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When testing, look at the big picture



When testing, look at the big picture

A common martech activity is testing different UI/UX (content, design, flow, etc.) elements in order to optimize some metric like lead form conversion rate.  While the actual responsibility for conducting such a test is usually assigned to a conversion rate optimizer, that doesn’t mean that other practitioners — regardless of whether they’re marketers, makers, modelers, or maestros — are not worth involving, since tests could have broad ramifications.

Indeed, A/B and multivariate testing typically have implications that reverberate throughout a martech stack — maybe beyond it.  In some cases that fact may present itself during the experiment phase, while in others the implications may pop up later on during an implementation phase.  That why it is important for martech practitioners to understand data flows.

Marketing and sales

The interaction between marketing and sales activities, is an example of a factor to consider.  Definitions for marketing qualified leads (MQL) and sales qualified leads (SQL), while related, differ for good reasons.  marketing and sales have different procedures, metrics, and goals.

For instance, a very common way people advocate to boost lead form conversion is to remove friction (in other words, fields).  In many cases, people are more likely to complete a form that requires less input and interaction than a form that requires a lot.  Simplifying a form could increase the quantity of leads while lowering the quality of leads.  What’s good MQL-wise is not necessarily beneficial from the perspective of SQL metrics.

Some of the “expendable” fields are likely for lead scoring.  They cause friction for marketing purposes, but they can help the sales team better understand a potential customer.  This enables salespeople to focus on targets who show strong intent to purchase, as well as tailor their efforts to the lead’s circumstances.

So it’s entirely possible for an experiment variation that has fewer lead scoring fields to boost marketing conversions while inhibiting salespeople from closing deals.  Thus, when conducting such an experiment, it’s important to consider and monitor downstream metrics.

Multi-departmental considerations

Another factor to consider is how and when to collect required information.  This is important, since removing or moving data collection points in order to boost MQL or SQL metrics could likely have downstream ramifications — even after the sales process is completed.

For example, when selling insurance (health, dental, life, etc.) the final cost a policy will require knowing a lot of detailed and sensitive information about the person covered.  Does it make sense to ask for a lot of that information on an initial lead form, so that when a salesperson follows up they can give the prospect a price estimate with some certainty?  Or does it make sense to gather just some general information and allow the salesperson to provide a ballpark estimate along with how various factors will influence the cost?  There are pros and cons to both approaches, and each could be pursued depending on whether more total leads or more qualified leads are called for.


However, it’s important to know what information is ultimately required.  A prospect will have to provide all of it at some point to turn into a customer.  Thus, toggling between those collection options will require a bigger picture view.  If the decision is to remove a field from a lead form, when and where downstream will that information be collected?

This may involve teams and systems that are outside of the martech stack, and therefore could require multi-departmental orchestration.  Then on top of that, if the decision is made to switch when certain information is collected, what happens to people who are in between the old and new collection points at the time of the switch?

Consider the big picture

It is crucial to consider the ramifications of common martech activities like conversion testing.  That perspective will help better ensure success.  For instance, there’s more to consider than MQL metrics, and at times, improved results with that one factor may come at a great cost down the line or in another crucial area.

As many practitioners can attest, sometimes changes are more complex than they may appear.  It’s really annoying to invest effort into finding a way to boost an upstream metric to only find that it will not work due to downstream issues.  Understanding data flows within and beyond the martech stack is one way to help mitigate against wasting time and effort on UI/UX testing that will ultimately not work out.

This story first appeared on MarTech Today.

Opinions expressed in this article are those of the guest author and not necessarily Marketing Land. Staff authors are listed here.

Steve Petersen is a marketing technology manager at Western Governors University in Salt Lake City, Utah. He started on WGU’s marketing website team where he helped create and implement several initiatives including site redesign and maintenance, multivariate testing, user testing and mobile app development. 




Google December Product Reviews Update Affects More Than English Language Sites? via @sejournal, @martinibuster



Google’s Product Reviews update was announced to be rolling out to the English language. No mention was made as to if or when it would roll out to other languages. Mueller answered a question as to whether it is rolling out to other languages.

Google December 2021 Product Reviews Update

On December 1, 2021, Google announced on Twitter that a Product Review update would be rolling out that would focus on English language web pages.

The focus of the update was for improving the quality of reviews shown in Google search, specifically targeting review sites.

A Googler tweeted a description of the kinds of sites that would be targeted for demotion in the search rankings:

“Mainly relevant to sites that post articles reviewing products.

Think of sites like “best TVs under $200″.com.

Goal is to improve the quality and usefulness of reviews we show users.”



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Google also published a blog post with more guidance on the product review update that introduced two new best practices that Google’s algorithm would be looking for.

The first best practice was a requirement of evidence that a product was actually handled and reviewed.

The second best practice was to provide links to more than one place that a user could purchase the product.

The Twitter announcement stated that it was rolling out to English language websites. The blog post did not mention what languages it was rolling out to nor did the blog post specify that the product review update was limited to the English language.

Google’s Mueller Thinking About Product Reviews Update

Screenshot of Google's John Mueller trying to recall if December Product Review Update affects more than the English language

Screenshot of Google's John Mueller trying to recall if December Product Review Update affects more than the English language

Product Review Update Targets More Languages?

The person asking the question was rightly under the impression that the product review update only affected English language search results.



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But he asserted that he was seeing search volatility in the German language that appears to be related to Google’s December 2021 Product Review Update.

This is his question:

“I was seeing some movements in German search as well.

So I was wondering if there could also be an effect on websites in other languages by this product reviews update… because we had lots of movement and volatility in the last weeks.

…My question is, is it possible that the product reviews update affects other sites as well?”

John Mueller answered:

“I don’t know… like other languages?

My assumption was this was global and and across all languages.

But I don’t know what we announced in the blog post specifically.


But usually we try to push the engineering team to make a decision on that so that we can document it properly in the blog post.

I don’t know if that happened with the product reviews update. I don’t recall the complete blog post.

But it’s… from my point of view it seems like something that we could be doing in multiple languages and wouldn’t be tied to English.

And even if it were English initially, it feels like something that is relevant across the board, and we should try to find ways to roll that out to other languages over time as well.

So I’m not particularly surprised that you see changes in Germany.

But I also don’t know what we actually announced with regards to the locations and languages that are involved.”

Does Product Reviews Update Affect More Languages?

While the tweeted announcement specified that the product reviews update was limited to the English language the official blog post did not mention any such limitations.

Google’s John Mueller offered his opinion that the product reviews update is something that Google could do in multiple languages.


One must wonder if the tweet was meant to communicate that the update was rolling out first in English and subsequently to other languages.

It’s unclear if the product reviews update was rolled out globally to more languages. Hopefully Google will clarify this soon.


Google Blog Post About Product Reviews Update

Product reviews update and your site

Google’s New Product Reviews Guidelines

Write high quality product reviews

John Mueller Discusses If Product Reviews Update Is Global

Watch Mueller answer the question at the 14:00 Minute Mark

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