Commentary

Better Understand Customers Through Smart Technologies, Predictive Analysis

Marketing, in its purest form, tells a story -- ideally, a good story. However, the reception of the story, similar to the reception of a marketing campaign, can be highly subjective. 

A nail-biting account of the sudden death, come-from-behind win of the Super Bowl underdog will likely command a better reaction, and have a higher degree of engagement, when delivered at a sports bar than shared at a bridge club meeting. The customer response to marketing efforts, similar to the response to fairy tales and sports stories, can fluctuate greatly depending on the audience. 

The level of audience engagement is also highly influenced by the timing and delivery method of the marketing story. As marketing budgets are bolstered by the promise of new delivery channels and formats (e.g., mobile devices, tablets, desktops), using smart technologies can ensure that your campaigns improve consumer engagement, drive sales, and increase ROI. 

Smart technologies have become a key component in efforts to learn about consumer behavior patterns. Pairing machine learning, artificial intelligence, and cognitive science technologies with collected (big) data has helped businesses build comprehensive consumer profiles and deliver personalized communications based on the information gathered. For example, an individual searching the Internet for a new car might receive a personalized and predictive ad for car insurance. 

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In order to sustain relationships with an established customer base and initiate effective communications with new customers, it is critical to extend or project the customer information learned by retargeting future customer behavior. Predictive analysis on consumer data collected will take that data and project it forward to identify future behavior patterns. Reacting to or pre-targeting future engagement behaviors will give companies the opportunity to develop personalized ads that reflect the future shift in behavior. 

For example, predictive analysis on a 30-something female consumer who is currently displaying an interest in maternity clothes would suggest a future shift in that consumer’s behavior patterns, including buying baby-care items. This predictive analysis leads to the creation of predictive ads that transition with the consumer, sometimes in advance of consumer behavior. The right ad is delivered to the right person at the right time.

Similarly, predictive analysis can also be deployed to prospect for new customers. Smart algorithms created by machine learning, artificial intelligence, and cognitive science technologies are used to constantly track consumer behavior. Responding to the changes in consumer engagement with personalized ads and messaging would lead to increased consumer engagement and increased sales opportunities.

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