Traditional manual segmentation based on user attributes has been a staple in marketing for many years, but it comes with several drawbacks, especially in today's data-driven and dynamic business environment.
Here are just a few of the drawbacks of relying solely on traditional manual segmentation:
- Limited Understanding of User Behavior: Traditional segmentation primarily relies on static user attributes such as age, gender, location, and income. While these attributes offer basic insights, they often fail to capture the nuances of user behavior, preferences, and intent, leading to a one-dimensional view of the audience.
- Lack of Real-time Insights: Manual segmentation tends to be a time-consuming process. Marketers must collect and analyze data periodically, which means that the insights are not always up-to-date. In a fast-paced digital landscape, real-time insights are crucial for making timely decisions and responding to market changes.
- Inefficiency and Scale Challenges: As businesses grow and their customer bases expand, manual segmentation becomes increasingly complex and resource-intensive. Manually sorting and categorizing users can be inefficient and error-prone, especially when dealing with large datasets.
- Homogeneous Segments: Traditional segmentation often leads to homogeneous groups based on a single attribute, such as age or gender. This approach overlooks the diversity within these groups and fails to recognize individual variations and interests.
- Inability to Adapt: User behavior and preferences are constantly evolving. Manual segmentation may not adapt quickly enough to capture these changes, leading to marketing strategies that are out of touch with the current audience.
- Limited Personalization: Effective personalization requires a deeper understanding of user intent and behavior. Traditional segmentation may struggle to provide the granularity needed for tailored content and experiences, resulting in generic marketing efforts.
- Risk of Bias: Manual segmentation can introduce biases based on the subjective interpretation of user attributes. These biases can affect marketing strategies, potentially alienating segments of the audience or reinforcing stereotypes.
- Missed Opportunities: Relying solely on predefined attributes may cause marketers to overlook valuable segments that do not fit neatly into predefined categories. These missed opportunities can impact revenue and growth potential.
- Cost and Time Constraints: Gathering, cleaning, and analyzing data for traditional segmentation can be resource-intensive. It often requires substantial time and financial investments, which may not be feasible for smaller businesses or startups.
- Difficulty in Combining Multiple Criteria: Traditional segmentation based on a single attribute may struggle to incorporate multiple criteria effectively. For instance, combining demographic and behavioral data to create nuanced segments can be challenging.
To address these drawbacks, many businesses are turning to advanced data-driven techniques, including machine learning and semantic tagging, to complement traditional segmentation. These approaches offer greater precision, real-time insights, and the ability to capture user behavior and intent more accurately, leading to more effective marketing strategies and improved customer experiences.
What is Semantic Tagging?
Semantic tagging involves adding descriptive tags or labels to specific elements within content or datasets, a process that might sound technical but is actually quite simple — and it offers huge advantages to marketing teams who implement the concept.
At its core, semantic tagging aims to provide context and structured information, making it easier to understand and sort not only for human readers but also to machines—those astute algorithms that power search engines, data analytics tools, and the very backbone of the digital realm.
Semantic tags are the markers that are attached to each of these elements which establish the relationship(s) between them.
These tags, in the context of data, connect the meaning and significance of different elements within a document, webpage, dataset, or, in our case, a message.
Semantic tagging, in essence, bolsters the accuracy of search engines, enabling them to sift through information with greater precision and serve up the most relevant results. It empowers data analytics tools to extract deeper insights, uncover hidden patterns, and decipher the underlying semantics of user interactions, but what makes semantic tagging particularly fascinating for marketers is its potential to enhance user segmentation. It also enables you to dissect your audience into distinct segments based on meaningful criteria—criteria that extend beyond the surface level of demographics and delve into the heart of user intent, preferences, and behavior.
In the following sections, we'll show you what semantic tagging looks like, how you can implement it, and the type of insights you can expect to see for yourself.
What does Semantic Tagging look like?
While it may sound complex, semantic tagging is actually quite straightforward.
For example, check out these abandoned cart push notification headers and their associated tags:
Abandoned carts are are an absolutely huge issue in the eCommerce space — In fact, the Baymard Institute found that almost 70% of all online shopping carts are abandoned, costing eCommerce businesses upwards of $18 billion in missed sales revenue each year.
In addition to figuring out the right timing and cadence for sending these abandoned cart messages, retailers can often benefit by understanding why these users failed to complete their transactions, and semantic tagging can allow just that.
In the picture above, you see the different headers on the left and their associated tags (in the grey boxes) on the right. These tags essentially group their associated message with other message copies that, in this case, emphasize the same value.
So, "Grab these top-selling picks in your cart" is associated with "Bestsellers," as does "Your cart is loaded with bestsellers!" Contrast this with "Don't let go of premium cart finds" which is more of an appeal to "Quality."
Aside from sheer organization, we can use these labels to understand each user's motivations through which messages they click (or don't click). We're essentially using semantic tagging to conduct psychographic segmentation.
What can Semantic Tagging teach you about your users?
Check out this latest example from one of our eCommerce customers:
They wrote a variety of different messaging options (SMS, Push Notifications, WhatsApp messages, etc.) and tagged them all different kinds of motivations like Novelty and Affordability to Convenience and Quality - 27 different options in all. Quite a rich feature set! (For more information on the importance of message tone, click here!)
Note: While it sounds intimidating, this process only took a couple of hours total in Aampe.
We then looked at how these different tags correlated with different buyer propensities — in other words, how likely were people who received each of these labeled messages to convert.
We expected we’d find clusters of users with particular message preference profiles (for instance, people who cared a lot about value and not much about luxury, or people who cared a lot about comfort but didn’t want to sacrifice affordability), but what came back from our cluster analysis surprised us!
The users largely seemed to fall into five groups:
- Groups 0 and 4 were much more likely to convert when they received personalized messages (although to varying degrees of significance)
- Group 1 wasn't likely to convert, no matter what message they received.
- Group 2 was much more likely to convert no matter what messaging they were sent
- Group 3 preferred messages about Encouragement, FOMO, Curiosity, and Gratification and Recommender messages...but not much else.
With this information in-hand, the customer could take much more intelligent and impactful actions, such as:
- Send more recommender messages (and less of everything else) to Groups 0 and 4 to increase conversions from these groups
- Throttle back messages to Group 1 to reduce churn
- Potentially increase messages to Group 2
- Focus messages to Group 3 around the topics that resonate with them most.
They can also use the full data from our analysis to develop more of their messaging and find new topics that could potentially resonate with their customers even more. The best part is that, aside from writing and labeling the messages (which GPT can help with), this analysis required zero effort from their internal teams.
How can you implement Semantic Tagging in your messaging?
With a standard CPaaS, tagging your messages can be difficult, but at Aampe, it's the core of what we do.
When you write messages in Aampe, our system lets you break them down into parts and tag them all. Our AI then intelligently sends these messages out to all of your users and collects these results for you:
Now you can understand which of your themes are performing the best, and which tags are resonating with which users.
Interested in understanding how you can get started with Semantic Tagging in your user messages?
Smash that big, orange button below.
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