Sales Forecasting Using Browsing Ratios and Durations
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Solution Overview
Problem
Conventional sales forecasting techniques are inaccurate due to their failure to account for browsing intentions, often confusing 'window-shoppers' with potential purchasers, leading to underestimated sales forecasts that affect business operations such as supply chains, inventories, and marketing activities.
Innovation Solution
The proposed solution involves using browsing ratios and browsing durations to differentiate between users who are likely to purchase products and those who are not, by calculating the number of times users visit product-related webpages versus non-product related webpages and the duration of their visits, respectively, to create a more accurate sales forecasting model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional sales forecasting techniques are used, then the forecasting process is simple and easy to implement, but the accuracy of sales forecasts deteriorates due to failure to account for window-shopper traffic
Solution Approach 1:
The patent segments website visitors into distinct categories: window-shoppers (those who visit product pages but do not purchase) and genuine purchasers. By dividing the user base into these segments and applying different forecasting weights to each, the system achieves more accurate sales forecasts while maintaining reasonable model complexity through clear categorical separation.
Solution Approach 2:
The patent introduces new parameters to the forecasting model: browsing ratio (ratio of product-related page views to total page views) and browsing duration (time spent on product pages). These additional parameters enable the model to distinguish between window-shoppers and genuine buyers, thereby improving forecast accuracy without overwhelming complexity.
2Loss of information
If simple calculations based solely on purchase histories are used, then the forecasting algorithm remains simple, but the ability to differentiate between window-shoppers and potential purchasers deteriorates
Solution Approach 1:
The patent performs preliminary analysis of browsing behavior before making purchase predictions. By pre-calculating browsing ratios and browsing durations for each visitor, the system prepares distinguishing characteristics that will later enable accurate differentiation between window-shoppers and potential buyers, preventing information loss upfront.
Solution Approach 2:
The patent introduces browsing ratio and browsing duration as intermediary metrics that mediate between raw browsing data and purchase predictions. These intermediary parameters translate complex browsing patterns into actionable insights, enabling the algorithm to differentiate user types without requiring overly complex direct analysis.
3Productivity
If window-shopper traffic is not accounted for, then the forecasting model remains simple, but the control of business operations deteriorates due to inaccurate forecasts
Solution Approach 1:
The patent incorporates feedback mechanisms that continuously monitor and adjust forecasting based on actual browsing behavior patterns. By feeding back browsing ratio and duration data into the forecasting model, the system continuously refines its ability to distinguish window-shoppers from buyers, improving operational efficiency through adaptive, increasingly accurate forecasts.
Data Source
AI summary
Sales Forecasting using Browsing Ratios and Browsing Durations is described. In one or more implementations, browsing ratios representative of how much users visited webpages associated with a product or service, and browsing durations representing how much time users spent visiting the webpages associated with the product or service are determined. Based on the determined browsing ratios and browsing durations, a sales forecast of the product or service can be accurately determined.