What Google Can Teach Us About Effective B2B Demand Generation

The chemistry of converting raw data into golden opportunities for demand generation is a mastery sought by many but perfected by few. The intersection of data and analytics with demand generation represents not merely a trend but a fundamental shift in how businesses approach market engagement and customer acquisition. With precision analytics, companies are not just passively observing market behaviors; they are actively predicting and shaping future demands.

Recently, I interviewedSteve Armenti, a Demand Gen Lead at Google.

Can you share a bit about yourself?

I’m from New Hampshire and lived most of my life in New England. After college, I landed a sales job at an accounting software company outside of Boston. I was an SDR making 100 phone calls a day trying to book meetings with CFOs. This was 10 years ago before marketing and sales automation was as prevalent as it is today.

However, I taught myself how to manually segment my territory using spreadsheets and designed my own A/B email experiments to get meetings. Eventually, I stopped making phone calls altogether and was producing the highest numbers on the team purely from emails. I didn’t know it but I was using ABM + personalization.

Being in sales taught me about the human element of marketing and sales. At the end of every marketing campaign is an actual person who is trying to solve problems. In marketing, we often forget that as we sit in our office buildings (or home offices) designing campaigns. I try to remember that there are real people on the other end, just like us, and they can smell your bullsh*t from a mile away.

How does data-driven decision-making impact B2B demand generation?

In my opinion, it’s everything. I’ve built most of my career on using data to inform a variety of decisions: small and large about B2B demand generation and growth. Data can remove human error and opinion from the situation. For example, let’s say marketing and sales are feuding about the quality of MQLs.

MQL quality can be very subjective. What matters is alignment between marketing and sales on what accounts you’re targeting, who in those accounts you’re acquiring, and what are their pain points. Who is the ideal customer profile?

All of those things can be measured with data. You can pull data to show that an MQL came from a target account and that the persona is precisely the proper seniority, title, and job function. The data is used to establish a baseline of what is true and what is opinion. If you don’t have the data to prove what is true, go get it before you get into a shouting match.

What specific data points are crucial for understanding B2B buyer behavior?

I like to first look at a combination of demographics, firmographics, and psychographics. You can get these data points from 1P and 3P research, persona development, your website and marketing data, your CRM, surveys, and focus groups. This will give you a basic understanding of who is in your total addressable market.

Second, I want to look at my data. How do my customers behave? This data requires analyzing the person-level data against all of your marketing activities. The question you want to answer is “How does [persona] engage with [marketing campaign] and what is the behavior they are taking as a result”

Doing this exercise at scale will unlock a trove of insights about your customer that you can then organize and segment to fit the needs of your campaigns.

What role does predictive analytics play in B2B demand generation?

Predictive analytics can play a big role in any B2B demand gen program. It requires a solid data analytics infrastructure though first. Assuming there is a data warehouse and process in place to clean and ingest data, predictive analytics can help marketers do many things:

• Identify the highest propensity accounts, personas, and segments.
• Predict the timing of purchase intent helping your sellers reach out at the most opportune time
• Personalize messages or notifications based on user behavior
• Predict the effectiveness of a campaign or activity

And now, with AI, this entire process is speeding up. AI will become better at collecting and processing large amounts of data to identify patterns and trends that marketers can take action on in real-time. As the prediction engine runs, and AI is feeding results back even faster than ever, marketers shift their role to directing the entire operation versus managing the hands-on creation process of marketing campaigns.

How can businesses effectively leverage CRM data in demand-generation efforts?

CRM data is a goldmine of information. It’s the source code to what makes your customers tick and what incentivizes them to buy. CRM data can be used to identify which accounts and personas have the highest propensity to buy. CRM data can be used to segment your audience into different groups based on their needs, interests, and purchase behavior. CRM data can be used to optimize your campaigns and improve performance.

At the end of the day, all marketing should be measured on its ability to drive revenue – and your CRM is one of the most critical data sources to be able to do that.

What are the common challenges in collecting and utilizing data for demand generation?

Data silos. When data is stored in different systems and applications, it becomes really difficult to access and analyze. The accuracy and completeness of your data is important once you break down the silos. As they say, garbage in garbage out. So if you organized the data but it’s still crap quality, you aren’t even close.

Collecting and utilizing data for demand generation can be a resource-intensive process often requiring skilled data scientists. This is a challenge in itself. Proper data infrastructure is essential to good B2B demand generation. That said, I think a big challenge the industry is facing is a lack of analytical skills for the average marketer. The marketer of the future needs to be as analytical as they are creative.

How do you ensure data quality and accuracy in B2B demand generation campaigns?

It starts with cleaning the data. Make sure you have a process to clean and join together the various data sources you want to use. You might find you are missing certain quality markers so you will need to look at enriching the data through other 1P sources or by licensing 3P data. Once you have a complete picture of your data (often called a data dictionary), you will want to validate that the data is accurate either through testing and QA or with an actual validation tool.

Could you explain how data segmentation enhances B2B lead nurturing?

Data segmentation is all about dividing your leads and contacts into groups based on certain characteristics, such as industry, company size, job title, location, or historical behavior. You want to use segmentation so you can tailor your marketing and sales messages to each group, increasing the chances they will take the action you want them to take.

Using segmentation allows you to improve your targeting and make it more precise, increase the relevance and personalization of your email, and improve the efficiency of email as a channel overall. You send less messages that produce better results.

What tools and technologies are indispensable for data-driven B2B demand generation?

There are a million of them but you will want a demand platform, marketing automation platform, CRM, customer data platform, web/SEO/content analytics, social media software, and A/B testing software. I’m partial to Demandbase, Marketo, Salesforce, GrowthLoop, Google Analytics, and SproutSocial. HubSpot, Kissmetrics, Mailchimp, Optimizely, and Buffer are also great products.

How does personalization based on data improve B2B lead conversion rates?

At a high level, when you personalize your message, you show people that you understand their needs and interests. This can make them more likely to pay attention to your messages and grow an affinity toward your brand. This will likely increase the chances that people will engage with your marketing and do what you want them to do.

Personalization is a game of compounding value. As your audience continues to feel like you understand them and can meet their needs, they go deeper into the funnel evaluating your product or service. Marketing continues to personalize until the person raises their hand and takes action or wants to talk to sales. The personalization doesn’t stop here. Onboarding, sales enablement, and product discovery all need to continue the experience the person felt during the evaluation process.

How do you bridge the gap between data analytics and actionable demand-generation strategies?

In my opinion, it’s a linear process that goes like this:

1. Get your house (data warehouse) in order. Collect, clean, and ingest all the data you want into one source of truth
2. Build the proper data pipelines to fuel your teams. Data needs to flow from the warehouse into dashboards, CDPs, reporting, etc. so teams can review that data in real-time
3. Automate insights and reporting. Most of the regular, day-to-day reporting you need to do is simply looking at the same KPIs weekly and/or monthly. Standardize those KPIs and the reports. Set calendar invites for when you want your team to look at it. Pull your top insights and develop commentary about what changed from the last reporting period and why.
4. Run optimization sprints. After you’ve collected insights, rank the top ones using some priority method [e.g. Reach, Interact, Convert, and Engage (RICE)] and go and A/B optimizations. Do this in a sprint fashion so you get quick feedback and quick data. That feedback and data flows back into your pipelines and the cycle becomes a perfect loop.

What emerging trends in data and analytics are shaping the future of B2B demand generation?

AI, of course. I’m stoked about what AI is going to do for B2B demand generation. On the data side, I’m personally exploring how AI can automate the collection and analysis of data, improve insights and decision-making, and identify patterns and trends in data that go unseen by humans to better understand customers and prospects.

On the creative side, I’m excited about personalizing our marketing to each person. Right now I use a framework that identifies target accounts, ICP (personas), and then segments. The next level of this is down to the actual person.

I’m also really interested in AI for the use of predictive performance and analysis in demand generation. Launching campaigns is a time-consuming process and when it fails, a lot of resources, budget, and time are wasted. With AI, predicting the success rate of campaigns is possible. Additionally, prediction around which accounts, personas, and people are most likely to buy is incredible. This plus the personalization is like Michael Jordan and Scottie Pippen in the 90s – unstoppable.