In the age of digital advertising, personalization plays a key role in boosting the performance of your communication strategies. Today, marketing professionals want to get as close as possible to their target audience. To do this, they use a customer-centric approach. To master this approach, agencies and advertisers use user-consented data to optimize their marketing campaigns and meet consumer expectations.
With the help of machine learning, this data can be combined into data sets that allow marketing professionals to create messages congruent with user behavior. The goal of this approach is to generate interest in the company’s brand, products, and physical stores by delivering flexible, creative messaging using hyper-personalization.
Making Use of Both Online Data…
Online data is a major tool for getting closer to your consumers. It provides information about each consumer’s affinity for certain sectors, brands, or products and helps determine an overall profile for them based on the social-demographic data they share online.
Through use of this data, marketers are able to identify the best market segments for their campaigns and reach qualified prospects who are likely to demonstrate interest in their brand and products.
While social-demographic data helps advertisers identify potential customers, affinity data allows them to ensure that their messaging will also be of interest to them. Marketers can use information about the sites, brands, and products target audiences are interested in to expose them to messaging containing similar content.
…And Offline Data, Too!
Offline data corresponds to users’ real-life, physical behavior. This type of user-consented data, although rarer and, therefore, more distinctive, plays a key role in optimizing your hyper-personalization strategy. When used with online data, offline data provides a deeper understanding of each user. It may be helpful for marketers wanting to better reach their target audience to think of online data as providing an incomplete portrait of their users that can be completed by pairing it with offline data.
There are 2 types of offline data:
- Real-time data, which allows you to determine the location of users at a given time.
- Historical data which, through analysis, allows you to identify locations frequently visited by users. Near uses this method to identify where users live and work as well as the locations they visit most often.
This information provides numerous opportunities for retailers to adapt their communication strategy by gaining insight into consumer buying habits (stores visited, brand competitors frequented, time spent in each store, etc.), refining prospecting and distribution zones, or integrating physical points of sale with digital marketing plans.
Adapting Your Message to Specific Contexts Using DCO
Personalization can take many forms. It can be adapted to consumers or their surrounding environment, because understanding the external factors that influence consumer behavior can be very useful.
As previously discussed, consumer geolocation data can be taken into account when implementing a personalized marketing strategy. Using mobile geolocation data and your brand’s marketing zones allows you to create a plan that takes each point of sale into account. This is particularly helpful for retailers who have a large number of stores in which they wish to generate qualified traffic.
Mobile geolocation data allows advertisers to identify where consumers live and work to create targeted advertising that corresponds to users’ nearest point of sale.
Examples of targeted messaging may include:
- Using a call to action to provide the shortest route to the nearest store
- Providing store-specific products with interactive visuals users can scroll through (e.g., available quantities, promotions, or prices that can vary from one point of sale to another)
- Adapting the message to the consumer’s life cycle or “situation” (store hours, peak times, sales, etc.)
Personalization allows marketers to adapt content to their target audiences based on their company’s brand, products, points of sale, etc. It allows the marketing plan to be adapted at both the national and local levels by leveraging companies’ physical distribution network and using geolocation advertising.
While geographic segmentation is important for communicating across different points of sale, timing plays an equally crucial role in implementing a personalized communication strategy.
In fact, timing is a key factor considered by advertisers when using DCO. Strategies based around timing may include:
- Using specific time periods to distribute messages. For example, certain brands may only wish to advertise during specific times or days (e.g., fast-food chains that only promote their breakfast offerings until 11 a.m. and then switch to their lunch menu).
- Adapting messaging during peak times of the year (year-end holidays, back-to-school, Mother’s Day, etc.). For example, ready-to-wear clothing brands may include in their messaging a real-time countdown of the time remaining until their summer sale.
- Taking the weather into account. For example, a restaurant chain may suggest having lunch on its patio when it’s nice out by preparing this type of content in advance.
- Today, DCO is a key aspect of creating personalized messaging and is essential to meeting the specific objectives set by advertisers. It allows them to adopt a data-driven strategy based on real-world data in order to better contextualize brand messaging.
Getting the Most Out of Personalization
One of the main advantages of digital advertising is the ability to evaluate and optimize marketing campaigns. In fact, several performance indicators can be identified depending on the company’s advertising objectives. These KPIs allow advertisers to study specific aspects of each campaign to determine which ones are most and least effective.
For example, today, retailers can identify:
- Which products generate the most interaction between users and content, allowing companies to suggest similar products to the same profile or identical segments
- Their most engaging messages and slogans
- Which content templates work best, so advertisers can increase their distribution in current or future campaigns
- Which CTAs result in the most clicks, so they can be used more often or be given a more significant creative role
Recurring campaigns or A/B testing provides retailers with the opportunity to determine what works in order to take their personalized messaging campaigns to the next level. the goal being to identify what appeals to the user and leads to conversion (e.g., the message, the visuals, etc.). This allows retailers to identify and adjust factors that generate traffic at their points of sale.
In addition, reliable, quantifiable performance indicators lead to better allocation of media advertising budgets, allowing retailers to achieve economies of scale by identifying which strategies within their communications plan are the most effective and profitable.
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