Drive predictable B2B revenue growth with insights from big data and CDPs, as seen on TechCrunch

As the world reopens and revenue teams are unleashed to meet growth targets, many B2B sellers and marketers are wondering how they can best prioritize prospect accounts. Everyone ultimately wants to achieve predictable revenue growth, but in uncertain times — and with shrinking budgets — it can feel like a pipe dream.

Slimmer budgets likely mean you’ll need more accurate targeting and higher win rates. The good news is your revenue team is likely already gathering tons of prospect data to help you improve account targeting, so it’s time to put that data to work with artificial intelligence. Using big data and four essential AI-based models, you can understand what your prospects want and successfully predict revenue opportunities.

Big data and CDPs are first steps to capturing account insights

Capturing and processing big data is essential in order to know everything about prospects and best position your solution. Accurately targeting your campaigns and buyer journeys necessitates more data than ever before.

Marketers today rely on customer data platforms (CDPs) to handle this slew of information from disparate sources. CDPs let us mash together and clean up data to get a single source of normalized data. We can then use AI to extract meaningful insights and trends to drive revenue planning.

That single source of truth also lets marketers dive into the ocean of accounts and segment them by similar attributes. You can break them down into industry, location, buying stage, intent, engagement — any combination of factors. When it’s time to introduce prospects to your cadence, you’ll have segment-specific insights to guide your campaigns.

AI realizes data-based insights

You might find that your data ocean is much deeper than you expected. While transforming all that data into a single source to drive actionable insights, you’ll also need the right resources and solutions to convert raw data into highly targeted prospect outreach.

This is where AI shines. AI and machine learning enable revenue teams to analyze data for historical and behavioral patterns, pluck out the most relevant intent data, and predict what will move prospects through the buyer journey.

Your CDP feeds your AI, and AIs love a big data feast: The more data you give it, the more accurate the predictions will be. As you gather data, it’s important to keep your CDP tidy — merging, cleansing and de-duplicating data deliver the freshest information to your AI for the best predictions.

4 AI models for predictable revenue growth

So you’ve deployed your AI. Great, now what? In practice, your AI — powered by your CDP — should use four essential models to analyze your account and person-level data and derive insights into your prospects. These models identify the top accounts worth targeting so you can develop a plan for predictable revenue growth.

ICP insights/account fit

Your revenue team should have an ideal customer profile (ICP) — the attributes and characteristics of the prospect you really want. But AI can go beyond what you think your ICP is. Some attributes don’t occur naturally to humans reading the data; AI can analyze historical opportunity data and find patterns you might’ve missed before.

AI will also refresh your ICP as companies and markets change. It’ll help you spot new verticals or lend insights to fine-tune your ICP. A highly accurate ICP model gives your revenue team confidence in making the next move on a prospect. They’ll know the accounts bearing a strong ICP fit are most worth pursuing.

Contact fit

Understanding accounts also requires understanding the individual roles involved in a prospect’s buying team. The contact fit model shows how well different personas match the typical buying team for your products or solutions. Is a prospect’s finance manager the right contact to engage and influence, or is it a director of sales and marketing instead?

Knowing your prospects’ buying teams and the right personas contributes to account-level success. Bring this contact fit score together with your ICP model to determine not only which accounts to prioritize but also who on the buying team should be engaged. Your revenue team can then budget their time and effort to generate the highest ROI.

Contact engagement

Also focused on the contact level, this model compares an individual contact’s current level of engagement with those of previous buyers. It examines which of your sales and marketing tactics are working and what that engagement looks like — and where there’s whitespace, or no activity. Once you see the buying center, you can engage the right contacts at the right time and plug holes with new contacts.

A focus on contact engagement lets us ditch the old point-based scoring method used in marketing automation. No more humans guessing that downloading an ebook is worth five points. AI determines which engagement activities matter the most and constantly analyzes data for the newest patterns.

Identifying in-market accounts

Is your prospect just starting their buyer journey, conducting research and identifying potential solutions? Or are they preparing to issue an RFP? By identifying in-market accounts, this model shows you where prospects are on their buyer journey and when you should reach out.

Prospects exhibit a typical behavioral pattern when engaging with your brand. This model maps those patterns and shows where prospects are on the buyer journey. Your revenue team can then launch the right sales and marketing tactics at the right time.

Combine these four models, and your revenue team will have a complete picture of the revenue opportunities available to your company. You’ll know which accounts best fit your ICP; who’s on the buying team and which individual contacts you should engage; how engaged key personas currently are with your brand; and where accounts are on the buyer journey. These key insights will enable you to prioritize accounts and contacts and create a data-driven plan to achieve predictable revenue growth.

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