B2B SaaS SEO Strategy and KPIs – A Brief Guide.

I have had the opportunity to lead the Search Engine Optimization (SEO) function at multiple B2B SaaS organizations. In the past few years, I have also talked to many talented folks in the B2B SaaS space to exchange ideas on what and how they are doing.

The conversation normally covers a few things (which all overlap with each other):

  1. SEO strategy
  2. KPIs
  3. Team structure
  4. Notable tactics

This post focuses on #1 and #2.

B2B SaaS SEO Strategy

For the most part, B2B SaaS companies all have a very similar SEO strategy.

It is because SEO is largely an open-book competition from Google. And SEO professionals have all figured out what needs to be done to rank on page 1.

SEO strategy is largely a three-part recipe:

  1. Content – the quality and quantity of your content production engine to meet search intent.
  2. Technical Development – technical knowledge and processes on implementing technical elements correctly for search engines, which are well-documented on Google Search Central.
  3. Backlinks – the quality and quantity links referred from other domains on the internet.
SEO Strategy Summary

So which part of the strategy differentiates a company and puts you at the front of the pack?

When it comes to Technical Development, every competitor of yours can hire the right people to build that.

For the domain authority generated from backlinks, your standing is typically influenced by (1) earned links from great content, and (2) business reach (i.e, the size and maturity of your business). In my experience, this is not commonly the first thing to tackle.

Content becomes the only workable competitive differentiator, and your content must be better to rank higher than your competitors.

Not only does the content need to provide value to the searchers by fulfilling their search intent, but it also needs to satisfy search engine criteria of “great content” in terms of technical elements and authority.

Great content is often easier said than done. Some of the best examples in B2B marketing are from Hubspot and Neil Patel.


After too-many meetings discussing SEO metrics, I have come to 3 key takeaways in forming SEO KPIs for your team.

1. Keep it simple.

As a north-star, I suggest picking organic traffic; and its conversion as a secondary KPI.

This way, you are capturing both qualitative and quantitative for your SEO team.

There are fancy tools such as Bizible that could help you with multi-touch attribution for the organic channels, but there are too many variables with the B2B sales cycle that are not within your SEO team’s control.

2. Focus on what you can control.

If you are managing a website with thousands of pages, there are simply too many opportunities to tackle using the SEO strategy above.

Whatever you are going to measure should proxy the direct influence of your SEO team.

A good example would be to target non-brand keywords. Focusing on this gives allows you to tell a story that you are attracting traffic that does not know your brand already.

3. Tell a great (intangible) story

Story-telling is important in all aspects of marketing, not just SEO.

SEO inherently is limited by the number of search queries related to your business. In other words, it is a zero-sum game.

Everyone searches on Google daily – if you can tell a story that relates to the search behavior of your audience, you are halfway there.

For the other half, what I found more successful is telling the story of growth, rather than absolute numbers.

If you can show either you are capturing more traffic from the same keyword or from adjacent topics, you are getting a bigger search impression in the competitive SERP.


While SEO strategy could be similar between B2B SaaS companies, where companies can stand out is strong execution and a clear vision to drive their organic search presence forward.

If you are operating in a B2B SaaS business, how do you formulate your SEO strategy and what are your primary KPIs?

It’d be great to exchange notes with you.

B2B Web Personalization Maturity Curve

Every B2B marketer in the world dreams of a state where every single touchpoint with a prospect or customer of your business is personalized.

But, what does it actually mean to be personalized?

My take on being personalized: content is relevant to the viewer. With that you, as the marketer, strives to provide value to that viewer about your business.

Value could take the form of education, a different perspective, or simply a spark of joy/emotion. You can measure the value you have delivered by means of engagement and action, such as a click and scroll.

In this post, let’s talk about the common phases or approaches that B2B companies are taking on the topic of web personalization.

B2B Web Personalization

If you have read my previous blog post, What is B2B Web Personalization, you may know that B2B web personalization centers on 3 elements:

  • Identification – the attributes to be used to identify that person
  • Content – the content that may be relevant to the person, including location of that content being shown
  • Algorithm – the logic behind the relationship between identification and content for the personalization

If you want to get an overview of B2B web personalization, read that blog post!

B2B Web Personalization Maturity

If that is B2B web personalization at a high-level, I would describe the B2B web personalization maturity journey as occurring in three phases.

1. Rules-Based Personalization with Reverse-IP Look-up

Personalization in this form is what most A/B testing platforms – Optimizely, Google Optimize and VWO – focus on.

These platforms act as a service layer between the three elements.

They take an identification attribute and read it via integration with a reverse-IP provider (e.g, Clearbit, Demandbase, 6sense) to surface the content.

The logic between the identification and content is rule-based and it’s often a 1-to-1 relationship.

For example, if site visitor is from the financial industry, show this image.

This type of personalization is table stakes at most mature marketing organizations, particular in B2B SaaS.

A/B testing (or experimental culture more generally) is something that every organization is driving for, especially where top-of-funnel volume and/or customer LTVs are high.

2. Dynamic or AI-Based Personalization

The core difference between the first and second phase is the personalization algorithm.

Instead of a if-this-then-that static rule, in the second phase there is a dynamic or AI-based algorithm (i.e, the personalization algorithm) that determines the content to optimize conversion based on the identification attributes.

With that, we see an increase in the number of inputs required in this second phase:

  1. Available identification attributes
    • [Individual] Behavioral – the activities that browser has done on your business web properties (e.g, page-view, IP-location)
    • [Account] Firmographic – the account information of that IP address (e.g, company industry, company revenue band)
  2. Available content

Both of these inputs are essential to determine which content is the best based on a single or multiple of identification attributes.

Having said that, the collection or aggregation of these two inputs could still be a rather manual effort into the personalization platform.

3. Full Scale Personalization – Customer 360 view

The last phase is built on top of phase 2, further leveraging the AI-based personalization algorithm with data.

The core differentiator in phase 3 is that the number and accuracy of inputs have exponentially increased, because of additional 1st party data (e.g, form fill provided by customers).

The available identification attributes now becomes:

  • [Individual] Behavioral – the activities that the browser has done on your business web properties plus activities within your products or free trial environments
  • NEW [Individual] Demographic – the static attributes of that individual once known (e.g, name, title)
  • [Account] Firmographic – the account information of that IP address plus what the individual provided.

With the massive amount of data becomes available, the business requires additional technology to organize the data.

Using the technology to blend and group the data attributes will be able to form identification segments.

If I use Amazon as an example: You (e.g., a 50 year old female in NYC) browsed product X, you are likely to put into a segment of “Interested in X”. And you can be in multiple segments at any given time.

On the other hand, the content algorithm organizes the available content and how they relate to other content.

Continuing the Amazon example, you will be shown product X in location B because you are in the segment of “Interested X”.

With the content algorithm, you might have a % of chances that you will be interested in product W and Y, in a similar lookalike audience.

To tie it all together, the personalization algorithm may also show product W and Y in location A and C.


Web personalization is a journey on which most B2B SaaS companies are currently embarked.

Although companies may be at a different points of the journey, the challenges and milestones are very similar depending on the volume of web traffic and relationships between website/product and go-to-market motions.

If you are interested to know more about my thoughts, add me on Linkedin!

Case Study: Digital Marketing Strategy in ABM for Monday.com

This is a case study of my other post: Digital Marketing Strategy in Account-Based Marketing (ABM).

My goal is to use a case study to illustrate the Digital Marketing Strategy in ABM and I will use monday.com as an example

Why monday.com?

Just because I have always seen their ads on Youtube and Instagram, and they are pretty good.

Disclaimer: I am not affiliated with monday.com any way, below is purely my outside-in reflections.

Business Fundamental

Before going deep into the ABM digital strategy, we should establish some shared understanding of monday.com ‘s business.

This could help answering the why bother doing ABM at all.

Monday.com is a software as a series business (SaaS). Their business model operates on a per-seat basis.

That means the larger the company, the more employees they have, the more potential revenue for monday.com.

The world largest organization are likely multi-national corporations with thousands of thousands of employees.

From an investment standpoint, there are two metrics that could potentially tell the story of how well monday.com is selling to the world’s largest organization.

  1. Number of customers with $100k annualized revenue
  2. The net dollar retention rate, which tells what percent of revenue from current customers you retained from the prior year, after accounting for upgrades, downgrades, and churn.

These two metrics are powerful because they tell a story that your product can be adopted in large corporations and they will pay you more and more every year.

With that, let’s continue with the case study.

ABM for Monday.com – Land and Expand

The lifecycle of a monday.com customer is likely started with individual users of small teams signing up to collaborate with each other.

Then, with product adoption and growth, it influences other teams within the enterprise to use the same platform to drive better alignment and collaboration.

This is a classic a land and expand playbook that many SaaS companies use, including Slack, Zoom, Dropbox…etc

So the ABM for monday.com could be framed as:

  1. How can the business land the Fortune 500 accounts?
  2. After land, how can the business expand within the account?

As an example of potential revenue, one of the brands in the Fortune 500 list is Levi Strauss, that has 15,800 employee.

Assuming 30% of the employees are corporate rather than retail, the potential revenue could be $1M+ for one account. (15k * 30% = 4500 users; 4500 * $20 user/mo* 12mos = $1M+)

There are many other tactics/programs could land and expand within Levi, and the next section will focus on how digital marketing can accelerate that.

Digital Marketing Strategy in ABM for Monday.com

If you have read my blog post, the digital marketing strategy in ABM is primarily around:

  1. What are the programs/campaigns we would deploy for 1:1, 1:few and 1:many in the Fortune 500 accounts.
  2. What are the programs/campaigns we would deploy to reach, drive them to website and convert.

If we put these two points together against a list of tactical ideas, you can put them into a 2×2 graph.

Each dot is a tactic / program, and they should be aligned with the ABM strategy.

2x2 graph for ABM Digital Program

Having said that, advertising is likely the core channel driving acquisition with a large media budget, thanks to the large consumer or SMB appeal.

And that SMB acquisition motion could potentially result in some good Fortune 500 logos.

Next section is going to illustrate some of the tactics applicable to ABM.


If I was in the marketing team, I would define landing an account to be at least have one paying team.

The rationale behind that is the product purchase flow should be easy enough for small teams to pay with credit card directly without much sales interactions for scalability.

Here are some ideas on 1:1, 1:few and 1:many for land tactics:

  • 1:1 – the most strategic account for monday.com are likely to be the likes of FAANG. Because the workers are technology-savvy and can adopt new tool very quickly.
    • Create 1:1 advertising creatives and landing page specific for that account. For example, “collaborate with your FB colleagues on XYZ”.
  • 1:few – the clusters of large enterprises are likely to be vertical-specific, e.g, large marketing agencies, or travel/hotel vertical.
    • Create vertical-specific advertising creatives and cluster landing page for that set of accounts. For example, “launch your next big marketing campaign.”
  • 1:many – the large clusters of targeted account could be focused on the product features that add most values to large organization.
    • Create air-cover advertising creatives and general landing page. For example, “Bring out the best in your remote teams

Depending on the maturity of the organization, 1:few and 1:many could be combined to streamline effort.

Remember, the objective here is to generate the first paying team.


On expand, there are more data available for the messaging creation.

It is because there are some workers from the first paying team are already using the product.

If permission allows, internal champion proof should be the most compelling case to drive adoption.

  • 1:1
    • The biggest opportunity I see in 1:1 ads is to leverage internal champion use case and get centralized functions (e.g, IT and procurement) buy-in.
    • The creatives here can be “Work with Sarah from Marketing” or “Learn how Mark from Finance uses Monday.com”
    • Note: If there are limited marketing resources, it may be beneficial to prioritize on customer internal events
  • 1:few
    • One idea is customer proof or even co-marketing. The key is to leverage the common characteristic to resonate with those accounts. For example, Booking.com collaborate with monday.com across 7 continents”
  • 1:many
    • Since the advertising campaigns are air-cover, the messaging here is likely to be very similar to overall brand or enterprise marketing.

Although there are several ideas on digital marketing campaigns above on expand.

I believe the most effective lever monday.com can have is an account plan on how revenue teams (Sales, Marketing and Customer Success) could work together to grow the account.

Every account is different on how they look at work collaboration, and has different maturity on cloud adoption/digital transformation/remote work.

It is essential to have a point-of-view (POV) on how to sell and add value to each individual account.

That POV could help forming different digital marketing tactics at each of account lifecycle stages, as shown in the below graph.

Digital Account Lifecycle with different stages
Digital Account Lifecycle with different stages


  • The objective is of digital marketing in ABM is to engage a specific set of account to generate revenue.
  • ABM technologies are essential to identify people in that company and orchestrate different campaigns, with 1:1, 1:few and 1:many programs in mind.
  • Moving account in different tier could be a manual or automatic effort, depending on the marketing stack.
  • Compared to Sales and Customer Success, Marketing is certainly most effective in spreading the brand at scale to drive adoption, particular on specific product launch and education.

What is Advertising Analytics? (Part 3 of 3)

This is a continued post of my thoughts on the topic of Digital Marketing Analytics, specializing in B2B advertising analytics.

What is Advertising Analytics?

To begin with, what is advertising?

Advertising is a marketing tactic to pay for space to promote products and services with the ultimate goal of improving sales.

The purpose of advertising analytics is to determine the return of investment of that tactic – i.e, drawing statistical conclusions on the relationship between the investment and sales.

Advertising Analytics is its own beast because of the large dollar investment in this industry and the huge complexity of the datasets involved.

This will ensure the advertising investment will get a decent return with speed-to-execution and sufficient efficiency analysis, depending on expertise and bandwidth.

Challenges in Advertising Analytics

A foundational challenge in advertising analytics is multi-touch attribution with cross-device adjustments.

No company has solved the challenges perfectly. The complexity of cookie compliance and rapidly changing technology landscape makes this even harder.

Nonetheless, the process to try to address to uncover insights on return remains.

In advertising analytics, the 5 steps of digital analytics are complicated due to:

  1. # of inputs, including # platforms (i.e, Google, Linkedin, FB..etc) and technologies involved (i.e, Marketo, Rollworks..etc). Not the mention the wide range of offline advertising (e.g, billboard, prints, radio/podcast, TV..etc) mixing with online mediums.
  2. # of outputs, including # of additional metrics related to #1, plus impression, clicks, CTR and any cost related metrics are additional to the metrics we can measure on web analytics in GA360.

The function of advertising analytics is to draw relationships between those inputs and outputs to see what is working and what is not working.

With enough data and analytical capabilities, the relationship between the two could even be predictable within a range.

I’ll continue to illustrate my thoughts in terms of outputs and inputs, below.

Advertising Outputs

Outputs are the results of digital advertising campaigns, mainly in quantifiable measures and sometimes in qualitative feedback.

In eCommerce, the return of advertising is revenue.

However, in B2B businesses, pipeline is often the quantifiable advertising return, driven from SAO, SAL, MQL and all the way from impressions.

Pipeline is often the north star because it is easier to measure and isolate marketing efforts and effectiveness, rather than direct revenue (which is heavily influenced by sales team effectiveness).

If we go further TOFU, outputs could be extended to include web visits, clicks and impressions.

That highlights why digital analytics is super complex, because the relationship between each stage/step changes from 1:1 (i.e, SAL to SAO) to many:1 (i.e, many web visits to MQL) to many to many (i.e, many impressions to many web visits).


All of this boils down to the fact that, unlike B2C settings, B2B businesses are ultimately selling to companies (i.e, accounts).

And because companies (aka accounts) are comprised of many people, it makes the sales/marketing funnel much more complicated, which in turns make the B2B attribution more difficult.

To complicate the matter further, we have not even scratched the surface of engagement.

Engagement is another way to measure an output that exists somewhere between true TOFU and a strict output – engagement is a signal that a humans are interested in your offering, even if they haven’t actually purchased yet.

If you can analytically measure the signals, that helps your business stay ahead of the competition.

In short, measuring outputs can be summarized as:

  • Ad-level awareness metrics: Impressions, click, CTR
  • Ad>Web-level engagement metrics: Engaged Users
  • “Demand” Funnel: SAO, SAL, MQL

No matter what your business chooses to measure, the key is to have the data available to reflect your effort/activities. If no data is available , you cannot measure at all.

Advertising Inputs

Advertising inputs are a mix of media spend and effort.

  • Media spend could be a top-down budget allocation based on the total marketing channel mix.
  • Effort is a function of time and headcount required to produce creative and configurations of campaigns.

With every dollar invested, the output should be measured across media spend and effort.

Measuring media spend across following dimensions:

  • Ads Platforms (Google, Linkedin, FB…etc)
  • Ads Channel (Search, Display, Direct Response…etc)
  • Messaging (Go-to-market Themes, Product…etc)
  • Location (Geo, Country, Region…etc)
  • Segment (Company size, vertical..etc)
  • Individual FTE
  • Budget Type:
    • Always on
    • Business Units (e.g, Geo, Product, and Campaign)

Measuring effort is more an art form because it is the management of human resources.

IMHO, the art form is a representation of how well does the advertising manage know about the interest or pain point of potential customers to produce the most effective ads.

Some companies may use a bottom-up approach — the team would need to commit how much output they are going to generate with an ask of the media budget based on the forecast.

Non-technical Challenges in Advertising Analytics

Now that you understand the technical challenges in advertising analytics and how to break down inputs and outputs.

This section talks about the non-technical challenges that relates to people.

Depending on the mature of the business, analytics function can be de-centralized and centralized.

  • De-centralized is when the analytics function lives within the advertising team.
  • Centralized is when the analytics function lives outside the advertising team, normally in a specalized analytics team.

The decision between de-centralized and centralized is largely driven by how the business processes are defined within the organization.

With talking to many organization on how they structure their teams, it seems to be that the decision of the team structure has no standard or set decision criteria.

It is more of a function of the 1) experience of marketing leadership and 2) existing team know-how.

No matter whether the analytics function is de-centralized or centralized, two things are key to success between ads and analytics teams:

  • Clarity of R&R across teams – how do the teams collaborate to address the 5 steps of digital analytics? Are the teams involved resourced (in terms of expertise and bandwidth) to support ads continuously, given ads run 24/7 globally and have a huge spend/impact.
  • Expertise in ads analytics – does the team know what needs to be done to be the leader in the space?

In my experience, depending on the maturity of the marketing organization, the in-house advertising team is typically structured with investment breakdown of: 60-70% media spend, 15-20% FTE/agency spend, 15-20% analytics/technology spend.


So here you have it on my brain-dump of digital analytics, and the specialization of web analytics and advertising analytics.

If you are in a startup, it is very likely to be in one big topic of digital analytics.

If you are in a larger B2B organization, specialization of web analytics and advertising analytics can give you some structure to ask the right questions to get the right answers.

At the end of the day, the devil is in the details of execution.

It not only requires technical knowledge of technology but also how to get alignment across teams and the right investment of resources to achieve the desired analytical engine.