These days we hear a lot about the notion of Account-Based Marketing (ABM). But the idea is not that new. We always had key account managers who drove the sales and marketing activities within a large target account.
However, recently there has been a surge of excitement in the marketing community about this concept with the intention to “flip the funnel.” Many new ABM products have emerged, and the existing marketing automation systems have extended their capabilities to make ABM more programmatic.
Wikipedia states, “Account-Based Marketing (ABM), also known as key account marketing, is a strategic approach to business marketing based on account awareness in which an organization considers and communicates with individual prospect or customer accounts as markets of one. Account-based marketing is typically employed in enterprise level sales organizations."
ABM has been contrasted with traditional the B2B marketing funnel to be more personalized and more effective.
There are a multitude of resources on the web to learn more about ABM and best practices. But even with a compelling value proposition, many companies find it hard to execute on the ABM strategy. A key issue that often comes up is data reliability.
Since the whole premise of ABM is deep customer understanding and providing highly tailored information to the customer at the most appropriate time, the account data needs to be impeccable.
For ABM strategies to be successful, companies must ensure that they have a reliable customer data foundation – one that enables comprehensive understanding of the account, understanding of relationships in the account, sales and marketing collaboration, relevant engagement and offers, and closed-loop analytics and data quality.
In a B2B scenario, customer understanding means creating a 360-degree account view that brings together information from CRM systems, marketing automation, support and supply-chain systems and enriching it with information from third-party sources like Dun & Bradstreet to create comprehensive account profiles.
The profile should include account hierarchy information, not just legal, but hierarchy with product penetration information or risk rollup across the account hierarchy. The profile should also include information about various contacts, teams and committees that influence the buying decision.
Next is to understand the contact, account and product relationships. Who are the key people in the purchase committee? Are there people within or outside the target account that can introduce you to the decision makers? Have contacts used your product in the past or are they connected to a person in another business unit who uses your product? Understanding such relationships in an organization helps identify key people that sales and marketing should engage.
Today, graph technology can help companies understand and visualize such relationships across people, products, accounts and locations in target accounts. The technology also allows companies to understand the influence of stakeholders in the account and reveal product whitespaces across large accounts. Understanding relationships and whitespaces help uncover cross-sell and upsell opportunities and fill product gaps in a large account.
Account-based marketing and selling efforts need close coordination between the two teams. They should be able to work off the same account information and collaborate with each other to enhance the account data. If marketing finds an incorrect email address or phone number, they can flag that information to be corrected after verification. If a new contact is identified in the account, sales can use voting or discussion threads to verify the importance of that contact in the account.
Even though the account information is presented in the context of sales and marketing roles, all views are provisioned from the same single source of data. Sales and marketing teams are always in sync and have access to current and complete account data.
Once reliable account information is established, including a full understanding of relationships between people, products and locations for the account, now we can start engaging with the contacts using the right information and offers.
B2B companies can now send personalized messages to targeted contacts that discuss their particular issues and interests. Many systems also use machine learning and predictive analytics to provide guided intelligent recommendations. Based on past multichannel interactions, systems can suggest the right time to connect with prospects, using the right channel and with the right offer.
ABM techniques need continuous fine-tuning of campaigns. Over time, companies learn more about contacts including their relationships and content and channel preferences. Capture all this information, analyze it, and make it a part of the contact and account profiles. Use this information to make your next campaign more personalized and relevant. This iterative process will help you deliver more relevant information to the key stakeholders progressively and move the prospect account closer to opportunity and closing.
ABM is an effective way to market to key new accounts or expand within a large account. Even though there are many applications and tools available to design and execute multichannel ABM campaigns, the data quality issues can be a roadblock.
As you embark on your ABM journey, take a look at all your data sources and understand how you are going to bring all the data together from internal, external and third-party sources. Then determine what you can glean from this information to turn account-based strategies into tactics and execution.