It’s no secret that modern digital marketers need more than just declarative data to build quality, personalized campaigns. They’ll need more than a snapshot of clicks, opens, retweets and other signals to build quality, personalized relationships with customers.
Personalization. It drives sales. It increases customer loyalty. And, to be truly effective, it needs to be rooted in a firm understanding of how a user is interacting with your brand – a.k.a. behavioral or engagement data.
What, then, is preventing marketers from fully utilizing their behavioral data? Why aren't we all reaping the benefits of personalization?
Let’s try answering that question with another question: Do you call your mother?
The answer is probably more nuanced than a simple “yes” or “no” can provide. Maybe you call her daily or weekly, just to say hello. Maybe you call her monthly, but the conversation is deeper than a typical weekly check-in. Maybe you just call on holidays. Or maybe it’s been years since you’ve called.
In data science, we’d call the answer to that question a flag field, which reduces a complicated answer to a simple one – yes or no. Black or white.
A lot of people apply the same simplification of behavioral data down to a single flag field or even a set of flag fields, which makes behavioral data become declarative. But, human behavior is seldom dichotomous – it’s gradual, nuanced and contextual. It’s multi-dimensional.
In other words, we’re going to need more colors.
Lytics provides cross-channel, multidimensional behavioral analysis out of the box by providing a suite of behavioral scores that identify specific aspects, or dimensions, of user behavior. Our process lets us see users in a variety of shades of their behavior, allowing us to group them in ways marketers can use to improve the level of personalization in their campaigns.
Here’s a list of Lytics behavioral scores:
Momentum: Measures the rate at which users are interacting with your brand. Users who are interacting more than usual with your brand will have a higher score.
Frequency: Measures how consistent a user is over time in interacting with your brand. More frequent interactions means higher score.
Quantity: Measures a user’s cumulative activity over their lifetime of brand engagement. The more activity the user registers, the higher the score.
Intensity: Measures the depth of a user’s typical interaction with your brand. More sustained intense/deep usage means a higher score.
Recency: Measures how recently the user’s general interaction has been. More recent activity means a higher score.
Propensity: Predicts how likely a user is to return with subsequent activity. Users exhibiting positive interaction patterns are more likely to return and have higher scores.
Consider two different theoretical users visiting an e-commerce retailer. Both visitors have 60 page views and one purchase in the month of May.
Allie is a long-time user who likes to peruse the site, often during lunch breaks.
Michael had a hard time finding a birthday present for a friend during his first visit to the site, but eventually found something that he liked.
By a lot of behavioral metrics, Allie and Michael look the same, but the nature of their engagement with the retailer is drastically different. Here’s how they would look by their Lytics behavioral scores:
The context around the conversation that Allie needs is much different than what Michael needs. Michael still needs more nurturing, and we’d characterize him as a binge user. Allie is well-aware of prices and products and always tries to find the best deal that she can. We’d characterize her as a peruser.
We now know that Allie and Michael are both recent purchasers, but their behaviors are completely different. Knowing this, would you send them the same marketing messages? This seems counter to actual personalization.
The fact that Allie is the type to know what she wants and is very price sensitive, we’ll want to respond to her habits of heavily perusing and rarely taking action. Therefore, we can hypothesize that she is more likely to respond to marketing campaigns that contain discounts or incentives to purchase.
Michael probably needs additional product recommendations based on his interest since he doesn’t know what he wants. We can put him into a retargeting campaign that will continue to drip-feed relevant products of interest to him until he finally decides to make another purchase.
Allie and Michael are two of many behavioral archetypes that businesses should classify their marketing segmentation towards. Lytics makes this kind of segmentation by behavior easy to do with your existing marketing data.
Lytics pre-packages behavioral scores into useful, consumable and actionable segments. We call these our Behavioral Segments.
These pre-built segments include:
New: Users who are new to your audience within the last week.
Power: Your most frequently and consistently engaged users.
Active: Users who are currently engaging with your brand.
Inactive: Users who are currently disengaged with your brand.
Dormant: Users showing minimal or no activity for a prolonged period of time.
Unscored: Users who have not registered enough activity to be scored by our behavioral algorithms.
Our Attribute Segments break down behaviors between each of our segments, letting marketers really see the motivations of how users traverse between each segment.
These Attribute Segments are:
At-Risk: Users showing signs of becoming inactive users.
Binge: Users who don’t engage frequently, but are very active when they do.
Peruser: Users who register activity on a regular basis but at a lower intensity than other users.
You can start using Lytics behavioral scores and pre-defined segments shortly after account creation. Explore different score combinations to identify behavioral clusters unique to your audience, or let us handle the segmentation so you can focus on execution.
However you use them, our behavioral scores probably won’t replace your existing segmentation. But they will definitely make it better.
Schedule a demo or contact your account rep today to learn more.
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