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September 7, 2022

Here’s why microsegmentation might not be as great as it’s made out to be

Ben Malka
Growth and Operations Manager

Customers these days are something else! As I’m sure you noticed, they are more demanding than ever from their favorite brands on the personalization front. And the brands that do this well are rewarded with long-term customer loyalty and increased revenue. Of course, using common characteristics - such as demographics, geography, psychographics, behavior and personas, to break customers into smaller groups does not guarantee that you'll be able to appeal to them on an individual basis. 

So this is where microsegmentation comes in, right? After all, it takes your regular marketing segmentation to the microscopic level. It’s not just about segmenting audiences into tinier, more granular groups. It’s also about using the data you hold on them to form more sophisticated segments that combine their various attributes. So that sounds pretty good, right? Sure, but also not really. At least not anymore. 

But first, let’s start off with some of the nice, sunny, and positive elements of microsegmentation, before diving into how it is also holding growth teams back from reaching their full potential.

The good things about microsegmentation

Most every industry has its own unique application of microsegmentation. When it comes to growth marketing, there are two forms of it which are pretty awesome (to an extent) at helping growth teams improve their ROAS, customer engagement, and conversions.

The first traditional form of microsegmentation is that of lookalike audiences, which refers to advertising to audience members that most closely resemble the people who have bought from you in the past. Through this approach, ad networks would compare demographics, interests, and behaviors to a list of customers you’ve provided, and take it from there.

{{banner}}

The other form of microsegmentation is interest segments, which refers to targeting audience members based on their personal attributes, interests, or demographics. There’s a little more cherry-picking involved here, in the form of hand-selecting your key audience. Many growth teams love this approach because these segments convert at higher rates, which naturally translates to better ROAS.

Microsegmentation has its own shortcomings

Now comes the interesting part. The shortcomings of microsegmentation.

I’ll preface by saying that yes— advertising randomly to everyone would be a waste of marketing budget. Targeting your ads to small audience segments that are most likely to buy from you would yield better results. BUT don't forget…. the individuals in these more targeted groups will still receive the same offers or blanket discounts on products, regardless of their underlying motivations or preferences, or what drives them to engage with your brand.

This is, in many ways, a binary approach, because people are either getting ads because they meet your segmentation rules, or they don’t. That seems fair enough, but there is a downside—when you choose to focus on only the customers that performed a certain action, you will end up missing out on the majority of your potential customers, and ultimately miss out on the highest-possible ROAS and profitability.

I mean, wouldn’t it absolutely stink if people that would have been interested in your products end up never hearing about it, simply because they didn’t “qualify” under Facebook’s interest segment or lookalike audience algorithm? If you strictly go by the Pareto Principle, you’re missing out on about 80 percent of potential customers.

And as a side note, many would argue that microsegmentation has other inherent limitations as well. For instance it's inefficient and time-consuming, as much manual effort is needed to create, fine-tune and deliver offers, resulting in months-long waits to launch campaigns. Also, it is not agile, because companies cannot quickly respond to changing trends or other customer insights. Ultimately, it is also not scalable, and companies can rarely handle more than 30 distinct cohorts because the manpower and time to do more is not the most cost-effective.

So what’s a growth marketer left to do? What’s the best way for growth teams to achieve maximum ROAS, maximum profitability, maximum scalability, and all the other good stuff? Well, ahem, this is where predictive AI, which refers to solutions that provide teams with predictability and scalability, comes to the rescue.

Here’s how predictive AI can help

Predictive AI supports constant ROI-positive growth by focusing on acquiring customers that are expected to create business value for your company for the long term. It is all done by optimizing acquisition campaigns by sending out signals for maximum LTV. LTV-based optimization is what makes acquiring valuable users, at scale, feasible. It is made possible by tapping into the power of ML and AI to create LTV models that predict the value of every single user, based on zero- and first-party data. So ultimately, by utilizing both zero- and first-party data, AI models predict key individual user-level metrics, including future LTV, as well as conversion, churn, and loyalty likelihood. 

So at this point you might be wondering, “Okay great, but how does predictive AI solve the microsegmentation issue exactly?” It all comes down saving time and money, while capitalizing on efficiency. Allow me to explain…

When you turn to using a predictive growth platform, your LTV data is used as a baseline to drive growth across your entire possible audience, by sending out signals to ad networks so you pay more for high value users and/or users that convert, while paying less for users that are less likely to convert. Your ROAS requirements will be satisfied in the short term, and exceed in the long run thanks to focus being placed on late bloomers, which refers to users who are more likely to make a purchase later on in their user journey. 

By nature, that completely negates the whole binary approach that’s brought on by microsegmentation, because everyone becomes fair game, with fair values being assigned to people based on how profitable they would be for your company. Instead of microtargeting, your bids are being based on LTV-optimized nonbinary signals, your budget and needs. Collectively, it is the conversion likelihood value for the highest engaged potential customers.

This is precisely why now is the time for growth teams to take next steps to achieve growth and profitability, without sacrificing ROAS, by combining smart bidding with predictive AI. You can finally target wider audiences than ever, and still cherry-pick the best, and it is all made easier (and possible) with an added boost from predictive AI.

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Here’s why microsegmentation might not be as great as it’s made out to be

September 7, 2022
4 mins
Ben Malka

Customers these days are something else! As I’m sure you noticed, they are more demanding than ever from their favorite brands on the personalization front. And the brands that do this well are rewarded with long-term customer loyalty and increased revenue. Of course, using common characteristics - such as demographics, geography, psychographics, behavior and personas, to break customers into smaller groups does not guarantee that you'll be able to appeal to them on an individual basis. 

So this is where microsegmentation comes in, right? After all, it takes your regular marketing segmentation to the microscopic level. It’s not just about segmenting audiences into tinier, more granular groups. It’s also about using the data you hold on them to form more sophisticated segments that combine their various attributes. So that sounds pretty good, right? Sure, but also not really. At least not anymore. 

But first, let’s start off with some of the nice, sunny, and positive elements of microsegmentation, before diving into how it is also holding growth teams back from reaching their full potential.

The good things about microsegmentation

Most every industry has its own unique application of microsegmentation. When it comes to growth marketing, there are two forms of it which are pretty awesome (to an extent) at helping growth teams improve their ROAS, customer engagement, and conversions.

The first traditional form of microsegmentation is that of lookalike audiences, which refers to advertising to audience members that most closely resemble the people who have bought from you in the past. Through this approach, ad networks would compare demographics, interests, and behaviors to a list of customers you’ve provided, and take it from there.

{{banner}}

The other form of microsegmentation is interest segments, which refers to targeting audience members based on their personal attributes, interests, or demographics. There’s a little more cherry-picking involved here, in the form of hand-selecting your key audience. Many growth teams love this approach because these segments convert at higher rates, which naturally translates to better ROAS.

Microsegmentation has its own shortcomings

Now comes the interesting part. The shortcomings of microsegmentation.

I’ll preface by saying that yes— advertising randomly to everyone would be a waste of marketing budget. Targeting your ads to small audience segments that are most likely to buy from you would yield better results. BUT don't forget…. the individuals in these more targeted groups will still receive the same offers or blanket discounts on products, regardless of their underlying motivations or preferences, or what drives them to engage with your brand.

This is, in many ways, a binary approach, because people are either getting ads because they meet your segmentation rules, or they don’t. That seems fair enough, but there is a downside—when you choose to focus on only the customers that performed a certain action, you will end up missing out on the majority of your potential customers, and ultimately miss out on the highest-possible ROAS and profitability.

I mean, wouldn’t it absolutely stink if people that would have been interested in your products end up never hearing about it, simply because they didn’t “qualify” under Facebook’s interest segment or lookalike audience algorithm? If you strictly go by the Pareto Principle, you’re missing out on about 80 percent of potential customers.

And as a side note, many would argue that microsegmentation has other inherent limitations as well. For instance it's inefficient and time-consuming, as much manual effort is needed to create, fine-tune and deliver offers, resulting in months-long waits to launch campaigns. Also, it is not agile, because companies cannot quickly respond to changing trends or other customer insights. Ultimately, it is also not scalable, and companies can rarely handle more than 30 distinct cohorts because the manpower and time to do more is not the most cost-effective.

So what’s a growth marketer left to do? What’s the best way for growth teams to achieve maximum ROAS, maximum profitability, maximum scalability, and all the other good stuff? Well, ahem, this is where predictive AI, which refers to solutions that provide teams with predictability and scalability, comes to the rescue.

Here’s how predictive AI can help

Predictive AI supports constant ROI-positive growth by focusing on acquiring customers that are expected to create business value for your company for the long term. It is all done by optimizing acquisition campaigns by sending out signals for maximum LTV. LTV-based optimization is what makes acquiring valuable users, at scale, feasible. It is made possible by tapping into the power of ML and AI to create LTV models that predict the value of every single user, based on zero- and first-party data. So ultimately, by utilizing both zero- and first-party data, AI models predict key individual user-level metrics, including future LTV, as well as conversion, churn, and loyalty likelihood. 

So at this point you might be wondering, “Okay great, but how does predictive AI solve the microsegmentation issue exactly?” It all comes down saving time and money, while capitalizing on efficiency. Allow me to explain…

When you turn to using a predictive growth platform, your LTV data is used as a baseline to drive growth across your entire possible audience, by sending out signals to ad networks so you pay more for high value users and/or users that convert, while paying less for users that are less likely to convert. Your ROAS requirements will be satisfied in the short term, and exceed in the long run thanks to focus being placed on late bloomers, which refers to users who are more likely to make a purchase later on in their user journey. 

By nature, that completely negates the whole binary approach that’s brought on by microsegmentation, because everyone becomes fair game, with fair values being assigned to people based on how profitable they would be for your company. Instead of microtargeting, your bids are being based on LTV-optimized nonbinary signals, your budget and needs. Collectively, it is the conversion likelihood value for the highest engaged potential customers.

This is precisely why now is the time for growth teams to take next steps to achieve growth and profitability, without sacrificing ROAS, by combining smart bidding with predictive AI. You can finally target wider audiences than ever, and still cherry-pick the best, and it is all made easier (and possible) with an added boost from predictive AI.

Ben Malka
Growth and Operations Manager

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