Customers desire shopping experiences that reflect their persona and buying patterns. Research shows that customers are willing to pay a price premium of up to 16 percent for brands offering tailored experiences.
Today, big data analytics and machine learning (ML) are helping marketers read the consumer’s mind and predict buying behavior. Several businesses are turning to predictive advertising to efficiently expand their targeting efforts, deliver great experiences, and boost their site’s rating.
Predictive advertising is a subset of predictive analytics that uses historical data, ML techniques, and algorithms to accurately target audiences and optimize ad copy and media spends. Given the value and competitive edge it brings, most businesses are looking at applying predictive advertising to improve the relevancy of their campaigns.
Read on to learn how predictive advertising is helping marketers achieve their goals with speed and accuracy.
Predictive advertising accesses customer data and third-party behavioral data to identify potential customers, allowing businesses to expand their user base. Tools like Google’s Lookalike Audience and Facebook Similar Audiences use predictive advertising to study audience behavior, anticipate customer needs, increase click-through rates, and drive business profits. These networks compare information pertaining to your website visitors with people who have similar traits and buying behavior and use the insights derived to build a new audience. They also use information, such as the location, age range, interests, and recent online activity to find new users who look and act like your existing visitors.
Most savvy marketers are using predictive advertising techniques to build lookalike models based on historical user data. Lookalike audiences help businesses expand their promotional strategies to new audiences who have the potential to become loyal customers. Since lookalike audiences have characteristics similar to your target audience, you can be assured that your ad budget is being used prudently.
Artificial intelligence (AI) has the ability to observe buying behavior and use this data to predict future buying patterns. Marketers can use cluster models to segment their audience and deliver relevant content that converts. Thus, content automation with AI can help ad-makers produce high-quality content tailored to a specific audience or persona type.
Amazon is already using predictive advertising to deliver relevant ads and cross-sell and up-sell its range. The ecommerce giant is coming up with new ways for brands to target their own customers and shoppers. Amazon’s advertising services can remove a lot of the guesswork by showing ads to the most relevant audience.
For instance, using cookies and other technical tools, Amazon can tell that a person who recently bought a protein bar on the website is now reading a post on the wellness blog, “Nerd Fitness”. Thus, the customer can be targeted on this site with a relevant fitness product.
Predictive advertising is allowing marketers to learn about customers based on their browsing history. They can determine where customers consume content and what kind of content they prefer. So, businesses can automate ad personalization based on user demography and situational factors, namely the device ID, domain, location, buying history, and interests to create the most relevant copy for that audience.
Target recently used its data-crunching ability to formulate a predictive model that helps them identify which of their female customers are pregnant and will buy diapers in the near future. The American retail store discovered that women who are in their early trimester typically purchase a combination of 25 different products. Later, female buyers who exhibited this buying behavior were sent coupon booklets through emails or the post.
Predictive advertising is making it possible for marketers to gain insights valuable for a limited period of time. In other words, businesses can use real-time data and optimize their ad placement strategy to deliver appropriate advertising and encash on the micro-moments.
According to Google, micro-moments are intent-rich moments when a user turns to a device (especially a smartphone) to learn, do, watch, discover, or purchase something. These are the moments when preferences are shaped or the user acts on a need.
The key micro-moments that marketers often target are:
The Dynamic Creative Optimization (DCO) technology creates real-time customized advertisements based on contextual signals about the user, such as the audience segment, the weather, and the time of day at the time of ad serving. Using these predetermined inputs the creative ad generation engine creates ads with personalized content for that user. That way, a prospect buying a car tire in Florida in the fall season doesn’t see ads related to snow tires.
Further, a customer’s historical data pertaining to online behavior or demography can be used to anticipate what segment of customers would be interested in a product or service. So, with predictive advertising, it’s possible to target these micro-moments before they even occur.
Each year, billions of online marketing dollars are wasted in advertising that targets the wrong audience or reaches the target audience at the wrong time. A report by Rakuten Marketing reveals that on average marketers waste 26% of their marketing budget by using the wrong marketing channels or strategies.
Through nuanced targeting and bid adjustments, predictive advertising can help marketers drive more ROI for their campaigns. Google’s automated bidding platform allows businesses to choose a marketing goal like site visits or conversions and uses audience and competitor data to automatically adjust bids in real-time.
Thus, predictive advertising can help digital marketers reduce their wasted ad spend and achieve marketing objectives.
Predictive advertising isn’t just a buzzword in the digital marketing arena. It’s a critical marketing tool that can be used to drive real business results. Given the benefits, value, and the competitive edge it offers, an increasing number of marketers are using predictive advertising to boost the relevance, efficiency, and ROI of their advertising campaigns.
So, go ahead and use the power of machine learning, statistical models, and behavioral data to identify relevant audiences and efficiently expand your targeting efforts.
George Konidis is the co-founder of Growing Search, a Canadian based digital marketing agency providing optimal SEO and Local SEO services worldwide.
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