AI Is Your GPS for Navigating the Complex Highways of Consumer Segmentation
Imagine for a moment that you’ve finally decided to take the cross-country road trip you've long dreamed about. You're meticulous in your planning by identifying all of the sites and cities you want to visit along the way. The PTO from work has finally been approved, you’re packed and ready to go, so now it's just a matter of navigating the endless highways and byways slithering across the country to get you to those destinations.
Given what the trip personally means to you as well as the deliberate planning that has been involved, choosing an antiquated roadmap printed at some point during the Clinton administration is not an ideal way to guide you along your path. Over the last quarter-century alone, those endless highways and byways have grown enormously in scope and complexity, making a simple roadmap an outdated and old-fashioned choice to lead the way.
The consumer segmentation that has resulted from the rapid expansion of the digital economy is remarkably similar to all those paved paths that blanket our country from coast-to-coast. Marketers are tasked with the daunting responsibility of targeting, engaging, and converting an audience that has been segmented into myriad components which are constantly changing.
Relying on traditional segmentation approaches is akin to using that old roadmap to wind your way across the continent. Instead, brands can leverage the vast reach, scope, and efficiency of AI-based machine learning to better navigate the innumerable twists and turns of a highly segmented consumer base.
Consumer Segmentation and You:
A Crash Course in Targeting Your Audience
In a nutshell, customer segmentation involves dividing a market into distinct consumer groups that have similar characteristics. It allows advertisers to employ a certain degree of uniformity in developing and distributing messages that are meant to engage each market segment, providing campaigns the opportunity to effectively target groups of consumers with a more tailored message. Segments can be analyzed and organized by a variety of different criteria -- variables like age, gender, location, psychographics, and brand affinities are some of the most common.
The true power of customer segmentation strategies exist in the ability to identify unmet customer needs or underserved segments of the marketplace. By identifying and specifically targeting these groups, a company can tailor offerings that are more likely to convert to sales and provide brands distinct competitive advantages. Companies use customer segmentation analysis to develop campaigns and price points to extract optimum value from both the high and low ends of the profit spectrum.
Up until a handful of years ago, the marketplace was simpler and more linear in nature. With the increased complexity of the digital marketplace, traditional segmentation approaches have inherent inefficiencies that can bias results and give advertisers a skewed perspective of the marketplace. A dynamic marketplace demands a more agile and insightful approach.
AI Gives Campaigns a Needed Precision
By leveraging the powerful combination of AI and big data, advertisers have their needed GPS units to successfully navigate the digital highways of the market. Rather than relying on old-fashioned tools, brands can quickly and thoroughly process huge amounts of consumer-generated content, including blogs, forums, social media, and product reviews.
These sources lack those inherent biases of outdated tools and give brands open and honest opinions without being prompted for them. Perhaps even more importantly, however, there's a spontaneity to the data that makes it exceptionally useful to quickly identify consumer segment trends that can provide distinct competitive advantages. Likewise, since the data is not a snapshot of a singular moment but, instead, a consistent and constant source of consumer information, a brand’s message can evolve in lockstep with changes within the market.
Machine learning is extremely helpful in recognizing and understanding the nuances of a consumer’s language and semantics, providing much more thorough insights for an advertising campaign. These nuances are important in defining the specific characteristics of each customer segment, allowing brands to optimize their message or product offering.
Using AI to Make Customer Segmentation Actionable
Most brands are already using CRM systems in some capacity to organize and monitor customer segment information. Machine learning can build upon these existing systems, enriching and enhancing the segmentation data by learning more about those customers from their social profiles and online dialogue.
Aside from the more basic information likely already stored in existing CRM databases, machine learning assistants, such as Oculus360’s Audience Affinity tool, can also extract the top interests, traits, related brands, media channels, music artists, celebrities, sports teams, television shows, and virtually any other distinguishing characteristic that can help a brand effectively engage those segments.
This diverse set of data points enables actionable ways to target and appeal to each individual segment. For instance, brands can play songs in ads that are already popular within a customer segment or run ads during television shows that the demographic segments enjoy. This integration of top interests and traits can effectively feed the messaging and themes included in marketing campaigns.
Campaigns Need Precise Direction to Find Their Way
The insights into the demographic segments within a brand’s customer base that are gathered through machine learning enable brands to leverage the landscape of public online conversations to deliver more targeted messages, products, campaigns and promotions for each customer segment.
AI helps advertisers extract significantly more accurate and specific consumer data for the various target segments they want to engage and convert. With the help and expertise of Oculus360, campaigns can rely on powerful consumer insights to improve marketing, pricing, and product development strategies and gain a competitive advantage in an extraordinarily crowded marketplace.