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!

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.

What is Web Analytics? (Part 2 of 3)

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

What is Web Analytics?

Web Analytics is the collection and analysis of data from a web property, let it be a single website or a collection of (sub-) domains.

The five steps of the analytical process is very similar to digital marketing overall.

  1. Data collection – Collect web touch-points via Google Analytics and Google Tag Manager
  2. Data piping – Depends, to be explained later
  3. Data analysis and insights – Perform analysis to obtain insights
  4. Visualization – Surface insights via platform reports, Google Data Studio, and/or other visualization tools
  5. Optimization – Digest insights and adjust programs run by marketing to optimize for conversion or KPI

What is the purpose of Web Analytics?

It is important to understand the core purpose of a function before diving into the detailed implementation.

What are the questions or insights is the business trying to answer?

In simple terms, the purpose of web analytics is to understand:

  1. The customer journey prior to a conversion (which is typically an email submission in B2B) via organic channels (paid channel journeys should fall under advertising analytics)
  2. How web projects impact web outputs to inform investment decisions

For #1 customer journey, think about every single pageview and how they relate to a conversion.

What are the most viewed pages in last month? What are the top converting pages?

Keep in mind, however, the relationship between sessions and users.

This adds complexity to the types of questions / answers we can ask because, as you can imagine, B2B web visitors do not just come to your website one time and convert immediately. They come to your website multiple times in different length of sessions and over different time periods.

For #2 web projects, think about the impact of a new page or a re-design and how it affects the KPIs that you care about.

Is the new pricing page more engaging? Does long-form content keep visitors on the site longer?

These are the questions you can answer to evaluate the time and resources you invest in your team to justify and prioritize projects.

The Complexity of Questions and Answers

At first glance, web analytics is relatively straight forward to understand.

However, once you spend a bit of time diving into the detail, you’ll quickly realize some answers are not that straight forward.

Here are two buckets that typically add complexity:

1. Joining data sources

Most businesses optimize their website with anonymous data assuming everyone is equally important.

That may be true for B2C sites where everyone could have an equal probability to check-out.

But the reality is, B2B businesses weight accounts and buyers differently depending on how they segment customers (e.g., potential account size and individual buyers’ persona/title).

While it is possible to know the attributes of a visitor once someone submits their email on your site, this capability requires an integration between your CRM and analytics solution.

While it is possible to uncover which advertising campaigns drive better quality leads, that capability also requires an integration between advertising platforms, your CRM and analytics solution.

A lot of companies also want to connect web data with their product data, especially if the company has a trial environment for people to try the product.

2. The customer journey is complex

If your marketing team has done a good job, there are probably hundreds, if not, thousands of touch-points before a record of any kind is surface-able to your CRM.

In addition, from a web analytics point-of-view, the relationship between landing pages (from different source/medium), sessions and users can be complex depending what types of answering you are looking for.

For example, you are launching Campaign A and Campaign B at the same time, each has its own conversion asset.

You want to know: Does Campaign A perform better than Campaign B, in terms of absolute conversion volume and conversion rate?

Under normal circumstances, you would look at 1) how many conversion each campaign generated and 2) # conversion over pageview for conversion rate.

However, there are some edge cases that complicate this picture:

  1. A single person can actually convert in both Campaign A and Campaign B
  2. The person may not convert in the asset of Campaign A or B, but other evergreen CTAs such as Contact-Sales or Free Trial, despite Campaign A or B may bring the traffic to site.
  3. When it comes to calculating a conversion rate, the traffic (or advertising budget) may not be equal in the same time period, influencing the # pageviews denominator.
  4. The CTA of Campaign A and Campaign B are different and therefore have different levels of friction, for example, one is watching a video and the other is downloading a whitepaper.

While the business question is straight forward, these edge cases muddle the comparison and make it hard to do an apples-to-apples comparison.

This is especially so if the sample volume is low (i.e, hundreds not thousands of conversions), which is common in B2B settings.

To address the complexity above, you either will have to understand and accept the limitations of a basics analytics solution, or decide that you want more precision.

If you prefer the latter, it’s probably time to look for a more advanced analytics solution, which will typically involve putting multiple data sources into a data warehouse for easy ETL and aggregation.


Again, it is important to start with figuring out what questions you’re asking for the business.

Then from there, understand what your current systems are capable of answering.

Remember, it will take time for your teams to get to the same page. Be patient and start smalll tackling one piece of analysis at a time.