The global Referral Marketing Software Market was valued at around US$ 302 Million in 2021. With a projected CAGR of 13.3% for the next ten years, the market is likely to reach a valuation of nearly US$ 1.2 Billion by the end of 2032.
| Attributes | Details |
|---|---|
| Global Referral Marketing Software Market Size (2021) | US$ 302 Million |
| Global Referral Marketing Software Size (2022) | US$ 348 Million |
| Global Projected Market Value (2032) | US$ 1.2 Billion |
| Global Market Growth Rate (2022 to 2032) | 13.3% CAGR |
| United States Market Growth Rate (2022 to 2032) | 13.1% |
| Key Companies Covered |
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Future Market Insights’ analysis reveals that a larger proportion of the market revenue was grossed through BFSI, with a CAGR of 15.2% from 2015 to 2021. Revenue through Large Enterprises is expected to be the highest among applications, with a forecasted CAGR of 12.8% in the coming decade.
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According to research, people who are referred to a product by a friend are 4 times as likely to buy a product, with word of mouth influencing a 54% increase in marketing efficiency. Referral marketing is preferred because it helps companies build a customer base and an interconnected referral network. It is also cost-effective for marketing since money is spent only on actual new conversions. Further, B2B Referral Marketing Software showed better retention rates, at around 37%.
Companies are increasingly switching over to automated referral marketing processes over manual ones. Referral marketing software can help automate manual processes so that companies can focus on optimizing their referral programs and figuring out what is working best for them. This software also helps with integration with other systems, such as with referral marketing platforms on which they sell products, or even integration with other vendor's sites.
Dropbox, an online storage and file transfer service provider, ran a program that did not run on monetary rewards but was still massively successful, showing an initial growth of 3900% in 15 months, going from 100k to 4 Million users in 2008 to 2010 period. The program rewarded existing customers for every contact referred, with additional space. Those on the Basic Membership can earn 500 MB each time, up to 16 GB; while those on the Plus and Professional Memberships can earn up to 32 GB total, equated to 1 GB per contact.
Harry’s, a mail-order-based shaving brand, ran a hugely successful pre-launch referral-marketing program, gaining 100,000 emails in a single week. Approximately 77% of these emails were based on referrals alone, with each referrer on average converting three consumers. The 4-tiered system provided tangible rewards for products. A single consumer could refer anywhere from 5 to 50 friends to gain the rewards. Over 200 people reached the 50-people mark and gained the reward of a free year of shaving. The product page also involved a tracker so consumers could keep up with referrals and provide them proof that their referrals were actually being accounted for.
A popular form of referral marketing is community-based referral marketing. The company ties up with community centers and non-profit organizations. A charitable donation on a consumer’s behalf can prove to be a strong reward incentive to a consumer as it combines the referral process with the idea of a positive social contribution. Consumers will be more willing to be part of a referral process if they believe they are also providing some kind of contribution to society as a result of their actions.
TOMS, for example, ran a Buy One, Give One model that incentivized customers by telling them that a pair of shoes would be donated to children in need every time a customer bought a pair. Vena, a software management SaaS company, donates US$ 2,000 to a charity of the customer’s choice for every converted lead.
Principal Consultant
According to studies, 60% of buyers look at a company’s social media presence before buying its products. Further, it was found that 18% of people in the 25-34 year category would not provide referrals to a firm that is not on social media. Further, in the current environment of social media, word of mouth travels the most through social media.
The phenomenon of Social proof, or informational social influence, is an idea that has been pushing companies toward investing in referral marketing. It refers to the idea that people take cues from how to behave by emulating the behavior of others they see. Therefore, people who notice brands in a story online will also tend to buy a product and post a story, which is then visible to all their friends and followers, creating a highly influential and rewarding process for brands. The benefits of extending referral marketing to social media are numerous. For one, sharing and promotion become easier. Secondly, customers can easily vet out a brand just based on comment sections and other engagements with the posts.
Businesses are looking at harnessing the wave of influencer popularity, especially amongst Micro and Nano influencers with niche audiences, who have relatively smaller followings but work very well for referral programs since they have better engagement and personal connections with their following. Influencers whose social media presence is closely associated with the product offer something which traditional advertising cannot do.
Unlike traditional forms of advertisement, using niche influencers can allow brands to benefit from the fact that niche influencers are renowned for being knowledgeable about the product concerned and the audience they have cultivated is highly likely to be people who fall into the target market. Under these programs, brands can pursue varied methods of referral marketing such as through a referral code specifically made for the influencer, or through incentives for the audience, an influencer has driven to the brand.
With a Referral Marketing Software market share of 25.2%, the market value of Europe is projected to reach a valuation of US$ 304.9 Million by 2032. According to a survey of over 32,000 consumers in Europe and the USA, 66% of repliers said they asked those around them before making purchasing decisions.
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The USA accounted for over 30% of the global Referral Marketing Software Market in 2021, with a forecasted market size of US$ 431.8 Million by 2032. According to research, an average American citizen mentions brands over 60 times weekly, with 90% of these name drops being online. Referral marketing software in the USA is projected to garner an absolute dollar opportunity of US$ 305.2 Million between 2022 and 2032.
Large industries are the key contributors to demand, having experienced a CAGR of 15%, with a forecasted CAGR of 12.8% for the next decade. These enterprises can afford to provide attractive rewards and require automation for any kind of referral marketing due to the massive volumes of customers that will be passing through the process.
Google, for example, has a referral program for its G Suite program, targeting mainly business professionals. The program is widely used in businesses. There is a monetary reward for referrals, through which businesses can earn US$ 30 for every person referred, capped at a maximum of US$ 3,000.
However, small businesses also stand to benefit from referral marketing. They benefit from decreased customer acquisition costs, increased brand awareness, higher ROI, and improvements in retention rates. In order for this marketing to be efficient and scalable, the use of automation through referral marketing software becomes essential, which has been boosting demand.
BFSIs are the top end-users for referral marketing software, having witnessed a CAGR of 15.2% with a forecasted CAGR of 13% for the upcoming decade. Fintech apps are being developed and released on a regular basis, which means the demand for financial services is at an all-time high compared to those competing for the business. This method also keeps clients interested in the company.
Both cloud-based and on-premise software have comparative advantages. Demand for cloud-based Referral Marketing Software has increased over the years due to its flexibility, scalability, and cost-effectiveness.
On-premise forms of software, on the other hand, are observed as more secure as they offer enterprises a degree of control, both in terms of security and possible breaches or malfunctions. The costs however are significantly higher in the case of on-premise software, for the maintenance and management as well as for the setup itself.
Referral Marketing Software companies are focused on increasing their presence and reach. The key companies operating include Impartner, Referral Candy, Genius Referral, Viral Loops Ltd, Hello Referrals, Tapfiliate, Mention Me, Annex Cloud, Invitevox, InfluitivE, Rocket Referrals, Extole, Refersion, InviteReferrals, OmniStar, Referral SaaSquatch, Friendbuy, and Buyapowa.
Some of the recent developments in Referral Marketing Software are as follows:
Similarly, recent developments related to companies manufacturing Referral Marketing Software have been tracked by the team at Future Market Insights, which is available in the full report.
The global Referral Marketing Software market was valued at US$ 302 Million in 2021.
The Referral Marketing Software industry is set to witness a growth rate of 13.3% over the forecast period and be valued at US$ 1.2 Billion by 2032.
The Referral Marketing Software Market expanded by 15.4% from 2015 through 2021.
Impartner, Referral Candy, Genius Referral, Viral Loops Ltd, Hello Referrals, Tapfiliate, Mention Me, Annex Cloud, Invitevox, InfluitivE, Rocket Referrals, Extole, Refersion, InviteReferrals, OmniStar, Referral SaaSquatch, Friendbuy, and Buyapowa are some of the key players.
The USA, UK, China, Japan, and South Korea are expected to be the key drivers of sales.
The market in the USA accounts for over 30% of the global market.
1. Executive Summary | Referral Marketing Software Market 1.1. Global Market Outlook 1.2. Summary of Statistics 1.3. Key Market Characteristics & Attributes 1.4. Analysis and Recommendations 2. Market Overview 2.1. Market Coverage / Taxonomy 2.2. Market Definition / Scope / Limitations 3. Market Risks and Trends Assessment 3.1. Risk Assessment 3.1.1. COVID-19 Crisis and Impact on Demand 3.1.2. COVID-19 Impact Benchmark with Previous Crisis 3.1.3. Impact on Market Value (US$ Million) 3.1.4. Assessment by Key Countries 3.1.5. Assessment by Key Market Segments 3.1.6. Action Points and Recommendation for Suppliers 3.2. Key Trends Impacting the Market 3.3. Formulation and Deployment Development Trends 4. Market Background 4.1. Market, by Key Countries 4.2. Market Opportunity Assessment (US$ Million) 4.2.1. Total Available Market 4.2.2. Serviceable Addressable Market 4.2.3. Serviceable Obtainable Market 4.3. Market Scenario Forecast 4.3.1. Demand in Optimistic Scenario 4.3.2. Demand in Likely Scenario 4.3.3. Demand in Conservative Scenario 4.4. Investment Feasibility Analysis 4.4.1. Investment in Established Markets 4.4.1.1. In Short Term 4.4.1.2. In Long Term 4.4.2. Investment in Emerging Markets 4.4.2.1. In Short Term 4.4.2.2. In Long Term 4.5. Forecast Factors - Relevance & Impact 4.5.1. Top Companies Historical Growth 4.5.2. Growth in Automation, By Country 4.5.3. Adoption Rate, By Country 4.6. Market Dynamics 4.6.1. Market Driving Factors and Impact Assessment 4.6.2. Prominent Market Challenges and Impact Assessment 4.6.3. Market Opportunities 4.6.4. Prominent Trends in the Global Market & Their Impact Assessment 5. Key Success Factors 5.1. Manufacturers’ Focus on Low Penetration High Growth Markets 5.2. Banking on with Segments High Incremental Opportunity 5.3. Peer Benchmarking 6. Global Market Demand Analysis 2015 to 2021 and Forecast, 2022 to 2032 6.1. Historical Market Analysis, 2015 to 2021 6.2. Current and Future Market Projections, 2022 to 2032 6.3. Y-o-Y Growth Trend Analysis 7. Global Market Value Analysis 2015 to 2021 and Forecast, 2022 to 2032 7.1. Historical Market Value (US$ Million) Analysis, 2015 to 2021 7.2. Current and Future Market Value (US$ Million) Projections, 2022 to 2032 7.2.1. Y-o-Y Growth Trend Analysis 7.2.2. Absolute Opportunity Analysis 8. Global Market Analysis 2015 to 2021 and Forecast 2022 to 2032, By Deployment 8.1. Introduction / Key Findings 8.2. Historical Market Size (US$ Million) Analysis By Deployment, 2015 to 2021 8.3. Current and Future Market Size (US$ Million) Analysis and Forecast By Deployment, 2022 to 2032 8.3.1. Cloud 8.3.2. On-premises 8.4. Market Attractiveness Analysis By Deployment 9. Global Market Analysis 2015 to 2021 and Forecast 2022 to 2032, By Enterprise Size 9.1. Introduction / Key Findings 9.2. Historical Market Size (US$ Million) Analysis By Enterprise Size, 2015 to 2021 9.3. Current and Future Market Size (US$ Million) Analysis and Forecast By Enterprise Size, 2022 to 2032 9.3.1. Application 9.3.2. SMEs 9.4. Market Attractiveness Analysis By Enterprise Size 10. Global Market Analysis 2015 to 2021 and Forecast 2022 to 2032, By End-use 10.1. Introduction / Key Findings 10.2. Historical Market Size (US$ Million) Analysis By End-use, 2015 to 2021 10.3. Current and Future Market Size (US$ Million) Analysis and Forecast By End-use, 2022 to 2032 10.3.1. BFSI 10.3.2. Retail 10.3.3. E-commerce 10.3.4. Education 10.3.5. Hospitality 10.3.6. Other 10.4. Market Attractiveness Analysis By End-use 11. Global Market Analysis 2015 to 2021 and Forecast 2022 to 2032, By Region 11.1. Introduction 11.2. Historical Market Size (US$ Million) Analysis By Region, 2015 to 2021 11.3. Current Market Size (US$ Million) & Analysis and Forecast By Region, 2022 to 2032 11.3.1. North America 11.3.2. Latin America 11.3.3. Europe 11.3.4. Asia Pacific 11.3.5. Middle East and Africa 11.4. Market Attractiveness Analysis By Region 12. North America Market Analysis 2015 to 2021 and Forecast 2022 to 2032 12.1. Introduction 12.2. Pricing Analysis 12.3. Historical Market Size (US$ Million) Trend Analysis By Market Taxonomy, 2015 to 2021 12.4. Market Size (US$ Million) & Forecast By Market Taxonomy, 2022 to 2032 12.4.1. By Country 12.4.1.1. United States of America 12.4.1.2. Canada 12.4.1.3. Rest of North America 12.4.2. By Deployment 12.4.3. By End-use 12.4.4. By Enterprise Size 12.5. Market Attractiveness Analysis 12.5.1. By Country 12.5.2. By Deployment 12.5.3. By End-use 12.5.4. By Enterprise Size 13. Latin America Market Analysis 2015 to 2021 and Forecast 2022 to 2032 13.1. Introduction 13.2. Pricing Analysis 13.3. Historical Market Size (US$ Million) Trend Analysis By Market Taxonomy, 2015 to 2021 13.4. Market Size (US$ Million) & Forecast By Market Taxonomy, 2022 to 2032 13.4.1. By Country 13.4.1.1. Brazil 13.4.1.2. Mexico 13.4.1.3. Rest of Latin America 13.4.2. By Deployment 13.4.3. By End-use 13.4.4. By Enterprise Size 13.5. Market Attractiveness Analysis 13.5.1. By Country 13.5.2. By Deployment 13.5.3. By End-use 13.5.4. By Enterprise Size 14. Europe Market Analysis 2015 to 2021 and Forecast 2022 to 2032 14.1. Introduction 14.2. Pricing Analysis 14.3. Historical Market Size (US$ Million) Trend Analysis By Market Taxonomy, 2015 to 2021 14.4. Market Size (US$ Million) & Forecast By Market Taxonomy, 2022 to 2032 14.4.1. By Country 14.4.1.1. Germany 14.4.1.2. France 14.4.1.3. United Kingdom 14.4.1.4. Italy 14.4.1.5. Benelux 14.4.1.6. Nordic Countries 14.4.1.7. Rest of Europe 14.4.2. By Deployment 14.4.3. By End-use 14.4.4. By Enterprise Size 14.5. Market Attractiveness Analysis 14.5.1. By Country 14.5.2. By Deployment 14.5.3. By End-use 14.5.4. By Enterprise Size 15. Asia Pacific Market Analysis 2015 to 2021 and Forecast 2022 to 2032 15.1. Introduction 15.2. Pricing Analysis 15.3. Historical Market Size (US$ Million) Trend Analysis By Market Taxonomy, 2015 to 2021 15.4. Market Size (US$ Million) & Forecast By Market Taxonomy, 2022 to 2032 15.4.1. By Country 15.4.1.1. China 15.4.1.2. Japan 15.4.1.3. South Korea 15.4.1.4. Rest of Asia Pacific 15.4.2. By Deployment 15.4.3. By End-use 15.4.4. By Enterprise Size 15.5. Market Attractiveness Analysis 15.5.1. By Country 15.5.2. By Deployment 15.5.3. By End-use 15.5.4. By Enterprise Size 16. Middle East and Africa Market Analysis 2015 to 2021 and Forecast 2022 to 2032 16.1. Introduction 16.2. Pricing Analysis 16.3. Historical Market Size (US$ Million) Trend Analysis By Market Taxonomy, 2015 to 2021 16.4. Market Size (US$ Million) & Forecast By Market Taxonomy, 2022 to 2032 16.4.1. By Country 16.4.1.1. GCC Countries 16.4.1.2. South Africa 16.4.1.3. Turkey 16.4.1.4. Rest of Middle East and Africa 16.4.2. By Deployment 16.4.3. By End-use 16.4.4. By Enterprise Size 16.5. Market Attractiveness Analysis 16.5.1. By Country 16.5.2. By Deployment 16.5.3. By End-use 16.5.4. By Enterprise Size 17. Key Countries Market Analysis 2015 to 2021 and Forecast 2022 to 2032 17.1. Introduction 17.1.1. Market Value Proportion Analysis, By Key Countries 17.1.2. Global Vs. Country Growth Comparison 17.2. US Market Analysis 17.2.1. Value Proportion Analysis by Market Taxonomy 17.2.2. Value & Analysis and Forecast by Market Taxonomy, 2015-2032 17.2.2.1. By Deployment 17.2.2.2. By End-use 17.2.2.3. By Enterprise Size 17.3. Canada Market Analysis 17.3.1. Value Proportion Analysis by Market Taxonomy 17.3.2. Value & Analysis and Forecast by Market Taxonomy, 2015-2032 17.3.2.1. By Deployment 17.3.2.2. By End-use 17.3.2.3. By Enterprise Size 17.4. Mexico Market Analysis 17.4.1. Value Proportion Analysis by Market Taxonomy 17.4.2. Value & Analysis and Forecast by Market Taxonomy, 2015-2032 17.4.2.1. By Deployment 17.4.2.2. By End-use 17.4.2.3. By Enterprise Size 17.5. Brazil Market Analysis 17.5.1. Value Proportion Analysis by Market Taxonomy 17.5.2. Value & Analysis and Forecast by Market Taxonomy, 2015-2032 17.5.2.1. By Deployment 17.5.2.2. By End-use 17.5.2.3. By Enterprise Size 17.6. Germany Market Analysis 17.6.1. Value Proportion Analysis by Market Taxonomy 17.6.2. Value & Analysis and Forecast by Market Taxonomy, 2015-2032 17.6.2.1. By Deployment 17.6.2.2. By End-use 17.6.2.3. By Enterprise Size 17.7. France Market Analysis 17.7.1. Value Proportion Analysis by Market Taxonomy 17.7.2. Value & Analysis and Forecast by Market Taxonomy, 2015-2032 17.7.2.1. By Deployment 17.7.2.2. By End-use 17.7.2.3. By Enterprise Size 17.8. Italy Market Analysis 17.8.1. Value Proportion Analysis by Market Taxonomy 17.8.2. Value & Analysis and Forecast by Market Taxonomy, 2015-2032 17.8.2.1. By Deployment 17.8.2.2. By End-use 17.8.2.3. By Enterprise Size 17.9. BENELUX Market Analysis 17.9.1. Value Proportion Analysis by Market Taxonomy 17.9.2. Value & Analysis and Forecast by Market Taxonomy, 2015-2032 17.9.2.1. By Deployment 17.9.2.2. By End-use 17.9.2.3. By Enterprise Size 17.10. UK Market Analysis 17.10.1. Value Proportion Analysis by Market Taxonomy 17.10.2. Value & Analysis and Forecast by Market Taxonomy, 2015-2032 17.10.2.1. By Deployment 17.10.2.2. By End-use 17.10.2.3. By Enterprise Size 17.11. Nordic Countries Market Analysis 17.11.1. Value Proportion Analysis by Market Taxonomy 17.11.2. Value & Analysis and Forecast by Market Taxonomy, 2015-2032 17.11.2.1. By Deployment 17.11.2.2. By End-use 17.11.2.3. By Enterprise Size 17.12. China Market Analysis 17.12.1. Value Proportion Analysis by Market Taxonomy 17.12.2. Value & Analysis and Forecast by Market Taxonomy, 2015-2032 17.12.2.1. By Deployment 17.12.2.2. By End-use 17.12.2.3. By Enterprise Size 17.13. Japan Market Analysis 17.13.1. Value Proportion Analysis by Market Taxonomy 17.13.2. Value & Analysis and Forecast by Market Taxonomy, 2015-2032 17.13.2.1. By Deployment 17.13.2.2. By End-use 17.13.2.3. By Enterprise Size 17.14. South Korea Market Analysis 17.14.1. Value Proportion Analysis by Market Taxonomy 17.14.2. Value & Analysis and Forecast by Market Taxonomy, 2015-2032 17.14.2.1. By Deployment 17.14.2.2. By End-use 17.14.2.3. By Enterprise Size 17.15. GCC Countries Market Analysis 17.15.1. Value Proportion Analysis by Market Taxonomy 17.15.2. Value & Analysis and Forecast by Market Taxonomy, 2015-2032 17.15.2.1. By Deployment 17.15.2.2. By End-use 17.15.2.3. By Enterprise Size 17.16. South Africa Market Analysis 17.16.1. Value Proportion Analysis by Market Taxonomy 17.16.2. Value & Analysis and Forecast by Market Taxonomy, 2015-2032 17.16.2.1. By Deployment 17.16.2.2. By End-use 17.16.2.3. By Enterprise Size 17.17. Turkey Market Analysis 17.17.1. Value Proportion Analysis by Market Taxonomy 17.17.2. Value & Analysis and Forecast by Market Taxonomy, 2015-2032 17.17.2.1. By Deployment 17.17.2.2. By End-use 17.17.2.3. By Enterprise Size 17.17.3. Competition Landscape and Player Concentration in the Country 18. Market Structure Analysis 18.1. Market Analysis by Tier of Companies 18.2. Market Concentration 18.3. Market Share Analysis of Top Players 18.4. Market Presence Analysis 18.4.1. By Regional Footprint of Players 18.4.2. Deployment Footprint by Players 19. Competition Analysis 19.1. Competition Dashboard 19.2. Competition Benchmarking 19.3. Competition Deep Dive 19.3.1. Request Rock Inc. 19.3.1.1. Overview 19.3.1.2. Deployment Portfolio 19.3.1.3. Sales Footprint 19.3.1.4. Strategy Overview 19.3.2. Impartner 19.3.2.1. Overview 19.3.2.2. Deployment Portfolio 19.3.2.3. Sales Footprint 19.3.2.4. Strategy Overview 19.3.3. Referral candy 19.3.3.1. Overview 19.3.3.2. Deployment Portfolio 19.3.3.3. Sales Footprint 19.3.3.4. Strategy Overview 19.3.4. Genius Referral 19.3.4.1. Overview 19.3.4.2. Deployment Portfolio 19.3.4.3. Sales Footprint 19.3.4.4. Strategy Overview 19.3.5. Viral Loops Ltd 19.3.5.1. Overview 19.3.5.2. Deployment Portfolio 19.3.5.3. Sales Footprint 19.3.5.4. Strategy Overview 19.3.6. Hello Referrals 19.3.6.1. Overview 19.3.6.2. Deployment Portfolio 19.3.6.3. Sales Footprint 19.3.6.4. Strategy Overview 19.3.7. Tapfiliate 19.3.7.1. Overview 19.3.7.2. Deployment Portfolio 19.3.7.3. Sales Footprint 19.3.7.4. Strategy Overview 19.3.8. Mention Me 19.3.8.1. Overview 19.3.8.2. Deployment Portfolio 19.3.8.3. Sales Footprint 19.3.8.4. Strategy Overview 19.3.9. Annex Cloud 19.3.9.1. Overview 19.3.9.2. Deployment Portfolio 19.3.9.3. Sales Footprint 19.3.9.4. Strategy Overview 19.3.10. Invitevox 19.3.10.1. Overview 19.3.10.2. Deployment Portfolio 19.3.10.3. Sales Footprint 19.3.10.4. Strategy Overview 19.3.11. InfluitivE 19.3.11.1. Overview 19.3.11.2. Deployment Portfolio 19.3.11.3. Sales Footprint 19.3.11.4. Strategy Overview 19.3.12. Rocket Referrals 19.3.12.1. Overview 19.3.12.2. Deployment Portfolio 19.3.12.3. Sales Footprint 19.3.12.4. Strategy Overview 19.3.13. Extole 19.3.13.1. Overview 19.3.13.2. Deployment Portfolio 19.3.13.3. Sales Footprint 19.3.13.4. Strategy Overview 19.3.14. Refersion 19.3.14.1. Overview 19.3.14.2. Deployment Portfolio 19.3.14.3. Sales Footprint 19.3.14.4. Strategy Overview 19.3.15. InviteReferrals 19.3.15.1. Overview 19.3.15.2. Deployment Portfolio 19.3.15.3. Sales Footprint 19.3.15.4. Strategy Overview 19.3.16. OmniStar 19.3.16.1. Overview 19.3.16.2. Deployment Portfolio 19.3.16.3. Sales Footprint 19.3.16.4. Strategy Overview 19.3.17. Referral SaaSquatch 19.3.17.1. Overview 19.3.17.2. Deployment Portfolio 19.3.17.3. Sales Footprint 19.3.17.4. Strategy Overview 19.3.18. Friendbuy 19.3.18.1. Overview 19.3.18.2. Deployment Portfolio 19.3.18.3. Sales Footprint 19.3.18.4. Strategy Overview 19.3.19. Buyapowa 19.3.19.1. Overview 19.3.19.2. Deployment Portfolio 19.3.19.3. Sales Footprint 19.3.19.4. Strategy Overview
Technology
May 2022
REP-GB-14688
299 pages
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Referral Marketing Software Market
