Ranga Kaliyur

Remember the days when the role of marketing was limited to promotions, campaigns, and branding? Because we…don't 😅. Marketing has evolved into an all-encompassing function that covers everything from demand generation and sales enablement to CX and pipeline growth. 

And at the helm of all this? The Chief Marketing Officer.

CMOs today are responsible for far more than just creative strategy. In addition to leading traditional marketing functions, It’s essential for CMOs to stay on top of product-market fit, consumer trends, competitive landscapes, and marketing’s bottom-line impact on revenue. 

And none of this would be possible without data. 

In fact, 64% of marketing executives strongly agree that data-driven marketing is crucial to business success. But how exactly is data, analytics and visualization influencing (and even improving!) the responsibilities of a CMO?

This article highlights everything you need to know about the evolution of CMO responsibilities and the profound impact of data and technology on the marketing function. 

Understanding The Current State Of CMO Responsibilities

Data and technology has transformed the current state of CMO roles and responsibilities. Here’s how:

Intuition can only take you so far

In the past, CMOs relied heavily on intuition and creative judgment to form strategies that may or may not work. These decisions were based on personal experience, high-level market trends, and subjective industry knowledge. 

Now, CMOs work with data-driven insights to guide their decision making process. No doubt, intuition and personal judgment still play an important role in successful marketing. But it certainly helps to back up a hypothesis with hard-hitting numbers. 

As businesses increasingly become digital-first, collecting relevant data across the customer journey has become far more accessible. Marketing leaders can leverage this data to drive results across brand strategy, customer acquisition, and retention by understanding what works and what doesn’t.

In addition to validating decisions, data-driven marketing also encourages dynamism and adaptability within various marketing functions. Experiments that would otherwise take months to produce results can be answered in a matter of days with journey analytics, heat maps, and A/B testing. This results in an agile, hyper-efficient marketing function that’s primed to optimize ROI and drive growth. 

Data delights marketers & customers alike

Just as data and analytics benefits marketers, so does it benefit buyers and the overall customer experience. Back in the day, marketing teams had very little information to work with. CMOs had no choice but to make broad assumptions and rely on spray & pray tactics to attract buyers. 

For one, targeting a wide audience with generic messaging can be expensive for smaller teams with limited budgets. Secondly, it can be ineffective (and annoying to customers) given that broad messaging that tries to appeal to everyone, generally appeals to no one. 

Today, CMOs can use cutting-edge visitor identification technology, account scoring, and intent data to specifically target sales-ready buyers with relevant marketing initiatives. This improves the buying experience for customers by swapping spammy email blasts and cold calls with personalized initiatives for the right accounts at the right time. Ultimately, this personalized marketing bolsters brand perception, improves conversions with fewer resources, and drives customer lifetime value — which is far more cost-effective than acquiring new customers.

The more things change, the more they stay the same 

Since the days of David Ogilvy, driving sales has been the north star for marketing. This, most definitely, hasn’t changed. That being said, the accuracy and granularity with which we can measure marketing's impact on revenue has improved dramatically in recent years.

Gone are the days of tedious, unintuitive marketing reporting. Several plug and play solutions can automatically consolidate marketing and revenue data across campaigns, content, website, CRM, and more under one roof. As we’ll see in later sections, this unified data can then be used for further analysis, visualization, and dashboarding. 

It’s also easier than ever to quantify the influence of every customer touchpoint on pipeline and revenue with sophisticated tools like multi-touch attribution. All this, to help CMOs’ prove and improve marketing’s impact on sales. 

How Is The Data Boom Shaping The World Of Marketing?

Now that we’ve established the importance of data and analytics, let’s explore a few data-based tools and techniques that CMOs can leverage to drive ROI and shape marketing strategy: 

1. Customer Segmentation & Personalization 

Customers, especially B2B ones, expect a personalized experience at every turn of the buyer journey. For instance, if you’re a CMO, you likely receive dozens of cold emails every week — but only respond to, if any, the well-researched, personalized mails that are actually relevant to you. It’s no different with any other buyer. 

Customer segmentation allows marketers to slice and dice their audience based on firmographics (revenue range, head count, etc), technographics (techstack), and intent data (engagement, page views, etc). This in turns allows marketers to personalize their efforts and target high-intent buyers with tailor-made efforts. Less spam, better conversions: win, win! 

2. Account Intelligence

B2B SaaS marketing teams invest heavily in driving relevant traffic to the company website. Unfortunately, even the most optimistic benchmarks find that only about 5% of website traffic actually convert through form submissions or sign ups. So is the remaining 95% of anonymous traffic simply taken to be potential pipeline down the drain? Well, until recently, yes 😳. 

Now, with IP-lookup technology, marketing teams can tap into databases with millions of companies to identify accounts that are already visiting the website but are yet to convert. 

How can CMOs and marketing teams use this?

• Optimize RoAS by retargeting accounts from paid ads who are yet to sign-up.
• Know in real-time when target accounts are live on the site, to strike while the iron’s hot
• Run relevant marketing efforts based on what target accounts are engaging with.

3. A/B Testing & Heatmaps 

“What would work better on this landing page: Headline A or Headline B?” 

Questions like this are exactly what A/B testing tools help answer with practical data. Rather than relying on individual judgment or biased surveys, A/B testing showcases multiple versions of a web page, creative, etc to a particular audience. Based on real-life performance, A/B testing can reveal what works better very quickly.

Heat maps are also valuable in identifying what visitors or users are engaging with within your website. This provides insight into points of resonance and friction for the target audience. 

4. Customer Journey Mapping

B2B customer journeys have always been lengthy, nonlinear, and complex. To solve for this, several solutions (including Factors.ai) can help unify and visualize various touchpoints along the journey in an intuitive manner. This helps CMOs achieve a bird’s eye view of the entire buying process from first visit, to sales engagement, all the way to deal closure. 

5. Multi-Touch Attribution

As businesses embrace digital transformation, CMOs and marketing teams are increasingly adopting multi/omni-channel marketing to deliver a consistent, persuasive experience to online buyers. Marketing channels range from search ads, email marketing, social media, organic blogs, marketplaces, and more. 

Without making sense of the numbers, it can be difficult to know which of these channels actually influenced conversions. Multi-touch attribution is a sophisticated analytics technique that collects and credits every touch point along a customer journey based on its relative influence on conversions. 

All the tools and analytics techniques discussed above rely crucially on data. The more voluminous and accurate your database, the more valuable the insights will be. The following section discusses a few practices for CMOs to make the most of their data.

Fulfilling CMO Responsibilities In The Age Of Data Analytics & Visualization

Here are a few key practices for CMOs to reap the benefits of data and analytics tools.

1. Build a culture around data 

As previously mentioned, none of the tools or techniques discussed in preceding sections would be possible without data. It’s essential for CMOs to create a strong, unequivocal culture around data-driven marketing — whether it be maintaining hygienic CRMs or qualifying a hypothesis with data-backed experiments.

It’s also just as important to eliminate siloed data by unifying numbers and KPIs under one roof. This ensures that the entire department, if not organization, is on the same page.

2. Pick the right tools

Every marketing department is built different. CMOs must invest in appropriate tools and marketing technologies to support their team based on size, scale, and objectives. For example,  heatmaps or attribution tools may not be essential to a smaller team that are just starting out. On the other hand, visitor identification, customer segmentation, and dashboarding tools can provide significant ROI for early-stage teams with limited budgets. 

In addition to functionality, here are a few more aspects to consider when investing in a martech tool:

• Pricing
• Customer success
• Integration with existing techstack 
• Ease of use
• Reliability
• Security

3. Create relevant dashboards

It’s definitely not feasible (or recommended) for CMOs to stay on top of every little marketing effort that the team’s working on. Instead, CMOs may rely on a bird eye’s view to guide strategy and improve performance at a higher level. CMO dashboards offer an intuitive view of all things marketing at a quick glance. 

Suggested reading: The complete guide to building a SaaS CMO dashboard

Based on the nature of your business, your CMO dashboard may reflect marketing spends, marketing sourced-pipeline by channel, MQLs generated by campaign, and other high-level marketing KPIs. You definitely don’t need to be bogged down by CTRs and likes, unless otherwise there’s a true anomaly in performance.  

4. Ensure privacy compliance

Lastly, in an increasingly privacy-first digital ecosystem, it’s important to ensure privacy compliance with all the tools and technologies that associate with customer data. SOC2 Type II and GDPR are industry-standard security frameworks that you should look for in every data-based product you’re considering investing in. (Psst…Factors is SOC2 Type II, GDPR, PECR, and CCPA compliant) 

How CMOs Can Take Marketing Data From Insights To Impact

Before concluding this article, here’s a quick highlight of the profound value that data can have on influencing and improving CMO responsibilities in this digital age.

• Optimize spends: Rather than relying on guestwork, CMOs can confidently allocate spend towards initiatives that work. This results in less marketing leakage all around. 
• Real-time decision-making: Rather than relying on intuition alone or waiting several weeks, CMOs can take a glance at a dashboard to make quick, data-driven decisions. 
• Drive marketing ROI: CMOs may adopt powerful tools like attribution to understand what works when. This results in the efficient allocation of resources and maximum ROI. 
• Reduce CAC: With the right set of data, marketers can personalize targeting and improve conversion rates with less spend. This, in turn, reduces the cost of acquiring customers and even improves the overall LTV of customers. 
• Prove marketing impact: Finally, marketing data and data-leveraging tools help CMOs quantify the impact of marketing on bottom-line business objectives like pipeline & growth. 

And there you have it. We’ve seen how data has well and truly disrupted the role and responsibilities of a CMOs. Luckily, it's only for the better. CMO responsibilities have transcended creative strategy to encompass a wide range of bottom-line objectives — all of which can be turbocharged with the right data analytics tools and technologies. 

Curious to see how Factors help CMOs drive marketing results and business growth? We’d be happy to have a quick chat! 

B2B marketing teams invest significantly across campaigns and content to drive qualified website traffic. However, benchmarks find that only about 2% of this traffic actually converts, with the majority of visitors simply bouncing off or browsing anonymously on the website.

Retargeting the remaining 98% of visitors via LinkedIn has proved to be an effective strategy to recapture interest from anonymous website traffic. While we won’t cover the specific benefits of LinkedIn retargeting here, this case study summarizes how Sage successfully leveraged LI retargeting to: 

• Generate 700,000 impressions in 6 weeks
• Improve lead generation by 4x
• And reduce cost-per-lead (CPL) by 80%

Simply put, LinkedIn retargeting works.

LinkedIn retargeting relies on the LinkedIn Insight tag (aka LinkedIn Pixel) to match website visitors with LinkedIn audiences. 

The LinkedIn insight tag is a simple piece of code placed on a website to help optimize campaigns. While the LinkedIn Pixel serves many functions, including conversion tracking and demographic insights, it’s challenged by shortcomings around website retargeting.

While LinkedIn Pixel works to some extent, we have found an alternate approach that can take your retargeting campaigns to the next level, also known as account-based retargeting. Account-based retargeting works by identifying, qualifying, and targeting anonymous accounts, as opposed to individual users visiting a website. Using a combination of identifiers, account-based retargeting has been shown to deliver:

• Larger, account-level audiences 
• Improved match rate accuracy
• Better segmentation and targeting

And the results? Well, they speak for themselves:

While the LinkedIn Pixel is a must-have solution given its wider functionality, we explore the limitations of Pixel-based retargeting and why Account-based retargeting is an effective alternative ⬇️

Limitation #1 - Match rates

The LinkedIn Pixel works by placing a cookie in visitors’ browsers, so when a LinkedIn user lands on your website, they may be identified and retargeted on LinkedIn. Note that this cookie-based identification takes place at a device and browser level for individual users. This means that, for the Pixel to match a website visitor to a LinkedIn user, the visitor must meet all 4 of the following criteria: 

1. Be an active member of LinkedIn 
2. Explicitly accept cookies on the website
3. Use the same device (phone/laptop/tablet) to visit the website and LinkedIn
4. Use the same browser (chrome/firefox/safari) to visit the website and LinkedIn

While a few visitors will probably fit this criteria, audience match rates via the Pixel are limited by the fact that the majority of traffic either doesn’t use LinkedIn, rejects cookies, or, most commonly, uses different devices/browsers for product research and LinkedIn browsing. 

In fact, only about 42% of B2B product research involves mobile touch points — with the majority of B2B buyers choosing to conduct their research on desktops. On the other hand, a whopping 80% of LinkedIn engagement is via mobile. This is not surprising, given that LinkedIn is primarily a social networking app. 

And so, despite the fact that LinkedIn Pixel works as designed, its match rates tend to be relatively poor, given the practical realities of B2B user behavior.

The limitation: Low match rates as a result of limited, cookie-based matching mechanisms by the LinkedIn Pixel. 

How Account-based Retargeting helps

The LinkedIn Pixel relies exclusively on cookie-based tracking to create its matched audiences. Factors, on the other hand, leverages a combination of three identifiers — IP address, advertising ID, and cookies, to triangulate a data connection and match anonymous traffic to a company.

Factors connects with over 4.2B+ IP addresses and 65M+ company profiles (in addition to cookies and ad IDs) to accurately identify which accounts are visiting your website. Note that this is regardless of whether the visitor in question is a member of LinkedIn, uses different browsers, etc. 

In fact, Factors can also identify remote companies by initially cookie-ing people using their corporate IP address to then re-identify them when they work remotely. To further explain how Factors achieves industry-leading match rates, here’s Viral from 6sense, one of our data partners:

“As a person moves around, their IP address changes. The platform adjusts for these changes by pulling in several additional markers to help match signals to an account. Now, with more variations in IP address data as remote working spreads across industries, our Graph deploys available secondary marker information, like cookies and mobile advertising IDs, to triangulate data connections. The Graph uses additional markers to sift through the noise so that confidence in the match rate remains consistent. Given the amount of signals we track, we don’t map every signal all the time, but we have observed accuracy over 85%.”

- CTO Viral Bajaria, 6sense, Data Partners

‍💡Build Better LinkedIn Retargeting Audiences with Factors‍

Limitation #2 -  User-level targeting

B2B buying decisions are rarely made by a single person. The typical buying committee comprises almost a dozen people from multiple departments and time zones. Selling a SaaS product today might involve gaining buy-in from multiple C-suite executives, individual stakeholders from operations to sales to marketing, and a chief revenue officer – along with legal and implementation teams.

Source: Challenger

Given that the Pixel focuses on individual, single users visiting your website, it fails to capture the wider buying group from each account those users are from. This shrinks your total matched audience size considerably, but more importantly, it inhibits your marketing efforts from reaching key stakeholders and decision-makers who may not have been the ones visiting your site. 

For example, if a junior marketer visits Factors.ai and is retargeted by the LinkedIn Pixel, the junior marketer alone will receive ads — with other stakeholders from the target account being ignored. As a result, this approach relies on the junior marketer being independently influential enough to convince the rest of the team to move forward with the deal. Definitely a tough sell. 

The limitation: User-level targeting, as opposed to account-level targeting, results in fewer stakeholders targeted per account and smaller audience sizes. 

How Account-based Retargeting Helps

While LinkedIn is best for targeting buying groups, it’s important to remember that there is no initial intent to buy on a social media platform. You need to layer in intent signals from multiple sources, such as your website and review sites like G2, to understand how you can best retarget relevant accounts.

Factors identifies intent signals and re-targets anonymous website traffic at an account level. This means that multiple decision-makers and stakeholders from the same company will be targeted on LinkedIn, regardless of which user actually visits the website. This bodes well for multiple reasons:

1. Increases audience size without compromising on the quality of accounts
2. Creates brand awareness at a company level rather than at an individual level
3. Improves odds of targeting the right decision-makers within each account

Even assuming that account-based targeting finds the same 100 accounts as Pixel-based targeting, the former would generate an audience size of 300-500 users (3-5 people from each account), while Pixel-based targeting would only target 100 users (1 from each account). More importantly, a larger audience will improve the odds of targeting decision-makers, ultimately resulting in more leads and conversions. Accordingly, account-based retargeting solves for the practical limitation of LinkedIn campaigns struggling to scale due to poor audience size. 

Limitation #3 - Audience segmentation

The previous two points discussed the LinkedIn Pixel’s limitations in terms of audience quantity. This third limitation highlights why the Pixel tends to fall short in terms of audience quality. In reality, a significant portion of your website traffic wouldn’t make a good fit for your business. Even within the subset of ICP accounts visiting your website, only a fraction would be “sales-ready” at any given moment (with the remaining accounts having to be nurtured until they’re prepared to buy). 

As important as having a large audience is, the quality of this audience plays a key role in determining conversions and RoAS as well. In an ideal scenario, marketing teams should only retarget this subset of sales-ready “3000-pound marlin” accounts.

The LinkedIn Pixel limits audience segmentation based on intent and engagement. With the Pixel, website traffic can only be segmented based on page views. While this is definitely a good starting point, it lacks granularity.

With Pixel, filtering out accounts that don’t match your target geographies, industries, sizes, or engagement levels can be challenging. This also translates to limited personalization options, as you can only segment campaigns by page views rather than by account and engagement properties. 

The limitation: Limited segmenting & filtering options resulting in subpar audience quality and limited scope for personalization. 

How Account-based Retargeting helps

Account-based retargeting with Factors supports granular segmentation based on a wide range of firmographics and engagement criteria. For example, with Factors, you can identify and retarget a list of accounts that meet the following rules:

“US-based Software companies with 100-500 employees visiting our pricing page & G2 profile for at least 10 seconds with a scroll-depth of 20% or more”

Here are a few ways in which Factors helps segment traffic data (in addition to regular old page views):

• Country
• City
• Industry
• Size
• Revenue range
• Time spent on page
• Scroll-depth
• Button clicks
• And a combination of all of the above

‍This level of filtering results in a list of precisely targeted ICP accounts that would make a great fit for your business. Additionally, by integrating your CRM, you may also include/exclude specific accounts, such as existing customers and competitors. 

And guess what? We found the solution to fix your list-building problems once and for all!

Factors has launched Audience Builder, which allows marketers to automatically segment based on their preferred criteria, push these segmented audience lists to LinkedIn, and activate personalized, targeted advertising. 

For example, you may choose to show accounts that visit high-intent pages such as factors.ai/pricing an ad creative offering a free trial. On the other hand, you can show accounts reading your competitor comparison blogs a “comparative” ad creative. The possibilities are endless. 

Real-life comparison: Pixel vs Account-based retargeting 

We’ve talked the talk - now we’ve got the numbers to back it up. Here’s how two campaigns, one that’s Pixel-based and another that’s Account-based, compare to each other. Note that all else (duration, budgets, creatives, copies) has remained the same through the course of this experiment.

Over the same period, we find that CTR is higher under Pixel-based retargeting, likely because this approach targets exact users visiting the website. That being said, Account-based remarketing significantly outperforms Pixel-based retargeting in every other key metric, including leads generated. 

And there you have it. 

Solve your ad targeting woes with AdPilot

If you want to maximise ROI for your LinkedIn ads, look no further than our latest offering: LinkedIn AdPilot! We offer a wide range of features that allow you to segment audiences based on intent data, implement exposure control for your campaigns and determine the true ROI for your ads.

Speak to our team today to understand how you can use AdPilot to improve your LinkedIn retargeting efforts.

B2B SaaS buying journeys are complex. Between independent research, ad campaigns, web sessions, events, sales outreach, social media, customer reviews, product demos, and more — buying journeys involve countless non-linear touchpoints across multiple channels and stakeholders.

While this may seem daunting at first, each of these touchpoints reflect unique buying intentions that may be leveraged to improve the customer experience and drive bottom of the funnel conversions. In some cases, the buying intent is obvious: if a customer submits their email ID to download an eBook, we know who they are and what they’re looking for. This challenge is further exacerbated by the fact that buyers are increasingly cautious about submitting their true email addresses. Professionals are educated to keep data safe and share contact details only if they’re absolutely sure of the need. Buying intent is generally the sum of incremental steps taken along the buying journey before reaching this inflection point. Recognizing these hidden intent signals — and the buyers behind those signals — is easier said than done…well, until now. 

This article explores interactive product demos as a high-intent touchpoint in B2B SaaS buying journeys. Specifically, we highlight how tools such as Factors.ai may be used in tandem with Demoboost to identify otherwise hidden intent from a ubiquitous element in SaaS today: the product demo.

Interactive Product Demos: Scale, Distribute & Analyze

Product demos have been at the cornerstone of SaaS buying journeys forever. They’re an effective way to showcase your software’s features, functionalities, and benefits all while addressing key use-cases and pain-points. Although live product demos continue to take place over real conversations with sales reps, businesses are increasingly adopting product demo softwares to support pre-sales efforts. This may be a result of B2C buying behaviors bleeding into B2B deals: Rather than submitting a demo form, finding a convenient time, and then speaking with sales reps, buyers today expect instant access to the info they need. Only after they educate themselves do they engage directly with sales reps. Businesses have adapted accordingly.

Product demo softwares help businesses build automated interactive product demos that are available to prospects on-demand. Interactive product demos are async product walkthroughs that users can access and navigate themselves without the involvement of sales reps or support personnel. Automated product demos are typically designed to be user-friendly, allowing potential customers to explore the product at their own pace. Among several other benefits, automated demos are scalable, easy to distribute, and provide helpful usage analytics. They may be embedded on websites, outbound emails, brand awareness campaigns, and more, so interested buyers have on-demand access.

So far so good…but you may be asking yourself: “but wait, who’s actually engaging with these demos?”

This would be a valid question. In the case of live demos, we know exactly who we’re showcasing our product to — they’re right there in front of us! But unless we gate an automated product demo (more on this later), how can we identify and analyze companies engaging with this touchpoint? In other words, what’s the full extent of intent signals from interactive product demos and how can we capture them?

Intent signals from product demos include information about who is engaging with the demos and what they're interested in. This helps marketers and salespeople know which companies are interested in their products and what parts of the demo they find most engaging.

Until recently, capturing this intent was a challenge. Intelligence and analytics tools could do their job on most web pages, but their functionality was limited within interactive product demos. 

Demoboost solves for this by uniquely supporting third-party tags (SDKs) inside its interactive demos. The following sections highlights how this ability may be leveraged by tools such as Factors.ai to:

• Identify and enrich anonymous companies engaging with interactive product demos
• Capture valuable intent signals beyond page views and clicks from demo engagement 
• Qualify, score, segment, and activate accounts based on demo engagement

But first, let’s establish why capturing intent signals from interactive product demos is so important. 

The Importance Of Intent Signals From Product Demo

There’s no doubt that the interactive product demo is a crucial touchpoint along the buying journey. Gartner’s analysis of buyer interactions finds that a supplier’s interactive tool (35%) is only behind the website (37%) and social media (36%) in terms of buyer engagement. Given that interactive product demos typically sit within the website, we can confidently claim its significance in the purchase process.

But even beyond the data, B2B marketers and sales folk would certainly be interested to capture intent signals from companies engaging with high-intent touch points such as pricing pages, paid landing pages, and in this case, interactive product demos. These intent signals help identify sales-ready accounts, determine winning touchpoints, and prove go-to-market’s wider influence and ROI. 

In a way, intent from product demos acts as a wonderful replacement for lead gen forms. Of course, marketing teams would love to place a lead gen form within the product demo as the resulting sign-ups wouldn’t need external intent data — we'd already know a lot about them via the form! However, given that buyers are increasingly growing to appreciate friction-free buying flows, capturing intent from ungated assets such as interactive product demos ensures the best of both worlds. This is where the Demoboost x Factors.ai integration comes in.

Demoboost + Factors.ai: Intent Signals From Product Demos

How it works

Factors.ai is an account intelligence and analytics software that uses industry-leading IP-lookup technology to identify, qualify, and activate anonymous companies engaging with websites and more. Tools like Factors work by placing a small piece of code (Javascript SDK) on the header of a website to de-anonymize website traffic, track account activity, and tie the dots between channels, website & CRM. Demoboost is a product demo software that offers all-in-one demo automation and demo-building functionality to reduce CAC, shorten sales cycles and increase win rates. Factors.ai now integrated with Demoboost to deliver the following use-cases:

As previously mentioned, such analytics tools have had the ability to track who clicked or landed on a product demo page. From there, however, users wouldn’t have visibility into what visitors are exactly engaging with inside the product demo. To solve for this, Demoboost’s open platform allows users to embed third-party javascripts within the product tour to capture account-level intent & engagement. This means that users can identify companies engaging with their demos as well as capture the extent of engagement — especially upon integrating Microsoft Clarity or Hotjar as well — at an account level. 

Demand capture to demand generation: The implications of this are significant. Typically, interactive demos have served the functions of evaluating product pre-sign ups and improving lead quality. Now, in addition to this, interactive demos may also be used to identify and retarget high-intent accounts based on demo engagement.

Use-cases

Integrating Factors.ai and Demoboost results in a wide range of use-cases. Here are a few of them:

1. Identify & enrich engaged accounts

A fundamental use-case of integrating Demoboost with Factors is the ability to identify and enrich otherwise anonymous companies engaging with your interactive product demos. Along with analyzing demo engagement with Demoboost, you’ll also know the accounts behind the engagement via Factors. 

2. Score & prioritize accounts

Given that several companies are likely engaging with your product demos, you may use demo usage insights from Demoboost in tandem with cross-channel engagement scoring across LinkedIn, G2, web sessions, and sales touchpoints to holistically score, qualify and prioritize high-intent accounts.

3. Relevant ABM campaigns

Once you identify and qualify high-intent accounts engaging with your product demos, you may then leverage this list of accounts for relevant account-based marketing. Rather than casting a wide net, you may initiate personalized ABM campaigns based on companies interacting with your product demos, website, LinkedIn ads, G2 review, sales touchpoints, etc to drive more conversions from existing efforts.

4. Personalized email & LinkedIn campaigns

Outreach and targeting is the next logical step after building your target accounts list. But rather than targeting every account with the same messaging — or tediously, manually orchestrating personalized campaigns, you may instead automate tailor-made campaigns based on engagement captured from Demoboost and other touchpoints. Configure your automation rules within Factors and every time an ICP company, say, completes more than half the interactive product demo, they’ll be pushed into a bottom of the funnel LinkedIn retargeting campaign or mail sequence to seal the deal. 

B2B buyer journeys involve a wide range of fragmented touchpoints across several channels. Factors.ai’s Demoboost integration empowers GTM teams to capture another source of intent data from interactive product demos to complement Factors.ai’s larger range of first-party intent signals across website, LinkedIn, G2 and more. As it stands, interactive demos are a mainstay amongst SaaS websites — and with this integration, marketers & sales folks have an opportunity to make the most of the data generated via these valuable touchpoints.   

The Challenge With Google Search Ads

Search advertising has established itself as the go-to channel for B2B marketers to capture low-hanging demand — and it’s easy to see why. As a marketer for an account intelligence product such as Factors.ai, it makes sense for me to bid on product keywords such as “ABM software” or “visitor identification tools” and competitor keywords such as “leadfeeder alternatives”, so I can attract relevant, in-market customers based on searcher intent. 

That being said, a closer look at the numbers reveals that conversions from search ads can actually be pretty disappointing (and expensive). For context, the average click-through rate (CTR) for search ads across industries is only about 3.17%. It’s even slimmer in the technology industry, at a meager 2.09% (Wordstream). Out of the few ad impressions that do translate into clicks, the average landing page conversion rate (sign-ups, demo form submissions, etc) is around 6% (HubSpot). And of the handful of visitors who do convert, only a fraction go on to become SQLs, opportunities, and ultimately, customers. 

Even the most optimistic benchmarks find that:

• Only around 30% of Leads become SQLs
• Out of which, 40% of SQLs become opportunities
• Out of which, 30% of opportunities become customers 

There are countless reasons for such significant drop-offs along the sales funnel:

• Most lead that land on your website, won’t sign-up
• Leads that do sign-up, may not schedule a meeting 
• Leads that do schedule a meeting, may not show up 
• Leads that do show up, may not be qualified (non-ICP)
• Leads that are qualified, may not be sales-ready (timing, budget, etc)
• Leads that are sales-ready, may choose to go with an alternate solution

All these factors suggest that to earn a single customer from search ads, you’d need more than 500 paid clicks (of course, this number varies widely based on category). That’s a lot of clicks…and a lot of money. 

To solve for this, marketers typically rely on three levers: 

1. Improve ad performance by optimizing keywords, budgets, etc
2. Improve website conversions with conversion rate optimization (CRO)
3. Improve quality of clicks via Google Click ID (GLCID) and conversion feedback

In this article, we’ll be exploring the latter of the three. Specifically, we’ll highlight an improved approach to training Google Ads to find the right clicks and traffic for your business via GCLID and conversion tracking. But first, let’s briefly discuss the current approach to Google conversion tracking — and its limitations. 

Google Conversion Tracking & GCLID: As It Stands

As a B2B marketer, you’re probably familiar with how conversion tracking and GCLID work to share conversion feedback with Google, but here’s a quick refresher:

Not all ad clicks are equal. A buyer that matches your ideal client profile is probably more valuable to your business than a student looking for an internship. However, to Google and other ad platforms, a paid ad click, regardless of whether it's by a buyer, a student, or a competitor, is a paid ad click.

To avoid the risk of burning through budgets on irrelevant paid engagement, Google supports the ability to digest feedback on the quality of clicks based on Google Click ID (GCLID) and preconfigured conversion actions. Via GCLID, Google assigns each click with a unique identifier. If the user behind a specific click goes on to perform a favorable action, marketers can flag that click to Google as a “high-quality lead”. Google’s algorithm then harnesses countless factors and historical records from its own database to surface your search ads to other audiences that match this criteria for a “high-quality lead”. Marketers typically tag sign-ups, MQLs, SQLs, and opportunities as favorable conversion actions. This lead-level feedback improves the quality of audience that receive your ads, which in turn, improves conversions. 

In theory, ad optimization with conversion tracking and GCLID sounds fantastic — a feedback loop between advertiser and advertising platform to continually improve ad performance and conversions. That being said, there are two challenges with Google Conversion Tracking and GCLID as it stands today:

1. Limited data: Google Ads recommends at least 30 conversions in 30 days for its algorithms to take effect in understanding what’s valuable and what’s not. In fact, for minimum CPA fluctuation and a quick learning period, Google suggests a whopping 500 conversions in 30 days. For early and mid-stage companies that are yet to hit these volumes of conversions, this lack of data can be a limiting factor. 
2. Lagging metrics: B2B sales cycles are notoriously lengthy and non-linear. After a visitor submits a demo form, for example, it might be a couple of days before their demo call, a few weeks before they become an opportunity, and more than a month before the deal is closed. Given that most marketers prefer quick iterations and experiments to squeeze the most ROI out of their campaigns, these extended periods between conversions lengthens the feedback loop when sending lead-level data back to Google. This lagging lead metrics is another limiting factor. 

With bids and cost per clicks becoming increasingly expensive as a result of growing competition, we need a fresh approach to overcome limitations with lead-level conversion tracking. Our hypothesis? Leverage traffic-level conversions to ensure sufficient, leading data availability for Google to work with.

Traffic-level Conversion Tracking: A Better Approach

Most marketers typically use sign-ups, M/SQLs, or other lead-level conversions as their conversion action goals. However, as noted earlier, only about 6% of visitors typically submit a form, with fewer still converting down funnel, after a delay. This results in small, lagging data sets for Google to work with. 

Rather than sending back lagging conversion data for 6 out of a 100 visitors on your paid landing pages, what if you could send leading data for 60? This is exactly what Traffic-level conversion tracking seeks to achieve via IP-based account enrichment, engagement tracking, workflow automations, and GCLID.  

Here’s how it works

Even though only a fraction of the traffic on your paid landing pages will sign-up, there’s still variable value in the remaining ninety something percent of visitors that are yet to convert. Say that 10 visitors land on your website from a search ad. Out of these 10, 2 are in-market ICP buyers that immediately sign up. 5 are ICP buyers that would make a good fit for your business, but decide that now is not the best time for a demo, so they drop off without submitting a form. And 3 are non-ICP visitors: a student, a job seeker, and a competitor — who also drop off without submitting a form.

The typical approach suggests sending the 2 ICP visitors that converted back to Google Ads as feedback. While this is helpful, it doesn’t encapsulate the full extent of data collected here. It fails to acknowledge the 5 clicks (50%!)  that albeit didn’t convert but matched our ideal client profile. While these clicks may not be as valuable as the 2 ICP clicks that converted, they’re certainly more valuable than the Non-ICP clicks. If ICP converted is worth $20, ICP not converted could be worth $10, while Non-ICP could be worth $2. This is valuable data for Google to make sense of ad clicks, even in cases where an explicit “conversion action" may not have taken place. By supplying Google with a larger set of relevant data, its algorithms will have a better understanding of what kind of visitors you value most. This data needn’t be limited to ICP data (firmographic) alone; it may be based on engagement (time-spent, scroll%) as well. 

Accordingly, traffic-level conversion tracking seeks to identify, qualify, and feed Google with a larger volume of granular, leading data by de-anonymizing website traffic and engagement at an account-level. This is where an account intelligence tool (*ahem* Factors.ai) comes into the picture.

How Factors Fits In: Your Data + Our Data = Ad Magic

The process we’re exploring here involves identifying website traffic, qualifying that traffic based on their firmographics (for ICP fit) and engagement (for intent fit), and pushing that data back to Google as feedback to attract better, more relevant audiences that *we hope* improves conversions and pipeline. Accordingly, we’ll need the following:

1. An IP-based intelligence tool to identify and enrich landing page traffic at an account-level 
2. Assign conversion value to incoming traffic based on your ICP and engagement criteria 
3. Automate a workflow that pushes this traffic-level conversion data to Google

As luck would have it, Factors.ai supports all three requirements with industry-leading account identification, engagement scoring, and workflow automations. Here’s an example of what a Factors-powered Search ads conversion tracking process could look like:

1. Identify up to 64% of anonymous companies landing on your website via search ads but are yet to convert 
2. Qualify and segment identified companies based on firmographics (industry, size, etc) and engagement (time-spent, scroll-depth, etc)
3. Push traffic-level conversion action data (along with lead-level data) back to Google automatically with the likes of Make, Zapier etc 
4. Google leverages a larger set of leading data to improve the quality of clicks and traffic
5. Improved audience quality results in better conversions and cost-effectiveness

Interested to see it in action? We’d be more than happy to set up a similar process for you over a trail with Factors.ai. 

The following article documents Chris Perrine's excellent presentation of the same name. Chris Perrine is VP & MD, Asia Pacific at G2 — and a leading mind on all things go-to-market. Check him out on LinkedIn.

B2C leads, B2B follows

Credit card payments, customer reviews, eCommerce, affiliate programs, and self-service are but a few selling trends that B2B companies have adopted from their trailblazing B2C counterparts. Without a doubt, implementing these practices have dramatically improved what was otherwise a tedious, time-consuming experience for B2B buyers and sellers alike. In fact, purchase decisions that once took several months (if not years), now take place in under a quarter.

As part of this evolution in buying patterns, B2B customers increasingly choose to conduct their own research — evaluating features, comparing alternatives, considering customer reviews, etc. — before speaking to sales or making a purchase decision. As you might have already guessed, this makes relevant, reliable content assets all the more valuable. Ironically, however, four of the top obstacles to make good software purchase decisions revolve around insufficient content.

This article explores what Chris refers to as The Trinity of Content — a content framework to resolve these common obstacles in making good software purchasing decision

What is the Trinity of Content?

The trinity of content refers to the idea that buyers view content as one entity, but it emanates from three key sources:

• Vendor content
• Expert content
• Customer content

Before diving into the B2B context of the Trinity of Content, let’s take an easier example: buying a car. Before you purchase a shiny new car (or most other things, for that matter), you consume a range of content — product specs, expert reviews, customer feedback, and more — before arriving at a decision.

What’s more? The consumption of this content is rarely ever linear. Instead, most of us go back and forth between vendor content (company website, dealer, test drive, etc), expert content (automobile magazines, awards, etc) and customer content (customer reviews, reddit, etc) — resulting in a great big mess of a customer journey. This mess is all the more pronounced in the case of B2B deals as they typically involve lengthy sales cycles and several stakeholders atop multiple channels and touchpoints. You might first learn about a product or service via a search ad, a blog, or a social media post — either way, the next few weeks and months would involve learning more about the offering, comparing alternatives, reading reviews, and eventually, speaking with sales. The next few sections explore the influence of vendor, expert, and customer content in this journey.

I. Vendor Content 

The most important aspect with vendor content is that you are in control of it. Everything from the messaging, volume, focus, and placement is in your hands. In the B2B context, few examples of Vendor content include:

• Website
• Sales collateral 
• Case studies
• Product demo
• Trials
• Blogs
• Ads
• Events
• Thought-leadership posts

While there’s no doubt that this type of content is absolutely essential to build up a brand as an authority in its space, there are a couple of reasons why it’s insufficient to rely exclusively on vendor content:

• For one, B2B verticals are becoming increasingly competitive across the board. If everyone is pumping out high-quality vendor content, there’s not much scope to stand out. 
• More importantly, however, the trust and influence that buyers have of vendor content has never been lower. In fact, only about 38% of buyers consider the website to be the most trust-worth resource. 

II. Expert Content

To solve this issue of trust, buyers typically consult industry experts for relevant, neutral feedback on products and services. You might have experienced expert content in the form of Gartner/Forrester reports, LinkedIn influencers, or management consultant reports from McKinsey, BCG, etc. 

The benefits of expert content are that they take neutral PoVs and are typically extremely well-researched and data-driven. That being said, expert content is not without its limitations:

III. Customer Content

Finally, we arrive at customer content. When was the last time you purchased a product on Amazon without reading the reviews? If you’re like most consumers, the answer is probably never. Customer content is, by far, the most trusted type of content out there — simply because of its in the hands of (generally) unbiased users that have actually adopted the product you’re considering. 

And there you have it! A combination of Vendor, Expert, and Customer content to drive trust, and ultimately, pipeline. Here are three key takeaways from the Trinity of Content: