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PHOTO: Jakob Owens

The modern B2B buyer has been increasingly conditioned to personalized buying experiences due to advances in the consumer world driven by companies such as Amazon, Uber and Google.

This trend comes at a time when, according to Forrester Research’s report "A Brand New Day for B2B Commerce" (fee charged), 60 percent of B2B buyers state they prefer not to interact with a sales rep as the primary source of information and 62 percent say they can now develop selection criteria or finalize a vendor list based solely on digital content. This means marketers are now under pressure to carry more of the client engagement burden and facilitate experiences that can rival those of companies like Netflix.

Managing the sophistication of today’s omnichannel, personalized buyer engagement process is a task that exceeds human cognitive capacity. But even though marketing technology has helped marketers achieve scale, its reliance on manually-built automation rules and segmentation (rather than segments of one) means that automated tools are not able to decide, for example, which piece of content or product reference should go inside every personalized email or be served onsite in a truly one-to-one manner.

For those businesses that are feeling this pain, artificial intelligence (AI) is providing a way out.

Here are some actionable steps for those who want to use AI to deliver a better B2B buying experience.

Related Article: How Machine Learning Will Tame the Explosion of Unstructured Data

Identify a Business Need for AI

The business applications of AI are by now well known, however Forrester Research identifies sales and marketing as the business unit that is most interested in AI in most organizations (over areas such as product management, customer support, engineering and logistics).

Yet even within such a narrow business category, the needs are myriad. In sales and marketing alone, we see use cases such as omnichannel engagement, buyer intent identification, contact scoring and prospect prioritization as the most common internal concerns driving chief marketing officers (CMO) to explore AI.

As such, if you want to deliver a personalized experience with AI, it’s crucial to understand where the need is greatest and, by extension, how success will be measured. Is it a shorter sales cycle? Faster marketing-to-sales lead handover? More net new leads? Revenue uplift? Fewer people handling manual tasks?

Conduct a Data Audit

AI isn’t about fancy math and algorithms — it’s about data. AI excels at locating relationships and patterns in your data, and AI-powered systems rely on a steady supply of accurate, updated and complete data to be able to deliver personalized experiences.

If your goal is to deliver personalized experiences for buyers, you will want to use the following data inputs:

  • Contact details: You can’t pursue personalization if you don’t have the lead’s contact information. It is the most basic requirement. Without contact information, no other data is useful.
  • “Firmographic” details: Information that falls into the category of “firmographic” details includes job title, company name and the company’s sector or industry. It might also include the revenue band of the company, the size of the company, where it is based and so on.
  • Product holding data: This refers to products that the buyer owns or the account the buyer is from.
  • Interaction data: This is a cumulative figure based on the number of engagements a buyer has with a company’s website or email program. It is most commonly presented in the form of an engagement or lead score.
  • Intent data: This dataset is derived from a buyer’s unique interactions with content and shows his or her current and evolving needs and interests. Marketers can use intent data to guide buyers toward particular pieces of content or product endpoints.

It’s also important to consider where you will get the above data. Will you be able to rely entirely on first-party data that is generated, owned and stored within your organization? Or will you have to obtain data from second- or third-party sources to feed your AI application the information it needs to develop a comprehensive understanding of the buyers you’re targeting?

A data audit will expose where you have gaps — both now and later — that could affect the success of your efforts to offer personalized experiences.

Related Article: 5 Signs Your Company Isn't Ready for AI

Vendor Selection

Artificial intelligence has entered the popular lexicon of B2B marketers, and legitimate AI-powered tools are now generally available from startups and major vendors alike. However, it’s important for marketers to develop a sense of what AI is and what it is capable of before they purchase AI-powered systems.

Analyst reports from research firms such as Forrester, SiriusDecisions and Ascend2 are useful for moving past the bumpf and hyperbole of AI vendors — and they often highlight the best solutions for your own particular use case. 

It’s also important to select vendors that have domain expertise and actively serve your industry: Nobody wants to be hampered with a system that is unable to accurately insert a product or content recommendation into a personalized email because the AI can’t understand a technical reference in your white paper or doesn’t recognize the specialized industry-specific terms describing your product.

Related Article: Do You Really Need That AI Solution?

Trial and Error Leads to Success

Delivering a personalized experience for buyers should absolutely be a business imperative, but not every AI implementation will be an unqualified or immediate success in helping you pursue that goal.

Lori Wizdo, a B2B analyst at Forrester, recommends CMOs dedicate 10 to 15 percent of their marketing budgets to innovation. Part of innovation is finding out what works and what doesn’t: The tips above will enable your organization to mitigate failures and deliver personalized experiences that reduce manual effort for your sales and marketing teams.