How Predictive Analytics Takes The Guesswork Out Of Campaign Performance30-Dec-2020
Just like everyone wants to be sure that they are getting the best bang for their bucks, advertisers today also love to get some level of guarantee on their ad spend. Until a few years ago, many companies wasted money on advertising based on their gut feelings while others collected plenty of data (or worse still, every available data) on their target audience only to display irrelevant advertisements that generated very little results,
These happenings led to many business leaders and performance marketers wondering about to use predictive analytics to optimize ads. To get the best results, they would need to figure out why are they are collecting data in first place so they can properly optimize their ads.
There are different reasons why businesses collect data to optimize their ads including increases in sales, customer acquisition or achievement of some other goals. Once you can figure out why you are collecting data, you immediately set yourself apart from a large chunk of performance marketers who collect all of the data available rather than data that is relevant to their goals.
For instance, ever read of Karen Heath from Teradata who wanted to help a local retailer boost diaper sales in 1992? By acquiring the right data, she saw that men who purchased diapers also bought bear. She then placed an offer for both beer and diapers, and the business witnessed a sharp rise in sales.
The story of how customers’ data changed the fortunes of a retailer might seem like mere history but it is what has birthed the more advanced predictive analytics that optimized multichannel marketing in 2011. Predictive analytics has further evolved such that most definitions of the terminology today mentions that it makes use of machine learning techniques. If you’re looking to leverage predictive analysis to optimize your ads for success, you need to use the tools to create different audience segments or explore new potential audiences. This offers an in-depth analysis of the preferences of your target audience from the comments, reviews, social media, interactions with ads, events and any relevant campaigns so you can develop unique plans for each segment.
Generally, there are three possible segments for every campaign
Prospects: Identify top-performing prospect ad audience segments based on demographic and psycho graphic similarities and content preferences
Active customers: These refers to customers that have purchased a similar product in times past
At risk or lapsed customers: Reach out to customers that are staged to attrition but are likely to respond to an offer as well as devoting efforts and strategies to lapsed customers.
Consider carrying out small tests everyday using different designs and strategies to figure out how effective the optimization is. Be sure to also identify the channels and segments that yielded the most results during the tests.
Also, keep in mind that just as no strategy works forever, you will need to revisit the predictive analytics process to optimize your ads. Sometimes, you might need to only make a few changes while other times, you will need to start from scratch.
In sum, predictive analytics makes it possible for performance marketers to improve their marketing predictability efforts by giving insight into past performance, aid the development of scoring models to predict buyer behavior, thus allowing for improved campaign performance.