From 50 to 5,000 to 5 Million!
B2B companies have been doing Account-Based Marketing (ABM) for years. Their efforts, however, have been targeted at their top 50 to 100 accounts because of the resources required for selecting your target accounts, researching their key challenges, developing customized offers and driving campaigns in a coordinated way between sales and marketing.
However, as the marketing technology stack has evolved, companies are now able to automate and scale ABM beyond their top 50 accounts to their top 500, top 5,000 and even millions of accounts.
- Demandbase targets 3,000 accounts as part of their program
- Dell targets tens of thousands of customers to drive cross-sell/up-sell
- EMC targets hundreds of thousands of customers
- A financial payments processor targets millions of accounts – they target the “S” side of the SMB market
I was fortunate to have hosted Sangram Vajre, CMO and Co-Founder of Terminus and Peter Isaacson, CMO of Demandbase last week in a webinar about how you can scale account-based marketing. According to them, step #1 to driving scale is getting strategy and alignment right. After you’ve done that, they highlight several pieces of the martech stack that you can put into place:
- Marketing automation
- Real-time web personalization
- Account-based Ad (re)-targeting
- Predictive analytics
Let me talk a little bit about what predictive analytics brings to the table.
Predictive analytics platforms provide the data and insights needed to execute on the sophisticated segmentation and personalization required for successful ABM programs. They bring several capabilities to bear.
360 Degree view of your prospects and customers
Predictive analytics platforms give you the ability to look at all the data you already have about your prospects and customers – e.g. marketing automation, sales interactions (CRM), support tickets, transactions, product usages, etc. Predictive analytics platforms also adds in external data you may not have about your prospects and customers or easily capture – e.g. growth rates, funding information, credit risk data, and technographic data (what technologies are they using).
Predictive analytics platforms combine all of this internal and external data to identify thousands of data points around each prospect and customer that you have. The diagram below provides specific examples of the different types of data predictive solutions make available to you.
Figure 1: Different categories of customer and prospect data surfaced by predictive platforms
Big data processing and machine learning
With predictive analytics, you can harness the power of big data processing and machine learning to easily create predictive models for scoring customers and prospects based on how likely they are to buy, what they’re likely to buy and when. You also get a prioritized list of attributes about your ideal buyer that you can use to enhance your personas.
Ability to operationalize insights at scale
Finally, predictive analytics platforms make the predictive scores and account-level insights and data available in real-time to your ad platforms, marketing automation systems and CRM systems. By directly integrating with other systems, users are able to drive the right campaigns and end user experiences.
To learn more, download our whitepaper on this topic – which features more in-depth examples of how companies like Dell, EMC, and Demandbase are scaling their account-based marketing programs using predictive analytics.
I’d love to hear your story and thoughts. Let me know what experiences you’ve had around your ABM programs in the comments section below.