A resident of Sparta, NJ, Michael Perry has driven conversions and revenue in his role as head of marketing at ClubsGalore over the last four years. Throughout his career in Sparta and beyond, Michael Perry has cultivated expertise in a number of areas, including business-to-business (B2B) marketing.
B2B marketing has long been a widely used practice among numerous businesses around the world. Throughout the years, these marketing strategies have developed in response to an ever-changing user base and increasing digital capabilities. Here are a few strategies that have proven most effective for B2B operations:
Even after more than two decades, email remains one of the most common B2B marketing tactics. In recent years, however, professionals have implemented new strategies to increase its effectiveness at generating online leads. Perhaps the most popular email marketing trend is marketing automation, in which businesses can easily create and manage their campaigns using email service providers (ESPs).
This strategy is steadily evolving and remains one of the most effective ways to target advertising campaigns online. One such platform for paid marketing is the Facebook sidebar ad, through which businesses can directly reach their subscribers and display the services they offer.
Businesses need to tap into their target market if they want to best provide their goods and services. Using precise targeting can help gain a look at each buyer’s online personality, which thus helps companies direct a more focused marketing message to them.
Michael Perry is an experienced Sparta, NJ, sales and business development executive who guides ClubsGalore’s marketing activities. His endeavors in Sparta include creating integrated campaigns that encompass social media, website content, telemarketing, and e-mail. Michael Perry has an interest in organizational design and economic trends defined through the use of econometrics.
One aspect of this field was recently explored in a Forbes article involving the impact of supervised machine learning (ML) on econometrics. In simple terms, machine learning involves the use of x covariates in predicting (y) outcomes. Current ML methods include systematic approaches such as regression trees, LASSO, and random forest.
Common to these approaches is their use of systems of cross-validation. This involves models being created using one part of available data, which is then tested on another data set. The robustness of the data generated from one model is reinforced by looking at these alternative models.
Supervised machine learning methods employing cross-validation have the advantage of providing actionable results in complex situations, with a multitude of variables. By “supervising” the variables inputted, researchers tailor the results to specific real-world situations.
In contrast, the main currents of social science rely on causal predictions, in which predictions based on trend observation take precedent. These tools may be more accurate in situations where unexpected changes occur and the variables that supervised ML relies on are themselves in flux.