Undeniably, machine learning has been the hot top in marketing circles in recent years.  In the marketing realm, higher expectations for more personalized marketing opportunities can be expected from AI. A big part of the opportunity for marketers is how AI will help enrich personalization and relevance. With platforms like Google Adwords and social media platforms like Facebook reaching billions of people every day, digital ad platforms are now data rich enough to learn about every user’s specific interests and behaviors at scale.

Current application of predictive modeling for marketing

Customer behavior and preferences prediction

Customer behavior prediction is to understand who your customers are and what specifically makes these people buy. Are their decisions driven by quality or price? What other variables contribute to the purchase? As all collected behaviors are backed up by real-time data, marketing is no longer just unconventional slogans or blind money investing, but a precise science. While I believe that traditional customer-facing strategies still definitely apply, real-time data is already being used to better understand customer behaviors through multiple platforms online.

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Customer targeting

Machine learning enables marketers to identify the consumers who have the intention to purchase instead of creating these intentions in the mind of the consumer. Think about that for a second: we’ve essentially flipped the entire frame of mind in marketing. While traditional marketing strategies are potentially burning billions of dollars trying to gather audience attention, AI marketing is able to precisely find the most likely customers by identifying variables like their browsing pattern and search history. So in this case, marketing is not telling people to buy what they never thought about, but helping them easily find what they have been searching for. It’s very clear what the result of this kind of targeting will look like.  

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Driving marketing strategies based on predictive analytics

Predictive marketing analytics correlates with better business results and metrics

  • Traditionally, marketers spend money on subjective topics and strategies. Thus, the outcome of a round of marketing is unknown and uncontrollable. However, if we can predict the result of marketing and change the direction of marketing even before the campaign starts, will that actually help? The answer is a big positive from marketers. With big-data supported prediction modeling, marketers are able to perform segmentation and audience targeting which will provide a clear idea of the outcome, or perhaps a necessary strategy modification in the middle of a campaign.

Predictive marketers use individual information to deliver greater impact across the customer lifecycle

Around the world – numerous startup companies that provide data analytics and machine learning training services are putting this theory into reality. Just like other machine learning domains such as finance, entertainment, healthcare, etc., startups will play a valuable role in the development of AI in marketing. Some of these companies will eventually also build powerful technologies to deliver gap filling solutions for digital marketing.