaign can result is essentially one of three outcomes. A recipient can complete the desired action, complete only part of the desired action, or take no action. Conventional wisdom dictates that those that completed part of the desired action are fit for remarketing; however, that’s not necessarily the case.
Behavior data can help to identify which recipients, if any, are legitimate remarketing opportunities, as well as provide insight on how to proceed. Rather than looking at “almost-conversions” as a uniform group, behavioral data provides context to the individuals. In all likelihood, there may be some contacts who view every landing page, but never download. At the same time, it’s just as likely that other contacts got distracted while filling out the form and would be receptive to another opportunity to complete it.
Let us know what you think:
- Do you use behavioral data in your marketing efforts?
- In which context is behavioral data most valuable?
- Would you add anything to the list?
