How Retailers use Segmentation by Purchase Intent?
In the last 3 years, digital marketing has become significantly more expensive. You may have noticed the following:
1. Facebook, the world’s biggest social platform and owner of Instagram, has been reducing organic reach for several years in a row. Other social networks are following suit, making it harder for you to reach your customers and followers.
2. In the US, ad revenue grew by 16% in 2017 alone. This means more competition for new clients, as well as old ones. More businesses are marketing online now.
3. Changes in technology are making it harder to compete. For example: Omnichannel Marketing is effective for 91% of businesses, but 64% of marketers lack the technology and resources to implement the strategy.
These are just 3 factors increasing costs and making it harder to compete. The bad news is, they aren’t going away.
The good news is that they don’t have to – because recent developments in marketing tech have made intent-based segmentation and targeting a reality. That may sound fancy, so here’s another way to look at it:
Today, digital tools can help you identify and target customers based on purchase intent. This is an easy way to get a competitive edge and make more money online.
In this article, we’ll tell you everything you want to know – but first, let’s talk about the reason generic segmentation and targeting is becoming less effective.
The First Problem with Generic Segmentation and Targeting
Forbes knows that data is now a major part of effective online marketing. Most brands are already using Google Analytics, Facebook Pixel and customer analytics tools like Webtrekk and Adobe Analytics. As a result, marketers understand the power of data-driven tactics like omni-channel tracking and retargeting.
The thing is, most managers we’ve spoken to are still having trouble collecting and using data. One reason is that Google, Facebook and other 3rd-party software makers segment using aggregated data about segments or cohorts. They don’t actually help you pinpoint your customers and figure out what they want.
And if you collect your own data using a custom tool that does give you individual data about visitors, guess what? You’ve still got to process that data every time you want to update your segments. This process is too labour-intensive for most businesses.
As a result, intent-based targeting is prohibitively expensive for most businesses to try. The tools and processes haven’t been standardized and commoditized yet. This is problem #1.
The Second Problem: Overwhelming Data Points
In addition to the above obstacle – a lack of effective tools – there’s a second one: the high number of data points.
Look at it this way. If you want to execute an omnichannel re-targeting campaign for old customers, you’ll want data from:
- Your website
- Your prior transactions
- Social media visitors
- Your e-mail list
- And so on and so forth
This can mean scraping, cleaning, organizing and processing tens of thousands of data points for a single segment. This is no short order, especially given the absence of effective tools and the need for real-time segments.
In other words, it’s not just that the tools we have are limited. It’s that there’s so much data to process that there’s no straightforward way to get real-time, automated, profitable segments.
So how do we go from that to intent-based targeting: the namesake of this article?
The Leap to Intent-Based Targeting
User intent isn’t a new concept. In SEO, the idea of predicting what users want and giving it to them has been a hot topic for years.
So why are we talking about intent now?
Because there are 2 proven segmentation frameworks that help segment (and target) potential customers by intent.
The problem is, these 2 frameworks were inapplicable to digital marketing until the recent evolution in analytics software. They required more data than all but the biggest, cash-rich marketing departments had – so the digital marketing world forgot about them.
This is unfortunate, because these 2 models help predict customer behavior patterns and their purchase intent.
The first model – RFM – measures the value of an existing customer based on the frequency, value and recency of their purchases.
The second model is called RFE. It gauges purchase intent using engagement, and can be applied to existing and new customers (even those without a purchase history). It uses multiple KPIs, like website visit data and interactions with customer service, to segment customers. When used alone, it can help you evaluate whether someone is likely to buy. When combined with RFM, it can show you which of your most valuable customers also have high purchase intent.
The advantage of using RFE is that it creates as many specific customer segments as you need, based on pure data. It doesn’t make assumptions; it simply separates customers into groups that are more or less likely to buy from you.
So Why Haven’t You Heard More About RFM and RFE?
This can seem surprising, given how effective the models are. But the explanation is simple:
Data Engineers Are Expensive!
Turning your disparate data into something your marketing team can work with – whether manually or with, say, Excel – means hiring a data engineer.
A full-time data engineer can costs over $99 per hour, this means that even rudimentary intent-based RFE segmentation will run you tens of thousands of dollars over the course of a year.
Then there’s the human factor. RFM and RFE are decent models, but if the data isn’t processed properly, your segments won’t be effective. Your conversions will fall. By the time you realize what happened, you stand to see your business affected negatively.
And That’s Why We Made Selma
Targeting and segmentation is perhaps the best way to sustain and improve digital marketing results in an increasingly competitive market.
Collecting data is the easy part. Nowadays, it’s easy to use 3rd-party tools or build your own for this purpose. Anyone can do it.
But turning this data into segments is expensive. Even if you’re using straightforward, proven frameworks like RFM and RFE, a data engineer is a 6-figure expense (you pay forward).
The solution we offer is Selma.AI: the world’s first A.I. marketing assistant with data science skills.
Selma helps you set up your segmentation and targeting with timely tips and recommended tactics. Once set up, she collects your data for you and helps you identify the perfect audience for your campaign – as well as the perfect messages to show them.
We believe in Selma’s ability to give every marketer an automated data scientist.
Want to know more about Selma.ai?