Have a beginner's mind. What worked in the past might not work in your new position (or it may? But you have to test it first before implementing something full blown). The challenges you have faced leading other teams are not going to be the same set of challenges you will face in your new role. I will think about my conversion path and buyer's journey before I even think about what go-to-market channels I need to build or optimize. Step 1: I would start off by listening to all the functional leads in your new company (sales, product, support, ops). I will then sit down with the data science team or someone from ops to help you draw out the exact conversion, purchasing and upsell funnel for your prospects and customers. Step 2: Identify from a marketing perspective when the key events happen (ie. web conversion, sales opp win/loss, what causes someone to convert, when upsells happen, what causes attrition...you get the point). Then figure out where the bottlenecks are that are preventing your users from taking the action you want them to take. Step 3: Once you have a good grasp on your bottlenecks and conversion point then you can start thinking about (in priority order) how these channels can be used to drive conversions and sales: 1) website/SEO, 2) email/marketing automation, 3) paid digital strategy, 4) sales alignment/training, 5) content buildout 6) webinars/events. 

One of the great things about Demand Gen is that there isn't a set path into it. For example, I started my career in sales and account management before transitioning over to marketing. While somewhat atypical, I've found having a sales background to be beneficial as I've grown my career in DG because it gave me a first hand look into what the sales and marketing relationship looks like from the other perspective, and a deep empathy for being quota carrying. I've worked with incredible DG marketers who have come to DG from different fields (both from other functions in marketing and fields outside of marketing) and landed on Demand Gen. My recommendation would be to think about how your skills in another field can transfer over to a Demand Gen role. Chances are they are transferable and will provide you with a differentiated view point because of them. Use that to your advantage!

I break out the DG metrics into two groups: 1. Demand generation Here I focused on generating demand through "push" campaigns to our ICP accounts. I call this "push" because I am essentially shoving our ads in front of folks who never asked for it. I look at things like ad impressions, CTR, engagement rates, and conversions. From an ABM perspective, I look at the same ad metrics, but I also look at account view-through rates (the % of accounts who've seen my ads and then visit our website by other means), intent signal increases, and funnel progression. Holistically, I want to ensure we are driving the right level of engagement to accounts on our target account list. Within our target account list, I want to make sure, period over period, I am creating awareness & demand at the same accounts our outbound teams are working so they have a higher rate of turning emails and calls into conversations. 2. Demand capture Here I focus on capturing demand through our "pull" campaigns to audiences that already have existing demand for our solutions. These are paid search, review websites, and content syndication with intent signals. The metrics I look for here are pure lead capture and lead nurture into MQLs that I can hand over to our sales teams to work. I review these metrics daily, but only when I see anomalies that last more than 3 days do I actually take action. On a weekly, monthly, and quarterly basis, we review the demand gen machine by channel (paid search, paid social, organic, direct, etc.), then break it out by campaign, down to the utm_term level and ultimately follow these down to revenue and LTV:CAC. The intent here is to analyze the numbers to surface insights and propose action items for the month or quarter.

Incorporating experimentation and A/B testing into growth marketing strategies is key for driving sustained growth. Without it, marketing results often plateau quickly. Here’s a 6-step process I’ve used in the past to structure a conversion rate optimization program around. Step 1 - Hypothesis Formation In this initial step, pinpoint crucial variables that influence user engagement and construct hypotheses regarding potential changes and their outcomes. * Identify Key Variables: Recognize the key variables (like webpage layout, email subject lines, ad creatives) that you think have a significant impact on user engagement or conversion. * Develop Hypotheses: Formulate hypotheses based on your observations, analytics data, or customer feedback, predicting how changes in these variables might affect the outcomes. Step 2 - Designing Experiments Here, the focus is on developing different variants based on the formed hypotheses, establishing a control group, and setting up appropriate analytics tools to track the performance metrics accurately. * Creating & Develop Variants: Create different variants of the webpage, email, or ad, incorporating the changes as per your hypotheses. * Control Group: Maintain a control group where no changes are made, to compare the results with the variants. * Define Key Metrics: Set up key metrics (like click-through rate, conversion rate, etc.) that will help you evaluate the performance of each variant. * Setting Up Analytics: Ensure that analytics tools are set up correctly to accurately track the performance metrics. Step 3 - Conducting Experiments During this phase, conduct the experiments by segregating the audience into different groups and launching all variants simultaneously to avoid time-based biases, ensuring a fair test. * Randomized Split Testing: Divide the audience randomly into different groups, each group being exposed to one variant. * Simultaneous Launch: Launch all the variants simultaneously to prevent any time-based biases from affecting the results. Step 4 - Data Collection and Analysis This step entails meticulous data collection and analysis to discern the most effective variant, followed by deriving insights to comprehend customer preferences and documenting the findings for future reference. * Collect Data: Gather data on how each variant performed based on the defined metrics. * Analyze Results: Analyze the data to find out which variant performed the best and if the differences are statistically significant. * Draw Insights: Draw insights from the experiment results, understanding customer preferences and behaviors. * Document Learnings: Document the learnings from each experiment to build a knowledge base for future reference. Step 5 - Implementation and Optimization At this point, your focus is implement any successful test, engage in continuous optimization based on the learnings, and accelerate growth by repeating the testing process with new hypotheses. * Implement Changes: Implement the changes based on the winning variant to optimize your marketing strategies. * Continuous Optimization: Use the learnings to continuously optimize and improve your marketing strategies. * Scale Successful Experiments: Scale up the successful experiments to a larger audience to maximize the benefits. * Iterative Process: Make experimentation an iterative process, continuously testing new hypotheses to foster growth. Step 6 - Knowledge Sharing Lastly, foster a data-driven culture within the organization by sharing the learnings with the team, encouraging collaboration, innovation, and developing a flexible marketing strategy adaptable based on the insights gathered from the experiments. * Share Learnings: Share the learnings with your team to foster a culture of data-driven decision-making. * Collaborative Approach: Encourage a collaborative approach where team members can propose new hypotheses for testing. * Fostering Innovation: Foster a culture of experimentation within the organization, encouraging innovation and agility. * Adaptability: Develop an adaptable marketing strategy that can pivot based on the insights from the experiments. By following this structured approach to experimentation and A/B testing, you can effectively incorporate them into your growth marketing strategies, driving improved results and fostering sustainable growth.

I don't think ABM at its core is all that different from landing net new vs cross/upsell/expansion. If you boil it down, you are taking a set of channels and tactics and deploying campaigns to get your prospects or customers to take a desired action or behavior. I will argue that you have more room for error when going into new prospects or markets where you might not have as much data or evidence to support your messaging, positioning and campaign strategy. When marketing to current customers, you better know what you're talking about. There is nothing worse that being an existing customer of a brand and receiving messaging and campaigns as if you had never worked with that brand in your life. With cross/upsell/expansion, you not only have to know your customer, but you better make sure you let your customer know you know them. For example, if you're already in at Amazon and looking to upsell, you better be able to discuss pain points that came up at prior QBRs, understand their org chart, tech stack, and review how you can help them achieve their goals,

My role evolved as the organization grew from $100 mil ARR to ~4X the size today. In earlier days, our GTM motion was primarily PLG. I was measured on Qualified Traffic as a leading KPI, and Trial volume and Sales CVR% as the lag KPIs. Today, we have a twin GTM engine - PLG & Direct Sales model. My role and success parameters have evolved accordingly. I'm measured on Marketing sourced and influenced pipeline. The leading metrics are Trial volume and # of accounts displaying category intent and engagement in a given period.

There is no other way. If you’re not going to invest in intent data, then don’t try to do ABM, you’ll waste money and employee calories. I’ve heard companies say “oh we will just focus our efforts on our ICP” Yes, of course, knowing what accounts have the most propensity to buy is critical. But where they are in their journey is just as critical. Intent data in your Account-Based Marketing (ABM) model is like having a crystal ball that helps you see which potential customers are genuinely interested in what you offer. Here's why it's important * Relevance: It helps you find and focus on businesses that are actively looking for products or solutions like yours. So, you're not wasting your time on those who aren't interested. This is why I love tools like 6Sense and Qualified. * Personalization and customization: Customize for your audiences! With intent data, you can customize your messages and content to fit exactly what these companies are looking for. It's giving them exactly what they want. Messaging for emails, ads, Outreach snippets, landing pages, all of it. * Priority: It lets you know which businesses are most likely to buy soon, so you can put your energy into them first. * The words that matter: It guides you on what to write and talk about. You create content that speaks to their needs and questions, making them more likely to choose you. * Timing: It tells you when they're most ready to hear from you, increasing your chances of making a sale. * Smart Decisions: You're not just guessing; you're using data to make your ABM strategy better, helping you make your marketing and sales work even smarter and work better together. In simple terms, intent data is the best kept secret that shouldn't be a secret in ABM, helping you find the right customers, talk to them in a way they like, and make your business grow faster.

Personally, I don't think there is a one single path for Demand Generation. I came into the role by surprise. I was interviewing for a Marketing Manager role at a company, almost 10 years ago and during the interview we were whiteboarding out pipeline funnels. I was talking through waterfall metrics and which type of assets I would place at each stage in the funnel. Then we went on to discuss global digital strategies and then lastly, my favorite, events and the campaigns that surround them. I had lots of experience in SFDC reporting, Pardot and over all dashboarding and my soon to be manager said, "Liz, you're not a Marketing Manager, you're a Demand Generation Manager" (shout out to one of my mentors Allen Johnson) He taught me how to mold all the different parts of marketing I loved into the Demand Generation role. Being a Sales driven marketer, who knows their data, how campaigns work and the best way to create growth quick was the trick. So, my advice as to career path ... Become passionate about all aspects of marketing and sales. Learn the numbers, ask questions to those who are in the roles you strive for. Connect with me on Linkedin (I'm always an open book), research - you've got this.

The one lesson I learned the hard way as a Demand Gen leader was that I was not as knowledgeable about my target audiences as I thought I was. I kept seeing different people respond differently to various assets and did not understand why. I learned that it was important to thoroughly understand the audience before building or when optimizing the campaign. It can't just be work from a 3rd party paper (e.g. - Forrester), it has to be true on the ground insights (from Sales or a Research team). This was so important to me because I sometimes did not see the results I had hoped to see. Moving forward, I kept in constant contact with Sales to understand what they were hearing from prospects/customers. 

The intersection of digital and direct mail can be a powerful combination. I have run many direct mail campaigns (actually direct FedEx!), where a package is sent to a prospect, and the materials point them back to a website, where they are prompted to enter a PIN or other code. From there, you've identified who you've reached and can capture data (through surveys or other means) on their current challenges, tech stack, etc., and even offer an incentive to book a meeting with sales. In some of these campaigns, I've seen 50%+ response rates.