Quecst: Quick and Easy Customer Segmentation Tool

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Quecst: Quick and Easy Customer Segmentation Tool 

Quecst (pronounced quest بحث) is a technology at the core of a critically needed business capability the rapid and accurate segmentation of customer behavioral and associated demographic data based on business Key Performance Indicators (KPIs). With the near universal collection of online analytics data for customers, Business-to-Consumer (B2C) companies have tremendous information about their customers but also face tremendous lag time in harvesting value from this data in the form of immediately actionable insights. Quecst addresses this pain point via a tailored interface for data upload combined with standardized backend algorithmic processes for analyzing data against specified KPIs, resulting in rapid, contextualized, and immediately actionable customer segmentation for B2C companies.

Brief Background: Many organizations, especially B2C businesses, now routinely collect customer behavioral data, along with customer demographics, including B2C businesses in the retail, product, and services sectors. For example, a retail company that fulfills orders online logs customer purchasing behavioral data. An airline company logs flight booking data. A telecom company stores data concerning data plans and usage. In each of these examples, the companies have numerous touchpoints for each customer, including variables such as inquiries or purchases, usually along with some set of demographic data (e.g., age, gender, nationality). As such, the companies possess rich stores of possible insights into their customer base for a variety of commercial activities, such as upselling, cross-selling, customer retention programs, brand enhancement, outbound marketing, and customer relationship management.

In response to this increase in customer data availability, there has been a corresponding increase in statistical analytics packages from which one can do segmentation, such as SPSS Enterprise, SAP, and R. There are also a variety of proposed analytics technology solutions, such as Adobe Analytics, Google Analytics, IBM Analytics, Salesforce, etc., aimed at streamlining the analytics of customer data. However, the statistical analytics packages require a degree of analytics sophistication and skills that are difficult for even the largest and most resourced firms to acquire and maintain. The analytics technology solutions usually provide a host of reports that are easy to generate, but these reports are generally exploratory, high-level, and not actionable. For example, Google Analytics provides customer flow report (i.e., where the customers of a website come from before visiting the website). While nice to know, there is little one can do with such information beyond some rudimentary action. Also, while the analytics technology solutions provide more in-depth investigative features, they also require a high degree of statistical sophistication. The learning curve for both of these types of technologies is high, and the analysis of customer data is not quick, causing companies to miss critical revenue generating opportunities concerning their customers.

In sum, the available technologies are (a) complex to employ, (b) require too long for insightful customer analysis, and (c) typically generate insights that are not directly actionable.

Quecst addresses these issues and provides a critically needed business capability for the rapid and accurate segmentation of customer behavioral and associated demographic data based on business KPIs. KPIs are metrics and measures that businesses use to gauge the process, success, etc. of customer initiatives and project. With this framework, Quecst addresses this pain point of complexity via a tailored interface for data upload by companies. We shorten the analysis lag period, by leveraging a built-in combination of algorithmic approaches that (1) identifies unique customer behavioral patterns, (2) automatically classifies customer preferences, including predicting possible future preferences, and (3) compresses the resulting customer segments into the smallest but still unique number. Finally, we address the issue of non-actionable insights by combined standardized backend algorithmic processes for analyzing data mentioned with company-specified KPIs. Quecst results are, therefore, rapid, contextualized, and therefore immediately actionable, customer segmentation for a B2C company.

Idea Description: Figure 1 shows the data flow of Quecst. To the best of our knowledge, the proposed system and outputs are novel and unique, relative to the existing state of the art. In our discussion with potential clients, who employ a variety of customer analytics service and technology, Quecst capabilities appear innovative and exclusive. 

Figure 1: Quecst data flow, from the input of customer data and KPIs to the output of actionable customer segments, along with the algorithmic processing of data and generation of segment clusters.