The global account-based data software market is estimated to be valued at around US$ 15.24 billion in 2023. With a projected CAGR of 8.2% for the next ten years, the market is likely to reach a valuation of nearly US$ 33.62 billion by the end of 2033.
| Attribute | Key Insights |
|---|---|
| Global Account-based Data Software Estimated Market Value (2023) | US$ 15.24 billion |
| Global Account-based Data Software Forecasted Market Value (2033) | US$ 33.62 billion |
| Global Account-based Data Software Market Growth Rate (2023 to 2033) | 8.2% CAGR |
| United States Account-based Data Software Forecasted Market Value (2033) | US$ 10.9 billion |
| United States Account-based Data Software Market Growth Rate (2023 to 2033) | 7.8% CAGR |
| Key Companies Profiled |
|
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As per the account-based data software market research by Future Market Insights - market research and competitive intelligence provider, from 2023 to 2033, the market value of the account-based data software market increased at around 8.2% CAGR. With an absolute dollar opportunity of US$ 15.24 billion, the market is projected to reach a valuation of US$ 33.62 billion by 2033.
Future Market Insights’ analysis reveals that most of the market revenue is grossed from Cloud-based Account-Based Data software, which is accounted for a forecasted CAGR of 7.9%. Account-Based Data software provides. The global market for account-based data software witnessed a CAGR of 6.1% over the last five years (2018 to 2022).
Benefits to ICP selection to provide a boost to demand in the forecast period
According to research, companies can gain the benefits of a 68% higher win rate if they create thorough and clear Ideal Customer Profiles (ICPs). The use of three different metrics of data can help companies define an ICP accurately. Qualitative data from teams must be used in comparison with actual data from data software such as analysis and historical sales and interaction data. There are numerous parts to the creation of the ICP.
For one, a company needs to use data to gain insights on which types and features of profiles have given rise to customer relations with high levels of engagement, high value deals and good retention rates. This requires the existence of a thorough and clear database, which is possible only through Account-Based Data Software utilization. The database must be up-to-date, cover all contacts and accounts, be scalable while maintaining accuracy, and be accessible and collaborative to all branches of the teams within the companies.
The database needs to be inclusive of as many accounts as possible, and regularly updates, since estimates have found that the accuracy of a list can decrease nearly at a rate of 20% per year. Further, the internal structure of the account needs to be collected to ensure that the right people and teams are being connected and engaged with.
Finally, the use of significant data can help define buyer personas more accurately. The buyer data is a key component of this since the persona must be personalized to individualized accounts activities, and goals. The gap between the accounts and buyer personas available needs to be covered.
Principal Consultant
Intent data to help organizations understand which accounts to target more efficiently.
Intent data, or sales intelligence about accounts that are showing intent or interest, is a key step to optimizing the benefits of B2B ABM marketing. According to research, 87% buyers search online for research before buying a product. Intent data allows an enterprise’s collaborating teams to understand elements such as if an account is actively consuming data that could indicate higher intent.
This data includes things like reviews, blog posts, research papers, news articles and community query sites related to the topic. When buyers with a high level of intent are identified, enterprises can target them through personalised campaigns and gain a competitive edge before the customer even begins engagement – to influence them in the early interest stages.
This can influence better target account selection and allow teams to rank accounts- giving higher priority to buyers showcasing more interest, which more often than not will mean higher conversion rates. It also benefits customer relations, since understanding intent patterns of existing consumers can allow for more efficient upselling strategy and improve relationships. The most efficient method of using this data, however, is through the use of data software that allows for analysis in combination with other data such as whether the customer is actually a good fit.
According to a survey conducted amongst marketing teams, 56% and 43% of participants believed that content and data management, respectively, are the most essential components of ABM. Using Data software, the databases concerned can be inspected to find which content is working the best for which types of clients and provide more personalized messages. Also, insights derived from this data are vital – with the insights collected having the ability to show teams possible new methods of content and advertising they might not even have considered. Thus, the teams can work on customized data for their accounts.
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Collaboration between teams leads to better account selection and overall benefits
According to research, the misalignment between sales and marketing teams can cost businesses over US$ 1 trillion in just a year. In a survey conducted amongst sales and marketing teams, 90% reported a misalignment. According to another study, enterprises where these teams are not misaligned, have 36% higher retention, 67% higher chances of customer conversion and 38% higher sales win rates. Effective data software ensures that sales and marketing teams are aligned and have access to the same datasets when coming up with ICP guidelines. Under traditional lead-based marketing, sales and marketing teams work separately and have a higher chance of misalignment. This can lead to unqualified leads with conversion rates and chancing of displaying lower retention. With the use of data software, both sales and marketing teams can come together to select accounts based on concrete data that both must agree upon, leading to higher chances of selecting qualified leads.
Using account-based software, companies can account for performance analysis, content engagement metrics across campaigns, activity and retention rates, and LTV. Further data automation can help contribute even after the data is already selected. Predictive analytics can also help teams understand where to adjust campaigns already in progress and where the techniques might need to be adjusted.
Large enterprises continue to contribute the most to the demand, however, small and medium companies can also benefit from Account-Based Data Software
Large Enterprises are the top application for account-based data software with a forecasted CAGR of 7.8% in the next 10-year epoch. While it does have several benefits, account-based data software requires high levels of data and resources to work, which suggests that large companies are the largest contributors to demand, since they benefit from the automation capabilities it can provide and they require the insights it provides. Small and medium-sized companies are increasingly looking to the adoption of account-based data software as the safety of cloud-based systems increases, since cloud systems come at significantly lower costs of setup, installation and upkeep, as well as require smaller IT teams when compared to on-premise options.
Europe is an emerging market with 23.4% market share captured in 2022 and a predicted market size of US$ 7.8 billion by 2033. According to a study conducted amongst 500 marketing professionals, it was observed that 86% of European companies reported plans to use account based marketing and account based data in the near future. The United Kingdom also emerged as a key country for demand, with the study indicating that 55% of respondents showed interest in implementing these strategies. The United Kingdom witnessed a CAGR of 8.6% in the past, and has an absolute dollar opportunity of US $5.8 billion at a CAGR of 7.8% with a forecasted market value of US$ 10.9 billion.
The United States to remain a key component of global Consumption
The United States accounted for over 17.1% of the global account-based data software market in 2022. The United States has a US$ 287.3 million absolute dollar opportunity of US$ 451.8 million between 2023 and 2033 at 10.6% CAGR.
Cloud Based Data Software is currently the leading type of account-based data software, with a historic CAGR of 8.9% and a forecasted CAGR of 8% in 2023 to 2033. Account based data software requires the storage and analysis of massive volumes of data, which needs to be scalable, cost-efficient and collaborative Cloud-based software, is the key solution type for companies to use in data software.
Another feature that makes cloud software preferable is the ability to manage routine processes like backup recovery if it is a feature provided by the service provider. Cloud storage is also beneficial across all sizes of enterprises, since it is significantly cheaper than on-premise solutions.
At present, account-based data software providers are focused on developments that will afford deeper insights and understanding of data The key companies operating include Terminus, Metadata, Integrate, 6sense, RollWorks, Madison Logic, Triblio, ListenLoop, Jabmo, Demandbase, Mintigo, Radiate B2B, Recotap, Bluebird, Kwanzoo Inc, MRP and IDG Communications, Intuit Inc., Sage Software Inc., SAP SE, Oracle Corporation, Microsoft Corporation.
Some of the recent development in Account-Based Data Software are as follows:
Similarly, recent developments related to companies manufacturing account-based data software have been tracked by the team at Future Market Insights, which is available in the full report.
By 2023-end, the fruit powders market will reach a valuation of US$ 15.24 billion.
The global market size is to reach US$ 33.62 billion by 2033.
The market is expected to flourish at a CAGR of 7.8% from 2023 to 2033.
Terminus, Metadata, Integrate, and 6sense are key market players.
Effective data software ensures that sales and marketing teams are aligned and have access to the same datasets when developing ICP guidelines. This boosts market demand.
1. Executive Summary
1.1. Global Market Outlook
1.2. Demand-side Trends
1.3. Supply-side Trends
1.4. Technology Roadmap Analysis
1.5. Analysis and Recommendations
2. Market Overview
2.1. Market Coverage / Taxonomy
2.2. Market Definition / Scope / Limitations
3. Market Background
3.1. Market Dynamics
3.1.1. Drivers
3.1.2. Restraints
3.1.3. Opportunity
3.1.4. Trends
3.2. Scenario Forecast
3.2.1. Demand in Optimistic Scenario
3.2.2. Demand in Likely Scenario
3.2.3. Demand in Conservative Scenario
3.3. Opportunity Map Analysis
3.4. Investment Feasibility Matrix
3.5. PESTLE and Porter’s Analysis
3.6. Regulatory Landscape
3.6.1. By Key Regions
3.6.2. By Key Countries
3.7. Regional Parent Market Outlook
4. Global Market Analysis 2018 to 2022 and Forecast, 2023 to 2033
4.1. Historical Market Size Value (US$ Million) Analysis, 2018 to 2022
4.2. Current and Future Market Size Value (US$ Million) Projections, 2023 to 2033
4.2.1. Y-o-Y Growth Trend Analysis
4.2.2. Absolute $ Opportunity Analysis
5. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Type
5.1. Introduction / Key Findings
5.2. Historical Market Size Value (US$ Million) Analysis By Type, 2018 to 2022
5.3. Current and Future Market Size Value (US$ Million) Analysis and Forecast By Type, 2023 to 2033
5.3.1. Cloud-Based Account-Based Data Software
5.3.2. On-Premise Account-Based Data Software
5.4. Y-o-Y Growth Trend Analysis By Type, 2018 to 2022
5.5. Absolute $ Opportunity Analysis By Type, 2023 to 2033
6. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Application
6.1. Introduction / Key Findings
6.2. Historical Market Size Value (US$ Million) Analysis By Application, 2018 to 2022
6.3. Current and Future Market Size Value (US$ Million) Analysis and Forecast By Application, 2023 to 2033
6.3.1. Large Companies
6.3.2. Small and Medium Companies
6.4. Y-o-Y Growth Trend Analysis By Application, 2018 to 2022
6.5. Absolute $ Opportunity Analysis By Application, 2023 to 2033
7. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Region
7.1. Introduction
7.2. Historical Market Size Value (US$ Million) Analysis By Region, 2018 to 2022
7.3. Current Market Size Value (US$ Million) Analysis and Forecast By Region, 2023 to 2033
7.3.1. North America
7.3.2. Latin America
7.3.3. Western Europe
7.3.4. Eastern Europe
7.3.5. South Asia and Pacific
7.3.6. East Asia
7.3.7. Middle East and Africa
7.4. Market Attractiveness Analysis By Region
8. North America Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country
8.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022
8.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033
8.2.1. By Country
8.2.1.1. USA
8.2.1.2. Canada
8.2.2. By Type
8.2.3. By Application
8.3. Market Attractiveness Analysis
8.3.1. By Country
8.3.2. By Type
8.3.3. By Application
8.4. Key Takeaways
9. Latin America Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country
9.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022
9.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033
9.2.1. By Country
9.2.1.1. Brazil
9.2.1.2. Mexico
9.2.1.3. Rest of Latin America
9.2.2. By Type
9.2.3. By Application
9.3. Market Attractiveness Analysis
9.3.1. By Country
9.3.2. By Type
9.3.3. By Application
9.4. Key Takeaways
10. Western Europe Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country
10.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022
10.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033
10.2.1. By Country
10.2.1.1. Germany
10.2.1.2. UK
10.2.1.3. France
10.2.1.4. Spain
10.2.1.5. Italy
10.2.1.6. Rest of Western Europe
10.2.2. By Type
10.2.3. By Application
10.3. Market Attractiveness Analysis
10.3.1. By Country
10.3.2. By Type
10.3.3. By Application
10.4. Key Takeaways
11. Eastern Europe Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country
11.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022
11.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033
11.2.1. By Country
11.2.1.1. Poland
11.2.1.2. Russia
11.2.1.3. Czech Republic
11.2.1.4. Romania
11.2.1.5. Rest of Eastern Europe
11.2.2. By Type
11.2.3. By Application
11.3. Market Attractiveness Analysis
11.3.1. By Country
11.3.2. By Type
11.3.3. By Application
11.4. Key Takeaways
12. South Asia and Pacific Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country
12.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022
12.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033
12.2.1. By Country
12.2.1.1. India
12.2.1.2. Bangladesh
12.2.1.3. Australia
12.2.1.4. New Zealand
12.2.1.5. Rest of South Asia and Pacific
12.2.2. By Type
12.2.3. By Application
12.3. Market Attractiveness Analysis
12.3.1. By Country
12.3.2. By Type
12.3.3. By Application
12.4. Key Takeaways
13. East Asia Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country
13.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022
13.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033
13.2.1. By Country
13.2.1.1. China
13.2.1.2. Japan
13.2.1.3. South Korea
13.2.2. By Type
13.2.3. By Application
13.3. Market Attractiveness Analysis
13.3.1. By Country
13.3.2. By Type
13.3.3. By Application
13.4. Key Takeaways
14. Middle East and Africa Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country
14.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022
14.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033
14.2.1. By Country
14.2.1.1. GCC Countries
14.2.1.2. South Africa
14.2.1.3. Israel
14.2.1.4. Rest of MEA
14.2.2. By Type
14.2.3. By Application
14.3. Market Attractiveness Analysis
14.3.1. By Country
14.3.2. By Type
14.3.3. By Application
14.4. Key Takeaways
15. Key Countries Market Analysis
15.1. USA
15.1.1. Pricing Analysis
15.1.2. Market Share Analysis, 2022
15.1.2.1. By Type
15.1.2.2. By Application
15.2. Canada
15.2.1. Pricing Analysis
15.2.2. Market Share Analysis, 2022
15.2.2.1. By Type
15.2.2.2. By Application
15.3. Brazil
15.3.1. Pricing Analysis
15.3.2. Market Share Analysis, 2022
15.3.2.1. By Type
15.3.2.2. By Application
15.4. Mexico
15.4.1. Pricing Analysis
15.4.2. Market Share Analysis, 2022
15.4.2.1. By Type
15.4.2.2. By Application
15.5. Germany
15.5.1. Pricing Analysis
15.5.2. Market Share Analysis, 2022
15.5.2.1. By Type
15.5.2.2. By Application
15.6. UK
15.6.1. Pricing Analysis
15.6.2. Market Share Analysis, 2022
15.6.2.1. By Type
15.6.2.2. By Application
15.7. France
15.7.1. Pricing Analysis
15.7.2. Market Share Analysis, 2022
15.7.2.1. By Type
15.7.2.2. By Application
15.8. Spain
15.8.1. Pricing Analysis
15.8.2. Market Share Analysis, 2022
15.8.2.1. By Type
15.8.2.2. By Application
15.9. Italy
15.9.1. Pricing Analysis
15.9.2. Market Share Analysis, 2022
15.9.2.1. By Type
15.9.2.2. By Application
15.10. Poland
15.10.1. Pricing Analysis
15.10.2. Market Share Analysis, 2022
15.10.2.1. By Type
15.10.2.2. By Application
15.11. Russia
15.11.1. Pricing Analysis
15.11.2. Market Share Analysis, 2022
15.11.2.1. By Type
15.11.2.2. By Application
15.12. Czech Republic
15.12.1. Pricing Analysis
15.12.2. Market Share Analysis, 2022
15.12.2.1. By Type
15.12.2.2. By Application
15.13. Romania
15.13.1. Pricing Analysis
15.13.2. Market Share Analysis, 2022
15.13.2.1. By Type
15.13.2.2. By Application
15.14. India
15.14.1. Pricing Analysis
15.14.2. Market Share Analysis, 2022
15.14.2.1. By Type
15.14.2.2. By Application
15.15. Bangladesh
15.15.1. Pricing Analysis
15.15.2. Market Share Analysis, 2022
15.15.2.1. By Type
15.15.2.2. By Application
15.16. Australia
15.16.1. Pricing Analysis
15.16.2. Market Share Analysis, 2022
15.16.2.1. By Type
15.16.2.2. By Application
15.17. New Zealand
15.17.1. Pricing Analysis
15.17.2. Market Share Analysis, 2022
15.17.2.1. By Type
15.17.2.2. By Application
15.18. China
15.18.1. Pricing Analysis
15.18.2. Market Share Analysis, 2022
15.18.2.1. By Type
15.18.2.2. By Application
15.19. Japan
15.19.1. Pricing Analysis
15.19.2. Market Share Analysis, 2022
15.19.2.1. By Type
15.19.2.2. By Application
15.20. South Korea
15.20.1. Pricing Analysis
15.20.2. Market Share Analysis, 2022
15.20.2.1. By Type
15.20.2.2. By Application
15.21. GCC Countries
15.21.1. Pricing Analysis
15.21.2. Market Share Analysis, 2022
15.21.2.1. By Type
15.21.2.2. By Application
15.22. South Africa
15.22.1. Pricing Analysis
15.22.2. Market Share Analysis, 2022
15.22.2.1. By Type
15.22.2.2. By Application
15.23. Israel
15.23.1. Pricing Analysis
15.23.2. Market Share Analysis, 2022
15.23.2.1. By Type
15.23.2.2. By Application
16. Market Structure Analysis
16.1. Competition Dashboard
16.2. Competition Benchmarking
16.3. Market Share Analysis of Top Players
16.3.1. By Regional
16.3.2. By Type
16.3.3. By Application
17. Competition Analysis
17.1. Competition Deep Dive
17.1.1. Terminus
17.1.1.1. Overview
17.1.1.2. Product Portfolio
17.1.1.3. Profitability by Market Segments
17.1.1.4. Sales Footprint
17.1.1.5. Strategy Overview
17.1.1.5.1. Marketing Strategy
17.1.2. Metadata
17.1.2.1. Overview
17.1.2.2. Product Portfolio
17.1.2.3. Profitability by Market Segments
17.1.2.4. Sales Footprint
17.1.2.5. Strategy Overview
17.1.2.5.1. Marketing Strategy
17.1.3. Integrate
17.1.3.1. Overview
17.1.3.2. Product Portfolio
17.1.3.3. Profitability by Market Segments
17.1.3.4. Sales Footprint
17.1.3.5. Strategy Overview
17.1.3.5.1. Marketing Strategy
17.1.4. 6sense
17.1.4.1. Overview
17.1.4.2. Product Portfolio
17.1.4.3. Profitability by Market Segments
17.1.4.4. Sales Footprint
17.1.4.5. Strategy Overview
17.1.4.5.1. Marketing Strategy
17.1.5. RollWorks
17.1.5.1. Overview
17.1.5.2. Product Portfolio
17.1.5.3. Profitability by Market Segments
17.1.5.4. Sales Footprint
17.1.5.5. Strategy Overview
17.1.5.5.1. Marketing Strategy
17.1.6. Madison Logic
17.1.6.1. Overview
17.1.6.2. Product Portfolio
17.1.6.3. Profitability by Market Segments
17.1.6.4. Sales Footprint
17.1.6.5. Strategy Overview
17.1.6.5.1. Marketing Strategy
17.1.7. Triblio
17.1.7.1. Overview
17.1.7.2. Product Portfolio
17.1.7.3. Profitability by Market Segments
17.1.7.4. Sales Footprint
17.1.7.5. Strategy Overview
17.1.7.5.1. Marketing Strategy
17.1.8. ListenLoop
17.1.8.1. Overview
17.1.8.2. Product Portfolio
17.1.8.3. Profitability by Market Segments
17.1.8.4. Sales Footprint
17.1.8.5. Strategy Overview
17.1.8.5.1. Marketing Strategy
17.1.9. Jabmo
17.1.9.1. Overview
17.1.9.2. Product Portfolio
17.1.9.3. Profitability by Market Segments
17.1.9.4. Sales Footprint
17.1.9.5. Strategy Overview
17.1.9.5.1. Marketing Strategy
17.1.10. Demandbase
17.1.10.1. Overview
17.1.10.2. Product Portfolio
17.1.10.3. Profitability by Market Segments
17.1.10.4. Sales Footprint
17.1.10.5. Strategy Overview
17.1.10.5.1. Marketing Strategy
18. Assumptions & Acronyms Used
19. Research Methodology
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Account-Based Data Software Market
