Analytics: Tactical Portfolio Analysis

Plan with confidence during times of uncertainty by detecting hidden trend deviations that could affect your business

In these times of high uncertainty, planning with last year’s data makes about as much sense as relying on a 3-month old forecast model – neither really is a good option. Instead, businesses must rely on the most recent available data and promptly identify and react to unprecedented changes in trends. For example, Idealo have shared a very insightful example of recent changes to e-commerce market trends as German households went into Covid-19 quarantine. Closely monitoring these sorts of trend changes is in the very best interest of every business leader. The required agility may, however, strain the usual annual planning and forecasting cycle used by many companies.

To help towards this end, Inspirient’s fully automated trend detection capability, including the detection of recent trend changes, can help businesses monitor for shifts in customer behavior at any level of granularity, e.g., individual products by region and customer segment, and at any frequency, e.g., multiple times a day if needed. As an example, on the publicly available ‘Online Retail Sales’ dataset for a UK-based online store, the automatically generated portfolio analysis examines the recent purchase patterns for every product sold by the online store and highlights that the product ‘Party Bunting’ drives the most revenue but orders for this product are declining. This indicates that an immediate business decision is required!

Other relevant use cases that would benefit from similar deep-dive trend analysis include internal costing, workforce and logistics planning, and lead engagement.

If you would like to prioritize your planning based on current market trends, please do not hesitate to get in touch!

The Inspirient Automated Analytics Engine automates the entire data analytics process end-to-end: From the assignment of input data, pattern and outlier detection, automated visualization of patterns, weak points and opportunities to automatic generation of textual explanations and recognition of the underlying relationships and rules. Most other analytics solutions rarely include these textual explanations and observations regarding the underlying data relations, which are both critical to provide a deeper level of analysis and more actionable conclusions.

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