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Predictive Analytics Can Bolster Marketing Strategies

Martech Outlook | Friday, August 23, 2019

The adoption of predictive marketing analytics has made a significant impact across the customer lifecycle, leading to better business results and metrics.

FREMONT, CA – The emergence and consolidation of big data, statistical models, and machine learning (ML) has paved the way for predictive analytics. The sophisticated approach leverages historical data to forecast the outcomes accurately in short periods. The developments in computing power and the accessibility to vast troves of data have empowered predictive analytics.

Organizations seeking to maintain a competitive edge in the market are incorporating robust data tracking and analytics tools to predict future trends and prepare for the demand. The predictive analytics approach has enabled businesses to analyze the available customer data and draw insights to develop informed marketing strategies.

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A robust predictive analytics strategy depends on several factors, including the availability of clean data, data analysis systems, and the essential tools and solutions to implement marketing strategies. Commercial giants leveraging predictive analytics to forecast consumer behavior have witnessed significant returns.

The technological success of the data-based approach and the accessibility to data has spurred many small and medium-sized businesses to adopt it and enhance their marketing processes. Solution providers have come up with different predictive models designed to assist the marketing sector.

Cluster models are leveraged to segment target customer groups based on multiple variables, including behavior, product, and brand. Propensity models predict customer behavior, including their lifetime value to the brand, level of engagement, loyalty to the brand, likelihood of conversion, the possibility of product purchase, and so on.

Recommendation and collaborative filtering techniques are empowered by predictive analytics. They enable businesses to recommend products and services to customers based on various factors such as purchase history and similar purchases. Marketing analysts often leverage regression analysis techniques to determine the relationship between customer variables and product purchase. The score of likelihood is based on the degree to which each of the variables affects future purchases.

One of the primary requirements for predictive analytics to works is the availability of clean and consistent data. Although robust mechanisms are available to marketers to gather data, they cannot ensure quality and consistency. Failure to maintain the quality of data can lead to dire consequences for the business. Since predictive analysis is based on the existing customer data, a database of quality information is crucial to the development of a successful predictive analytics strategy.

However, organizations should keep from leveraging predictive analytics if there is not sufficient customer data to draw insights. It will not only lead to adverse effects but may also alienate the customers. Also, identifying trends from the insights requires extensive experience in data analysis, not to mention, robust tools to implement the marketing strategies.

Adopting a robust data management solutions enable business organizations to collect and consolidate data across the marketing channels. By utilizing ML algorithms, predictive insights can be drawn, allowing the implementation of robust strategies to expand into new markets. Predictive analytics insights can enhance marketing strategies, streamlining the conversion of prospects into leads.

Predictive analytics is a cyclical process, as the data gathered from converted customers can serve as the fuel for the next round of predictive analytics, which can inform the succeeding stages of marketing activities. Also, predictive scoring allows businesses to prioritize their prospects and leads. Identification models and automated segmentation techniques can be leveraged to identify new prospects and segment them for personalized messaging.

Although the developments in ML techniques and solutions have improved the accessibility of small businesses to predictive analytics capabilities, the approach requires vast sales data to develop and train robust predictive models. This variance places large firms at a significant advantage when compared to companies with fewer customer data.

Regardless, there is no question that the accessibility to data has improved significantly, what with organizations moving their various processes into the digital space. It has enabled them to personalize their marketing campaigns and deliver their brand messages to targeted audiences. It has facilitated not only greater customer engagement but also a significant increase in lead conversions.

Top Marketing Analytics Solutions Companies : ( ZS Associates, MarketBridge, Keen Decision Systems )

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