Power eCommerce Sales With Predictive Audiences
In the programmatic advertising landscape, our attention and use of algorithms has proliferated. The difficulty to determine which are working and which are not as clear as they seem until now.
By NICOLE PAOLINO
Capitalize on and exploit these newly-categorized market recommendations of Predictive Audiences from Google Analytics.
When Facebooks lookalike audience modeling began to gain traction within marketing plans, agencies and advertisers alike were excited about the possibilities of their impacting campaigns by understanding more about their existing customers.
Facebook lookalike audience modeling maps out users based on a brand’s first-party data by informing us what target audience we should test to reach new but similar users that will demonstrate the most likely to purchase intent to drive additional conversions.
The process to create Facebook’s lookalike Audiences takes up to 24 hours and if active targeting ads remain it’ll refresh every 3 – 7 days.
However, when applying this strategy within the direct response e-commerce (DR) space, success is limited.
Then there was the new release of Google Analytics App + Web property Predictive Audiences. It gives eCommerce advertisers the understanding of purchase and churn probability of audiences.
Predictive audiences surface who a potential new customer looks like, allowing Googles audience builder to search the internet and dynamically pinpoint audience segments most likely to convert.
Purchase probability predicts the likelihood that a user who was active visiting your web and app properties in the last twenty-eight days, and will purchase within the next week.
The churn probability defines a user who was active on your site or app within the last week will not be active within the next week taken from twenty-eight most-recent days of data.
When Google’s machine learning models are applied, it can analyze your data and predict future actions people may take.
These metrics drive growth for businesses by reaching the people most likely to purchase and retaining the people who might not return to your app or site via Google Ads.
When compared to Facebooks lookalike audience, Google Analytics Predictive Audiences created in the Audience Builder is far more powerful because it has far more data to model from, since it isn’t tasked to input only one brand’s 1st party data.
Previously, to reach people most likely to purchase, you built audiences by adding customer data into Facebook to create custom audiences and then create lookalike audiences.
With Google, it’s people who had added products to their shopping carts but didn’t purchase.
However, with Predictive Audiences, there are far more opportunities. You are not missing the possibility of reaching people who never selected an item on your website but the people who are likely to purchase in the future.
Predictive audiences in an instant can pinpoint people who are likely to convert at scale.
Imagine you have a hardware store and are trying to drive more online sales this month. Google Analytics will now suggest an audience that includes every person who is likely to purchase in the next week on either your website or app by presenting them with a personalized message using Google Ads.
In addition to building predictive audiences, predictive metrics are available to analyze your data with the Analysis module. The User Lifetime LTV analysis shows your users behaviour during their lifetime as a customer of your app or website.
This is excellent way to help decision-makers determine the best way to allocate your marketing dollars towards that high potential campaign by pinpointing which marketing campaign helped you acquire users with the highest Purchase or Churn Probability.
Google Analytics free state-of-the-art testing, attribution and conversion tools help businesses small or large build better user experiences and maximize digital strategies.
With a better understanding of today’s restless and curious consumers, top marketers can employ Google’s insight technology to turn customers anxiousness into an opportunity to build better customer relationships.
Businesses that take better control of their marketing and advertising data and technology will have the agility to respond quickly to a customer’s zig-zags and precisely send a tailored Ad message at the right moment.
Thus raising the odds of seizing attention for the immediate sale and building brand equity in the longer run.