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How AI and Account-Based Marketing are Changing the Game for B-to-B Marketers

Christine Birkner

Over one in three B-to-B marketers are using artificial intelligence (AI), according to a new study by Salesforce Pardot, but the massive amounts of data generated by this new technology make it a challenge for marketers to realize its true business impact. We caught up with Nate Skinner, vice president of product marketing at Salesforce, to discuss how B-to-B marketers can best put AI to work for them, how marketers can make the most of AI in their account-based marketing programs, and what the future might hold for these technologies.

Q: How is AI impacting B-to-B marketers’ jobs on a daily basis, changing how they gather and segment customer data, personalize customer experiences, and other tasks?

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A:  AI is actually a distraction for marketers, because there’s a lot of noise about what AI actually is and how people get value from it. Marketers need to figure it out and don’t know where to start. We found that 30% of marketers are using AI in some capacity, 24% more than last year. It’s clearly not a fad. But for AI to be done properly, you have to have all of your data in one place. Salesforce’s AI platform, Einstein, can generate automated insights on campaign performance and lead scoring. That’s the power of AI done properly – it works on your behalf to provide information. For key accounts, it assigns a higher-rated lead score, and shows you next-step actions and suggestions on how to engage the prospect.

Q: Despite the growing popularity of AI, no dominant use case for the technology has emerged. Why do you think this is?

A: So much of AI is in our everyday lives. Amazon implements it, and it’s in Gmail, which autocompletes email text. Salesforce Einstein does that, too. For B-to-B marketers, there are so many opportunities to apply it, but the power of AI comes from data. If you don’t have data, AI doesn’t work. For many customers, data is all over the place: in your CRM, in your ads platform, in your data management platform. If you don’t have a single place for all the data, aggregated, then you can’t use machine learning to tell you things you didn’t already know. If you have siloed implementations of AI, you won’t get the biggest bang for your buck. Our customers are seeing specific use cases that are impactful because their data is on the same platform, where machine learning models can run against all the data. Einstein shows you campaigns that are highest performing and makes suggestions about running campaigns again. Those are the insights and better use cases you get when you run all the data in the same place.

Q: What are the biggest challenges of implementing AI so that it has real business impact, and how can B-to-B marketers address those challenges?

A: All marketers can implement AI, but actually getting it to move the needle for your business is hard because there are so many niche vendors who aren’t using marketing muscle to understand buyer need. Sales data doesn’t match marketing data, or vice versa. For most B-to-B companies, the sales team is selling to an account, not to one person. Most companies aren’t aligned around a committee of buyers, they’re aligned around getting one person to do something. That’s why it’s so challenging. If you connect all of those things and have a clear line of sight across sales and marketing, accounts you’re going after and people you need to engage, you could start to see massive business impact. We’re seeing game-changing results in ABM when you put martech, adtech and sales tech together, with AI at the center. The challenge is bringing it all together and finding truth around data that powers the AI initiative. You can address it by stepping back and looking at whether your martech stack works for you, or vice versa. Many customers keep buying technology to solve individual problems, but they need to make sure their process connects to that technology to drive business results.

Q: Account-based marketing (ABM) now accounts for 28% of total user marketing budgets, and 45% of ABM users are seeing at least double ROI compared to other marketing methods, according to Salesforce Pardot. Do you expect these trends to continue, and why?

A: ABM isn’t new, but the reason it’s gotten so much traction is because it’s now scalable. Marketing can now take an account-first approach and have it be economically viable, and it’s easier to prove ROI. With the value in increased conversion rates and deal sizes getting bigger across every market, ABM strategies are going to continue, because the results speak for themselves. Most customers we speak to are getting started on an account-first marketing approach and are thinking about the technology stack that will allow them to do that, so we’re nowhere near the tipping point.

Q: How is AI helping supercharge ABM, and how can B-to-B marketers make the most of AI in their ABM programs?

A: AI helps supercharge ABM in the same way that it helps supercharge broad-based marketing campaigns – if you have your arms around your data. If you don’t, if you’re tracking sales data separate from marketing data, you’ll run into trouble. AI is only as powerful as the data that’s generating the recommendations, so you can’t execute your strategy if the data is in different places. ABM starts with accounts, so to make the most of AI in B-to-B marketing programs, your content and data should be in the same place, so you have access to it in the machine layer. For account and context selection, and engagement orchestration, you need to measure it based on the same source of truth, otherwise, your marketing team is measuring the impact of ABM whiles sales is looking at whole different set of metrics.

Q: Salesforce Pardot reported that high-performing B-to-B sales teams are 2.2 times more likely than underperformers to execute ABM programs jointly with marketing teams. What’s your advice for achieving better sales/marketing alignment to optimize ABM?

A: Tracy Eiler, the CMO of InsideView Technology, wrote a book that says that data is the ultimate equalizer when it comes to sales/marketing alignment. If both departments are talking about the same data sets and metrics, if they have the same view of the world, they’ll have alignment. When you add people to the mix, and give them a single dashboard of the same metrics, that takes it to the next level. Imagine the opposite of that, which most companies have: marketing is measuring its performance, and sales is measuring its performance, and nobody’s measuring them together. If sales and marketing can’t agree on metrics that matter, you can’t build an ABM program. Sit in a room together and agree on three metrics that are most important for both of you, build an ABM strategy together, and measure your efforts based on those three metrics. Bring the teams together and realize a common mission.

Q: What are the biggest challenges B-to-B marketers face when it comes to ABM?

A: For most organizations, marketing and sales are divided. Processes and tools are separate. Sales teams identify accounts and develop account-specific content and campaigns, then measure and optimize efforts without touching marketing at all. When it comes to ABM, we’re using different words to describe the same thing. When the customer doesn’t think you’re speaking to them in one voice, your AMB strategy is failing. Breaking down siloes is the biggest challenge, and bringing common nomenclature to both sales and marketing is the next-biggest challenge. Marketing is ironically a little behind in terms of account-first thinking: marketers have been thinking about ABM for the first time maybe in the last five years, but sales teams and B-to-B companies have been thinking account-first forever. If marketers come in hot with an account-based strategy but don’t include the sales point of view, they’ll be out of sync with reality, because the sales team is already thinking that way.

Q: What do you think the future holds for AI and ABM, and how can marketers prepare?

A: We’ll continue to see B-to-C marketing and B-to-B marketing converge as it relates to best practices for both AI and ABM. B-to-C marketers are reaching customers via LinkedIn, Instagram and Google, and B-to-B marketers are starting to do that, too. A lot of these trends are powered by AI. Imagine the kind of technology where AI tells you, based on all the campaigns you’ve run, here’s one to try, and one you’ve never thought of. For ABM, it’s the same: AI-powered ABM campaigns will recommend new account types to consider. For instance, in financial services, there are several sub-groups, like wealth management and brokerage firms, and AI might tell you to run a campaign that targets a whole new sub-group, high-net-worth individuals, by taking everything you’ve already learned about marketing to wealth mangers and brokers and marketing to high-net-worth individuals. There are a lot of things that I can’t imagine possible in the next three to five years, but those that are out front of the AI and ABM trends will change their business.

Christine Birkner is a freelance writer in Chicago.