Predictive advertising: Informed efforts for optimized results
It is an agency’s dream: Build the campaign and they will come. Or rather, build a campaign that you already know will best suit and reach your highest converting audience, and they will come. And guess what? It is a brand’s dream to have this certainty from their agency – with the knowledge that the brand will be optimizing its budget.
How do we leverage the power of predictive advertising to help us optimize creative campaigns, targeting and budgets? We now use data science to do three things: (1) predict which audiences will engage with a given campaign; (2) score certain device and advertiser-specific targeting factors with a view towards maximizing engagement and conversion; and (3) score things to help optimize your campaign.
At the end of the day, it is data science that provides the predictive analytics that can drive an uplift in conversion, decreased costs and ultimately delivery of the right ad creative at the right time and place.
Steps 1 and 2: Scoring optimum audiences in real time, predicting advertisers’ ability to engage and convert
There are now data science tools and tactics that advertisers can use to analyze the audience profile of each potential ad impression in real time – we are talking millions per second – and then enrich the profile with demographic, behavioral, psychographic, environmental, sentiment, keyword, contextual and social insight.
Add to that data about device features and other advertiser-specified audience targeting criteria. Now you have enough to create a reliable predictive model that identifies and segments impressions with the highest propensity for conversion and awareness lift.
Besides the obvious knowledge-is-power advantage, this data empowers advertisers with the opportunity to use real-time bidding (RTB) to secure higher conversion rates at lower prices.
Step 3: Creative campaign optimization
Many advertisers feel that machine-driven learning and data analytics only helps drive effective performance-based or conversion-driven campaigns. But they are wrong.
These types of algorithms can also score a publisher’s value from an awareness perspective, so it can meet a brand’s awareness goals as well.
After creating a predictive model that flags the top-ranked audience profile, we can use post-click analytics to optimize campaign creative towards users most likely to convert or engage.
We can even go so far as to determine which individual user is more likely to get an impression or click.
In addition, consider price optimization. Here, mathematics and data can decide whether you should bid a certain price based on the value of that high-converting or highly engaged user.
Machine learning and data science can optimize price against virtually any metric. You can determine which publishers are more valuable to your brand, what creative element or content an individual is most responsive towards, and how much it will cost to convert that consumer.
At the end of the predicting scoring and analytics exercise, you will have predictive models for click-through rates (CTR), cost per click (CPC) and cost per acquisition (CPA), with continuous, real-time data that learns and reacts to changes in the marketing environment.
Which advertisers would not want to purchase only ideal impressions and maximize that user-brand engagement?
What is next?
Machine learning is the next big technology that will allow the brain to do the work that humans used to do.
It is unproductive to continue to manually optimize campaigns, targeting and price when a machine can take this on, and scale it across millions of impressions per second in real time.
The value we have in predictive advertising today is astronomical when you consider the benefit of price optimization when married to the insights provided by predictive scoring optimization. It is unparalleled and right at the fingertips of advertisers.