Using your Existing Customers’ Data for more Efficient Customer Acquisition

We often talk about how a Customer Data Platform, or CDP (read the basics), and 1st party data will help you know your customers better and allow for a smoother user experience orchestration. On the other side, what we do not mention as much is how your CDP can help you increase reach and find new customers within but also outside your ecosystem.   

The below use cases highlight how you can leverage your CDP and your 1st party data for quicker and more efficient customer acquisition.  

Use AI to find higher propensity prospects in your own 1st party data  

In your CDP, you will not only capture actual buyers of your brand but also potential prospects with no sign of purchase intent just yet. For the sake of this article, we’ll call them the “agnostic” profiles. With the lack of strong data signal, it is often challenging to assess those users: what products would they be likely to respond to or what customer journey do they belong to. For instance, some profiles will be created based on a newsletter sign up or a visit without any specific product page view.  

Those so-called “agnostic” profiles can be enriched, or in other words actionable, through predictive modeling. For customer acquisition; we tend to see propensity modeling as the most effective method. What does that mean? You can leverage historical transactional data to predict propensity of your prospect base to buy a product. The higher your propensity score, the more high-value your prospects will be. This will make them a perfect target group for your customer acquisition paid tactics.

A more advanced usage of predictive modelling will also refine those profiles with crucial data points. You can perform, for instance, product recommendation, clustering or even discount sensitivity to help you tailor messaging down the line.  

Run lookalike model based off of your best customers 

Another way to increase reach based on your 1st party data is to run lookalike models. These will allow you to find similar audiences. Often time, this use case will go beyond the walls of your CDP, and will exist in the advertising platform itself.  

What is a lookalike? It is a media tactic to reach new people who look similar to your selected and custom audience. In our case, let’s say your custom audience is composed of your best and most valued customers. The advertising platform will find similar profiles within their own environment. This will exponentially increase your reach among a key target with higher likeliness to be interested in your brand.  

In other words, lookalike audiences will allow you to reach greater media efficiency by targeting at scale prospects who look like your most valued customers. 

A CDP can bring tremendous value for your customer acquisition efforts. It’s time to stop taking any guesses with 3rd party data or data that your brand simply doesn’t control. Your 1st party data offers you the confidence to respect users’ privacy and the guarantee of finding higher value prospects faster and more efficiently.  

Firstly, you can use Data Sciences to score prospects based on their propensity to convert for specific offer. This will let you tailor the experience of users who have previously shown interest but have not given strong enough data signal to place them into a specific customer journey.  

Secondly, most advertising platforms allow you to find similar profiles to your most valued customers through lookalikeIn conclusion, this means you can efficiently tap into the broad universe of those media partners while trusting that you are reaching out to the right people for your brand.  

About the Author: Elaine Lorent
Elaine is our Head of Data Strategy at NOBI. She spent 8+ years in Media and Digital Analytics, with focus on CDP, DMP and CRM strategies. Elaine has a Master of Science In Integrated Marketing & Analytics from the New York University. Her passion is to help brands leverage their 1st, 2nd & 3rd party data in a meaningful and responsible way, to become fully data-driven organisations.

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