Overview

Master the tricks and techniques of business analytics consulting, specifically applicable to small-to-medium businesses (SMEs). Written to help you hone your business analytics skills, this book applies data science techniques to help solve problems and improve upon many aspects of a business' operations.  SMEs are looking for ways to use data science and analytics, and this need is becoming increasingly pressing with the ongoing digital revolution. The topics covered in the books will help to provide the knowledge leverage needed for implementing data science in small business. The demand of small business for data analytics are in conjunction with the growing number of freelance data science consulting opportunities; hence this book will provide insight on how to navigate this new terrain. This book uses a do-it-yourself approach to analytics and introduces tools that are easily available online and are non-programming based. Data science will allow SMEs to understand their customer loyalty, market segmentation, sales and revenue increase etc. more clearly.  Data Science and Analytics for SMEs is particularly focused on small businesses and explores the analytics and data that can help them succeed further in their business.  What You'll Learn Create and measure the success of their analytics project Start your business analytics consulting career Use solutions taught in the book in practical uses cases and problems  Who This Book Is For Business analytics enthusiasts who are not particularly programming inclined, small business owners and data science consultants, data science and business students, and SME (small-to-medium enterprise) analysts 

Full Product Details

Author:   Afolabi Ibukun Tolulope
Publisher:   APress
Imprint:   APress
Edition:   1st ed.
Weight:   0.545kg
ISBN:  

9781484286692

ISBN 10:   1484286693
Pages:   335
Publication Date:   29 September 2022
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

Table of Contents

​ INTRODUCTION We introduce data science generally and narrow it down to data science for business which is also referred to as business analytics. We then give a detailed explanation of the process involved in business analytics in form of the business analytics journey. In this journey, we explain what it takes from start to finish to carry out an analytics project in the business world, focusing on small business consulting, even though the process is generic to all types of business, small or large. We also give a description of what small business refers to in this book and the peculiarities of navigating an analytics project in such a terrain.  To conclude the chapter, we talk about the types of analytics problems that is common to small business and the tools available to solve these problems given the budget situation of small businesses when it comes to analytics project. ·         DATA SCIENCE ·         DATA SCIENCE FOR BUSINESS ·         BUSINESS ANALYTICS JOURNEY ·         SMALL AND MEDIUM BUSINESS (SME) ·         BUSINESS ANALYTICS IN SMALL BUSINESS ·         TYPES OF ANALYTICS PROBLEMS IN SME ·         ANALYTICS TOOLS FOR SMES ·         ROAD MAPS TO THIS BOOK ·         PROBLEMS ·         REFERENCES   CHAPTER 1: DATA FOR ANALYSIS IN SMALL BUSINESS In this chapter, we would look at the various sources of data generally and in small business. This chapter is important because the major challenge of consulting for small business is the lack of data or quality data for analysis. This chapter will therefore detail the sources of data for analysis explaining first the type or form that data exists and some general ideas of how to collect such data. It gives an overview on data quality and integrity issues and touches on data literacy. The chapter also includes the typical data preparation procedures for the common types of techniques used in small business analytics and by extension used in this book. To conclude the chapter, we look at data visualization, particularly towards preparing data for various analytics task as explained in section 1.3. ·         SOURCE OF DATA ·         DATA QUALITY & INTEGRITY ·         DATA GOVERNANCE ·         DATA PREPARATION ·         DATA VISUALIZATION ·         PROBLEMS ·         REFERENCES CHAPTER 2: BUSINESS ANALYTICS CONSULTING In this chapter, we will look at business analytics consulting, particularly what the concept implies and how to build such a career path. We will explain the types of business analytics consulting that exist and then narrow it down to how to navigate the world of business analytics consulting for small business. In this chapter, we will look at how to manage a typical analytics project and measure the success of analytics projects. In conclusion, we will discuss issues revolving around how to bill analytics project particularly as a consultant. ·         BUSINESS ANALYTICS CONSULTING ·         MANAGING ANALYTICS PROJECT ·         SUCCESS METRICS IN ANALYTICS PROJECT ·         BILLING ANALYTICS PROJECT ·         PROBLEMS ·         REFERENCES CHAPTER 3: BUSINESS ANALYTICS CONSULTING PHASES In this chapter we will look at the stages involved business analytics consulting, particularly when the analytics service is offered as a product from either within or outside the business. We will look at the proposal and initial analysis stage which gives direction to the analytics project. Then we look at the details involved in the pre-engagement, engagement and post engagement phase. It is important to know that the stages are presented in a typical or generic way but when implemented, there might be reason to modify or customize them for the application scenario. ·         PROPOSAL & INITIAL ANALYSIS ·         PRE- ENGAGEMENT PHASE ·         ENGAGEMENT PHASE ·         POST ENGAGEMENT PHASE ·         PROBLEMS ·         REFERENCES   CHAPTER 4: DESCRIPTIVE ANALYTICS TOOLS This chapter is focused on the mostly common descriptive analytics tools used in business generally and specifically in small businesses. The chapter will help  to use descriptive analytics tools to understand your business and make recommendations that can improve your business profits. For small business, descriptive analytics helps SMEs to make sense of available data in order to monitor business indicators at a glance, helps SME owners to observe sales trends and patterns on an overall basis, as well as deep-dive into product categories and customer groups.  It also helps SME’s to plan product strategy, pricing policies that will maximize their projected revenues and derive a lot of valuable insights for getting more customers.   ·         INTRODUCTION ·         BAR CHART ·         HISTOGRAM ·         LINE GRAPHS ·         SCATTER PLOTS ·         PACKED BUBBLES CHARTS ·         HEAT MAPS ·         GEOGRAPHICAL MAPS ·         A PRACTICAL BUSINESS PROBLEM I ·         PROBLEMS ·         REFERENCES   CHAPTER 5: PREDICTION TECHNIQUES In this chapter, we will explore the popular techniques used for prediction, particularly in retails business. The approach used in explaining these techniques us to use them in solving a business problem. The second business problem to be addressed is the sales prediction problem which is common in retail business. The chapter first explain the fundamental concept of prediction techniques, next we look at how such techniques are evaluated. After this, we describe the business problem we intend solving. We then pick each of the selected techniques one by one and explain the algorithms involved and how they can be used to solve the problem described. The prediction techniques used and compared are the Multiple linear regression, the Regression Trees and the Neural Network. To conclude the chapter, we compare the results of the three algorithms and conclude on the problem in question.  In this chapter therefore, the analytics products being offered is to solve sales prediction problem for small retail business. ·         INTRODUCTION ·         PRACTICAL BUSINESS PROBLEM II (SALES PREDICTION) ·         MULTIPLE LINEAR REGRESSION ·         REGRESSIN TREES ·         NEURAL NETWORK (PREDICTION) ·         CONCLUSION ON SALES PREDICTION ·         PROBLEMS ·         REFERENCES   CHAPTER 6: CLASSIFICATION TECHNIQUES In this chapter, even though there are several classification techniques, we will explore the popular ones used for classification in the business domain.  In doing this, we will use the third business problem centered on customer loyalty comparing neural network, classification tree and random forest algorithms. In solving this problem, we are particular about how to get and retain more customers for our small business.  We will also introduce some other classification based techniques such as K-nearest neighbour logistic regression and persuasion modelling.  We will use persuasion modelling for the fourth practical business problem. In using these techniques to solve the problem we explain the fundamental concepts in the chosen algorithms and use them to demonstrate how this problems solving process can be adopted in real business scenarios. ·         CLASSIFICATION MODELS & EVALUATION ·         PRACTICAL BUSINESS PROBLEM III (CUSTOMER LOYALTY) ·         NEURAL NETWORK ·         CLASSIFICATION TREE ·         RANDOM FOREST & BOOSTED TREES ·         K NEAREST NEIGHBOUR ·         LOGISTIC REGRESSION ·         PROBLEMS ·         REFERENCES   CHAPTER 7: ADVANCED DESCRIPTIVE ANALYTICS This chapter is focused mainly on advanced descriptive analytics techniques. In this chapter, we will first explain the concept of clustering which is a type of unsupervised learning approach. We will then pick one clustering technique which is the K means clustering. Using the fourth practical business problem, we will explain how we can use the K means clustering technique to solve a real business problem. Next will explain the association rule example and finally Network analysis. We conclude with the fifth business problem which is focused on using network analytics for employee efficiency. ·         CLUSTERING ·         K MEANS ·         PRACTICAL BUSINESS PROBLEM IV (Customer Segmentation) ·         ASSOCIATION ANALYSIS ·         NETWORK ANALYSIS ·         PRACTICAL BUSINESS PROBLEM V (Staff Efficiency) ·         PROBLEMS ·         REFERENCES   CHAPTER 8: CASE STUDY PART I This chapter is the beginning part of major consulting case study for this book. We will explain what transpired during a typical business analytics consulting and help to create a road map or an example of how to navigate a business analytics consulting project. We start with a description of the SME Ecommerce environment generally, since this is the business environment of our selected case study, we then talk about the sources of data for analytics peculiar this environment. Next we describe the business to be used as case study briefly, followed by the analytics road map peculiar to consulting for this business. This chapter ends with the results of the initial analysis and pre engagement phase which forms the bases for the detailed analytics and implementation phase in chapter 10. ·         SME ECORMERCE ·         INTRODUCTION TO SME CASE STUDY ·         INITIAL ANALYSIS ·         ANALYTICS APPROACH            ·         PRE –ENGAGEMENT ·         PROBLEMS ·         REFERENCES   CHAPTER 9: CASE STUDY PART II In this chapter, we will conclude the case study used for illustration of a typical business analytics consulting for an SME by presenting the details of the engagement phase for the case study in question. The post engagement phase is left  out as the implementation of the recommendations is determined by the systems and procedures of the business. It is important to note that the consulting steps can be customized for any small business based on the intended problem. The whole steps described in chapter 9 and 10 have been made simple for understanding, though in real life business application there might be need to iterate the process until satisfactory results have been gotten. This is because you constantly need to incorporate feedback from the stakeholders and domain experts. ·         GOAL 1:        INCREASE WEBSITE TRAFFIC ·         GOAL 2:       INCREASE WEBSITE SALES REVENUE ·         PROBLEMS ·         REFERENCES

Reviews

“By reading the book and working out the use case, subject matter experts will be able to get a coherent roadmap to the main techniques available for both descriptive and predictive data analytics, as well as be able to provide simple services related to their company data and future prospects.” (Rosario Uceda-Sosa, Computing Reviews, October 2, 2023)

Author Information

Afolabi Ibukun is a Data Scientist and is currently a Senior Lecturer in the Department of Computer and Information Sciences, Covenant University. She holds a B.Sc in Engineering Physics, an M.Sc and Ph.D in Computer Science. Afolabi Ibukun has over 15 years working experience in Computer Science research, teaching and mentoring. Her specific areas of interest are Data & Text Mining, Programming and Business Analytics. She has supervised several undergraduate and postgraduate students and published several articles in international journals and conferences. Afolabi Ibukun is also a Data Science Nigeria Mentor and currently runs a Business Analytics Consulting and Training firm named I&F Networks Solutions

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