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उपभोक्ता व्यवहार एवं विपणन शोध (Consumer Behaviour & Marketing Research)

₹ 175.00 ₹ 148.75

• For M.Com Final Year of Pt. Deen Dayal Upadhyaya Gorakhpur University (DDU) and Siddharth University Kapilvastu Siddharth Nagar
• Thoroughly Revised and Enlarged Edition 2023

Description

• प्रस्तुत उपभोक्ता व्यवहार एवं विपणन शोध Consumer Behaviour & Marketing Research Book विश्वविद्यालय अनुदान आयोग (U.G.C) द्वारा आधारित एम. काॅम. पाठ्यक्रम के अनुसार प्रथम प्रस्तुति है। उपभोक्ता व्यवहार एवं विपणन शोध जैसे कठिन विषय का हिन्दी अनुवादित पुस्तक उपलब्ध नहीं होने की वजह से विद्यार्थियों को अधिक कठिनाइयों का सामना करना पड़ रहा था। इसी सन्दर्भ में विद्यार्थियों के हितों को ध्यान में रखते हुए अत्यन्त सरल, आकर्षक और उपयोगी भाषा में इस पुस्तक को लिखने का प्रयास किया गया है, जिससे कि विद्यार्थियों को उपभोक्ता व्यवहार एवं विपणन शोध की विषय-वस्तु को समाहित किया गया है। प्रथम भाग में उपभोक्ता व्यवहार एवं द्वितीय भाग में विपणन शोध की विषय-वस्तु को समाहित किया गया है। विद्यार्थी वर्ग की आवश्यकता को ध्यान में रखते हुए विषय के स्तर को बनाए रखने के साथ-साथ समस्त विषय-सामग्री को एक ही स्थान पर संगृहीत करने का प्रयास किया गया है तथा सभी अध्यायों के अन्त में दीर्घ उत्तरीय, लघु उत्तरीय व वस्तुनिष्ठ प्रश्न दिए गए हैं, जो सामान्यतया परीक्षा में पूछे जाते हैं।

उपभोक्ता व्यवहार एवं विपणन शोध Consumer Behaviour and Marketing Research Book Syllabus For M.Com II Year of Pt. Deen Dayal Upadhyaya Gorakhpur University (DDU) and Siddharth University Kapilvastu Siddharth Nagar

Marketing: Paper-II

• Consumer Behaviour; Theory and its Application to Marketing Strategy,Buying Process, Internal and External determinants of Consumer Behaviour, Opinion Leadership and Innovation Diffusion.
• Stimulus-Response Model of Consumer Behaviour, Industrial Buying Behaviour, Globalisation of Consumer Markets and Marketing Implications; Impact of Information Technology on Consumer Behaviour.
• Marketing Research: Importance, Nature and Scope of Marketing Research; Marketing Research in India, Marketing Information System and Marketing Research; Marketing Research Process and Organization. Problem Identification and Definition; Developing a Research Proposal; Determining Research Type-exploratory, Descriptive and Conclusive research; Experimental designs. Data Resources and Collection: Primary and Secondary
• Data Sources; On-line Data Sources; Data Collection Methods-Organisation of Fieldwork and Survey Errors-Sampling and Non-Sampling Errors.
• (i) Sampling plan: Universe, Sample Frame and Sampling Unit; Sampling techniques; Sample size determination. (ii) Data Analysis: Univariate, Bivariate and Multivariate Data Analysis; Report Preparation and Presentation.

उपभोक्ता व्यवहार एवं विपणन शोध Consumer Behaviour and Marketing Research Book विषय-सूची

• उपभोक्ता व्यवहार
• उपभोक्ता व्यवहार के सिद्धान्त और माॅडल
• क्रय-प्रक्रिया
• उपभोक्ता व्यवहार के आन्तरिक एवं बाह्य निर्धारक
• औद्योगिक उत्पाद क्रय व्यवहार
• उत्पाद नवाचार प्रसारण
• उपभोक्ता बाजार का वैश्वीकरण एवं सूचना तकनीकी
• विपणन शोध विपणन सूचना पद्धति
• समस्या की पहचान एवं शोध डिजाइन
• शोध समंकों का संकलन
• निदर्शन योजना
• विश्लेषण विधि (एक अस्थिर और बहु-अस्थिर)
• रिपोर्ट लेखन एवं प्रस्तुतीकरण
• भारत में विपणन शोध

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Market Research क्या है? उपयोगिता, फायदे एवं कम बजट में करने के तरीके।

Market Research किसी भी ऐसे व्यक्ति के लिए बेहद जरुरी उपकरण हो जाता है जो या तो पहले से बिजनेस कर रहा हो या फिर खुद का व्यवसाय शुरू करने की सोच रहा हो। जहाँ मौजूदा उद्यमी को अपना व्यवसाय बढाने के लिए मार्केट रिसर्च करने की आवश्यकता हो सकती है वहीँ व्यवसाय शुरू करने के इच्छुक व्यक्ति को आंशिक ग्राहक एवं उस एरिया विशेष में उस वस्तु या सेवा विशेष की माँग इत्यादि का पता करने के लिए ये सब गतिविधियाँ करने की आवश्यकता हो सकती है।

चूँकि इस पूरी प्रक्रिया में उद्यमी को अपने बिजनेस से समबन्धित बाजार की जानकारी वैज्ञानिक तौर पर संग्रहित करके उनका अति सूक्ष्मता के साथ अध्यन करना पड़ता है। ताकि उद्यमी अपने बिजनेस सम्बन्धी उचित निर्णय ले सके। यद्यपि Market Research किसी भी व्यवसायी के लिए एक बेहद महत्वपूर्ण उपकरण है इसमें कोई दो राय नहीं हैं लेकिन दुर्भाग्यवश इस विषय पर किये गए अध्यन दर्शाते हैं की इस विषय को स्टार्टअप उद्यमी बेहद कम गंभीरता के साथ लेते हैं और परिणाम कुछ समय बाद बिजनेस की विफलता के तौर पर सामने आता है।

वास्तव में अगर देखा जाय तो Market Research किसी भी बिजनेस की स्टार्टअप प्रक्रिया में एक बेहद अहम् चरण है लेकिन बहुत सारे उद्यमी मार्केट रिसर्च की अनदेखी कर देते हैं। वह शायद इसलिए क्योंकि उन्हें कम बजट में मार्केट रिसर्च करने के विकल्पों के बारे में जानकारी नहीं होती है।

मार्केट रिसर्च किसी प्रोजेक्ट या बिजनेस की व्यवहार्यता को निर्धारित करता है और इसका अध्यन करने के बाद उद्यमी स्वयं के बिजनेस को बढ़ाने की रणनीति बना सकता है। इसलिए इस लेख में हम आगे कम बजट में मार्केट रिसर्च करने के तरीकों के बारे में बताएँगे ताकि स्टार्टअप उद्यमी जिनके पास इस गतिविधि को करने के लिए भारी भरकम बजट नहीं होता वे इससे लाभान्वित हो सकें।

मार्केट रिसर्च क्या है (What is Market Research in Hindi):

Market Research को बाजार एवं विपणन सम्बन्धी आंकड़ो का व्यवस्थित संग्रह, इनका विश्लेषण एवं प्राप्त डेटा का अध्यन करके उसकी समुचित व्याख्या करने की प्रक्रिया कहा जा सकता है। साधारण शब्दों में इसे हम लक्ष्यित बाजारों एवं ग्राहकों के बारे में जानकारी एकत्रित करने का एक व्यवस्थित प्रयास भी कह सकते हैं।

कहने का आशय यह है की ग्राहकों या उपभोक्ताओं की आवश्यकताओं एवं वरीयताओं के बारे में जानकारी एकत्रित करने की क्रिया या गतिविधि ही Market Research कहलाती है। मार्केट रिसर्च मार्केटिंग स्थितियों से सम्बंधित प्रासंगिक जानकारी एवं आंकड़े प्रदान करता है जिससे मार्केटिंग चुनौतियों से निबटने में उद्यमी को मदद मिलती है। किसी भी बिजनेस को जो सबसे बड़ी चुनौती का सामना करना पड़ता है वह है उसके सामने आने वाली मार्केटिंग की चुनौतियाँ और मार्केट रिसर्च इसी चुनौती से निबटने में उद्यमी की मदद करता है। इसलिए यह बिजनेस प्लानिंग का एक अहम् अंग है। 

कम बजट में मार्केट रिसर्च करने के तरीके (Best ways to do market research with low budget)

उद्यमी चाहे मौजूदा बिजनेस को बढ़ाना चाह रहा हो या कोई व्यक्ति नया बिजनेस शुरू करना चाह रहा हो स्थिति कोई भी हो लेकिन किसी भी व्यवसाय के लिए Market Research बेहद महत्वपूर्ण होती है। लेकिन इस गतिविधि को अंजाम देने में उद्यमी के बहुत सारे पैसे खर्च हो सकते हैं जो की किसी भी नए या स्टार्टअप उद्यमी के लिए खर्च कर पाना आसान नहीं होता है। इसलिए ऐसे उद्यमी जो मार्केट रिसर्च कराने की सोच रहे हैं उन्हें सर्वप्रथम इस कार्य के लिए अपना बजट ही चेक करना चाहिए। उसके बाद ही उद्यमी मार्केट रिसर्च प्लान बनाने में सक्षम हो पायेगा।

1. फ्री ऑनलाइन स्रोतों का इस्तेमाल करें  

वर्तमान में आम तौर पर अधिकांश देशों में इन्टरनेट पर लगभग हर इंडस्ट्री से जुड़े ऑनलाइन संसाधन मौजूद हैं। कुछ देशों में छोटे उद्यमियों की मदद के लिए सरकारी प्लेटफोर्म उपलब्ध हैं तो कुछ देशों में सरकारी एवं निजी प्लेटफॉर्म दोनों उपलब्ध हैं। जहाँ से उद्यमी हर इंडस्ट्री से जुड़ी मार्किट समबन्धी जानकारी प्राप्त कर सकता है। जैसे Market Research करने वाला उद्यमी चाहे तो गूगल ट्रेंड, गूगल कंज्यूमर सर्वे इत्यादि फ्री ऑनलाइन स्रोतों का इस्तेमाल यह जानने के लिए कर सकता है की लोग क्या ढूंढ रहे हैं और उनकी क्या माँग है।

2. फोरम वेबसाइट चेक करें

Market Research करने वाले उद्यमी के पास यदि बजट की कमी है तो वह किसी विशेष इंडस्ट्री, प्रोडक्ट, सर्विस की जानकारी फोरम वेबसाइट से भी एकत्रित कर सकता है। इन वेबसाइट में लोग तरह तरह के प्रश्नों पर अपनी राय प्रकट करते हैं। इसलिए उद्यमी चाहे तो Quora, Reddit जैसी वेबसाइट पर प्रश्न डालकर लोगों की राय भी ले सकता है।

3. छोटे सर्वे आयोजित करें

एक छोटा व्यवसाय करने वाले उद्यमी को मार्केट से जुड़ी पूरी जानकारी की आवश्यकता नहीं है इसलिए कम बजट में Market Research करने के लिए उद्यमी को छोटे सर्वे आयोजित कराने चाहिए। ताकि यह काम उद्यमी या तो खुद या फिर किसी व्यक्ति को सस्ते में नियुक्त करके अपने आंशिक ग्राहकों के बीच भेजकर करा सके। इस छोटे से सर्वे में उद्यमी को केवल कुछ बुनियादी सवालों को ही इसका हिस्सा बनाना चाहिए। जैसे उद्यमी के आंशिक ग्राहक किन उत्पादों का उपयोग कर रहे हैं?

क्या वे इनसे संतुष्ट हैं, वे उस उत्पाद या सेवा में क्या अतिरिक्त लाभ देखना पसंद करेंगे, क्या उत्पादों को खरीदने में उनकी कीमत एक प्रमुख कारक होती है? इत्यादि प्रश्न पूछे जा सकते हैं। और यदि उद्यमी के पास बजट की कमी नहीं है तो वह किसी मार्केट रिसर्च एजेंसी को यह काम सौंप सकता है।

4. मौजूदा रिसर्च से मदद लें (Take help from Available Market Research)

यदि Market Research के लिए उद्यमी का बजट कम है तो उसे थोड़ा समय निकालकर इस बात का पता लगाने की कोशिश करनी चाहिए। की कहीं उसके उत्पाद या सेवा के जैसी पहले से कोई रिसर्च हुई तो नहीं है। इस बात का पता लगाने में समाचार पत्र के लेख, पत्रिकाएं, वेबसाइट में छपे आर्टिकल, औद्योगिक समीक्षा लेख काम आते हैं। यदि उद्यमी खर्च करने की क्षमता रखता है तो वह किसी प्रतिष्ठित संगठन से इस तरह की रिपोर्ट खरीद भी सकता है।

बिजनेस में मार्केट रिसर्च क्यों जरुरी है (Why Market Research is Important in a Business) 

किसी भी बिजनेस में Market Research को निम्नलिखित कारण महत्वपूर्ण बनाते हैं।

• इससे उद्यमी को बिजनेस से सम्बंधित आईडिया फाइनल करने में सहायता प्राप्त होती है ।
• इससे किसी भी बिजनेस को व्यवहार्यता के मापदंडों एवं विश्वसनीयता पर खरा उतारा जा सकता है। क्योंकि इस रिपोर्ट का अध्यन करने के बाद उद्यमी को संचार, मूल्य निति एवं उत्पाद सीमा निर्धारित करने में मदद मिलती है।
• बिजनेस सेटअप करने के दृष्टिकोण को ठीक से व्यवस्थित किया जा सकता है।
• Market Research के बाद उद्यमी वित्तीय भागीदारों एवं अन्य लोगों के सामने और अधिक विश्वसनीय तथ्य प्रस्तुत कर पाने में सक्षम हो पाता है।

वर्तमान में ऑनलाइन रिसर्च ने मार्केट रिसर्च में क्रांति लाने का काम कर दिया है और यह रिसर्च करने वालों एवं इसका इस्तेमाल करने वालों को अवसर एवं चुनौतियाँ दोनों प्रदान कर रहा है । इन दिनों ऑनलाइन Market Research रिसर्च गतिविधियों के लिए जानकारी या डेटा संग्रह करने का एक महत्वपूर्ण साधन बन गया है।

मार्केट रिसर्च के फायदे ( Benefits of Market Research in Hindi):

Market Research किसी कंपनी, सेवा प्रदाता, व्यक्तिगत व्यक्ति या संगठन को अपने बिजनेस के प्रति और बेहतर निर्णय लेने में सहायता प्रदान करता है। क्योंकि बिजनेस की रणनीतिक योजनायें बनाने से पहले उससे सम्बंधित जितना अधिक रिसर्च किया जाता है रणनीतिकारों या उद्यमी को उसकी उतनी गहरी जानकारी होती जाती है । उस हिसाब से वे आने वाली चुनौतियों या कठिनाइयों से लड़ने की योजना बना सकते हैं। Market Research के लाभों की लिस्ट कुछ इस प्रकार से है।

• Market Research के माध्यम से उद्यमी अपनी मार्किट एवं टारगेट कस्टमर के बारे में जानकारी प्राप्त कर पाता है जिससे वह अपनी फर्म को प्रतिस्पर्धा में आगे रहने की योजना बना सकता है। यही कारण है की यह व्यवसायों को उनकी स्थिति मजबूत करने में सहायता प्रदान करता है।
• Market Research के माध्यम से उद्यमी को मार्केट एवं ग्राहकों की आवश्यकता, पसंद, नापसंद इत्यादि के बारे में पता चल जाता है। जिसके चलते उद्यमी बिजनेस प्लानिंग भी उसी के अनुसार करता है इस प्रकार मार्केट रिसर्च से निवेश जोखिम को भी कम किया जा सकता है।
• मार्केट रिसर्च के माध्यम से उद्यमी को बिजनेस से सम्बंधित संभावित खतरों एवं अवसरों के बारे में पता चल जाता है।
• यह उद्यमी एवं उसके प्रतिस्पर्धियों की ताकत एवं कमजोरी को पहचानने में भी मदद करता है जिनका अध्यन करके उद्यमी अपने बिजनेस को बढाने की योजना बना सकता है।
• यह कंपनी, सेवा प्रदाता, बिजनेसमैन इत्यादि को बिजनेस की रणनीतिक योजना बनाने में मदद प्रदान करता है।
• Market Research से उद्यमी लोगों से नई नई बातें उसके उत्पाद या सेवा के बारे में जुटा पाता है जिससे उसे उभरती प्रवृत्तियों को प्रदर्शित करने में मदद मिलती है।
• यह व्यवसायों को प्रतिस्पर्धा में आगे बने रहने में मदद करता है।
• इससे उद्यमी को ग्राहकों की आवश्यकताओं एवं माँगो का पता चल पाता है।

यह भी पढ़ें:

• बिजनेस की मार्केटिंग करने के तरीके
• एफिलिएट मार्केटिंग क्या है कैसे शुरू करें?
• नेटवर्क मार्केटिंग क्या है? इसके फायदे एवं जुड़ने का तरीका.

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Download MBA Marketing Research Books PDF Notes Study Material: Hey There Today We talk About MBA MARKETING RESEARCH is most Important and Searching Notes In India and Pakistan and Bangladesh  MBA MARKETING RESEARCH One the Most Important Topics For All MBA MARKETING RESEARCH Students We provide All Topic Wise Notes and Study Material in PDF Download for English and Hindi Medium Students  If we talk about you can find books and PDF online.  Study Materials notes Questions Paper Sample Papers in Hindi for All MBA MARKETING RESEARCH Students Here in this post, we are sharing MBA MARKETING RESEARCH Books Notes Study materials for Exam Preparation. BBA BTech CCC CTET you have landed on your destination page.

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Marketing Research Notes, PDF, Syllabus I MBA, BBA, BCOM 2024

• Post last modified: 5 April 2022
• Reading time: 9 mins read
• Post category: MBA Study Material / BBA Study Material / BCOM Study Material

Download Marketing Research Notes , PDF, Books, Syllabus for MBA, BBA, BCOM 2024. We provide a complete marketing research pdf. Marketing Research study material includes marketing research notes, book, courses, case study, syllabus, question paper, MCQ, questions and answers and available in marketing research pdf form.

Marketing Research subject is included in MBA so students are able to download marketing research notes for MBA, BBA, BCOM 2nd year and marketing research notes for MBA, BBA, BCOM 4th semester.

Table of Content

• 1 Marketing Research Syllabus
• 2 Marketing Research Notes PDF
• 3 Marketing Research Notes
• 4 Marketing Research Questions and Answers
• 5 Marketing Research Question Paper
• 6 Marketing Research Books

Marketing Research Notes can be downloaded in marketing research pdf from the below article.

Marketing Research Syllabus

A detailed marketing research syllabus as prescribed by various Universities and colleges in India are as under. You can download the syllabus in marketing research pdf form.

Introduction to marketing Research – marketing research as a tool of Management – relevance of marketing research in the Indian Context.

Basic concepts – Scientific method – Types of Research – basic method of collection data – Secondary Data – The Marketing research process – planning the research project.

The data collection forms – attitude measurement.

Introduction to sampling – applications of sampling methods of marketing problems.

UNIT V Data collection and the field force – tabulation of collected data – analysis techniques – research report presentations.

Marketing Research Notes PDF

Marketing research notes.

Market research is defined as the process of evaluating the feasibility of a new product or service, through research conducted directly with potential consumers. This method allows organizations or businesses to discover their target market, collect and document opinions and make informed decisions.

Marketing Research Questions and Answers

If you have already studied the marketing research notes, then it’s time to move ahead and go through previous year marketing research question papers.

• What is primary data?
• What is secondary data?
• What are the classical methods of collecting primary data?
• Mention some important sources of economic data.
• Distinguish between primary and secondary data.
• Distinguish between cross section and time-series data.
• Distinguish between qualitative and quantitative data
• What is a sampling frame?
• What is a complete enumeration?
• Define sampling.
• Write a note on simple random sampling
• Write a note on stratified random sampling.
• Write a note on systematic sampling.
• Write a note on multi-stage sampling.
• Write a note on sequential sampling.

Marketing Research Question Paper

If you have already studied the marketing research notes, then it’s time to move ahead and go through previous year marketing research question paper.

It will help you to understand the question paper pattern and type of marketing research question and answer asked in MBA 2nd year marketing research exam. You can download the syllabus in marketing research pdf form.

Marketing Research Books

Below is the list of marketing research books recommended by the top university in India.

• Boyd, Harper W. Jr., Westfall, Ralph and Stasch, Stanley, Marketing Research: Text and Cases, Richard D. Irwin Inc., Homewood, Illinois.
• Green, P. E. and Tull, D. S., Research for Marketing Decisions, 5th edition, Prentice-Hall of India, New Delhi.
• Luck D. J., Wales, H.G., Taylor, D. A. and Rubin R. S., Marketing Research, 7th Edition, Prentice-Hall of India, New Delhi.
• Tull, D. S. and Hawkins D. I., Marketing Research : Measurement and Method, 6th Edition, Prentice-Hall of India, New Delhi.

In the above article, a student can download marketing research notes for MBA, BBA, BCOM 2nd year and marketing research notes for MBA, BBA, BCOM 4th semester. Marketing Research study material includes marketing research notes, marketing research books, marketing research syllabus, marketing research question paper, marketing research case study, marketing research questions and answers, marketing research courses in marketing research pdf form.

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Rural Marketing PDF

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Table of contents.

Cover About the Author Brief Contents Table of Contents Foreword Preface Chapter 1: The Call of Rural India Defining Rural Markets Rural Myths Rapid Urbanization Rural India Is an Agrarian Economy The Rural Marketing Mix: Challenges Affordability Availability Awarenes Acceptability The Evolving Rural Consumer Rural India: The Exploding Middle Class Rural Marketing Snapshot: Changing Rural Settlements Rural Marketing Memo: Startling Facts about Rural India Review of Objectives Discussion and Application Rural Marketing Case: Rural Markets Chapter 2: The Rural Marketing Environment The Evolution of Rural Marketing Phase I (Prior to the 1960s) Phase II (1960s–1980s) Phase III (1990s–2000) Phase IV (After 2000) The Rural Environment The Demographic Environment The Physical Environment The social and Cultural Environment The Political Environment The Technological Environment The Rural Economic Environment The Changing Face of Rural Development The Rural Economic Structure The Farm Sector: Agriculture and Allied Activities The Non-farm Sector and Rural Industries Changing Migration Trends Incomes and Expenditure The Rural Infrastructure Road Connectivity Electrifi cation Rural housing Telecommunications Drinking Water Rural Marketing Snapshot: Rural Infrastructure Rural Marketing Insight: Road Connectivity Bringing Rural Economy on Track in Bihar Rural Employment Generation Programmes: Government Initiatives The Mahatma Gandhi National Rural Employment Guarantee Act (MNREGA) The Swarnjayanti Gram Swarozgar Yojna (SGSY) The Pradhan Mantri Rojgar Yojna Review of Objectives Discussion and Application Rural Marketing Case: MNREGA Chapter 3: Rural Consumer Behaviour The Consumer Buying Behaviour Model What Influences Consumer Behaviour? Cultural Factors Social Factors Personal Factors Psychological Factors Rural Marketing Memo: Reference Groups in Rural The Buyer Decision Process Need Recognition Information Search The Evaluation of Alternatives The Purchase Decision Post-purchase Behaviour Rural Marketing Snapshot: Key Opinion Leaders The Product Adoption Process Rural Marketing Insight: Aircel Diffusion of Innovation HPCL Rasoi Ghar Review of Objectives Discussion and Application Rural Marketing Case: HPCL Rasoi Ghar Appendix: The New SEC System Chapter 4: Rural Marketing Research The Rural Marketing Research Process Defining the Objectives Determining the Research Budget Designing the Research Sampling Designing the Research Instrument Organizing the Field and Collecting the Data Collating and Analysing the Data Reporting the Findings Special Tools Used in Rural Marketing Research Participatory Rural Appraisals Scaling Tools for Rural Quantitative Research Rural Marketing Insight: New Age Innovation in Rural Research Field Procedures and Rural Realities Dos and Don’ts in Rural Marketing Research Limitations of Rural Research Rural Marketing Snapshot: Data Collection in Rural India Rural Marketing Memo: Attributes of a Rural Researcher The Rural Research Business Review of Objectives Discussion and Application Rural Marketing Case: Participatory Rural Appraisal Chapter 5: Segmenting and Targeting Rural Markets Segmentation Heterogeneity in Rural Markets Pre-requisites for Effective Segmentation Degrees of Segmentation Bases for Segmenting Rural Consumer Markets Rural Marketing Insight: MART Market Attractiveness Score: Urban and Rural ‘Prosperity Plus’ Index Targeting Evaluation of Segments Selection of Segments Coverage of Segments Choosing a Coverage Strategy Positioning Identifying the Positioning Concept Selecting the Positioning Concept Developing the Concept Communicating the Concept Review of Objectives Discussion and Aplication Rural Marketing Case: Bru Instant Coffee Connect Chapter 6: Product Strategy The Product Concept and the Classification of Rural Products The Product Concept Rural Product Classification Product decisions and Strategies Individual Product Decisions Product Line and Mix Decisions Product Lifecycle Strategies Rural Marketing Snapshot: Product Acceptability Rural Marketing Insight: Product Designing for Rural Needs—Godrej Chotu Kool Product Branding in Rural Markets Building Brands in Rural Markets Brand Loyalty Versus Brand Stickiness Fake Brands Rural Marketing Insight: Dealing with Piracy Packaging for Rural Markets Packaging Material Pack size and Convenience Packaging Aesthetics Rural Marketing Insight: The Sachet Revolution Rural Marketing Insight: After-sales Service Initiatives by Companies in Rural Markets Product Warranty and After-sales Service New Product Development in Rural Markets Review of Objectives Discussion and Aplication Rural Marketing Case: New Product Development: Improved Biomass Stove by Shell Foundation Chapter 7: Pricing Strategies Pricing in Rural India How Do Companies Price? Consumer Psychology and Pricing Rural Marketing Insight: Rural Consumers Prefer Value for Money Setting the Price for Rural Products and Services Internal Factors External Factors Price Setting Strategies Market Entry Strategies Product Mix Pricing Strategies Price Adjustment Strategies Rural-specific Pricing Strategies Rural Marketing Insight: Caution for Marketers Giving Discounts to Rural Consumers Rural Marketing Snapshot: Rising Consumption of Branded Products Review of Objectives Discusion and Application Rural Marketing Case: Coca-Cola’s Strategy for Rural Markets Chapter 8: Distribution in Rural Markets Availability: The Challenge and the Dilemma Distribution Channels The Evolution of Rural Distribution Channels Channel Dynamics Rural Channel Members The Rural Retail Environment Traditional Retail The Emergence of Modern Retail in Rural Areas Rural Marketing Snapshot: The Changing Face of Rural Retail Channel Behaviour in Rural Areas Sourcing of Stocks and Purchase Cycles Stocking Behaviour and Seasonality Credit Patterns Transfer of Capital Pricing by Channel Channel Promotion Retailer–Consumer Dynamics Distribution Models in Rural Markets The Distribution Models for FMCGs The Distribution Model for Durables Rural-centric Distribution Models Haats/Shandies Modern Distribution Models Vans The Public Distribution System Cooperative Societies Petrol Pumps and Extension Counters Non-government Organizations Rural Mobile Traders: Last Mile Distribution Rural Marketing Insight: Project Shakti Rural Marketing Insight: Gaon Chalo: A Tata Tea Initiative Rural Logistics The Hub and Spoke System Syndicated Distribution Rural Marketing Insight: Syndicated Distribution for Rural Dominance Review of Objectives Discussion and Application Rural Marketing Case: Colgate DISHA: The Evolution of a Sustainable PPP Model in the BoP Market Chapter 9: Communication Strategies for Rural Markets Challenges in Rural Communication Heterogeneity and spread Low Literacy and varying Comprehension Abilities Different media reach and Habits The Communication Process: An Overview Rural Marketing Insight: Rural and Urban Responses to Television Advertisements Developing Effective Rural Communication Identifying and Profiling the Target Audience Determining Communication Objectives Designing the Message Selecting the Communication Channels Budgeting for Rural Communication Designing the Communication Mix Strategy Companies Face the Task of Distributing the Total Communication Measuring the Impact of Communication Rural Marketing Insight: Religion: A Key Profiling Factor for Designing Communication Rural Marketing Memo: Developing an Effective Rural Communication Message Creating Advertisements for Rural Audiences Rural Marketing Insight: Hitting the Bull’s Eye Rural Media Conventional Media Rural-centric Non-conventional Media Rural Marketing Insight: Increasing Role of DTH in Rural India Rural Marketing Snapshot: Changing Means of Rural Communication Rural Marketing Insight: Clutter-free Communication at the Mela Sales Promotion and Events and Experiences Sales Promotion Events and Experiences Review of Objectives Discussion and Application Rural Marketing Case: Tata Shaktee GC Sheets Chapter 10: Rural Services Marketing Telecommunications in Rural India Reuters Market Light IFFCO Kisaan Sanchar Limited Nokia Life Tools Rural Marketing Snapshot: The Telecom Revolution in Rural india Rural Marketing Insight: M-VAS Initiatives in Rural India Information and Communications Technology (ICT) in Rural Areas ITC’s e-choupal Common Service Centres n-Logue Village Internet Kiosks Drishtee The Relevance of ICT Services in Rural India Financial Services in Rural India Banking Services Microfinance and Credit Services Insurance Life Insurance Rural Marketing Insight: Cloud Computing for Rural Banking Rural Healthcare Services An Overview of Rural Healthcare in India The Challenges in Rural Healthcare The Healthcare Infrastructure The Healthcare Market Government Initiatives in Rural India Growth Drivers of Rural Healthcare Review of Objectives Discussion and Application Rural Marketing Insight: Arogya Parivar: A Rural Health-care Delivery Business Model Chapter 11: Marketing in Small Towns Small Towns: A Definition The Potential of Small Towns Rural Marketing Snapshot: The Changing Face of Small-town India Small-town Consumer Behaviour Rural Marketing Insight: The Dhoni Effect Rural Marketing Insight: Consumer Behaviour in Small Towns of India The Strategic Importance of Small Towns for Rural Marketers As Selling and Redistribution Centres As Servicing Centres As a Hub for Availing Services As the Agricultural Linkage As a Place for Leisure and Entertainment Review of Objectives Discusion and Application Rural Marketing Case: Philips Lighting: Bulb ka Badshah Chapter 12: Role of Government in Rural India Why Is Government’s Intervention Important for Rural Development? What is the Government doing? Rural Infrastructure Connecting the Rural Housing in Rural Electrifying the Rural Houses Urbanizing the Rural e-Governance in Rural India Education Health Skill Development Employment National Rural Employment Guarantee Act (NREGA) Financial Inclusion Sanitation Agriculture Conclusion Review of Objectives Discussion and Application Rural Marketing Case: Rural Markets Chapter 13: The Future of Rural Marketing The Rural Boom The Way Forward Forward Innovation A New Price–Performance Paradigm Innovative Rural Distribution Inclusive Marketing Dedicated Rural Teams Rural Dividend New Opportunities Rural Marketing Memo: Some Startling Facts Review of Objectives Discussion and Application Rural Marketing Case: 3G Video Telephony Endnotes Photo Credits Index

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Best Share Market Books In Hindi PDF | Free Download

Best Share Market Books In Hindi PDF | Trading Books in hindi pdf | Share Market – a to z pdf free download in hindi

दोस्तों आज हम आपको Share Market Books in Hindi PDF के बारे में जानकारी देंगे और इस PDF को आप डाउनलोड भी कर सकेंगे दोस्तों जैसा कि मैंने पहले आपको Best Share Market Books In Hindi के बारे में जानकारी दी है लेकिन अब हम आपको इन बुक्स की पीडीएफ के बारे में जानकारी देंगे और मनी सेविंग हिंदी साइट के माध्यम से आपको Best Share Market Books in Hindi PDF को Free में डाउनलोड करने के लिए शेयर मार्किट की महत्वपूर्ण बुक्स उपलब्ध की गई है और आप इस पीडीएफ को डाउनलोड भी कर सकेंगे आइए जानते हैं कैसे! और इसे आर्टिकल को आप अंत तक जरूर पढ़े 

Share Market Books in Hindi PDF Free Download

• 1.1 1. शेयर मार्केट में मुनाफे के मंत्र
• 1.2 2. द इंटेलीजेंट इन्वेस्टर 
• 1.3 3. शेयर मार्केट के सक्सेस मंत्र
• 1.4 4. ट्रेडनीति : कैसे बने सफल प्रोफेशनल ट्रेडर
• 1.5 5. शेयर बाज़ार में सफल कैसे हों
• 1.6 6. रिच डैड गाइड टू इन्वेस्टिंग

1. शेयर मार्केट में मुनाफे के मंत्र

इस बुक को शेयर मार्केट की विशेषज्ञ सुधा श्रीमाली द्वारा लिखा गया है, और इस बुक में उन्होंने शेयर बाजार की बारीकियों को समझाने का एक सफल प्रयास किया है। और इस बुक में शेयर मार्केट के व्यावहारिक और उपयोगी ज्ञान के साथ निवेशकों को एक महत्वपूर्ण जानकारी प्राप्त होगी, जिससे वे शेयर मार्केट में सफलता प्राप्त कर सकेंगे।

इस बुक में आपको शेयर मार्केट से जुड़ी सभी पहलुओं के बारे में जानकारी मिलेगी और आपको इस बुक में यह भी जानने को मिलेगा कि आप शेयर मार्केट से अपने मुनाफे को कैसे बढ़ा सकते हैं और इस बुक की पीडीएफ डाउनलोड करने के लिए नीचे डाउनलोड बटन पर क्लिक करें

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2. द इंटेलीजेंट इन्वेस्टर 

“द इंटेलिजेंट इंवेस्टर” एक महत्वपूर्ण वित्तीय बुक है जिसे महान निवेशक बेंजामिन ग्राहम ने लिखा है। इस बुक में शेयर मार्केट के बारे में बेहद मूलभूत जानकारी दी गई है, और यह अब तक की सबसे बेहतरीन शेयर मार्केट सिखने की पुस्तकों में से एक मानी गई है। यह पुस्तक शेयर मार्केट की बाइबिल के रूप में जानी जाती है, जिसमें एक श्रेष्ठ निवेशक बनने के लिए महत्वपूर्ण और उपयोगी साबित होगी

Share Market book in Hindi Pdf free download : आप इस बुक पीडीएफ में शेयर मार्केट से जुड़ी सभी चीजों को एकदम बारीकी से जान पाएंगे और यह शेयर मार्केट लर्निंग के लिए बेस्ट बुक मानी गई है और इस बुक पीडीएफ  में शेयर मार्केट के संपूर्ण जानकारी और फाइनेंस विश्लेषण से जुडी जानकारी भी प्राप्त कर सकेंगे और इस बुक को शेयर मार्केट की बाइबिल के रूप में जानी जाती है इस बुक की पीडीएफ डाउनलोड करने के लिए नीचे डाउनलोड बटन पर क्लिक करें

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3. शेयर मार्केट के सक्सेस मंत्र

इस बुक को सौरभ मुखर्जी द्वारा लिखी गई है और यदि आप शेयर मार्केट में सफल होना चाहते हैं तो यह बुक आपके लिए सर्वोत्तम विकल्प हो सकती है क्योंकि यह बुक आसानी से पढ़ी जा सकने वाली बुक है यानि की नए इन्वेस्ट भी इस बुक के द्वारा शेयर मार्केट से जुड़ी चीजों को आसानी पढ व समझ सकेंगे इसे बुक के बारे आप सम्पूर्ण जानकारी प्राप्त कर पाए इसलिए हमने आपके के लिए इस बुक की पीडीएफ उपलब्ध की है और पीडीएफ डाउनलोड करने के लिए नीचे डाउनलोड बटन पर क्लिक करें

4. ट्रेडनीति : कैसे बने सफल प्रोफेशनल ट्रेडर

“ट्रेडनीति – कैसे बने सफल प्रोफेशनल ट्रेडर” जो Best Trading Books in hindi pdf भी है जिसे भारतीय लेखक युवराज कलशेट्टी ने लिखा है। इस बुक में शेयर बाजार से जुड़ी सभी महत्वपूर्ण जानकारी सरल और सुगम भाषा में प्रस्तुत की गई है, जो आपको ट्रेडिंग के क्षेत्र में सफलता प्राप्त करने के लिए मार्गदर्शन प्रदान करेगी

इस बुक की खास बात यह भी है कि यह एक ही स्त्रोत में आपको ट्रेडिंग से संबंधित सभी महत्वपूर्ण जानकारी प्रदान करती है, जो ट्रेडर बनने के लिए आवश्यक है। इस बुक में अनुभवी ट्रेडिंग विशेषज्ञों के अनुभव और सुझाव हैं जो आपको प्रोफेशनल ट्रेडर बनने के लिए आवश्यक ज्ञान और कौशल प्रदान करेगी

इस बुक में आपको शेयर बाजार, ट्रेडिंग स्ट्रैटेजीज, रिस्क मैनेजमेंट, और निवेश के मूल सिद्धांतों के बारे में अच्छी जानकारी मिलेगी, जो नए और अनुभवी ट्रेडरों को उनके लक्ष्यों की प्राप्ति में मदद करेगी। इस बुक की मदद से आप शेयर मार्केट में अच्छे व सफल ट्रेडर बन सकते हैं। जो ट्रेडिंग के क्षेत्र में सफलता पाने के लिए उपयोगी हो सकती है, चाहे आप नए हों या प्रोफेशनल ट्रेडर बनने का सपना देख रहे हों। और नीचे इस बुक की पीडीएफ की लिंक दी गयी जिसे आप डाउनलोड कर सकते हो

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Note : दोस्तों अभी ट्रेड नीति कैसे बने सफल प्रोफेशनल ट्रेडर की पीडीएफ बुक उपलब्ध नहीं है और जैसे ही हमे उपलब्ध होगी मैं इस पेज पर अपडेट कर दूंगा

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5. शेयर बाज़ार में सफल कैसे हों

दोस्तों इस बुक के लेखक महेंद्र चंद्र कौशिक जी है और आपको इस बुक में शेयर मार्केट से जुडी वो सभी महत्वपूर्ण बातों को समझने में आपकी मदद करेगी जिसे अच्छे से समझ कर आप एक सफल ट्रेडर बन सकते हैं इस बुक में आपको शेयर मार्केट के एनालिसिस और शेयर के विश्लेषण के बारे में पढ़ने को भी मिलेगा और आप नीचे डाउनलोड बटन पर क्लिक करें पीडीएफ डाउनलोड कर सकते है 

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6. रिच डैड गाइड टू इन्वेस्टिंग

Share Market Books In Hindi PDF : दोस्तों इस बुक को रॉबर्ट कियोसकी द्वारा लिखा गया है और रॉबर्ट कियोसकी एक ऐसे व्यक्ति हैं जो अपनी इन्वेस्टिंग के स्ट्रेटजी के दम पर ना सिर्फ अपनी जिंदगी बल्कि लाखों लोगों की जिंदगी में भी बदलाव लाये है और वह एक महान इन्वेस्टर व बिजनेस कोच भी रह चुके हैं और इस बुक में आपको इन्वेस्टिंग करने के बेहतरीन टिप्स और आप अपना रिच माइंडसेट कैसे बना सकते हैं और यह भी जाने को मिलेगा कि अमीर कैसे बना जा सकता है और इस बुक से शेयर मार्केट समझ में पहले बेहतरी होगी इस बुक की पीडीएफ डाउनलोड करने के लिए नीचे डाउनलोड बटन पर क्लिक करें

इसे भी पढ़े : Rich dad Poor dad in hindi pdf

निष्कर्ष – हम आशा करते हैं कि आपको शेयर मार्केट बुक इन हिंदी pdf के बारे में यह लेख पसंद आया होगा। इस लेख में शेयर मार्केट की मुख्य बुक्स का उल्लेख किया गया है, जो अच्छे लेखकों द्वारा लिखी गई हैं। इन बुक्स pdf को पढ़ने के बाद, आपकी शेयर मार्केट की समझ काफी हद तक आ जाएगी। इस लेख से जुड़ा हुआ कोई भी सवाल हो तो आप कमेंट बॉक्स के माध्यम से हमें संदेश भेज सकते हैं, हम आपके सवाल का उत्तर देने का पूरा प्रयास करेंगे। धन्यवाद!

NOTE – दोस्तों मनी सेविंग हिंदी साइट पर आपको सिर्फ शेयर मार्केट की महत्वपूर्ण बुक्स की pdf के बारे में बताया गया है वो भी सिर्फ एजुकेशनल परपज के लिए उपलब्ध की गयी है जिससे आपको Share Market Books In Hindi PDF को आसानी से डाउनलोड कर पढ सके और हाँ आप किसी भी शेयर को खरीदने या इन्वेस्ट करने से पहले शेयर बाजार के बारे में अच्छे से जानकारी प्राप्त कर ले या फिर आप किसी एक्सपर्ट की राय जरूर ले

18 thoughts on “Best Share Market Books In Hindi PDF | Free Download”

Jatin Kushwah

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A generative AI reset: Rewiring to turn potential into value in 2024

It’s time for a generative AI (gen AI) reset. The initial enthusiasm and flurry of activity in 2023 is giving way to second thoughts and recalibrations as companies realize that capturing gen AI’s enormous potential value is harder than expected .

With 2024 shaping up to be the year for gen AI to prove its value, companies should keep in mind the hard lessons learned with digital and AI transformations: competitive advantage comes from building organizational and technological capabilities to broadly innovate, deploy, and improve solutions at scale—in effect, rewiring the business  for distributed digital and AI innovation.

About QuantumBlack, AI by McKinsey

QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe.

Companies looking to score early wins with gen AI should move quickly. But those hoping that gen AI offers a shortcut past the tough—and necessary—organizational surgery are likely to meet with disappointing results. Launching pilots is (relatively) easy; getting pilots to scale and create meaningful value is hard because they require a broad set of changes to the way work actually gets done.

Let’s briefly look at what this has meant for one Pacific region telecommunications company. The company hired a chief data and AI officer with a mandate to “enable the organization to create value with data and AI.” The chief data and AI officer worked with the business to develop the strategic vision and implement the road map for the use cases. After a scan of domains (that is, customer journeys or functions) and use case opportunities across the enterprise, leadership prioritized the home-servicing/maintenance domain to pilot and then scale as part of a larger sequencing of initiatives. They targeted, in particular, the development of a gen AI tool to help dispatchers and service operators better predict the types of calls and parts needed when servicing homes.

Leadership put in place cross-functional product teams with shared objectives and incentives to build the gen AI tool. As part of an effort to upskill the entire enterprise to better work with data and gen AI tools, they also set up a data and AI academy, which the dispatchers and service operators enrolled in as part of their training. To provide the technology and data underpinnings for gen AI, the chief data and AI officer also selected a large language model (LLM) and cloud provider that could meet the needs of the domain as well as serve other parts of the enterprise. The chief data and AI officer also oversaw the implementation of a data architecture so that the clean and reliable data (including service histories and inventory databases) needed to build the gen AI tool could be delivered quickly and responsibly.

Our book Rewired: The McKinsey Guide to Outcompeting in the Age of Digital and AI (Wiley, June 2023) provides a detailed manual on the six capabilities needed to deliver the kind of broad change that harnesses digital and AI technology. In this article, we will explore how to extend each of those capabilities to implement a successful gen AI program at scale. While recognizing that these are still early days and that there is much more to learn, our experience has shown that breaking open the gen AI opportunity requires companies to rewire how they work in the following ways.

Figure out where gen AI copilots can give you a real competitive advantage

The broad excitement around gen AI and its relative ease of use has led to a burst of experimentation across organizations. Most of these initiatives, however, won’t generate a competitive advantage. One bank, for example, bought tens of thousands of GitHub Copilot licenses, but since it didn’t have a clear sense of how to work with the technology, progress was slow. Another unfocused effort we often see is when companies move to incorporate gen AI into their customer service capabilities. Customer service is a commodity capability, not part of the core business, for most companies. While gen AI might help with productivity in such cases, it won’t create a competitive advantage.

To create competitive advantage, companies should first understand the difference between being a “taker” (a user of available tools, often via APIs and subscription services), a “shaper” (an integrator of available models with proprietary data), and a “maker” (a builder of LLMs). For now, the maker approach is too expensive for most companies, so the sweet spot for businesses is implementing a taker model for productivity improvements while building shaper applications for competitive advantage.

Much of gen AI’s near-term value is closely tied to its ability to help people do their current jobs better. In this way, gen AI tools act as copilots that work side by side with an employee, creating an initial block of code that a developer can adapt, for example, or drafting a requisition order for a new part that a maintenance worker in the field can review and submit (see sidebar “Copilot examples across three generative AI archetypes”). This means companies should be focusing on where copilot technology can have the biggest impact on their priority programs.

Copilot examples across three generative AI archetypes

• “Taker” copilots help real estate customers sift through property options and find the most promising one, write code for a developer, and summarize investor transcripts.
• “Shaper” copilots provide recommendations to sales reps for upselling customers by connecting generative AI tools to customer relationship management systems, financial systems, and customer behavior histories; create virtual assistants to personalize treatments for patients; and recommend solutions for maintenance workers based on historical data.
• “Maker” copilots are foundation models that lab scientists at pharmaceutical companies can use to find and test new and better drugs more quickly.

Some industrial companies, for example, have identified maintenance as a critical domain for their business. Reviewing maintenance reports and spending time with workers on the front lines can help determine where a gen AI copilot could make a big difference, such as in identifying issues with equipment failures quickly and early on. A gen AI copilot can also help identify root causes of truck breakdowns and recommend resolutions much more quickly than usual, as well as act as an ongoing source for best practices or standard operating procedures.

The challenge with copilots is figuring out how to generate revenue from increased productivity. In the case of customer service centers, for example, companies can stop recruiting new agents and use attrition to potentially achieve real financial gains. Defining the plans for how to generate revenue from the increased productivity up front, therefore, is crucial to capturing the value.

Upskill the talent you have but be clear about the gen-AI-specific skills you need

By now, most companies have a decent understanding of the technical gen AI skills they need, such as model fine-tuning, vector database administration, prompt engineering, and context engineering. In many cases, these are skills that you can train your existing workforce to develop. Those with existing AI and machine learning (ML) capabilities have a strong head start. Data engineers, for example, can learn multimodal processing and vector database management, MLOps (ML operations) engineers can extend their skills to LLMOps (LLM operations), and data scientists can develop prompt engineering, bias detection, and fine-tuning skills.

A sample of new generative AI skills needed

The following are examples of new skills needed for the successful deployment of generative AI tools:

• data scientist:
• prompt engineering
• in-context learning
• bias detection
• pattern identification
• reinforcement learning from human feedback
• hyperparameter/large language model fine-tuning; transfer learning
• data engineer:
• data wrangling and data warehousing
• data pipeline construction
• multimodal processing
• vector database management

The learning process can take two to three months to get to a decent level of competence because of the complexities in learning what various LLMs can and can’t do and how best to use them. The coders need to gain experience building software, testing, and validating answers, for example. It took one financial-services company three months to train its best data scientists to a high level of competence. While courses and documentation are available—many LLM providers have boot camps for developers—we have found that the most effective way to build capabilities at scale is through apprenticeship, training people to then train others, and building communities of practitioners. Rotating experts through teams to train others, scheduling regular sessions for people to share learnings, and hosting biweekly documentation review sessions are practices that have proven successful in building communities of practitioners (see sidebar “A sample of new generative AI skills needed”).

It’s important to bear in mind that successful gen AI skills are about more than coding proficiency. Our experience in developing our own gen AI platform, Lilli , showed us that the best gen AI technical talent has design skills to uncover where to focus solutions, contextual understanding to ensure the most relevant and high-quality answers are generated, collaboration skills to work well with knowledge experts (to test and validate answers and develop an appropriate curation approach), strong forensic skills to figure out causes of breakdowns (is the issue the data, the interpretation of the user’s intent, the quality of metadata on embeddings, or something else?), and anticipation skills to conceive of and plan for possible outcomes and to put the right kind of tracking into their code. A pure coder who doesn’t intrinsically have these skills may not be as useful a team member.

While current upskilling is largely based on a “learn on the job” approach, we see a rapid market emerging for people who have learned these skills over the past year. That skill growth is moving quickly. GitHub reported that developers were working on gen AI projects “in big numbers,” and that 65,000 public gen AI projects were created on its platform in 2023—a jump of almost 250 percent over the previous year. If your company is just starting its gen AI journey, you could consider hiring two or three senior engineers who have built a gen AI shaper product for their companies. This could greatly accelerate your efforts.

Form a centralized team to establish standards that enable responsible scaling

To ensure that all parts of the business can scale gen AI capabilities, centralizing competencies is a natural first move. The critical focus for this central team will be to develop and put in place protocols and standards to support scale, ensuring that teams can access models while also minimizing risk and containing costs. The team’s work could include, for example, procuring models and prescribing ways to access them, developing standards for data readiness, setting up approved prompt libraries, and allocating resources.

While developing Lilli, our team had its mind on scale when it created an open plug-in architecture and setting standards for how APIs should function and be built.  They developed standardized tooling and infrastructure where teams could securely experiment and access a GPT LLM , a gateway with preapproved APIs that teams could access, and a self-serve developer portal. Our goal is that this approach, over time, can help shift “Lilli as a product” (that a handful of teams use to build specific solutions) to “Lilli as a platform” (that teams across the enterprise can access to build other products).

For teams developing gen AI solutions, squad composition will be similar to AI teams but with data engineers and data scientists with gen AI experience and more contributors from risk management, compliance, and legal functions. The general idea of staffing squads with resources that are federated from the different expertise areas will not change, but the skill composition of a gen-AI-intensive squad will.

Set up the technology architecture to scale

Building a gen AI model is often relatively straightforward, but making it fully operational at scale is a different matter entirely. We’ve seen engineers build a basic chatbot in a week, but releasing a stable, accurate, and compliant version that scales can take four months. That’s why, our experience shows, the actual model costs may be less than 10 to 15 percent of the total costs of the solution.

Building for scale doesn’t mean building a new technology architecture. But it does mean focusing on a few core decisions that simplify and speed up processes without breaking the bank. Three such decisions stand out:

• Focus on reusing your technology. Reusing code can increase the development speed of gen AI use cases by 30 to 50 percent. One good approach is simply creating a source for approved tools, code, and components. A financial-services company, for example, created a library of production-grade tools, which had been approved by both the security and legal teams, and made them available in a library for teams to use. More important is taking the time to identify and build those capabilities that are common across the most priority use cases. The same financial-services company, for example, identified three components that could be reused for more than 100 identified use cases. By building those first, they were able to generate a significant portion of the code base for all the identified use cases—essentially giving every application a big head start.
• Focus the architecture on enabling efficient connections between gen AI models and internal systems. For gen AI models to work effectively in the shaper archetype, they need access to a business’s data and applications. Advances in integration and orchestration frameworks have significantly reduced the effort required to make those connections. But laying out what those integrations are and how to enable them is critical to ensure these models work efficiently and to avoid the complexity that creates technical debt  (the “tax” a company pays in terms of time and resources needed to redress existing technology issues). Chief information officers and chief technology officers can define reference architectures and integration standards for their organizations. Key elements should include a model hub, which contains trained and approved models that can be provisioned on demand; standard APIs that act as bridges connecting gen AI models to applications or data; and context management and caching, which speed up processing by providing models with relevant information from enterprise data sources.
• Build up your testing and quality assurance capabilities. Our own experience building Lilli taught us to prioritize testing over development. Our team invested in not only developing testing protocols for each stage of development but also aligning the entire team so that, for example, it was clear who specifically needed to sign off on each stage of the process. This slowed down initial development but sped up the overall delivery pace and quality by cutting back on errors and the time needed to fix mistakes.

Ensure data quality and focus on unstructured data to fuel your models

The ability of a business to generate and scale value from gen AI models will depend on how well it takes advantage of its own data. As with technology, targeted upgrades to existing data architecture  are needed to maximize the future strategic benefits of gen AI:

• Be targeted in ramping up your data quality and data augmentation efforts. While data quality has always been an important issue, the scale and scope of data that gen AI models can use—especially unstructured data—has made this issue much more consequential. For this reason, it’s critical to get the data foundations right, from clarifying decision rights to defining clear data processes to establishing taxonomies so models can access the data they need. The companies that do this well tie their data quality and augmentation efforts to the specific AI/gen AI application and use case—you don’t need this data foundation to extend to every corner of the enterprise. This could mean, for example, developing a new data repository for all equipment specifications and reported issues to better support maintenance copilot applications.
• Understand what value is locked into your unstructured data. Most organizations have traditionally focused their data efforts on structured data (values that can be organized in tables, such as prices and features). But the real value from LLMs comes from their ability to work with unstructured data (for example, PowerPoint slides, videos, and text). Companies can map out which unstructured data sources are most valuable and establish metadata tagging standards so models can process the data and teams can find what they need (tagging is particularly important to help companies remove data from models as well, if necessary). Be creative in thinking about data opportunities. Some companies, for example, are interviewing senior employees as they retire and feeding that captured institutional knowledge into an LLM to help improve their copilot performance.
• Optimize to lower costs at scale. There is often as much as a tenfold difference between what companies pay for data and what they could be paying if they optimized their data infrastructure and underlying costs. This issue often stems from companies scaling their proofs of concept without optimizing their data approach. Two costs generally stand out. One is storage costs arising from companies uploading terabytes of data into the cloud and wanting that data available 24/7. In practice, companies rarely need more than 10 percent of their data to have that level of availability, and accessing the rest over a 24- or 48-hour period is a much cheaper option. The other costs relate to computation with models that require on-call access to thousands of processors to run. This is especially the case when companies are building their own models (the maker archetype) but also when they are using pretrained models and running them with their own data and use cases (the shaper archetype). Companies could take a close look at how they can optimize computation costs on cloud platforms—for instance, putting some models in a queue to run when processors aren’t being used (such as when Americans go to bed and consumption of computing services like Netflix decreases) is a much cheaper option.

Build trust and reusability to drive adoption and scale

Because many people have concerns about gen AI, the bar on explaining how these tools work is much higher than for most solutions. People who use the tools want to know how they work, not just what they do. So it’s important to invest extra time and money to build trust by ensuring model accuracy and making it easy to check answers.

One insurance company, for example, created a gen AI tool to help manage claims. As part of the tool, it listed all the guardrails that had been put in place, and for each answer provided a link to the sentence or page of the relevant policy documents. The company also used an LLM to generate many variations of the same question to ensure answer consistency. These steps, among others, were critical to helping end users build trust in the tool.

Part of the training for maintenance teams using a gen AI tool should be to help them understand the limitations of models and how best to get the right answers. That includes teaching workers strategies to get to the best answer as fast as possible by starting with broad questions then narrowing them down. This provides the model with more context, and it also helps remove any bias of the people who might think they know the answer already. Having model interfaces that look and feel the same as existing tools also helps users feel less pressured to learn something new each time a new application is introduced.

Getting to scale means that businesses will need to stop building one-off solutions that are hard to use for other similar use cases. One global energy and materials company, for example, has established ease of reuse as a key requirement for all gen AI models, and has found in early iterations that 50 to 60 percent of its components can be reused. This means setting standards for developing gen AI assets (for example, prompts and context) that can be easily reused for other cases.

While many of the risk issues relating to gen AI are evolutions of discussions that were already brewing—for instance, data privacy, security, bias risk, job displacement, and intellectual property protection—gen AI has greatly expanded that risk landscape. Just 21 percent of companies reporting AI adoption say they have established policies governing employees’ use of gen AI technologies.

Similarly, a set of tests for AI/gen AI solutions should be established to demonstrate that data privacy, debiasing, and intellectual property protection are respected. Some organizations, in fact, are proposing to release models accompanied with documentation that details their performance characteristics. Documenting your decisions and rationales can be particularly helpful in conversations with regulators.

In some ways, this article is premature—so much is changing that we’ll likely have a profoundly different understanding of gen AI and its capabilities in a year’s time. But the core truths of finding value and driving change will still apply. How well companies have learned those lessons may largely determine how successful they’ll be in capturing that value.

The authors wish to thank Michael Chui, Juan Couto, Ben Ellencweig, Josh Gartner, Bryce Hall, Holger Harreis, Phil Hudelson, Suzana Iacob, Sid Kamath, Neerav Kingsland, Kitti Lakner, Robert Levin, Matej Macak, Lapo Mori, Alex Peluffo, Aldo Rosales, Erik Roth, Abdul Wahab Shaikh, and Stephen Xu for their contributions to this article.

This article was edited by Barr Seitz, an editorial director in the New York office.

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