scholarly journals Data-driven innovation in shopper marketing

2019 ◽  
Author(s):  
Αναστασία Γρίβα
Keyword(s):  
Data Driven ◽  
Business Analytics ◽  
Shopper Marketing

Η επιχειρηματική αναλυτική είναι ένα ισχυρό εργαλείο των λιανεμπόρων για να αποκτήσουν γνώση σχετικά με τις αγοραστικές συνήθειες και προτιμήσεις των πελατών τους. Ωστόσο, στις μέρες μας αυτό γίνεται ολοένα και πιο δύσκολο, μιας και ο σύγχρονος αγοραστής αλλάζει συχνά συμπεριφορά και αναζητά συνεχώς νέες εμπειρίες στα λιανεμπορικά καταστήματα. Πλέον, ο αγοραστής εκτελεί ένα περίπλοκο ταξίδι με σκοπό να ικανοποιήσει τις αυξανόμενες απαιτήσεις του. Αυτό έχει ως αποτέλεσμα, οι έμποροι λιανικής πώλησης να έχουν αρχίσει να συνειδητοποιούν ότι οι τεχνικές κατάτμησης των αγοραστών δε μπορούν να περιγράψουν τις νέες, ασταθείς συνήθειες και προτιμήσεις των αγοραστών. Από την άλλη πλευρά, και οι ερευνητές αναγνωρίζουν αυτή την ανάγκη και υποδεικνύουν ότι πρέπει να δώσουμε προσοχή σε κάθε επίσκεψη ενός αγοραστή. Μέσα σε αυτό το πλαίσιο, στην παρούσα διατριβή εισάγουμε τον όρο κατάτμηση των επισκέψεων (visit segmentation). Ο προαναφερθείς όρος επικεντρώνεται στις ανάγκες που ώθησαν τον πελάτη να επισκεφθεί ένα κατάστημα π.χ. για να αγοράσει προϊόντα για το πρωινό του. Παράλληλα, αναπτύσσουμε μια προσέγγιση βασισμένη σε τεχνικές επιχειρηματικής αναλυτικής (business analytics) για την κατάτμηση των επισκέψεων στο λιανεμπόριο. Επιπλέον, εφαρμόζουμε και επικυρώνουμε την προτεινόμενη προσέγγιση μέσω τριών ετερογενών μελετών περίπτωσης (λιανεμπόριο τροφίμων, ρούχων, προϊόντων βελτίωσης σπιτιού). Η αξία μιας τέτοιας καινοτόμας προσέγγισης, διαφαίνεται όταν χρησιμοποιούμε την εξαγόμενη γνώση για την αποτελεσματικότερη στόχευση του πελάτη, το σχεδιασμό ενεργειών μάρκετινγκ και τη δεδομενοκεντρική λήψη των αποφάσεων. Για το σκοπό αυτό στην παρούσα διατριβή διερευνούμε μέσω μιας μελέτης πεδίου πως η αξιοποίηση της γνώσης από την κατάτμηση των επισκέψεων μπορεί να έχει επίδραση στα αποτελέσματα μιας προωθητικής ενέργειας. Επιπροσθέτως, προσδιορίζουμε και συζητούμε όλους τους παράγοντες που, επηρεάζουν τα συστήματα κατάτμησης. Έτσι, η έρευνα αυτή θέτει επίσης τις βάσεις για τις αρχές ανάπτυξης σχετικών εργαλείων. Τέλος, η παρούσα έρευνα φιλοδοξεί να γεφυρώσει τους ερευνητές και τους διευθυντές μάρκετινγκ με τους επιστήμονες δεδομένων και τους σχεδιαστές συστημάτων κατάτμησης των επισκέψεων και των αγοραστών.

How to turn managers into data-driven decision makers

2019 ◽  
Vol 25 (3) ◽  
pp. 553-578 ◽  
Author(s):  
Kevin Daniel André Carillo ◽  
Nadine Galy ◽  
Cameron Guthrie ◽  
Anne Vanhems
Keyword(s):  
Measurement Instrument ◽  
Data Driven ◽  
Business Analytics ◽  
Business World ◽  
Content Type ◽  
Multi Stage ◽  
Attitudes Toward Statistics ◽  
Attitude Toward Business ◽  
Professional Contexts ◽  
The Way

Purpose The purpose of this paper is to emphasize the need to engender a positive attitude toward business analytics in order for firms to more effectively transform into data-driven businesses, and for business schools to better prepare future managers. Design/methodology/approach This paper develops and validates a measurement instrument that captures the attitude toward business statistics, the foundation of business analytics. A multi-stage approach is implemented and the validation is conducted with a sample of 311 students from a business school. Findings The instrument has strong psychometric properties. It is designed so that it can be easily extrapolated to professional contexts and extended to the entire domain of business analytics. Research limitations/implications As the advent of a data-driven business world will impact the way organizations function and the way individuals think, work, communicate and interact, it is crucial to engage a transdisciplinary dialogue among domains that have the expertise to help train and transform current and future professionals. Practical implications The contribution provides educators and organizations with a means to measure and monitor attitudes toward statistics, the most anxiogenic component of business analytics. This is a first step in monitoring and developing an analytics mindset in both managers and students. Originality/value By demonstrating how the advent of the data-driven business era is transforming the DNA and functioning of organizations, this paper highlights the key importance of changing managers’ and all employees’ (to a lesser extent) mindset and way of thinking.

Knowledge Management and Business Analytics

2019 ◽  
pp. 1-42
Author(s):  
Nirali Nikhilkumar Honest ◽  
Atul Patel
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Business Intelligence ◽  
Business Performance ◽  
Virtual Organization ◽  
Data Driven ◽  
Business Analytics ◽  
Broad Category ◽  
Business Operations

Knowledge management (KM) is a systematic way of managing the organization's assets for creating valuable knowledge that can be used across the organization to achieve the organization's success. A broad category of technologies that allows for gathering, storing, accessing, and analyzing data to help business users make better decisions, business intelligence (BI) allows analyzing business performance through data-driven insight. Business analytics applies different methods to gain insight about the business operations and make better fact-based decisions. Big data is data with a huge size. In the chapter, the authors have tried to emphasize the significance of knowledge management, business intelligence, business analytics, and big data to justify the role of them in the existence and development of an organization and handling big data for a virtual organization.

Role of Pharmacy Analytics in Creating a Data-Driven Culture for Frontline Management

2020 ◽  
pp. 001857872092079
Author(s):  
Whitley M. Yi ◽  
Adam Bernstein ◽  
Mary-Haston Vest ◽  
Evan W. Colmenares ◽  
Suzanne Francart
Keyword(s):  
Data Driven ◽  
Organizational Level ◽  
Business Analytics ◽  
Management Culture ◽  
Effective Strategies ◽  
Standard Of Practice ◽  
Day To Day Operations ◽  
Data Elements ◽  
Data Driven Decisions ◽  
Operational Metrics

Purpose: The purpose of this article is to offer key recommendations based on the authors’ experiences for utilizing pharmacy analytics to support moving beyond standard-of-practice operational metrics towards high impact reporting to drive day-to-day decisions for frontline leaders. Summary: There is a continuous and vast amount of data generated through all facets of a health system’s daily operations, yet many data elements go unused and fail to contribute to value creation and increased performance at an organizational level. It is critical, therefore, for departments of pharmacy to identify and implement effective strategies to leverage data through robust business analytics and reporting, ensuring managers at every level are provided the information they need to support data-driven decisions and meaningful interventions in the day-to-day operations of the organization. At the authors’ institution, development and growth of a dedicated Pharmacy Analytics (PA) team has been instrumental to the pharmacy department for generating value and proactively supporting a business intelligence strategy that focuses on a data-driven management culture. Key recommendations to leverage pharmacy analytics are provided within four overarching themes: building transparency, leveraging synergy, optimizing actionability, and prioritizing partnerships. Conclusion: Through creation of a data-driven management culture, the authors provide recommendations for leveraging pharmacy analytics to reduce costs and impact outcomes across a range of hospital pharmacy operations.

Storytelling, business analytics and big data interpretation

2019 ◽  
Vol 43 (2) ◽  
pp. 204-222 ◽  
Author(s):  
Valeriia Boldosova ◽  
Severi Luoto
Keyword(s):  
Decision Making ◽  
Data Interpretation ◽  
Daily Basis ◽  
Data Driven ◽  
Complex Data ◽  
Business Analytics ◽  
Content Type ◽  
Individual Level ◽  
Business Practitioners ◽  
Data Driven Decisions

Purpose The purpose of this paper is to explore the role of storytelling in data interpretation, decision-making and individual-level adoption of business analytics (BA). Design/methodology/approach Existing theory is extended by introducing the concept of BA data-driven storytelling and by synthesizing insights from BA, storytelling, behavioral research, linguistics, psychology and neuroscience. Using theory-building methodology, a model with propositions is introduced to demonstrate the relationship between storytelling, data interpretation quality, decision-making quality, intention to use BA and actual BA use. Findings BA data-driven storytelling is a narrative sensemaking heuristic positively influencing human behavior towards BA use. Organizations deliberately disseminating BA data-driven stories can improve the quality of individual data interpretation and decision-making, resulting in increased individual utilization of BA on a daily basis. Research limitations/implications To acquire a deeper understanding of BA data-driven storytelling in behavioral operational research (BOR), future studies should test the theoretical model of this study and focus on exploring the complexity and diversity in individual attitudes toward BA. Practical implications This study provides practical guidance for business practitioners who struggle with interpreting vast amounts of complex data, making data-driven decisions and incorporating BA into daily operations. Originality/value This cross-disciplinary study develops existing BOR, storytelling and BA literature by showing how a novel BA data-driven storytelling approach can facilitate BA adoption in organizations.

scholarly journals How do top- and bottom-performing companies differ in using business analytics?

2017 ◽  
Vol 30 (6) ◽  
pp. 874-892 ◽  
Author(s):  
Guangming Cao ◽  
Yanqing Duan
Keyword(s):  
Organizational Performance ◽  
Business Processes ◽  
Academic Research ◽  
Data Driven ◽  
Business Analytics ◽  
Analytical Strategy ◽  
Content Type ◽  
And Performance ◽  
Data Driven Decisions ◽  
Effective Use

Purpose Business analytics (BA) has attracted growing attention mainly due to the phenomena of big data. While studies suggest that BA positively affects organizational performance, there is a lack of academic research. The purpose of this paper, therefore, is to examine the extent to which top- and bottom-performing companies differ regarding their use and organizational facilitation of BA. Design/methodology/approach Hypotheses are developed drawing on the information processing view and contingency theory, and tested using multivariate analysis of variance to analyze data collected from 117 UK manufacture companies. Findings Top- and bottom-performing companies differ significantly in their use of BA, data-driven environment, and level of fit between BA and data-drain environment. Practical implications Extensive use of BA and data-driven decisions will lead to superior firm performance. Companies wishing to use BA to improve decision making and performance need to develop relevant analytical strategy to guide BA activities and design its structure and business processes to embed BA activities. Originality/value This study provides useful management insights into the effective use of BA for improving organizational performance.

scholarly journals The effect of individuals’ analytical orientation and capabilities on decision quality and regret

2022 ◽  
Vol 9 (1) ◽  
pp. 0-0
Keyword(s):  
Decision Making ◽  
Least Squares ◽  
Business Students ◽  
Decision Makers ◽  
Data Driven ◽  
Decision Quality ◽  
Business Analytics ◽  
Path Modeling ◽  
Individual Decision ◽  
Data Driven Decisions

Decision makers are exposed to an increasing amount of information. Algorithms can help people make better data-driven decisions. Previous research has focused on both companies’ orientation towards analytics use and the required skills of individual decision makers. However, each individual can make either analytically based or intuitive decisions. We investigated the characteristics that influence the likelihood of making analytical decisions, focusing on both analytical orientation and capabilities of individuals. We conducted a survey using 462 business students as proxies for decision makers and used partial least squares path modeling to show that analytical capabilities and analytical orientation influence each other and affect analytical decision-making, thereby impacting decision quality and decision regret. Our findings suggest that when implementing business analytics solutions, companies should focus on the development not only of technological capabilities and individuals’ skills but also of individuals’ analytical orientation.

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