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Time Series Analysis for B2B Trend Mining

Seasonality has a huge impact on sales and revenues for B2B ecosystems. As companies are run by humans and cater to humans, the myth that B2B organizations are exempt from these cycles is completely untrue. Due to the close correlation between time and resources, business practices affect a wide variety of managerial decisions. Seasonality influences both advertisers’ and consumers’ decisions as well, thus having a large impact on B2B markets.

The whitepaper by Valasys Media uses multiple time series models to understand sales predictions. Sales forecasting is an important part of modern business strategies, particularly when the data needed to link the dots is either missing or incomplete.

The paper employs the application of machine learning for predictive sales analytics. It also discusses the importance of how ML tech can be used in a variety of methods for increased efficiency of sales forecasting. One can also learn home machine learning generalization can be deployed when data is absent or incomplete.

The paper also takes B2B marketing into consideration and explains the various activities that are impacted by seasonal activities. Recognizing seasonal trends helps marketers ensure a continuous supply of fresh leads and allows them to stock marketing materials and sales resources to provide them with optimal sales opportunities.

They suggest that marketers should be aware of various business trips and events that might be taking place to optimize their chances of getting in touch with leaves and converting them. We should also be cognisant of various holidays as clients are usually with their families and might not be that receptive.

Knowing behavioral patterns can provide great insight into how to reach your leads during the holidays. Keeping the budget cycle in mind is an important time factor that needs to be taken into consideration.

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