By Imanol González, Data Analysis and CRO Team Lead at Making Science
Targeting in advertising is a necessary practice to ensure the right messages reach the right people at the right time. While this has been a focus since the inception of advertising, today’s sophisticated technology dramatically enhances the process and can greatly improve results.
Many brands are relying on newer technologies such as artificial intelligence (AI), which enables advertisers to engage with their audiences — in real-time — across the web. By understanding behaviors and needs, smart technology can create relevant and personalized advertising experiences that resonate with audiences and drive them to take action.
However, now we are entering the next level of personalization — ultra-personalization will open the doors to new opportunities to engage and connect consumers with your brand.
Let’s look at a few takeaways that advertisers need to keep in mind with ultra-personalization.
Go beyond Personalization
Ultra-personalization goes beyond traditional personalization practices. The term refers to collecting real-time customer data related to behavior and online preferences.
Advertisers use this information to inform their programmatic approach and create contextual ads with customized messages and creatives that are extremely relevant to users based on their contextual information.
As an example, Netflix leverages users’ viewing history to present displays for an upcoming movie or TV show with different artwork and copy that will best attract a specific user’s attention.
Despite the recent push for increased user privacy and the demise of third-party cookie, consumers still crave timely and relevant experiences and are willing to trade their data for highly personalized advertising interactions.
This influx of first-party data is key to allowing advertisers to take a step past traditional personalization practices by ultra-personalizing their programmatic capabilities.
Advertisers that can implement effective ultra-personalization strategies adapt their ads according to a given consumer’s current circumstances — leading to higher ROIs for their brand and better experiences for their users.
Make The Most of First-Party Data
Developing the right predictive models is crucial for advertisers to get the most out of their first-party data when striving for ultra-personalization prowess.
First, it’s important to determine who your most valuable customers might be, taking into consideration their purchase frequency, lifetime value and churn probability. Then, set custom objectives for them based on their specific commercial needs.
Smart bidding is another key step towards better value matching and improving the customer journey through first-party data. AI-powered technology allows advertisers to activate the bidding process in real-time for maximal impact with fresh data and cross-conversions.
Those who can effectively do this through search, display, video and social channels optimize return on ad sales (ROAS), cost per acquisition (CPA) and any custom objectives by presenting the right messages to the right audiences.
Take search, for example. By dynamically presenting the right keywords to a target user based on their specific online searches, advertisers capture the full range of search intentions and drive higher quality scores for a lower cost per click (CPC) — all while connecting with consumers’ specific interests and needs.
Implementing an Ultra-Personalized Programmatic Strategy
When planning an ultra-personalized programmatic strategy, here are some key considerations that will help develop an optimal, performance-focused strategy:
- Identify and Understand Your Ad Tech Stack: Clearly identify the strengths and weaknesses of the tech stack you’re planning on using to carry out your ultra-personalization strategy. Ensure you have the right tools to accurately predict buying behavior and provide tactical-level behavioral analysis in order to achieve your campaign goals. Make sure its programmatic capabilities sync up with your ultra-personalization goals.
- Break Down The Target Audience: Through machine learning and analysis, group users from your first-party data set that might be interested in the given product or service. Then, break down your initial target audience into smaller, micro cohorts. Take into account key demographic, behavioral and psychographic factors when doing so. Trust in your platform’s ability to provide ultra-personalized advertising experiences for each cohort at the most opportune times — with specific messaging and creative tailored to each segment’s preferences ready to deliver.
- Activate and Analyze: Initiate your ultra-personalization campaign and keep a close eye on the results. Find out which specific tactics are and aren’t working well and adjust accordingly. Constantly be looking for ways to improve the conversion rate for current and future campaigns.
Consumers are still yearning for timely, relevant and ultra-personalized advertising experiences. Smart marketers know how to keep an appropriate balance by providing these experiences without making it seem like their audience is being watched or listened to by their devices.
With the right ultra-personalization strategy, an adequate programmatic technology stack, and high-quality first-party data, it can be done.