Ranga Kaliyur

‍With GA4 here to stay, here’s why you might want to leave

[July 5th 2023 Update] As of this month, GA4 has been sunsetted. What's more? Sweden has recently announced a comprehensive ban of Google Analytics due to security concerns. The Swedish Authority for Privacy Protection has cautioned users against the use of GA as a result of privacy risks posed by the U.S. government. This makes Sweden one of several European nations to have elected to ban Google Analytics in recent months.

It’s official — on July 1st, 2023, GA4 will permanently replace Universal Analytics (GA3) as Google’s primary marketing analytics platform. While ga4 vs universal analytics (ua) is still hotly  debated, the general verdict emerging within the marketing community is that ga4 falls short in several, fundamental aspects. Criticism ranges from ga4’s exceptionally unintuitive UI to limitations around ga4 events, event parameters, and reporting mechanisms. The following article lists out a few of these major drawbacks to highlight why it may be time for B2B marketers to consider ga4 alternatives. 

I usually can find my way round any piece of software quickly. But Google Analytics 4 is making me cry...

I've never seen a tool upgrade that made simple things sooo complicated :face_palm: Non-tech business owners were already struggling to use it. But now they have NO chance.

Gill Andrews (@StoriesWithGill)

GA4 migration challenges

The most pressing issue with migrating to GA4 is that the platform is not ready for independent use as of yet. Several bugs continue to persist, third-party integrations are scarce, and many features, including core ones like internal filtering, continue to remain under development. To be fair, ga4 is likely to squash these issues by the time it's standardized in 2023. But at the moment, ga4 is a half baked product. 

How to set-up GA4? Well, the logistics of migrating to ga4 isn’t all that straightforward either. While former universal analytics users have the option to upgrade for free, this facility is not available for all ua properties. Depending on your Google Tag Manager implementation, setting-up GA4 can take significant time and effort (depending on developer bandwidth) — in some cases, as long as a month!

Marketing analytics on GA4

Missing metrics and reports on GA4

A big change from UA to ga4 is the shift away from sessions and pageviews. Hit types like page views, social, transaction, use-timing, and more have been consolidated into a single measurement property on ga4 — events. Familiar metrics like average session duration and bounce rate have been stripped as well. The latter is an especially jaring loss because it’s a valuable metric for marketers to understand and compare landing page performance.

Standard reports have also taken a hit in google analytics update from UA to ga4. For instance, acquisition reporting on UA had as many as 30 standard reporting techniques. This included useful features such as traffic acquisition reports and source/medium reports. Unfortunately, ga4 has adopted only 10% (just 3) of its predecessors standard reports! One explanation for this is that ga4 is transitioning from a full fledged marketing analytics platform to a solution that enables you to capture and transport data elsewhere for further analysis. 

Conversion tracking on GA4

Universal analytics offered 4 types of goals — session duration, page/sessions, destination, and event. Conversion goals could easily be configured, for example, a “thank you” page could be tagged as the destination to measure form-fill conversions, in a matter of seconds. Because ga4 misses out on this “destination” goal type, ga4 requires tedious, manual GTM configurations to set-up “form-fills” as a conversion goal. In fact, Zack Duncan from the Root and Branch Group found that it takes around 16 minutes (along with adequate knowledge of GTM) to configure submission tracking on GA4 (as compared to a minute on UA). This is a major limitation for B2B SaaS websites and marketers as a significant proportion of leads come through demo form fills.

Event collection on GA4

Other Ga4 mechanisms have also faced significant backlash for a couple of reasons. Let’s start with event collection limits. As a rule, ga4 will not log events, event parameters, and user properties that exceed these limits: 

• Distinctly named events: 500 per app instance 
• Event parameters per event: 25 event parameters only
• User properties: 25 properties only

While these limits may suffice for early-stage teams, event collection on ga4 will almost certainly become an issue once the organizations starts to scale and garner complex events on relatively high-traffic websites.

Character limits on GA4

What’s especially concerning is that on ga4, distinctly named events and user properties can not be deleted/updated if you’re close to hitting their limits. In addition, ga4 heavily restricts character length on event and user names and values. For example, ga4 will truncate page names to a maximum of 300 characters. So, if your landing page has a url longer than 300 characters (which is far from uncommon), it will consider only the first 300 characters and perform attribution and analytics based on that. This could also mean that the entirety of the UTM may not be sent to google analytics servers, which in turn means a significant loss in data.

Data sampling and Processing time on GA4

Credit where credit is due — ga4 has taken a big step in the right direction by eliminating data sampling for standard reports. The keyword here, however, is standard. Advanced reporting (explore, advertising, configure) on ga4 continues to sample data under certain conditions. These advanced reports include core techniques like funnel exploration, path exploration, user explorer and more.

A drawback of unsampled data analytics on ga4 is the processing time. Standard ga4 claims up to 24 hours of processing time for intraday reporting and as much as 48 hours for complex features like multi-channels funnels and attribution modeling. To put this in perspective, Factors.ai delivers standard reports near instantly and will require at most 24 hours (half that of ga4!) for multi-touch attribution reporting.

While on the topic of data, it’s worth mentioning that ga4 offers data-retention for up to 14 months only. What’s more? XL properties are limited to a measly 2 months! This can be of great hindrance to B2B SaaS marketing analytics — wherein customer journeys can easily stretch across a couple of years. 

Custom events, properties, and dimensions on GA4

As of today, GA4 supports only 2 scopes for custom dimensions: event scopes and user scopes. This is two less than UA’s custom scopes which covered session and product dimensions as well. What’s worse is that the pair of custom dimensions offered on GA4 are heavily limited (even with GA360!). Here’s how the limits break down for standard GA4:

• Event-scoped custom dimensions: Max 50
• User-scoped custom dimensions: Max 25
• Custom metrics: Max 50

If you reach the ceiling on these custom dimensions, unfortunately your only option on ga4 is to archive infrequently used dimensions and hope for the best.

And there you have it…

This article explicitly covers a non-exhaustive list of shortcomings with GA4. Other concerns include useability, privacy-risks, lack of third-party integrations, and challenges at scale. While Google Analytics has dominated the marketing and web analytics space for years now (mostly because it’s a free tool), its limitations are starting to catch up with it. With dozens of robust Google Analytics alternatives emerging from the market, now is the time to replace ga. 

Factors is an end-to-end marketing analytics and revenue attribution platform that goes above and beyond the likes of Google Analytics to help you make sense of (and optimize) your marketing efforts. Here’s how Factors compare to Google Analytics. 

Interested in learning more? Book a personalized demo here! 

Not all SaaS marketing metrics are made equal

Between traffic, conversion rates, MQLs, CAC, churn, and more, there’s no shortage of key marketing metrics for SaaS companies to track.

Each of these metrics allows teams to capture the pulse of marketing health, which in turn helps make iterative improvements to marketing performance and ROI.

No doubt, SaaS marketing metrics are important.

But it can also be overwhelming for teams to know which metrics matter more than others. Given that monitoring marketing metrics can be an investment in and of itself, it’s vital to prioritize a few key ones to begin with.

This blog explores 9 of the most important SaaS metrics that every marketing team should regularly keep tabs on. But first, let’s briefly discuss what marketing metrics are and why they’re important. 

Related reading: 9 ABM metrics to track campaign success

What are SaaS marketing metrics?

SaaS marketing metrics are standards of measurement used to monitor the efficacy of SaaS marketing campaigns and assets. 

These metrics provide a frame of reference to compare past and present performance in order to continue to make iterative improvements to desired objectives.

For instance, observing that the signups have dramatically increased by 40% after a landing page design overhaul is clear evidence of improvement in performance. At a deeper level, SaaS marketing metrics like return on investment helps marketers prove the impact of their campaigns on pipeline.

In summary, marketing metrics help SaaS companies track performance, improve ROI, and quantify bottom line impact.

9 key marketing metrics for SaaS companies

1. Website traffic  

Definition: Website traffic refers to the total number of web sessions or website visitors over a certain period of time.

Especially in SaaS, the website is at the heart of business. It acts as a hub for prospects to learn more about your work and reach out for a demo call or free trial. Needless to say, not all traffic is from high-intent prospects. In fact, only a fraction of traffic is likely to be relevant to your business. That being said, when used in tandem with other metrics, website traffic can help SaaS companies asses how the number of visitors interested in your brand and product.

Several tools including Google Analytics and Factors.ai measure website traffic. It’s a helpful metric to understand high-level website health as well as the immediate impact of marketing campaigns and content. While traffic in and of itself may not provide granular insights, growing traffic is generally a positive sign as it means more visitors are likely to eventually convert to paying customers. 

2. Conversion rate

Definition: Conversion rate measures the proportion of users who complete a certain event or action.

Conversion rate % = total conversions ÷ total visitors x 100 

Conversion rate is a broad SaaS marketing metric that can apply to a wide range of scenarios such as webinar registrations, demo form submissions, or trial sign-ups. 

One of the most common uses of conversion rate is in landing pages. 

For example, say 50 people click on a search ad and arrive at a landing page with a demo form. 2 people actually submit the demo form and schedule time to speak with a sales rep. In this case, the conversion rate is 2/50 x 100 = 4%. Maybe improving headline relevancy and page design could increase conversions even further.

The average benchmark landing page conversion rate is 9.7%

3. Bounce rate

Bounce rate is defined as the percentage of website visitors who click away from a website without viewing or interacting with any other page apart from the one they initially landed on. 

As much fun as it sounds, bounce rate is a serious marketing metric that reflects the quality of your web pages. A high bounce rate indicates that your web page design/content does not resonate with the visitor, causing them to leave without exploring any further. 

Bounce rate = total one-page visits  ÷ total visitors x 100

Note that a landing page with a high-bounce rate isn’t necessarily a cause for concern given that the purpose of the landing page is almost always to bring in a visitor, have them submit a form, and leave. 

Instead, bounce rate is more relevant for the homepage, feature page, pricing page, or blogs. High bounce rates in such pages indicate that the content or design isn’t relevant or captivating enough for the visitor to continue exploring the website.

Bounce rate benchmarks:

• 0-40% bounce rate: excellent performance
• 40-55% bounce rate: decent performance 
• 55% - 70%: mediocre performance 
• 70%+ bounce rate: poor performance 

Average bounce rates by channel:

• Display ads: 56%
• Social: 54%
• Direct: 49%
• Paid search: 44%
• Organic: 43% 

In addition to tracking traditional bounce rates, Factors.ai shows granular insight into exit and engagement rates as well. This provides complete insight into where visitors are dropping off and what content resonates most with the audience.

4. Marketing Qualified Leads (MQLs)

It’s all well and good to improve website traffic but real marketing impact involves driving qualified visitors who show explicit potential to eventually become paying customers. Marketing qualified leads is a metric that captures the number of leads early along the customer journey — but nonetheless on the path to becoming customers. 

Marketing qualified lead (MQL) measures the number of top-of-the-funnel leads that exhibit explicit interest in what a company has to offer based on their interactions across paid campaigns, social media, website, and other touchpoints. 

For example, a visitor downloading an eBook on “customer journey mapping”  is likely interested in addressing this use-case and is at the very least open to learning more about Factors. Generally speaking, this lead can be considered an MQL.

Factors.ai connects the dots between campaigns, website, and CRM to showcase which marketing efforts and assets are contributing to MQLs, SQLs, deals, and other lifecycle stages. 

5. Sales velocity 

Sales velocity is defined as the rate at which leads and prospects move through the sales funnel and generate pipeline. 

Sales velocity = (opportunities x deal value x % win rate) ÷ length of sales cycles

Sales velocity indicates the health and performance of sales and marketing teams to herd buyers towards becoming paying customers. 

Go-to-market teams can improve sales velocity by:

• Increasing number of opportunities by scaling marketing initiatives and sales outreach
• Increasing deal values by targeting larger customers
• Increasing % win rate by improving sales pitches and enablement material
• Decreasing the length of the sales cycle with incentives like free trials or limited time deals 

Funnel analytics on Factors.ai allows users to calibrate custom sales cycles to identify the velocity between one stage to the next. With this, users can understand how long it takes for visitors to progress from ad campaigns to web sessions to button submissions to deal won. In turn, this helps identify points of weaknesses or friction to eliminate across the journey. 

6. Customer Acquisition Cost 

Most marketing teams invest significant resources in paid campaigns, social, SEO, and offline events with the hopes that these initiatives attract further customers to cover their costs several times over. 

Customer acquisition cost (CAC) or cost per acquisition (CPA) is a metric that measures the amount of money spent to acquire a single new customer.

‍In theory, this includes employee compensation, overheads, and, of course, marketing expenses. In practice, most teams only consider the latter. 

For example, if a marketing team spends $70 on ads and $30 a website redesign to acquire 20 new customers, the CAC works out to be: ($70 + $30) ÷ 20 = $5 per customer. 

7. Customer lifetime value

Customer lifetime value (CLV) is the total expected revenue from a customer during the entire relationship with a business. 

For instance, long-term, enterprise customers with large contract values are bound to have greater CLV than mid-market customers with short-term contracts.

While it certainly helps to know the cost of acquiring a single customer, it’s crucial to measure the lifetime value of each of these customers to truly understand if acquisition initiatives are worth it. 

For example, if it costs $300 to acquire a single customer with a customer lifetime value of $250, it’s actually a loss of $50 to the business. Alternatively, if CAC is $500 but CLV is $5000, the customer pays back the CAC several times over. Hence, it’s important to look at CAC and CLV in conjunction. 

8. Return on marketing investment (RoMI)

Now more than ever, SaaS marketing teams are urged to prove their impact on bottom line metrics like pipeline and revenue. This is where RoMI comes in. 

Return on marketing investment (RoMI) measures the revenue won from marketing campaigns against the cost of that campaign. 

RoMI = revenue earned from campaign ÷ cost of campaign x 100

In theory, the RoMI is a straightforward concept. But in practice, calculating RoMI without the right multi-touch attribution tools can be an unintuitive, time-consuming chore. Given that SaaS sales cycles involve several touch-points across several campaigns and stakeholders, it’s hard to pin-point exactly which campaign contributed to revenue.

Factors.ai solves for this challenge with a wide range of powerful revenue attribution models to quantify marketing ROI. In turn, this helps allocate budgets towards campaigns that drive results and prove marketing’s impact on revenue. 

9. Retention & Churn

We’ve combined retention & churn together as they’re two sides of the same coin. 

Customer retention measures the number of customers that a business retains over time through repeated purchases or contract renewals. 

Customer retention is an important SaaS metric as retaining existing customers works out to 5-10 times cheaper than acquiring new ones. Hence, businesses should always look to improve retention rates. 

On the flip side, Churn refers to the number of customers who discontinue their relationships as buyers with a business. 

A high rate of churn indicates that customers are not receiving the value or service they expect from the business. It’s a strong signal of dissatisfaction. Hence, businesses should always look to limit churn rates.

And there you have it. While there are several other important SaaS marketing metrics out there, the 9 metrics we’ve covered in this blog should give any SaaS marketing team an idea of their top and bottom line performance. 

Want to learn more about how Factors.ai can help ll the metrics that matter to you under one roof? Request a personalized demo today! 

B2B marketing strategies that maximize growth

B2B marketing may be in trouble. Research suggests that B2B organizations are inadvertently transforming marketing into a supporting tool for the sales function. In reality, however, marketing is at the very core of a business. With the right principles securely in place, B2B marketing may well transform into the growth engine of B2B organizations. 

It’s about time we rethink the principles of marketing 

Linkedin’s B2B institute recently conducted wonderful research using B2B effectiveness data in collaboration with Peter Field, Les Binet and the IPA. The primary motivator of this research was to identify the best marketing principles that correlated with growth. Keep in mind that in this case, growth does not mean improving CTR, impressions, engagements or other traditional digital marketing metrics. Instead, we’re referring to growth in terms of market share, revenue, profitability, and other bottom line business metrics.

What makes this research especially special is the fact that it's never been done before through the lens of B2B marketing. That is, of course, until now. The following series will delve into each of these principles one article at a time with hopes of providing an intuitive, straightforward explanation of cutting-edge B2B marketing research.

If you had to take away one thing from this series, it’s this:

Extensive research and anecdotal evidence point to one thing — The key to marketing-sourced growth is balance. While this may seem obvious, the truth is that modern B2B marketing is almost always unbalanced. They tend to involve solely short-term, volume-based endeavors that play to logic and reasoning as opposed to a balanced view of short AND long term strategies that consider volume AND price, logic AND emotion, awareness AND fame. 

With that out of the way, let’s finally move on to the first principle of B2B marketing strategies that maximize growth.

Brand Building Vs. Sales Activation

A. Have Your Cake & Eat It Too:  Brand Building and Sales Activation I

In their research, Binet and Field identify two types of marketing: 

1. Sales activation: Sales activation definitely provides short-term growth. But while sales activation captures existing demand, it does not create it. Results with sales activation often produce results that decay just as fast they appear – which isn’t necessarily a bad thing, just something to keep in mind for the long term.

2. Brand building: Brand building provides long-term growth. In a sense, this creates and captures demand together. Note that when executed well, brand building delivers short-term growth as well. So the big takeaway is that you don’t have to pick between one or the other. In a sense, brand building contributes to future demand to ensure a durable pipeline of future sales and profits.

Ideally, a combination of both types of marketing will yield the best results. If you had to pick one, however, the choice is easy. Brand building is the only strategy that delivers both short-term and long-term growth. 

B. The 60/40 Rule: Brand Building & Sales Activation II

In B2C marketing, organizations with the most short-term and long-term growth spend most of their budget towards branding (60%) as opposed to sales activation (40%). In most B2B orgs, this marketing investment is skewed in the opposite direction; with most spend being allocated towards activation (54%). 

What might explain this variation? Put simply, B2B sales is harder. It involves several touch points across several stakeholders over several months. It also necessitates far more exposition around the product, use-cases, functional benefits, and more. There’s no doubt that in B2B, sales activation, especially in early-stages, has an important (albeit expensive) role to play. But as the novelty and needs of a new business fades, marketing needs to mature towards a brand-focused distribution to ensure sustainable growth.

C. Flip The Funnel: From ToFu/BoFu To In/Out Market

The funnel is a well-known construct in B2B marketing. The conventional B2B funnel depicts a voluminous top-of-the-funnel that wittles down along each step of the funnel towards the bottom of the funnel. Interestingly, Binet and Field suggest flipping the funnel. Rather than ToFu and BoFu, they recommend thinking of the funnel as “in market” buyers and “out market” buyers. In this case, activation spend is mostly for limited market  buyers while branding spend is for the much larger, out market buyers. 

This approach tends to be more customer-centric because:

1. Customers don’t think of themselves as being in the “brand building” phase or “sales activation phase”. Instead, customers think of themselves as being “in-market” to buy a product or “out-market” to not buy a product at this moment. 
2. Marketers have two customers: Your external customers and your internal finance team. Thinking about the funnel as current cash flow customers and future cash flow customers will help align marketing with the CFO or finance team as well. 

D. Different Stroke For Different Folks: 

Of course, in-market buyers are inherently very different from out-market buyers. This necessitates different approaches for creative, distribution, and measurement.

For in-market approach:

• Rational Messaging - for immediate ROI and value
• Narrower targeting - for a narrower market
• Sales metrics - revenue and pipeline are the most relevant KPI for in-market

For out-market approach: 

• Emotional Messaging - for long term brand retention
• Broder targeting - for a larger market
• Memory metrics - brand sense is an example of a relevant memory metric 

And there you have it. The first principles delved deep into the pros and cons of Sales Activation and Brand Building. While employing both approaches in unison are crucial to long-term success, the verdict is that, at the end of the day, the goal should be to prioritize brand building. We also highlight an unconventional perspective of the good old sales funnel. Join us next week to go over the second principle: Awareness vs Fame.

All our homies LOVE HubSpot. No doubt, it's a reliable CRM and marketing automation platform. In fact, Factors.ai integrates seamlessly with HubSpot to deliver full-path analytics and attribution across campaigns, website, and CRM. That being said, HubSpot’s own in-platform analytics and attribution engine, is fraught with serious limitations. The following article highlights these issues with HubSpot — and how you can overcome them with Factors.ai. Ultimately, we find Factors.ai to be a far better fit for data-driven B2B marketers.

Before we jump into the limitations of HubSpot analytics and attribution, it’s only fair to address a couple of positives. Although premium reporting (advanced analytics, revenue attribution, etc) is only available on HubSpot’s enterprise plans, it delivers a robust range of multi-touch attribution models in a simple, user-friendly framework. Additionally, if your company uses HubSpot CRM, MAP, and life cycles stages religiously, HubSpot could possibly be an effective all-in-one solution for reporting. As we shall now see, however, most teams do not use HubSpot in the dedicated manner that’s required for it to function well.

Limitation #1: Rigidity & Inaccuracy

1.1. Fixed Lifecycle Stages

One glaring limitation with HubSpot’s in-platform analytics solution is its rigidity around the sales funnel — and especially its life cycle stages. HubSpot analytics only offers fixed definitions for events and stages along the customer journey — Subscriber, Lead, MQL, SQL, Opportunity, Customer, and Evangelist. Now, this set of stages may fit in perfectly with your organization’s funnel structure; but in reality, most B2B teams follow unique customer stages based on the nuance and particulars of their business model. B2B SaaS firms for example, may care about including a “Demo Done” stage to flag high intent leads. HubSpot’s analytics engine does not provide the flexibility to include, or even edit lifecycle stages to match this preferences. 

If your team does not adhere to HubSpot’s predetermined structure, Factors.ai may be the right fit for you. On Factors, users have limitless flexibility to set, track, and analyze their own internal life-cycle stages.

1.2 Inaccurate Lifecycle Stage Tracking

In continuation with the previous point — not only is HubSpot’s lifecycle stage tracking rigid, it’s also blatantly inaccurate. Rather than considering the leads in lifecycle stage “B” to be a subset of the previous lifecycle stage “A”, HubSpot only counts the contacts in a particular stage at that point in time. Here’s an example to illustrate:

Say you have 50 leads tagged MQLs. 20 of them become SQLs. This, of course, does not mean that you now only have 30 MQLs. Rather, it means that the set of 20 SQLs are a subset of the total set of 50 MQLs. 

This is a major issue with HubSpot analytics — leading to inaccurate readings, insights, and ultimately; marketing decisions. Rest assured, Factors.ai ensures no such fallacies in logic. You can also guarantee a far wider range of filters, breakdowns, and visualization techniques on Factors.ai as compared to HubSpot analytics. 

Limitation #2: Attribution Troubles

2.1 Campaign Attribution

It’s impossible to create attribution reports on HubSpot at a keyword level across campaigns and ad groups. If you want to look at keyword level attribution reports on HubSpot, you’ll need to examine keywords within a specific ad group from a specific campaign. Why is this an issue? Well because a specific keyword can (and usually does) belong to multiple campaigns

On Factors, you can do what HubSpot attribution does AND look at keyword attribution reports across campaigns and ad groups for granular, and more importantly, accurate insights.

2.2 Attributing Offline Events

Offline touchpoints are those touchpoints along the customer journey that cannot be tracked digitally. These include outbound emails, webinars, in-person events, corporate gifts, etc. While HubSpot does enable you to document these “events”, it is not possible to analyze or visualize them within HubSpot analytics. As a company scales, it’s likely to have a good combination of digital and offline touchpoints, making it imperative to account and analyze for both in union. 

Factors.ai makes it possible to track, analyze, and attribute offline touchpoints by fetching contact tags and UTMs. These touchpoints are also completely customizable with no-code. Needless to say, unlike Factors.ai, HubSpot does not enable users to attribute custom properties, events, or KPIs. 

2.3 Comparing Attribution Models

Factors.ai is one of the few attribution solutions that allows users to compare attribution models against each other. B2B sales cycles can be complex, and the ability to compare results across first-touch and multi-touch models gives marketers an unequivocal advantage in identifying trends accurately. Unlike Factors.ai, HubSpot does not offer the ability to compare attribution models. 

Limitation #3: Lack of Granularity 

Another major drawback with HubSpot analytics & attribution is that it considers lead source only at a channel level. That is, lead sources may be viewed as “Organic”, “Paid ads”, “Social” and so on. We all know that the devil’s in the details — and channel level data simply will not cut it in this day and age. How is one to know which campaigns or content to scale, if they are unable to view performance data for the same? Factors.ai is all about granularity. We ensure detailed analytics at a channel, campaign, ad group and keyword level to help you make the best possible marketing decisions. Our extensive line of no-code integrations across the most popular ad platforms guarantees a proper data-driven marketing experience. 

Limitation #4: Data Integration Woes

So here’s the thing: you can integrate HubSpot with third-party data-sources, including other CRMs like Salesforce — but it’s no easy task. It requires tedious onboarding, strict vigilance, and developer dependency. You need to make sure all your sales data is either on or linked to HubSpot. If you use a combination of HubSpot and Salesforce or LeadSquared or Marketo, a platform like Factors.ai would make your life a lot easier. IF, however, you religiously use HubSpot exclusive products — CRM, MAP, Website, etc, then HubSpot may be a more convenient option for you.

Limitation #5: It’s The Little Things…

By design, Factors.ai is a robust, intuitive marketing analytics, attribution, and journey mapping platform. Above all, we pride ourselves on delivering the best possible experience to our users. This entails end-to-end onboarding support, sustained customer success management, and smooth, reliable performance. The same, unfortunately, cannot be said about HubSpot analytics. 

Here’s why Factors.ai has the edge over HubSpot when it comes to user experience:

1. HubSpot imposes limited users or seats per hub. Factors grants unlimited seats, free of charge. 
2. HubSpot requires tedious, developer dependent onboarding and training over several weeks, if not months. You can get started with Factors.ai in 30 minutes.
3. HubSpot charges an independent fee for tech support. Factors.ai is an extension of your team — with dedicated customer success management guaranteed.
4. HubSpot aggressively up-sells its features to nickel and dime existing customers. Factors.ai recommends tailor-made plans based on the scale and growth of your team. 

And there you have it. Still curious to learn why Factors.ai would be better suited for your B2B team over HubSpot Marketing Hub? Book a personalized demo here to see our work in action.

Website Account Identification with Factors.ai

Table of Contents:

1. Introduction
2. How does website account identification work on Factors?
3. How can website account identification help?
4. Why Factors?

Your B2B website is a goldmine. Here’s how you can make the most of it.

Now more than ever, B2B marketers & sales folk are being asked to do more with less. Teams are constantly on their toes trying to make limited resources stretch a long way. What’s more? As buyers become increasingly adept at spotting campaign & sales pitches from a mile away, it becomes that much harder to build new relationships from scratch.

The solution? Making the most of what you already have — like the goldmine of acccount data buried away in your website. 

Your website is arguably the most voluminous, high-traffic touch-point for B2B buyers. That being said, only about 1-5% of this traffic actually converts. So what happens to the remaining 95% of accounts you’ve worked tirelessly to drive to your site? We can’t just let all that potential pipeline slip-away, can we?

The following blog highlights how you can identify anonymous companies already visiting your site using cutting-edge account deanonymization and website  tracking technology. 

Factors.ai’s account identification tool identifies who your B2B audience is and how they engage with your brand — so you can reach out with the right message at the right time. But how does account identification work? And what makes Factors.ai the best account identification solution for B2B teams? Let’s find out. 

How does Factors work?

Account identification like Factors use reverse DNS lookup to discover companies visiting your site based on their IP addresses. In short, reverse DNS identifies the host name of a particular IP address to provide company location information. 

Factors ai matches IP data with an extensive database to identify which company visited your site as well as other firmographic features like revenue, employee headcount, industry, etc. Note that Factors is a privacy-compliant intelligence platform that does not identify, collect, or distribute anonymous user-level data.

How can account identification help?

For sales: 

1. Find ready-to-buy accounts: Accounts that visit your website are aware of your brand. And accounts that are aware of your brand are far more likely to convert as compared to those that are yet to hear of you. With account identification, sales can reach out to otherwise anonymous prospects, capitalize on an untapped pool of buyers, and prevent low hanging revenue from slipping away. 

2. Close better deals, faster: The early bird gets the worm and the early salesperson closes the deal. It’s no secret that time is off the essence when it comes to B2B sales. With Factors, sales teams can reach out to high-intent prospects before they have a chance to interact with competitors.

3. Create better sales pitches: Sales can see exactly what content — features, blogs, case-studies, use-cases etc — prospects are engaging to anticipate their needs and personalize sales interactions accordingly. 

For marketers: 

1. Delight your sales team: Make your sales team happy with a list of high-intent, high-quality prospects for your sales team to engage with. And the best part? These are leads generated with zero additional ad spend as they’re simply companies who already visit your site.

2. Understand traffic sources: Learn which channels and traffic source high-quality accounts come from. Scale the right campaigns and close the gap between impressions and revenue. This benefit is all the more pronounced when account identification is used in unison with Factors’ end-to-end attribution and journey mapping.

3. Improve website engagement: See how companies engage with content on your website and understand what topics and themes resonate most with buyers. Gauge what’s helping and hurting website conversions and optimize performance accordingly. 

4. Optimize retargeting: Once you understand which accounts are visiting your site and what they’re looking for, it becomes that much easier to retarget the right audience. Don’t waste your ad budget retargeting everyone who visits your site — just the select few who show real buyer intent. 

Why Factors?

Well, because Factors.ai is the most accurate, cost-effective, and well-integrated account identification software for B2B companies:

1. Data accuracy

Factors.ai delivers the most accurate IP-matching in the industry. In a case wherein customers provided 22,000 unique IPs (where the answers were known), different vendors were asked to match IPs with companies and provide firmographic information. Factors.ai’s cutting-edge IP-technology delivered a whopping 64% match rate and nearly 30% more matches than the nearest competitor. Factors provide a matchrate that’s 10-15% better than alternatives like Clearbit, Kickfire, and Demandbase.

2. Cost-efficiency

Factors.ai is one of, if not the most cost-effective de-anonymization solution in the industry. Learn more about our pricing here: factors.ai/pricing

3. Unified account analytics

Another benefit with Factors is the wide range of complementary features it has to offer along with account identification. End-to-end marketing analytics, revenue attribution, journey mapping and more — all of which provide a layer of depth and direction once you identify which accounts are visiting your site. Answer questions like:

• What campaigns are driving the most traffic to my website?
• What channels should I scale to improve demo conversions on my website?
• What content resonates most with my target audience? 
• How do our customers progress from impressions to revenue? 
• How does marketing performance vary by buyer persona and firmographic features?

4. Website behavior and account timelines

Along with knowing which company is visiting your site, Factors light-weight script will also shed light onto what accounts are engaging with on your site. Understand website activity — including page views, button clicks, time spent, scroll depth and more. All of this data is collected using only first-party cookies — so there’s no impact on third-party restrictions.

What's more? Factors provides intuitive account timelines and user journeys to visualize, in real-time, how accounts are progressive from awareness to intent. This is a valuable feature for B2B marketers to identify buyer intent and strike with marketing material, targeted campaigns, or a simple email while the iron's still hot.

It’s no secret that the B2B SaaS funnel involves several touchpoints across campaigns, website, offline events, and CRM. Given that customer journeys are complex and nonlinear, measuring and optimizing marketing’s impact on revenue may seem like a daunting task. To solve for this, there’s been an influx of plug and play B2B marketing attribution and analytics tools in recent years. 

While there’s no shortage of marketing attribution tools out there, each solution has its own unique set of features, strengths, and limitations. This blog compares two popular B2B marketing attribution tools — Dreamdata and Hockeystack — to help readers decide which solution may be better suited to their needs.

Note that this blog won’t cover the basics of what marketing attribution is. Instead, you can find a wide range of resources on marketing attribution here:

• A Comprehensive Guide To Marketing Attribution
• B2B Marketing Attribution
• Challenges With B2B Attribution (And How To Get Over Them)

About Dreamdata

Dreamdata is a Denmark-based B2B revenue attribution platform that works to connect and crunch revenue related data across the customer journey.  At a high level, much like any other competent marketing analytics tool, Dreamdata helps teams identify what GTM effort drives revenue, where to cut costs, and how to scale the right campaigns. 

As following sections highlight, Dreamdata provides a robust analytics suite, a wide-range of integrations, and a strong customer success experience. That being said, the platform seems to fall short when it comes to implementation, custom reporting and dashboarding, and ease of use. Each of these features and limitations are covered in detail below. 

About HockeyStack

HockeyStack is a B2B analytics and attribution platform that helps teams track data across campaigns, website, and CRM to measure marketing ROI, view account-based intent signals, and improve budget allocation. 

HockeyStack claims a rapid implementation process and customizable dashboards. That being said, HockeyStack offers fewer integrations and limited granularity when it comes to reporting. Again, each of these features and shortcomings are highlighted in detail below.

Dreamdata vs. HockeyStack: Key Features

Both Dreamdata and HockeyStack are effective marketing attribution tools in their own right — but no product is perfect. The next couple of sections examine key features, strengths and limitations of each solution. Naturally, there’s bound to be significant overlap; but the devil is in the details. After covering a few key common features, we explore where each platform outperforms the other.

#1 Tracking & Analytics: 

As most analytics solutions do, both Dreamdata and Hockeystack unify marketing and revenue data under one roof. Both tools also provide a wide range of analytics capabilities to help teams make well-informed decisions across campaigns, website, content, and more. 

Both solutions employ javascript codes that are added to a website to track visitor interactions and engagement. They can measure standard website performance metrics like pageviews, scroll depth, clicks, form submissions, and more at an account and user level. In turn, teams can gauge customer behavior and learn how different content and webpages influence pipeline by cohort. 

Dreamdata and HockeyStack also integrate with ad platforms, marketing automation platforms, and CRMs to consolidate campaign metrics, offline events, and revenue metrics. This helps marketing teams monitor their efforts and understand what’s helping or hurting bottom line conversions. Note that Dreamdata currently provides a wider range of integrations than HockeyStack — more on this later. 

#2 Multi-touch attribution

Attribution analysis is at the core of Dreamdata and Hockeystack. Unsurprisingly, both solutions do a good job of measuring performance across marketing activities and attributing each touchpoint back to revenue.  

They can stitch and credit measurable touchpoints across channels, campaigns, website, and offline events (from CRM) based on their influence on pipeline. Using a range of multi-touch attribution models, marketing teams can quantify their impact on revenue from first-touch to deal won at an account level. Here are a few use-cases multi-touch attribution on Dreamdata and Hockeystack can solve for:

• Measuring ROAS across ad campaigns
• Attributing revenue back to marketing channels 
• Tracking the impact of organic social and SEO efforts
• Learning which content and channels drive bottom-line metrics

#3 Journeys

Journeys analytics is a relatively recent feature that’s not as common amongst other marketing analytics and attribution tools. That being said, both Dreamdata and HockeyStack offer variants of journey analytics.

In short, journey analytics helps teams visualize complex, non-linear customer journeys by mapping each stakeholder’s touch-points at an account level. Why is this helpful? It provides an intuitive timeline of profiles, behavior, and intent across each account within the pipeline. This information may in turn be used to personalize further marketing efforts, optimize retargeting campaigns, customize sales pitches, and identify buying patterns. 

HockeyStack and Dreamdata work well for all three features covered above. Still, both tools have their own strengths and limitations. The following section highlights stand-out reasons why users may prefer one over the other. 

What Dreamdata Does Better

1. Out-of-the-box Integrations

Dreamdata offers a wider range of out-of-the-box integrations as compared to HockeyStack. While both solutions provide integrations with the most popular ad platforms, CRMs, MAPs, and CDPs, Dreamdata goes the extra mile to cater to relatively niche platforms and data warehouses as well. 

Key integrations supported by Dreamdata and HockeyStack*: HubSpot, SalesForce, Google Ads, Facebook Ads, Linkedin Ads, Marketo, Pardot, Intercom, Segment

Key integrations supported by Dreamdata but not HockeyStack*:  Zoho, G2, Zapier, Outreach, AdRoll, Google Data Studio, BigQuery 

*based on HockeyStack website

Pro Tip: Note that in case Dreamdata and HockeyStack doesn’t support an integration for a specific platform, both tools offer custom integrations as per demand. 

2. Detailed & Granular Reporting 

Although this isn’t necessarily a drawback with HockeyStack, users have complained about its lack of granularity. Reviews compare HockeyStack’s reporting capabilities to that of Google Analytics (GA4) — decent, but not detailed enough. Given that Dreamdata is a relatively mature product, their reporting capabilities provide deeper insights across conversion rates, customer lifetime value, and revenue attribution, and more. 

3. Customer Success

B2B analytics and attributions platforms are complex. While tools are becoming increasingly intuitive, it’s important for non-technical users to have easy access to timely, effective CSM. Fortunately, Dreamdata seems to support robust customer success servicing. This is especially valuable since Dreamdata’s implementation is reportedly an involved process. 

4. Templatized Reporting + UI

This is a double edged sword. Dreamdata delivers a structured, non-customizable dashboard and event framework that offers little room for flexibility. Dashboards are broadly grouped into the following categories: Engagement, Content, Performance, Journeys and Revenue. 

On one hand, this may be beneficial to smaller SaaS teams with limited technical resources as it’s likely to cater to most of their analytics and reporting needs. 

However, as the business starts to scale, its requirements may include custom dashboards and events that are company-specific. At this point, Dreamdata’s templatized reporting may be a drawback.

Although reviews suggest that Dreamdata involves a steep learning curve, it’s fair to assume that its UI is a step ahead of HockeyStack. HockeyStack is a relatively younger product and users tend to find the platform a little rough around the edges. That being said, reviews also suggest that they’re showing quick improvement. It’s likely only a matter of time before both platforms are on par with each other.

What HockeyStack Does Better

1. Implementation

HockeyStack makes strong claims about its rapid implementation process, suggesting that users can onboard and get started in a matter of minutes. This is in stark contrast to Dreamdata, which, as a more sophisticated tool, requires an involved, drawn-out implementation process. HockeyStack’s intuitive onboarding is a big advantage to smaller teams that don’t have the resources for dedicated onboarding or maintenance support. 

2. Custom Dashboards

Dreamdata’s platform focuses on solving the most common SaaS use-cases. As a result, the platform tends to be relatively less flexible. HockeyStack, on the other hand, promotes far more customizations across events, reports, dashboards, and visualizations. HockeyStack provides the option of preconfigured templates, but lets users build reports from scratch as well. While granularity may be lacking when compared to Dreamdata, this ability for flexible dashboarding may be helpful for teams looking for tailor-made, high-level reports. 

3. Funnels, Surveys & Impression Tracking 

Along with the key analytics and attribution features discussed, HockeyStack provides a few features that Dreamdata doesn't.

The most valuable of these features is probably Funnels. Funnels is a powerful analytics technique that helps users graphically visualize different stages of the sales cycle. These stages can be configured by users to, for example, see how website visitors are progressing from the home page, to the pricing page, and to a blog before scheduling a demo. 

Surveys is another feature that, as the name suggests, allows users to create surveys for self attribution. Finally, Linkedin Impression Tracking is another nifty feature that enables users to identify companies viewing Linkedin campaigns.

Dreamdata vs. HockeyStack: Pricing

[December 2023 Update]: Both HockeyStack and Dreamdata have revised pricing since this article was published. While HockeyStack have increased their starting price, Dreamdata have decreased theirs. Here's an updated rundown of pricing:

• Dreamdata pricing now starts at $599/mo for up to 30,000 MTUs
• HockeyStack pricing now starts at $1399/mo for up to 10,000 monthly visitors

‍

[Pricing as of February 2023]

• Dreamdata’s paid plans start at $999/month for 10 seats and up to 10,000 MTUs
• HockeyStack’s paid plans start at $949/month for 10 seats and up to 10,000 monthly visitors
• HockeyStack offers a 14-day free trial 
• Dreamdata offers a free web analytics tool as an alternative to Google Analytics

Still On The Fence About What B2B Attribution Tool To Go With?

And there you have it. A breakdown of Dreamdata and HockeyStack, and the reasons why one could be a better fit for you over the other. Still On The Fence About What B2B Attribution Tool To Go With? Here are a few reasons why you might want to consider Factors as well:

• Rapid, no-code integrations across ad platforms, CRM, MAP, and more 
• Granular, end-to-end analytics, attribution, and journeys across ad campaigns, website content, offline events, organic content, and more 
• Fully customizable events, properties, dimensions, and dashboard
• Dedicated customer success management 
• Funnels, path analysis and website tracking

And…

• Website visitor identification
• AI-fueled conversion insights
• Real-time Slack alerts
• Cost-effective analytics pricing plans starting at $399/month