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Predictive Modeling for Advanced Audience Targeting

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Media advertising in this decade is superfast and all about accurate customer engagement. Smartphones, iPads, and the internet bring unprecedented access to information, and publishers are facilitating carefully customized content that caters to old-fashioned as well as new-age audiences. Content is developed with focus on customer needs and brand loyalty. Tailored product or service information is vital.

Predictive analytics help publishers understand customers better across all services and brands. With predictive modeling, audience data is sorted for in-depth and actionable insights. That provides recommendations on how to target audiences and engage them whenever required. The objective is always more revenue and greater customer loyalty.

Challenges that brands are facing without predictive modelling:

  1. Lack of incentive in sharing information across channels and brands
  2. Generic product-based information which doesn’t benefit like audience-centric information can
  3. Expensive rates for integrated internal database of multi-channel users


These cause inaccurate marketing and lead to failure in audience engagement.

With cloud-based predictive modeling, brands can achieve what they need. They can target their audiences better and achieve higher ROIs. Here are the reasons:

-Analytics reveals customer preferences to develop marketing engagements with exclusive data sorting. This results in tailored inbound and outbound interactions with the most relevant contextual data.

-Data-driven mechanics can analyze those elements that drive customer loyalty and customer spend at the micro level. Publishers can invest in customers having the highest potential towards lifetime value.

-Analytics can optimize decisions about customer service to improve measures of customer satisfaction and retention. Using historical data, the technology can sort information to identify those elements that churns and retains customers. It further provides the insight that can help in offering proactive service or offer required for customers moving out.

– Predictive modelling comes with features like integrating survey information. A similar approach is used to deliver customer experience across all channels. This not only helps in capturing customer responses to enhance the models continuously, but also towards relevant and consistent audience journey.

Audience engagement in the digital market has to be relevant, consistent, and personalized. Real-time data is no doubt expensive, but hardly useful without predictive modeling software. Developers can provide technology as well as real-time data cost-effectively, but data alone makes very little sense in terms of cost-to-benefit in the market.

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