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.

B2B SaaS SEO KPI

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.

Summary

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.

Summary

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.

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.

LAND

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.

EXPAND

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

Summary

  • 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.

Digital Marketing Analytics – Introduction (Part 1 of 3)

This is Part 1 of 3 in a series of digital marketing analytics. This series is more of a brain-dump of my thoughts when thinking about the topic.

What is Digital Marketing Analytics?

The number 1 SERP from Google of “Digital Marketing Analytics” is from Hubspot: “the translation of customer behavior into actionable business data.

I agree with the definition at a high-level.

To take it one step deeper, Digital Marketing Analytics is “driving data-driven decisions (with a focus on calculating return on investment) to provide insights on obtaining a maximal return on every dollar invested across all marketing channels or campaigns”.

If you have a good foundation of digital marketing analytics, there is always more your business can do.

An example of next level would be using the data to forecast and predict return with data modeling.

What is “return” in B2B digital marketing?

If getting return is one of the objectives of digital analytics, we need to define what is return.

Return can be calculated on a number of metrics in B2B – it could be revenue, pipeline and leads.

Why?

Given the whole point of B2B digital marketing is to fuel the business, the metrics on which you measure return should match the sales/marketing funnel progression that the business is built upon.

This is the most intuitive way for digital marketers and their work to form a direct relationship with revenue and other revenue-generating teams.

Once you have those output metrics established. You can join it against cost data to calculate cost per revenue, cost per pipeline and cost per lead. These become your efficiency metrics.

If efficiency is ‘good’ (i.e., falls within your threshold of what’s acceptable), it makes sense to continue to invest with positive returns.

What about other metrics?

Many other metrics closer to the top of funnel – for instance impressions, clicks, web visits, conversions – have a less direct relationship to revenue.

They are typically have a many-to-many relationship, which makes their impact very difficult to measure accurately.

However, there is a temptation to anchor on these as they are typically available out-of-the-box (via ad and campaign platforms) and require less computation.

Definitions are important

We touched on the definition of “return” above, but there are many more business metrics, in terms of inputs and outputs, that are critical to unpack the drivers of any business.

To start with Sales, the inputs are often headcount — assuming each headcount performs as expected to hit 100% attainment (the efficiency), the business will get predictable revenue (i.e, outputs).

Many may say that the unit economics of sales staffing is science, the management of sales selling is art.

For marketing, the marketing activities that drive the outputs are the art, and the science is what we are trying to unpack with digital analytics.

To set a baseline, let’s align on the following digital marketing metrics, which may give more context later:

  • Top of the Funnel (TOFU): A web visit
  • Middle of the Funnel (MOFU): A lead (or conversion event, e.g., email capture)
  • Bottom of the Funnel (BOFU): A stage X opportunity accepted by AE

The Ultimate Challenge

The real challenge of digital marketing analytics is to understand the relationships between pre-TOFU and MOFU in a cross-device, multi-touch environment.

From MOFU to BOFU, most companies can do it pretty well with a Marketing Automation Platform (MAP) (i.e, Marketo, Pardot, Hubspot..etc) and Customer Relationship Management software (CRM) (i.e, Salesforce)

To understand the relationships between pre-TOFU and MOFU by factual data analysis, you will need to collect the data.

And it is difficult, because of 2 reasons.

  1. While Lead>Account>Opportunity is a 1-to-1 relationship, impressions>web visits>leads are many-to-many relationships
  2. The sources of impressions and web visits are complex — it could be search, paid, direct, referral
    • Compliance regulation like GDPR, ad-blocking technologies, browsers removing cookies and privacy mode make it even more difficult to attribute the sources

It is worth noting that if the business has no data, there is no science to be done.

Now, we should be on the same page on business understanding in terms of digital analytics and the marketing funnel.

Let’s dive deeper into data/digital analytics with inputs and outputs.

Inputs and Outputs in Digital Analytics

The framework of inputs and outputs are important to structure what is need to produce certain outcome, that includes both technology and human effort.

In broad stroke, a digital analytics process cycle can be split into 5 steps:

  1. [Input] Data collection – the process to collect raw data
  2. [Input] Data piping – the process to manipulate raw data to a structured format
  3. [Output] Data analysis and insights – the process to extract insights from structured data
  4. [Output] Visualization – the process to surface structured data to be digested easily
  5. [Input & Output] Optimization – the process to translate the insights to execution literally

Depending on the maturity of the business, the implementation of the 5 steps could look very different.

For a startup, a general analytics tool like Google Analytics makes step 1 – 4 easy and step 5 becomes the iterative process that your team is executing.

If your business has grown out of Google Analytics from an analysis standpoint, you could consider expanding the analytical capability by extending the number of data sources available and performing the analysis out of a data warehouse directly.

Conclusion

Business stakeholders at each organization need to identify the requirements and subsequence actions that would be performed with the insights in order to make the investment of data analytics worthwhile.

As the business continues to grow, it may be necessary to specialize in web analytics and advertising analytics. I will share more details in part 2 and part 3.