In today’s highly competitive digital environment, the ability to reach the right individual at the right time has become a real differentiator for brands. However, traditional approaches to segmentation using historical data are not always the best to capture the ever-changing behaviors of modern consumers. Using real-time data can change all that.
With real-time data, brands are able to react swiftly, engage intelligently, and personalize in the moment. Let’s take a look at some examples of how real-time data changes the execution of proper segmentation strategies.
1. Moving From Static Segmentation to Dynamic Segmentation
Traditional segmentation typically uses fixed categories (age, geography, income, etc.), which may not completely capture a given interest or behavior. Real-time data allows segmentation to utilize a dynamic approach where users are segmented and grouped based on current activity (e.g., what they are viewing, what they have on their carts, and maybe even what their social circles are engaging with).
Example: An e-commerce site may see a surge in interest for hiking gear from urban millennials as the spring season approaches, and develop a micro-segment to send relevant promotional offers.
2. Better Personalization Across Channels
Real-time insights allow marketers to personalize context-aware content, offers, and experiences powered by data. This allows for more contextually aware personalization across email, social channels, websites, and apps. GoAudience is a wonderful tool for personalization. It empowers marketers and brands to create highly personalized and targeted ad campaigns. It uses cutting-edge AI technology to ensure that you get the best value for your money.
Example: A person sees luxury perfumes on a website and may shortly start seeing personalized email campaigns and social ads for those products or brands.
3. Faster Response to Behavioral Triggers
When businesses use real-time data, they can act on behavioral triggers — like how much shopping was abandoned on a cart, how fast someone clicked, or the time they spent on a page — during the actual events. Being responsive to potential conversion situations maximizes the likelihood of a conversion, as it reduces lead decay.
Example: If someone adds some items to a cart, but doesn’t finish checking out within 10 minutes, businesses can trigger an automated message with an offer of a discount or free shipping.
4. Better Prediction of Customer Lifetime Value (CLV)
When doing analytics in real-time, businesses can make predictive decisions about segments that are likely to become high LTV customers or churners. Not being reactive with the data affords businesses the ability to optimize segmentation for loyalty programs or retention activities.
Example: Video streaming companies monitor viewing habits in real-time and therefore can identify “binge-watchers,” as they are likely to convert from free trials to premium subscriptions.
5. Optimized Ad Spend and Target Audience
Real-time segmentation enables marketers to make more informed budget decisions and invest their ad dollars towards the most relevant audiences. It eliminates wasted impressions and improves ROI.
Example: A travel brand can increase investment in audiences actively searching for flights, or general travel content, instead of relying on broad demographic definitions.
6. Better A/B Testing and Campaign Optimization
Real-time feedback loops provided by segmentation allows marketers to test multiple versions of a campaign. This allows them to understand the variations that resonate better with certain segments and optimize their performance in real-time.
Example: A SaaS company can monitor real-time sign-up rates from multiple landing pages and alter the ads shown to an audience to see which one works better on getting more people to join.
7. Enhanced Capability of AI and Predictive Analytics
Real-time segmentation further extends the power of AI. With machine learning capabilities, it’s now possible to continuously sift through massive volumes of behavioral data to categorize audiences and predict potential next best actions.
Example: Retailers are now capable of using AI-enhanced real-time data to suggest next-best offers and products based on the stage of the customer’s journey. This can effectively increase upsell and cross-sell opportunities.
Conclusion
Real-time data is no longer an added source of value; it is a precondition for any audience segmentation to be competitive. Brands that shift to a stance of real-time insights and incorporate behavioral components (in place of pure demographics) will offer smarter targeting alternatives to buyers in exchange for their time and attention. In addition, brands can move much faster and refine the customer journey if they have the capability to use real-time data to conduct behavioral segmentation.
At a time when consumers demand relevance at every interaction, real-time segmentation is a quick tool that easily meets consumer demands and exceeds their expectations.
To read more content like this, explore The Brand Hopper
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