Investigating the Factors for Predictive Marketing Implementation in Algerian Organizations

Investigating the Factors for Predictive Marketing Implementation in Algerian Organizations

Soraya Sedkaoui (Djilali Bounaama University, Algeria & Montpellier University, France & SRY Consulting, France)
DOI: 10.4018/978-1-5225-5993-1.ch005

Abstract

This chapter examines and identifies the factors that influence the implementation of predictive marketing in Algeria enterprises. A structured questionnaire was used to collect data from 30 respondents comprised of CEOs of selected enterprises. Some analytical methods were applied to analyze the data and evaluate the point of view of the enterprises with regard to the adoption and implementation of predictive marketing techniques. The major findings of the study indicated that the adoption of predictive marketing requires the relevant tools and software to extract knowledge “data mining.” In addition, the existence of start-up (for analytics) and the level of development of e-commerce and digital marketing in Algeria will undoubtedly encourage the use of these techniques. This chapter also provides some suggestions for further research.
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Introduction

The Internet and the Web has attracted considerable attention and research from both academics and practitioners. Numerous studies anticipated a “marketing revolution” (Hoffman & Novak, 1997; Keeny & Marshall, 2000) as businesses changed their modes of operation and customers adapted to novel and different ways of purchasing goods and services. With the advent of digital technology and smart devices, a large amount of digital data is being generated every day. Individuals are putting more and more publicly available data on the web. Thus, not only the quantity of digitally stored data is much larger, but the type of data is also tremendously diversified, due to various new technologies (Sedkaoui & Monino, 2016).

Customer databases have grown significantly larger over the last decade. Many companies collect information on their customers and their respective behavior. Thanks to technology advent which enables companies to produce a granular record of every touchpoint consumers make in their purchase journey. However, firms still depend on aggregate measures to guide their marketing investments in multiple channels (display, paid search, referral, e-mail …). If they want to predict how customers will respond in the future, there is one place to turn “predictive analytics”. Organizations enabled with analytical tools can incorporate better strategies to use their resources in more efficient way (Ngai et al., 2009).

Predictive analytics comprise collection of statistical and empirical models with the goal of creating empirical predictions and further assessing the quality of those predictions in practice. These techniques are applicable in both theory building and theory testing approaches. Predictive analytics methods help to analyze and understand customer behaviors and acquire and retain customers and also maximize customer value. Thus it facilitates decisions making and supports development of businesses strategies. The notion of “big data” and the potential of producing actionable information from the existing databases are the main drivers of predictive analytics application (Halper, 2011).

Big data is often obtained by aggregating different sources of very different nature of data. We may have to deal simultaneously with numerical, categorical data, but also with text, preference data, browsing histories, historical purchase on e-commerce websites, social media data, analyzed by using methods of natural language processing, being fused with sales data to determine the effect of advertising on consumer sentiment about a product and behaviors of purchase. Indeed, marketing strategy, supported by the predictive analysis techniques is a project that is not limited to define ideas but especially to translate them into action and to control its state of evolution for better understand customer behavior.

The application of predictive analytics in Algerian enterprises is an emerging trend. Despite the transition from a planned economy to a market economy, many experts and researchers believe that the practice of Marketing companies is still weak and affecting their competitiveness. Moreover, these companies are also exposed to the universe of big data. Data is therefore collected and analyzed to support efficient business processes and to create significant additional value. This leads us to question the reasons for Algerian enterprises’ delay in adopting predictive marketing tools and the factors likely to influence them to adapt predictive analytics in their marketing strategies.

Many researches have been examined in understanding analytics benefits. However, few studies, especially in Algeria, have examined this theme. It is on this premise that this study wants to examine the factors influencing adoption and implementation of predictive tools with special reference to the Algerian enterprises. This makes the present research one of the first studies analyzing this point. This paper seeks to investigate the issues relating to the adoption of predictive analytics methods for marketing purposes by enterprises. In particular it focuses on the factors that affect a need for such methods in Algerian enterprises. It builds primarily on existing based research and develops a conceptual framework to understand why organizations do or do not adopt predictive marketing.

Key Terms in this Chapter

Analytics: Emerged as a catch-all term for a variety of different business intelligence (BI)- and application-related initiatives. For some, it is the process of analyzing information from a particular domain, such as website analytics. For others, it is applying the breadth of BI capabilities to a specific content area (e.g., sales, service, supply chain and so on). In particular, BI vendors use the “analytics” moniker to differentiate their products from the competition. Increasingly, “analytics” is used to describe statistical and mathematical data analysis that clusters, segments, scores, and predicts what scenarios are most likely to happen. Whatever the use cases, “analytics” has moved deeper into the business vernacular. Analytics has garnered a burgeoning interest from business and IT professionals looking to exploit huge mounds of internally generated and externally available data.

Predictive Marketing: Predictive marketing is a marketing technique that involves using data analytics to determine which marketing strategies and actions have the highest probability of succeeding. It has a place in the marketing technology (MarTech) landscape, as companies make use of general business data, marketing and sales activity data, and mathematical algorithms to match patterns and determine the best-fit criteria for their next marketing actions. Companies that utilize this strategy strive to make data-driven decisions to yield better results.

Big Data: The term big data is used when the amount of data that an organization has to manage reaches a critical volume that requires new technological approaches in terms of storage, processing, and usage. Volume, velocity, and variety are usually the three criteria used to qualify a database as “big data.”

Marketing: The management process through which goods and services move from concept to the customer. It includes the coordination of four elements called the 4 Ps of marketing: (1) identification, selection, and development of a product; (2) determination of its price; (3) selection of a distribution channel to reach the customer's place; and (4) development and implementation of a promotional strategy.

Data Mining: This practice consists of extracting information from data as the objective of drawing knowledge from large quantities of data through automatic or semi-automatic methods. Data mining uses algorithms drawn from disciplines as diverse as statistics, artificial intelligence, and computer science in order to develop models from data; that is, in order to find interesting structures or recurrent themes according to criteria determined beforehand, and to extract the largest possible amount of knowledge useful to companies. It groups together all technologies capable of analyzing database information in order to find useful information and possible significant and useful relationships within the data.

Data Analysis: This is a class of statistical methods that makes it possible to process a very large volume of data and identify the most interesting aspects of its structure. Some methods help to extract relations between different sets of data, and thus, draw statistical information that makes it possible describe the most important information contained in the data in the most succinct manner possible. Other techniques make it possible to group data in order to identify its common denominators clearly, and thereby understand them better.

Customer Relationship Management (CRM): A business strategy that optimizes revenue and profitability while promoting customer satisfaction and loyalty. CRM technologies enable strategy, and identify and manage customer relationships, in person or virtually. CRM software provides functionality to companies in four segments: sales, marketing, customer service, and digital commerce.

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