The Predictive Dialer Software Market size was USD 2.6 billion in 2022 and is expected to Reach USD 32.6 billion by 2030 and grow at a CAGR of 37.2 % over the forecast period of 2023-2030.
The market is going to grow as a result of consumers' rising preference for telemarketing as a way to communicate with businesses in real-time and increase customer satisfaction. A common automated dialing technology used in contact centers is predictive dialer software, which makes outbound calls in sequence from a list of phone numbers. The program may be able to identify busy signals, disconnected numbers, voicemails, and unanswered numbers. It will then connect just those calls to the available contact center employee. By doing away with manual dialing, the predictive dialer software connects contact center personnel to calls only after they have been answered. It guarantees a smooth changeover between customer conversations and produces a constant stream of calls with the least amount of downtime. The Software is flexible enough to be customized for use by businesses of all sizes. The software can be used for various applications, including debt collection, sales and telemarketing, professional services in BPO, and multiple outbound campaigns. With a revenue share of more than 68.0% in 2020, the software category dominated the market. The program can help agents increase the client calling rate by efficiently ignoring busy signals and phony phone numbers. The shorter average call duration provided by the software aids agents in improving the customer experience. As a result, companies frequently use predictive dialer software to cut down on idle time. The demand for mobile solutions is increasing, and vendors are offering predictive dialer software that can be used on mobile devices. This trend is expected to continue as more businesses adopt mobile-first strategies. Predictive dialer software is being used to collect and analyse data to gain insights into customer behaviour and preferences. This data can be used to improve customer engagement strategies and drive business growth. The potential of power dailer to greatly increase agent productivity is one of their main advantages. In fact, using a power dialer instead of the more conventional techniques of searching the database and manually dialing numbers will make agents almost 60% more productive. Higher contact rates, greater customer happiness, and improved sales outcomes for firms can all arise from this boost in efficiency. The predictive dialer software market is expected to be dominated by a few large players, including Five9, NICE inContact, Genesys, and Convoso. These companies are expected to account for a combined market share of over 60% in 2023.
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KEY DRIVERS
Potential to increase outbound calls drive the market growth.
Predictive dialer software help reduce misdialing, call drops, and excessive waiting times which lead to growth of this market.
Predictive dialer software can potentially allow IT and telecom contact centers to significantly increase the number of outbound calls, which is driving the growth of the market in this segment
RESTRAIN
Due to Predictive Accuracy it may hinder the market growth.
The success of predictive dialer software depends on accurate predictions of when agents will become available to take the next call. If the prediction algorithms are not precise, it can result in over-dialing or under-dialing, leading to inefficiencies or missed opportunities.
OPPORTUNITY
Increasing adoption by small and medium-sized businesses Create Opportunity.
Cloud-based solutions offer cost-effectiveness, scalability, and flexibility, which can benefit businesses of all sizes
The adoption of predictive dialer software is growing among small and medium-sized businesses, as it can help them increase productivity and reduce costs
CHALLENGES
Challenges with Adapting to Consumer Behaviours
With the focus on increasing call volumes, maintaining call quality and providing adequate training to agents can be challenging.
Consumer behaviour is continually changing, and people are becoming increasingly averse to unsolicited calls. Predictive dialer software needs to adapt to these changing preferences and ensure that the calling approach remains relevant and effective.
The war in Ukraine is expected to slow down the growth of the market in the near term. the Russia-Ukraine war has had a negative impact on the global predictive dialer software market. The war has disrupted supply chains, led to rising inflation, and created uncertainty and risk. These factors have made it more difficult and expensive for businesses to purchase and deploy predictive dialer software. As a result, some businesses have postponed or cancelled their plans to implement predictive dialer software. Five9 is a leading provider of cloud-based predictive dialer software. The company has seen a decline in revenue and customer demand in recent months due to the war in Ukraine. Businesses are increasingly using predictive dialer software to improve customer engagement. Predictive dialer software can help businesses to reach more customers, increase the number of successful calls, and improve customer satisfaction. According to a recent report, VanillaSoft saw a decline in sales of 15% in the first quarter of 2023 compared to the same quarter in 2022. The company also saw a decline in customer demand of 10%. the decline in sales and customer demand is attributed to the Russia-Ukraine war. However, the predictive dialer software market is expected to grow in the coming years.
IMPACT OF ONGOING RECESSION
The ongoing recession is expected to have a mixed impact on the predictive dialer software market. On the one hand, the recession is likely to lead to a decline in demand for predictive dialer software as businesses cut costs. On the other hand, the recession is also likely to lead to an increase in the need for predictive dialer software as businesses look for ways to improve efficiency and save money. The recession is also likely to lead to an increase in the need for predictive dialer software as businesses look for ways to improve efficiency and save money. Predictive dialer software can help businesses to reach more customers, increase the number of successful calls, and save time and money. Sales at NICE Ltd., a top supplier of customer interaction solutions, fell 10% in the first quarter of 2023 compared to the corresponding period in 2022. The organization also noticed a 5% drop in client demand. the continuing recession is to blame for the drop-in sales and client demand. Business expenditure has decreased as a result of the recession, which has made it more challenging for companies to acquire and implement NICE's software. Although sales and demand are down, NICE is nevertheless optimistic about its long-term growth prospects. The business is concentrating on creating new features and functionalities for its software as well as stepping up its sales and marketing initiatives. In order to continue operating, NICE is attempting to cut expenditures as well. competitive in the market.
By Component
Software
Services
By Organization Size
SME
Large Organization
By Deployment
Public
Private
Hybrid
By End-Use
BFSI
Government
Healthcare
IT
Telecom
Others
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North America
USA
Canada
Mexico
Europe
Eastern Europe
Poland
Romania
Hungary
Turkey
Rest of Eastern Europe
Western Europe
Germany
France
UK
Italy
Spain
Netherlands
Switzerland
Austria
Rest of Western Europe
Asia Pacific
China
India
Japan
South Korea
Vietnam
Singapore
Australia
Rest of Asia Pacific
Middle East & Africa
Middle East
UAE
Egypt
Saudi Arabia
Qatar
Rest of Middle East
Africa
Nigeria
South Africa
Rest of Africa
Latin America
Brazil
Argentina
Colombia
Rest of Latin America
REGIONAL ANALYSIS
North America dominated the market with a revenue share of more than 36.0% in 2020. Some of the major market players are based in North America. Predictive dialer system popularity in North America is also being fuelled by the enormous number of contacts centers operating there. These solutions are being aggressively adopted by contact centers in the area in order to improve operational efficiency, information delivery effectiveness, and internal process execution. Predictive dialer software has seen tremendous growth in North America, particularly in the United States and Canada. Predictive dialers have been widely adopted to increase agent productivity and customer engagement in the region's developed telemarketing, contact center, and customer service sectors. Predictive dialers are increasingly being used for market research, lead generation, and customer assistance in addition to more conventional applications. For vendors doing business in this area, adherence to legal regulations such as the Telephone Consumer Protection Act (TCPA) in the U.S. has been crucial.
The Asia Pacific market is anticipated to show the fastest growth. Over the course of the projection period. the regional market is anticipated to increase as a result of the increasing adoption of predictive dialer systems by both major and small and medium-sized businesses. It is projected that cost-effective and cloud-based systems will be used widely. the growth of the BPO sector, and rising customer service expectations. Predictive dialers are becoming increasingly popular as a result of the rise of countries like Malaysia, the Philippines, and India as major players in the contact center industry. Additionally, there is more interest in adopting predictive dialers for sales and customer service as a result of the expansion of e-commerce and online enterprises in nations like China. The focus on delivering better customer experiences and optimizing call center operations has contributed to the adoption of predictive dialers in select countries within this region.
The major players in the Predictive Dialer Software Market are Agile CRM, Chase Data Corporation, Convoso, NICE inContact, Phone Burner, RingCentral, Inc., Star2Billing S.L., VanillaSoft, Ytel Inc. Five9, Inc. and other players.
Five9: In February 2023, Five9 announced that it had partnered with Google Cloud to offer its predictive dialer software on Google Cloud Platform. This partnership gives businesses the ability to use Five9's predictive dialer software on Google's reliable and scalable infrastructure.
NICE inContact: In March 2023, NICE inContact announced that it had acquired InVision, a provider of cloud-based customer experience solutions. The acquisition of InVision gives NICE inContact a broader portfolio of products and services that can help businesses to improve their CX.
| Report Attributes | Details |
| Market Size in 2022 | US$ 2.6 Bn |
| Market Size by 2030 | US$ 32.6 Bn |
| CAGR | CAGR of 37.2 % From 2023 to 2030 |
| Base Year | 2022 |
| Forecast Period | 2023-2030 |
| Historical Data | 2020-2021 |
| Report Scope & Coverage | Market Size, Segments Analysis, Competitive Landscape, Regional Analysis, DROC & SWOT Analysis, Forecast Outlook |
| Key Segments | • By Component (Software, Services) • By Organization Size (Large Enterprises, Small & Medium Enterprises) • By Deployment (Cloud, On-premise) • By End Use (BFSI, Government, Healthcare, IT, Telecom, Others) |
| Regional Analysis/Coverage | North America (US, Canada, Mexico), Europe (Eastern Europe [Poland, Romania, Hungary, Turkey, Rest of Eastern Europe] Western Europe] Germany, France, UK, Italy, Spain, Netherlands, Switzerland, Austria, Rest of Western Europe]). Asia Pacific (China, India, Japan, South Korea, Vietnam, Singapore, Australia, Rest of Asia Pacific), Middle East & Africa (Middle East [UAE, Egypt, Saudi Arabia, Qatar, Rest of Middle East], Africa [Nigeria, South Africa, Rest of Africa], Latin America (Brazil, Argentina, Colombia Rest of Latin America) |
| Company Profiles | Agile CRM, Chase Data Corporation, Convoso, NICE inContact, Phone Burner, RingCentral, Inc., Star2Billing S.L., VanillaSoft, Ytel Inc. Five9, Inc. |
| Key Drivers | • Potential to increase outbound calls drive the market growth. • Predictive dialer software help reduce misdialing, call drops, and excessive waiting times which lead to growth of this market. |
| Market Restraints | • Due to Predictive Accuracy it may hinder the market growth. |
Ans. The Compound Annual Growth rate for Predictive Dialer Software Market over the forecast period is 37.2 %.
Ans. USD 32.6 Billion is the projected Predictive Dialer Software Market size of the Company by 2030.
Ans. Predictive dialer Software can help businesses save time and money, increase productivity, and improve customer engagement strategies. These benefits make predictive dialer software a valuable tool for businesses that make a lot of outbound calls.
Ans. The large enterprises segment dominated the market in 2020 and accounted for a revenue share of more than 54.0%. The adoption of predictive dialer software for cold-calling and inside sales and customer support activities is growing continuously.
Ans. Predictive dialer software can help reduce agent idle time by automating the dialing process, filtering out unproductive calls, and optimizing the pacing algorithm. These benefits can help businesses increase productivity and improve customer engagement strategies.
TABLE OF CONTENTS
1. Introduction
1.1 Market Definition
1.2 Scope
1.3 Research Assumptions
2. Research Methodology
3. Market Dynamics
3.1 Drivers
3.2 Restraints
3.3 Opportunities
3.4 Challenges
4. Impact Analysis
4.1 Impact of Ukraine- Russia War
4.2 Impact of Recession
4.2.2.1 US
4.2.2.2 Canada
4.2.2.3 Germany
4.2.2.4 France
4.2.2.5 United Kingdom
4.2.2.6 China
4.2.2.7 Japan
4.2.2.8 South Korea
4.2.2.9 Rest of the World
5. Value Chain Analysis
6. Porter’s 5 forces model
7. PEST Analysis
8. Predictive Dialer Software Market Segmentation, by Component
8.1 Software
8.2 Services
9. Predictive Dialer Software Market Segmentation, by Organization Size
9.1 SME
9.2 Large Organization
10. Predictive Dialer Software Market Segmentation, by Deployment
10.1 Public
10.2 Private
10.3 Hybrid
11. Predictive Dialer Software Market Segmentation, by End-Use
11.1 BFSI
11.2 Government
11.3 Healthcare
11.4 IT
11.5 Telecom
11.6 Others
12. Regional Analysis
12.1 Introduction
12.2 North America
12.2.1 North America Predictive Dialer Software Market by Country
12.2.2North America Predictive Dialer Software Market by Component
12.2.3 North America Predictive Dialer Software Market by Organization Size
12.2.4 North America Predictive Dialer Software Market by Deployment
12.2.5 North America Predictive Dialer Software Market by End-Use
12.2.6 USA
12.2.6.1 USA Predictive Dialer Software Market by Component
12.2.6.2 USA Predictive Dialer Software Market by Organization Size
12.2.6.3 USA Predictive Dialer Software Market by Deployment
12.2.6.4 USA Predictive Dialer Software Market by End-Use
12.2.7 Canada
12.2.7.1 Canada Predictive Dialer Software Market by Component
12.2.7.2 Canada Predictive Dialer Software Market by Organization Size
12.2.7.3 Canada Predictive Dialer Software Market by Deployment
12.2.7.4 Canada Predictive Dialer Software Market by End-Use
12.2.8 Mexico
12.2.8.1 Mexico Predictive Dialer Software Market by Component
12.2.8.2 Mexico Predictive Dialer Software Market by Organization Size
12.2.8.3 Mexico Predictive Dialer Software Market by Deployment
12.2.8.4 Mexico Predictive Dialer Software Market by End-Use
12.3 Europe
12.3.1 Eastern Europe
12.3.1.1 Eastern Europe Predictive Dialer Software Market by Country
12.3.1.2 Eastern Europe Predictive Dialer Software Market by Component
12.3.1.3 Eastern Europe Predictive Dialer Software Market by Organization Size
12.3.1.4 Eastern Europe Predictive Dialer Software Market by Deployment
12.3.1.5 Eastern Europe Predictive Dialer Software Market by End-Use
12.3.1.6 Poland
12.3.1.6.1 Poland Predictive Dialer Software Market by Component
12.3.1.6.2 Poland Predictive Dialer Software Market by Organization Size
12.3.1.6.3 Poland Predictive Dialer Software Market by Deployment
12.3.1.6.4 Poland Predictive Dialer Software Market by End-Use
12.3.1.7 Romania
12.3.1.7.1 Romania Predictive Dialer Software Market by Component
12.3.1.7.2 Romania Predictive Dialer Software Market by Organization Size
12.3.1.7.3 Romania Predictive Dialer Software Market by Deployment
12.3.1.7.4 Romania Predictive Dialer Software Market by End-Use
12.3.1.8 Hungary
12.3.1.8.1 Hungary Predictive Dialer Software Market by Component
12.3.1.8.2 Hungary Predictive Dialer Software Market by Organization Size
12.3.1.8.3 Hungary Predictive Dialer Software Market by Deployment
12.3.1.8.4 Hungary Predictive Dialer Software Market by End-Use
12.3.1.9 Turkey
12.3.1.9.1 Turkey Predictive Dialer Software Market by Component
12.3.1.9.2 Turkey Predictive Dialer Software Market by Organization Size
12.3.1.9.3 Turkey Predictive Dialer Software Market by Deployment
12.3.1.9.4 Turkey Predictive Dialer Software Market by End-Use
12.3.1.10 Rest of Eastern Europe
12.3.1.10.1 Rest of Eastern Europe Predictive Dialer Software Market by Component
12.3.1.10.2 Rest of Eastern Europe Predictive Dialer Software Market by Organization Size
12.3.1.10.3 Rest of Eastern Europe Predictive Dialer Software Market by Deployment
12.3.1.10.4 Rest of Eastern Europe Predictive Dialer Software Market by End-Use
12.3.2 Western Europe
12.3.2.1 Western Europe Predictive Dialer Software Market by Country
12.3.2.2 Western Europe Predictive Dialer Software Market by Component
12.3.2.3 Western Europe Predictive Dialer Software Market by Organization Size
12.3.2.4 Western Europe Predictive Dialer Software Market by Deployment
12.3.2.5 Western Europe Predictive Dialer Software Market by End-Use
12.3.2.6 Germany
12.3.2.6.1 Germany Predictive Dialer Software Market by Component
12.3.2.6.2 Germany Predictive Dialer Software Market by Organization Size
12.3.2.6.3 Germany Predictive Dialer Software Market by Deployment
12.3.2.6.4 Germany Predictive Dialer Software Market by End-Use
12.3.2.7 France
12.3.2.7.1 France Predictive Dialer Software Market by Component
12.3.2.7.2 France Predictive Dialer Software Market by Organization Size
12.3.2.7.3 France Predictive Dialer Software Market by Deployment
12.3.2.7.4 France Predictive Dialer Software Market by End-Use
12.3.2.8 UK
12.3.2.8.1 UK Predictive Dialer Software Market by Component
12.3.2.8.2 UK Predictive Dialer Software Market by Organization Size
12.3.2.8.3 UK Predictive Dialer Software Market by Deployment
12.3.2.8.4 UK Predictive Dialer Software Market by End-Use
12.3.2.9 Italy
12.3.2.9.1 Italy Predictive Dialer Software Market by Component
12.3.2.9.2 Italy Predictive Dialer Software Market by Organization Size
12.3.2.9.3 Italy Predictive Dialer Software Market by Deployment
12.3.2.9.4 Italy Predictive Dialer Software Market by End-Use
12.3.2.10 Spain
12.3.2.10.1 Spain Predictive Dialer Software Market by Component
12.3.2.10.2 Spain Predictive Dialer Software Market by Organization Size
12.3.2.10.3 Spain Predictive Dialer Software Market by Deployment
12.3.2.10.4 Spain Predictive Dialer Software Market by End-Use
12.3.2.11 Netherlands
12.3.2.11.1 Netherlands Predictive Dialer Software Market by Component
12.3.2.11.2 Netherlands Predictive Dialer Software Market by Organization Size
12.3.2.11.3 Netherlands Predictive Dialer Software Market by Deployment
12.3.2.11.4 Netherlands Predictive Dialer Software Market by End-Use
12.3.2.12 Switzerland
12.3.2.12.1 Switzerland Predictive Dialer Software Market by Component
12.3.2.12.2 Switzerland Predictive Dialer Software Market by Organization Size
12.3.2.12.3 Switzerland Predictive Dialer Software Market by Deployment
12.3.2.12.4 Switzerland Predictive Dialer Software Market by End-Use
12.3.2.13 Austria
12.3.2.13.1 Austria Predictive Dialer Software Market by Component
12.3.2.13.2 Austria Predictive Dialer Software Market by Organization Size
12.3.2.13.3 Austria Predictive Dialer Software Market by Deployment
12.3.2.13.4 Austria Predictive Dialer Software Market by End-Use
12.3.2.14 Rest of Western Europe
12.3.2.14.1 Rest of Western Europe Predictive Dialer Software Market by Component
12.3.2.14.2 Rest of Western Europe Predictive Dialer Software Market by Organization Size
12.3.2.14.3 Rest of Western Europe Predictive Dialer Software Market by Deployment
12.3.2.14.4 Rest of Western Europe Predictive Dialer Software Market by End-Use
12.4 Asia-Pacific
12.4.1 Asia Pacific Predictive Dialer Software Market by Country
12.4.2 Asia Pacific Predictive Dialer Software Market by Component
12.4.3 Asia Pacific Predictive Dialer Software Market by Organization Size
12.4.4 Asia Pacific Predictive Dialer Software Market by Deployment
12.4.5 Asia Pacific Predictive Dialer Software Market by End-Use
12.4.6 China
12.4.6.1 China Predictive Dialer Software Market by Component
12.4.6.2 China Predictive Dialer Software Market by Organization Size
12.4.6.3 China Predictive Dialer Software Market by Deployment
12.4.6.4 China Predictive Dialer Software Market by End-Use
12.4.7 India
12.4.7.1 India Predictive Dialer Software Market by Component
12.4.7.2 India Predictive Dialer Software Market by Organization Size
12.4.7.3 India Predictive Dialer Software Market by Deployment
12.4.7.4 India Predictive Dialer Software Market by End-Use
12.4.8 Japan
12.4.8.1 Japan Predictive Dialer Software Market by Component
12.4.8.2 Japan Predictive Dialer Software Market by Organization Size
12.4.8.3 Japan Predictive Dialer Software Market by Deployment
12.4.8.4 Japan Predictive Dialer Software Market by End-Use
12.4.9 South Korea
12.4.9.1 South Korea Predictive Dialer Software Market by Component
12.4.9.2 South Korea Predictive Dialer Software Market by Organization Size
12.4.9.3 South Korea Predictive Dialer Software Market by Deployment
12.4.9.4 South Korea Predictive Dialer Software Market by End-Use
12.4.10 Vietnam
12.4.10.1 Vietnam Predictive Dialer Software Market by Component
12.4.10.2 Vietnam Predictive Dialer Software Market by Organization Size
12.4.10.3 Vietnam Predictive Dialer Software Market by Deployment
12.4.10.4 Vietnam Predictive Dialer Software Market by End-Use
12.4.11 Singapore
12.4.11.1 Singapore Predictive Dialer Software Market by Component
12.4.11.2 Singapore Predictive Dialer Software Market by Organization Size
12.4.11.3 Singapore Predictive Dialer Software Market by Deployment
12.4.11.4 Singapore Predictive Dialer Software Market by End-Use
12.4.12 Australia
12.4.12.1 Australia Predictive Dialer Software Market by Component
12.4.12.2 Australia Predictive Dialer Software Market by Organization Size
12.4.12.3 Australia Predictive Dialer Software Market by Deployment
12.4.12.4 Australia Predictive Dialer Software Market by End-Use
12.4.13 Rest of Asia-Pacific
12.4.13.1 Rest of Asia-Pacific Predictive Dialer Software Market by Component
12.4.13.2 Rest of Asia-Pacific Predictive Dialer Software Market by Organization Size
12.4.13.3 Rest of Asia-Pacific Predictive Dialer Software Market by Deployment
12.4.13.4 Rest of Asia-Pacific Predictive Dialer Software Market by End-Use
12.5 Middle East & Africa
12.5.1 Middle East
12.5.1.1 Middle East Predictive Dialer Software Market by Country
12.5.1.2 Middle East Predictive Dialer Software Market by Component
12.5.1.3 Middle East Predictive Dialer Software Market by Organization Size
12.5.1.4 Middle East Predictive Dialer Software Market by Deployment
12.5.1.5 Middle East Predictive Dialer Software Market by End-Use
12.5.1.6 UAE
12.5.1.6.1 UAE Predictive Dialer Software Market by Component
12.5.1.6.2 UAE Predictive Dialer Software Market by Organization Size
12.5.1.6.3 UAE Predictive Dialer Software Market by Deployment
12.5.1.6.4 UAE Predictive Dialer Software Market by End-Use
12.5.1.7 Egypt
12.5.1.7.1 Egypt Predictive Dialer Software Market by Component
12.5.1.7.2 Egypt Predictive Dialer Software Market by Organization Size
12.5.1.7.3 Egypt Predictive Dialer Software Market by Deployment
12.5.1.7.4 Egypt Predictive Dialer Software Market by End-Use
12.5.1.8 Saudi Arabia
12.5.1.8.1 Saudi Arabia Predictive Dialer Software Market by Component
12.5.1.8.2 Saudi Arabia Predictive Dialer Software Market by Organization Size
12.5.1.8.3 Saudi Arabia Predictive Dialer Software Market by Deployment
12.5.1.8.4 Saudi Arabia Predictive Dialer Software Market by End-Use
12.5.1.9 Qatar
12.5.1.9.1 Qatar Predictive Dialer Software Market by Component
12.5.1.9.2 Qatar Predictive Dialer Software Market by Organization Size
12.5.1.9.3 Qatar Predictive Dialer Software Market by Deployment
12.5.1.9.4 Qatar Predictive Dialer Software Market by End-Use
12.5.1.10 Rest of Middle East
12.5.1.10.1 Rest of Middle East Predictive Dialer Software Market by Component
12.5.1.10.2 Rest of Middle East Predictive Dialer Software Market by Organization Size
12.5.1.10.3 Rest of Middle East Predictive Dialer Software Market by Deployment
12.5.1.10.4 Rest of Middle East Predictive Dialer Software Market by End-Use
12.5.2. Africa
12.5.2.1 Africa Predictive Dialer Software Market by Country
12.5.2.2 Africa Predictive Dialer Software Market by Component
12.5.2.3 Africa Predictive Dialer Software Market by Organization Size
12.5.2.4 Africa Predictive Dialer Software Market by Deployment
12.5.2.5 Africa Predictive Dialer Software Market by End-Use
12.5.2.6 Nigeria
12.5.2.6.1 Nigeria Predictive Dialer Software Market by Component
12.5.2.6.2 Nigeria Predictive Dialer Software Market by Organization Size
12.5.2.6.3 Nigeria Predictive Dialer Software Market by Deployment
12.5.2.6.4 Nigeria Predictive Dialer Software Market by End-Use
12.5.2.7 South Africa
12.5.2.7.1 South Africa Predictive Dialer Software Market by Component
12.5.2.7.2 South Africa Predictive Dialer Software Market by Organization Size
12.5.2.7.3 South Africa Predictive Dialer Software Market by Deployment
12.5.2.7.4 South Africa Predictive Dialer Software Market by End-Use
12.5.2.8 Rest of Africa
12.5.2.8.1 Rest of Africa Predictive Dialer Software Market by Component
12.5.2.8.2 Rest of Africa Predictive Dialer Software Market by Organization Size
12.5.2.8.3 Rest of Africa Predictive Dialer Software Market by Deployment
12.5.2.8.4 Rest of Africa Predictive Dialer Software Market by End-Use
12.6. Latin America
12.6.1 Latin America Predictive Dialer Software Market by Country
12.6.2 Latin America Predictive Dialer Software Market by Component
12.6.3 Latin America Predictive Dialer Software Market by Organization Size
12.6.4 Latin America Predictive Dialer Software Market by Deployment
12.6.5 Latin America Predictive Dialer Software Market by End-Use
12.6.6 Brazil
12.6.6.1 Brazil Predictive Dialer Software Market by Component
12.6.6.2 Brazil Africa Predictive Dialer Software Market by Organization Size
12.6.6.3 Brazil Predictive Dialer Software Market by Deployment
12.6.6.4 Brazil Predictive Dialer Software Market by End-Use
12.6.7 Argentina
12.6.7.1 Argentina Predictive Dialer Software Market by Component
12.6.7.2 Argentina Predictive Dialer Software Market by Organization Size
12.6.7.3 Argentina Predictive Dialer Software Market by Deployment
12.6.7.4 Argentina Predictive Dialer Software Market by End-Use
12.6.8 Colombia
12.6.8.1 Colombia Predictive Dialer Software Market by Component
12.6.8.2 Colombia Predictive Dialer Software Market by Organization Size
12.6.8.3 Colombia Predictive Dialer Software Market by Deployment
12.6.8.4 Colombia Predictive Dialer Software Market by End-Use
12.6.9 Rest of Latin America
12.6.9.1 Rest of Latin America Predictive Dialer Software Market by Component
12.6.9.2 Rest of Latin America Predictive Dialer Software Market by Organization Size
12.6.9.3 Rest of Latin America Predictive Dialer Software Market by Deployment
12.6.9.4 Rest of Latin America Predictive Dialer Software Market by End-Use
13 Company profile
13.1 Agile CRM
13.1.1 Company Overview
13.1.2 Financials
13.1.3Product/Services/Offerings
13.1.4 SWOT Analysis
13.1.5 The SNS View
13.2 Chase Data Corporation
13.2.1 Company Overview
13.2.2 Financials
13.2.3Product/Services/Offerings
13.2.4 SWOT Analysis
13.2.5 The SNS View
13.3 Convoso,
13.3.1 Company Overview
13.3.2 Financials
13.3.3Product/Services/Offerings
13.3.4 SWOT Analysis
13.3.5 The SNS View
13.4 NICE inContact
13.4.1 Company Overview
13.4.2 Financials
13.4.3Product/Services/Offerings
13.4.4 SWOT Analysis
13.4.5 The SNS View
13.5 Phone Burner
13.5.1 Company Overview
13.5.2 Financials
13.5.3Product/Services/Offerings
13.5.4 SWOT Analysis
13.5.5 The SNS View
13.6 RingCentral, Inc.
13.6.1 Company Overview
13.6.2 Financials
13.6.3Product/Services/Offerings
13.6.4 SWOT Analysis
13.6.5 The SNS View
13.7 Star2Billing S.L.
13.7.1 Company Overview
13.7.2 Financials
13.7.3Product/Services/Offerings
13.7.4 SWOT Analysis
13.7.5 The SNS View
13.8 VanillaSoft
13.8.1 Company Overview
13.8.2 Financial
13.8.3Product/Services/Offerings
13.8.4 SWOT Analysis
13.8.5 The SNS View
13.9 Ytel Inc.
13.9.1 Company Overview
13.9.2 Financials
13.9.3 Product/Service/Offerings
13.9.4 SWOT Analysis
13.9.5 The SNS View
13.10 Five9 Inc.
13.10.1 Company Overview
13.10.2 Financials
13.10.3 Product/Service/Offerings
13.10.4 SWOT Analysis
13.10.5 The SNS View
14. Competitive Landscape
14.1 Competitive Benchmarking
14.2 Company Share Analysis
14.3 Recent Developments
14.3.1 Industry News
14.3.2 Company News
14.3.3 Mergers & Acquisitions
15. USE Cases and Best Practices
16. Conclusion
An accurate research report requires proper strategizing as well as implementation. There are multiple factors involved in the completion of good and accurate research report and selecting the best methodology to compete the research is the toughest part. Since the research reports we provide play a crucial role in any company’s decision-making process, therefore we at SNS Insider always believe that we should choose the best method which gives us results closer to reality. This allows us to reach at a stage wherein we can provide our clients best and accurate investment to output ratio.
Each report that we prepare takes a timeframe of 350-400 business hours for production. Starting from the selection of titles through a couple of in-depth brain storming session to the final QC process before uploading our titles on our website we dedicate around 350 working hours. The titles are selected based on their current market cap and the foreseen CAGR and growth.
The 5 steps process:
Step 1: Secondary Research:
Secondary Research or Desk Research is as the name suggests is a research process wherein, we collect data through the readily available information. In this process we use various paid and unpaid databases which our team has access to and gather data through the same. This includes examining of listed companies’ annual reports, Journals, SEC filling etc. Apart from this our team has access to various associations across the globe across different industries. Lastly, we have exchange relationships with various university as well as individual libraries.
Step 2: Primary Research
When we talk about primary research, it is a type of study in which the researchers collect relevant data samples directly, rather than relying on previously collected data. This type of research is focused on gaining content specific facts that can be sued to solve specific problems. Since the collected data is fresh and first hand therefore it makes the study more accurate and genuine.
We at SNS Insider have divided Primary Research into 2 parts.
Part 1 wherein we interview the KOLs of major players as well as the upcoming ones across various geographic regions. This allows us to have their view over the market scenario and acts as an important tool to come closer to the accurate market numbers. As many as 45 paid and unpaid primary interviews are taken from both the demand and supply side of the industry to make sure we land at an accurate judgement and analysis of the market.
This step involves the triangulation of data wherein our team analyses the interview transcripts, online survey responses and observation of on filed participants. The below mentioned chart should give a better understanding of the part 1 of the primary interview.
Part 2: In this part of primary research the data collected via secondary research and the part 1 of the primary research is validated with the interviews from individual consultants and subject matter experts.
Consultants are those set of people who have at least 12 years of experience and expertise within the industry whereas Subject Matter Experts are those with at least 15 years of experience behind their back within the same space. The data with the help of two main processes i.e., FGDs (Focused Group Discussions) and IDs (Individual Discussions). This gives us a 3rd party nonbiased primary view of the market scenario making it a more dependable one while collation of the data pointers.
Step 3: Data Bank Validation
Once all the information is collected via primary and secondary sources, we run that information for data validation. At our intelligence centre our research heads track a lot of information related to the market which includes the quarterly reports, the daily stock prices, and other relevant information. Our data bank server gets updated every fortnight and that is how the information which we collected using our primary and secondary information is revalidated in real time.
Step 4: QA/QC Process
After all the data collection and validation our team does a final level of quality check and quality assurance to get rid of any unwanted or undesired mistakes. This might include but not limited to getting rid of the any typos, duplication of numbers or missing of any important information. The people involved in this process include technical content writers, research heads and graphics people. Once this process is completed the title gets uploader on our platform for our clients to read it.
Step 5: Final QC/QA Process:
This is the last process and comes when the client has ordered the study. In this process a final QA/QC is done before the study is emailed to the client. Since we believe in giving our clients a good experience of our research studies, therefore, to make sure that we do not lack at our end in any way humanly possible we do a final round of quality check and then dispatch the study to the client.
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