Advertising used to be a (mostly) numbers game. The more people who saw your ad or brand, the more opportunities you had to convert viewers into customers. But times have changed, and for the better. Mass messages have lost their luster and companies are more in favor of tailored marketing that speaks to a certain type of customer. Enter the audience segmentation strategy.
A Quick Review of Audience Segmentation
Audience segmentation is the process of dividing a broad audience into various subcategories. For instance, if you look at all of your customers who have ever bought from you, you can segment them by date of last purchase, type of item they purchased, or how much they spent. You might zoom in on how you acquired each customer (e.g., social media, direct mail, paid ads, etc.) or their total lifetime value.
Applying audience segmentation to marketing allows marketers to create offers, messages, and content that doesn’t rely on mass appeal. Instead, it homes in on real problems, needs, or interests that only apply to a few select audience members. As a result, brands can build deeper, stronger connections with those customers.
For instance, let’s say your company sells vacuum cleaners. Just about anyone with carpeted floors will need what you sell. But why would a consumer choose your brand over another? That’s where an audience segmentation strategy can come in handy. You might tailor your marketing to speak directly to pet owners and how your vacuum handles pet hair better than a competitor. Or you might focus on families with small children and play up the convenience angle for time-starved parents.
Segmentation helps people remember your marketing and advertising because it’s more relevant to them than a mass message. It also allows brands to better understand their customers and learn more about their intentions, buying triggers, and habits that could make their marketing even more effective.
Old vs New: How New Technology Impacts Audience Segmentation
The Old Way
In the past, brands have segmented their audiences with high-level data. Age, geographic location, income range, and education often played a role in the process. Using data from a CRM, sales history, website analytics, and other first-party resources, companies could generate unique “customer personas” that represented each type of customer they wanted to market to. From there, marketers would create offers and other content specific to a subgroup of its customer base.
But this method of customer segmentation isn’t completely foolproof, despite using your own data to drive your marketing.
For starters, your own data doesn’t offer any insights into your customers beyond how they’ve interacted with your company. You may not know what else they’re interested in beyond the items they’ve bought from you. You may not know what other brands they buy or what motivates them to buy. The data lacks significant detail that could help you get to know your audience better, and therefore doesn’t help you to learn more about them.
Also, remember that consumer preferences can shift quickly and are constantly evolving. This happens with very little or no warning!
The customer journey is a prime example of this. Research suggests that most customers complete two-thirds of the customer journey digitally. Because of this, more companies are investing in content marketing and digital resources that empower customers to discover, learn, and make buying decisions compared to just a decade ago.
It’s essential for companies to continue feeding their data with current insights. Data expires quickly when using it to gauge a customer’s buying intent. That’s why real-time data collection and usage are becoming top priorities for companies. They aim to remain competitive by knowing exactly when users are consuming content that may indicate readiness to buy. But first-party data isn’t enough to deliver these deeper insights.
The New Way
Technology has changed the traditional approach to audience segmentation, shifting the focus from first-party data to third-party data. Companies can now get richer, up-to-date insights across multiple channels in real time about customers that fit your personas. With a greater level of detail, brands can develop marketing strategies that are more customer-centric.
Leading the evolution of audience segmentation are three technologies that help brands get inside the customers’ shoes at scale: artificial intelligence, natural language processing, and social media listening.
Artificial intelligence (AI) is being deployed in marketing in a variety of ways, including audience segmentation. By definition, AI is a form of computer science that allows robots to do tasks that would normally require human discernment.
When applied to audience segmentation, the benefits of AI are easy to see. For starters, robots can scour the web and collect and analyze data on your customers and prospects in a fraction of the time as a human team. AI works in the background non-stop to deliver the most current insights. What’s more, it can analyze its findings similar to a human analyst (but faster) and find connections between data that a human might not know to look for.
For example, AI can track millions of topics or keywords simultaneously and connect them with other topics or keywords used by the same audience. When tracking your brand mentions, AI might also discover that a lot of your customers are interested in a particular sport, hobby, TV show, or travel destination. From there, you might integrate this shared interest into your marketing, such as product placement on a TV show or developing an ad campaign connecting your brand to a sport or hobby.
Given that we create 2.5 quintillion bytes of data every day — a number that continues to grow alongside the Internet of Things — it’s increasingly important to have an efficient and agile way to collect, analyze, and use our data to our advantage. There’s just too much information at one time to manually review it and turn it into usable insights.
Natural Language Processing
A slice of the larger AI pie is natural language processing, or NPL. This helps computers “process” human language to understand what people are talking about — and the meaning behind conversations.
Take this sentence for example:
I never said she stole money.
This sentence can take on very different meanings if you emphasize a different word each time (go ahead, try it!).
Finding nuances like these is a huge benefit of using NPL. It doesn’t just look at the words, but also tries to find the context behind the words. It spots what’s important in large amounts of text and ignores the rest — a process that would be extremely time-consuming for human analysts.
It’s also beneficial when sorting through large sets of unstructured data. For instance, data you collect via a form on your website is usually structured, with each field representing a specific type of data. But answers to open-ended questions on that same form, or online reviews and forum discussions, have no structure to speak of. People on the web use slang and jargon. They misspell words and use wrong terms when they mean something else (e.g. specific vs pacific, you’re vs your, etc.).
NPL proves useful when making sense of the data AI tools collect. As you understand how your customers talk about a given topic, you can join the conversation in more meaningful ways.
Social Media Listening Tools
Over 3.6 billion people worldwide use social media. It’s no wonder that this digital channel has captured the eyes and ears of marketers, given its potential to glean rich insights about consumers. Social listening tools were designed to do exactly this in real time and at scale.
Social listening tools can track mentions of brands or other keywords that might relate to your products or services. For instance, if you are an art supply company, you might track terms related to art supplies, art techniques, famous artists, art marketplaces, various media, or even online art classes.
When users mention these terms on social media, your social listening tools jump into action. Brands can analyze how people are using those terms, what other topics they’re talking about in relation to you, and even the characteristics your audience members share beyond your brand.
Social listening leverages AI technologies to find and analyze millions of conversations on social media in real time. Brands gain up-to-the-minute insights so they can tweak and tailor their messaging based on what their ideal customers are currently doing and talking about.
In addition, you can also collect other details that will make your marketing more effective. For instance, discover which social media platform has the most engaged audience for your brand. You might even find a brand advocate or influencer who can put you in front of their audiences for even more credibility and exposure.
How Tribes Identifies and Activates Your Audience
Marketers have had their sights set on audience segmentation for years, but AI-powered technology is widening the scope. No longer limited to just first-party, high-level data, audience segmentation is now a continuous process with real-time insights across multiple digital channels.
It’s no longer about taking a reactive approach to segmentation based on past behaviors. Brands can now be proactive by understanding current intent and positioning themselves in front of the right people at the right time.
Linkfluence combines audience segmentation with AI-powered technologies in our Tribes product. Identify your most engaged audiences, discover their interests and how they talk about specific topics, and join their conversations with greater authenticity. Most importantly, get a better sense of how others view and talk about your brand so you can keep shaping your image.
See how Tribes brings audience segmentation into a new era; get a demo today!