How to Crack ABM for Enterprise Sales: A Data-Driven Approach

Account-Based Marketing (ABM) has become a cornerstone strategy for businesses looking to scale enterprise sales. ABM offers a targeted, data-driven approach to connecting with high-value accounts – emphasizing precision and personalization over broad, generalized outreach. 

In a recent conversation with Draup, Sameer Shariff, CEO of Impelsys, shared his insights on how his company transitioned from traditional sales methods to adopting ABM strategies powered by data and AI.  

Full video of the conversation here: 

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From Spreadsheets to AI: The ABM Evolution 

Many businesses start ABM with basic tools, eventually adopting CRMs. However, true ABM requires AI-powered sales intelligence. Impelsys leveraged Draup to gain a competitive edge, using data to understand customer intent and personalize outreach. As CEO Sameer Shariff notes, “We started with spreadsheets, moved to CRMs, and then to platforms like Draup. With AI tools, we now have the intelligence to target accounts with precision, understand customer intent, and have the right conversations at the right time.” 

The takeaway is clear: the path to ABM success lies in embracing continual evolution and adopting technologies that turn data into a competitive advantage. 

Leveraging Sales Intelligence for  ABM Success 

This shift to AI-driven sales intelligence platforms equips businesses with the tools they need to unlock the true potential of ABM. By transforming multi-dimensional market data  -spanning account pain points, digital initiatives, M&A activities, tech stack evolutions, center expansions and more – into actionable insights, companies can align their sales and marketing efforts with precision, ensuring every decision is informed by a deep understanding of their target accounts. 

Impelsys’s success with sales intelligence highlights key ABM principles: 

1. Narrow Your Focus: Precision Over Volume

One of the fundamental shifts in ABM is moving from broad, unfocused outreach to a targeted, precision-driven approach. Instead of casting a wide net, enterprises should focus on: 

• Deepening Existing Relationships: For many companies, the majority of revenue comes from a small percentage of their existing customers. By layering data insights on top of existing knowledge, businesses can uncover upsell opportunities and strengthen these relationships. 
• Targeting High-Value Prospects: For new accounts, curating a focused list of high-value prospects is essential. With tools like Draup, businesses can identify accounts that align closely with their solutions and prioritize those actively exploring providers. 

“Pre-ABM, we were shooting everywhere, hoping to hit something. With a more focused approach, we selected a handful of high-value accounts and worked on understanding them deeply. The results were far better.” -Sameer Shariff 

This targeted approach not only optimizes resource allocation but also ensures that every interaction is meaningful and aligned with the specific needs of high-priority accounts. 

2. Leverage AI to Drive Personalization

Personalization is no longer a “nice-to-have” but a “must-have” in B2B sales. AI empowers sales teams to deliver hyper-personalized experiences at scale. By analyzing data points like intent signals (what prospects are researching online), technographics (what technologies they use), and behavioral insights (how they interact with your website and content), AI can provide a deep understanding of each account’s specific needs and challenges.  

This allows sales teams to tailor their messaging, offer relevant solutions, and build stronger connections with decision-makers.  

At Impelsys, the adoption of AI tools helped them achieve a deeper understanding of their target accounts. These tools provided insights that allowed them to have more meaningful conversations, increasing their chances of conversion. 

“AI helps us understand not just the company but the individual decision-makers. It’s like mass customization at scale, where every outreach is personalized and intentional.” – Sameer Shariff 

Such AI-driven personalization ensures that sales teams are better prepared, making every customer interaction more impactful.  

3. Build Collaborative Partnerships

Effective ABM isn’t accomplished in isolation—it requires collaboration. Businesses are increasingly turning to strategic technology partners to help them refine their sales and marketing strategies. These partnerships provide access to: 

• Advanced tools and platforms for data collection and analysis. 
• Expertise in implementing and scaling ABM strategies. 
• Continuous support in fine-tuning approaches based on real-time results. 

Impelsys’ partnership with Draup is a prime example of how working with a technology provider can enhance sales and marketing strategies. 

“Partnerships are at the core of our success. With Draup, it’s not just about the technology—it’s about aligning on goals and working together to refine strategies. That’s where the real value lies.” – Sameer Shariff 

Collaborative partnerships enable businesses to leverage external expertise, access advanced tools, and continuously improve their ABM strategies. 

4. The Role of Data and KPIs in ABM

Data is the backbone of ABM, and measuring success through clear metrics is essential. Businesses must define and track key performance indicators (KPIs) such as: 

• Conversion Rates: How effectively are targeted accounts converting into customers? 
• Time-to-Close: How quickly are deals being finalized? 
• Customer Lifetime Value (CLV): How much revenue is generated from each account over time? 

At Impelsys, these metrics are central to their ABM strategy. By analyzing KPIs, they fine-tune their approach and ensure they’re achieving ROI—not just on financial investments but also on the time spent engaging with accounts. 

As Sameer notes, “We measure everything—conversion rates, time-to-close, and ROI. This data-driven approach helps us refine our strategies and continuously innovate.” 

IT Market Size in Telecom Industry – Market Share and Trends

 

The telecom industry is rapidly evolving, fuelled by 5G expansion, cloud adoption, AI-driven automation, and IoT integration.    

IT spending in this sector is surging as companies modernize their infrastructure to enhance network efficiency, customer experience, and security. These advancements are not just shaping telecom’s future but also creating new revenue streams, operational efficiencies, and market opportunities. 

This market report on IT in Telecom curated with insights from Draup’s sales intelligence platform includes –  

• Market Size and Hiring Trends of IT in Telecom: Examining the IT spending surge in telecom and global hiring hubs in telecom IT. 
• Key IT Investment Areas in Telecom: NewGen technologies are shaping the telecom sector’s future. 
• Top IT Outsourced Workloads and Major Players: Understanding the rise of IT outsourcing and key players driving software, security, and cloud innovations. 

Market Size and Growth of IT in Telecom 

The telecom sector is experiencing a surge in IT and digital investments, with global spending projections reaching $1.595 trillion in 2024. This growth is fuelled by 5G rollouts, cloud adoption, and the increasing demand for AI-driven automation, as telecom companies look to enhance network performance and monetize digital services. 

Regional Growth Trends: Who’s Leading and Why? 

• The Americas lead as the largest telecom market, driven by high demand for data and rapid 5G expansion. Operators like Verizon invest heavily in 5G, AI-powered network optimization, and cloud transformation to meet connectivity needs.
• Asia-Pacific is the fastest-growing region, fuelled by rising smartphone adoption, digital commerce, and IT outsourcing. Countries like India, China, and South Korea are at the forefront of 5G deployments, IoT integration, and enterprise digitalization. 
• EMEA is seeing steady digital transformation, focusing on network automation, cybersecurity, and Open RAN adoption to enhance flexibility and reduce vendor dependency.

Fig: Region-wise Telecom Services Spending

Key Opportunities for Telecom Ecosystem Players 

For telecom operators, this means increased spending on 5G networks, cloud migration, and AI-powered automation to stay competitive. For IT service providers, it presents a massive opportunity to offer cloud solutions, cybersecurity services, and AI-driven analytics to telecom clients.  

Enterprise businesses using telecom services can benefit from more reliable, faster, and intelligent networks, enabling innovations like remote work, IoT automation, and smart cities. 

Key IT Investment Areas in Telecom 

As telecom companies embrace digital transformation, they are focusing on strategic IT investments to drive innovation, enhance network performance, and meet customer expectations.  

Here are the key areas where telecom companies are channelling their resources to shape the future of the industry:  

1. Cloud and Edge Computing: The Backbone of Digital Transformation

Telecoms are shifting from legacy infrastructure to cloud-based operations for on-demand scalability and low-latency applications like cloud gaming and self-driving cars. 

Companies like O2 Telefonica and Verizon are using AWS, Google Cloud, and Azure to migrate core network functions to the cloud. 

2. AI and Automation: The Future of Network Operations

AI is transforming network management, fraud detection, and customer interactions. By 2025, AI will handle 95% of telecom customer interactions. 

Cisco, T-Mobile, and Vodafone are investing in AI-driven fraud detection, network optimization, and predictive maintenance. 

Fig : Major IT spend areas of Telecommunication Technology

3. 5G Expansion and Network Slicing: Enabling High-Performance Applications

5G will power two-thirds of smartphone connections by 2027. Network slicing will enable customized connectivity for industries like healthcare, manufacturing, and smart cities.

4. IoT and Smart Connectivity: The Next Big Data Boom

With 25.1 billion connected devices expected by 2027, telecoms are expanding IoT-powered smart cities, industrial automation, and connected vehicles. MTN and Swisscom invest in IoT-driven infrastructure and enterprise solutions.

5. Cybersecurity: Telecom’s Biggest Challenge

Growing network complexity increases cyber threats. Telecoms are adopting AI-driven security solutions and STIR/SHAKEN protocols to combat fraud, robocalls, and identity spoofing.

Top IT Outsourced Workloads and Major Players

Telecom operators are increasingly outsourcing IT workloads to reduce costs and accelerate digital transformation indicating faster deployment and growing opportunities for IT vendors specializing in telecom software, security, and AI-driven automation.

Key Players:

• Tech Mahindra, TCS, and Infosys handle ~26% of outsourced workloads.
• Capgemini, Ericsson, and HCLTech specialize in network equipment testing and automation.

Top Outsourced Workloads:

• Application Development & Maintenance (20%) – Cloud-native telecom apps.
• Software Testing (30%) – Automated security and performance testing.
• Data Engineering (9%) – Scalable data pipelines for AI and network analytics.

 

Hiring Trends and Top Hiring Locations

With telecom IT investment rising, there’s a growing demand for skilled tech professionals.

Top job roles in demand:

• Software Engineers – Developing AI-based telecom applications.
• Network Engineers – Deploying 5G networks.
• DevOps Engineers – Automating cloud operations.

Top hiring locations:

Bengaluru, Washington D.C., San Francisco – Key hubs for telecom IT talent.

Key Takeaways and Future Outlook

• Telecom is no longer just about connectivity—it’s a data-driven, AI-powered industry with massive opportunities for businesses, IT service providers, and innovators.

• 5G, cloud, and AI are the top telecom IT investment areas, with network slicing and IoT creating new revenue streams.
• AI and automation will dominate customer service, cybersecurity, and network management by 2025.
• IT outsourcing is increasing, creating opportunities for service providers in software, cybersecurity, and cloud migration.
• Hiring in telecom IT is at an all-time high, particularly for AI, cloud, and 5G specialists.

Book a demo now to get hands-on real-time industry/market trends and reports with Draup’s AI powered Sales Intelligence platform.

Bridge the Skills Gap: 7 Customer Benefits of a Modern Skills Architecture

Addressing the skills gap is essential for organizations to remain competitive. A robust skills architecture aligns workforce capabilities with strategic objectives, enabling businesses to navigate market dynamics and capitalize on opportunities. Effective skills gap analysis is a key component. 

This post delves into seven major benefits of implementing a cutting-edge skills architecture, showcasing its advantages in the context of skills gap analysis. 

1. Granular, peer-level skills data: The foundation for accurate skills gap analysis

A modern skills architecture distinguishes itself by collecting skills data at a highly granular, peer level. This detailed approach allows for reporting on emerging skills with specificity across occupations, job families, and job roles. Instead of relying on broad generalizations, you gain actionable insights – the bedrock of effective skills gap analysis. You can now pinpoint specific skills deficiencies within your organization, enabling data-driven decisions about targeted training programs and resource allocation to close the skills gap. 

2. Integrating skills data for comprehensive skills gap analysis

A key benefit of a strong skills architecture is its ability to seamlessly integrate with your existing HR and talent management systems, like Workday. This interoperability is crucial for incorporating skills data into your established processes, creating a unified view for skills gap analysis. Consistent skills data across platforms ensures a holistic view of workforce capabilities, streamlines workflows for improved efficiency in conducting skills gap analysis, and enhances data accuracy by reducing silos, leading to more reliable results. 

3. Identifying digitally replaceable skills: A proactive approach to skills gap analysis

A forward-thinking skills architecture goes beyond identifying current and emerging skills by also pinpointing skills that are declining or can be automated through AI. This is achieved through understanding the Digitally Replaceable Quotient (DRQ) of individual skills. By understanding which skills are becoming obsolete, you can proactively plan for automation, develop targeted reskilling strategies for employees to address the skills gap, and optimize your workforce composition for the future, minimizing the impact of automation. 

4. Skill cluster quadrant for strategic workforce planning

A modern skills architecture helps organizations develop a Skill Cluster Quadrant, which maps emerging skill clusters for each function (e.g., HR, Finance, Engineering, Maintenance). This is essential for understanding the functional skills gap. This strategic tool provides a clear visual representation of the skills needed for each function, facilitating strategic workforce planning to close the skills gap, enabling the development of function-specific training programs, and ensuring that skills development initiatives are aligned with the specific needs of each function and the overall business strategy. 

5. Workload-based job descriptions: Building job roles aligned with skills gap analysis

A cutting-edge skills architecture transforms traditional job descriptions by mapping tasks and grouping them into workloads. This allows for a more accurate assessment of the skills gap required for specific roles. This approach helps you translate tasks into manageable workloads for more effective planning, align current and future skills with specific workloads to ensure job descriptions are future-ready and accurately reflect the skills gap, and provide the assets needed to update job descriptions and plan for future skills requirements. 

6. Big Data-driven skill gap analysis: Uncovering hidden deficiencies

A modern skills architecture leverages big data to interpret skills from professional databases and identify key areas of skills gaps. This data-driven approach supports reskilling initiatives by identifying the skills that need to be developed within the existing workforce to close the skills gap, focuses recruitment efforts on candidates who possess the skills that are most needed to fill the skills gap, and enables data-informed decision-making about talent management based on comprehensive skills data. 

7. Leapfrogging with AI agents: strategically addressing the skills gap

In certain areas, a robust skills architecture identifies leapfrog opportunities, where AI Agents can handle tasks while humans focus on higher-level responsibilities. This allows for a strategic approach to the skills gap, focusing human effort where it’s most impactful. This approach enables you to optimize human-AI collaboration by determining the optimal division of labor between humans and AI Agents, avoid overloading reskilling efforts by focusing on areas where humans can add the most value to close the skills gap, and drive innovation by freeing up human employees to focus on creative and strategic tasks. 

Draup’s: Your partner in bridging the skills gap 

Draup’s skills architecture offers a comprehensive suite of tools and capabilities to help organizations align their workforce with strategic objectives, address the skills gap, and prepare for the future of work. By providing granular data, seamless integration, and AI-powered insights, Draup empowers businesses to make informed decisions about talent management and drive sustainable success. With Draup, you’re not just reacting to the skills gap; you’re proactively closing it and shaping your workforce for the challenges and opportunities ahead. 

Addressing the skills gap for AI and automation-driven roles

 

AI and automation are fundamentally reshaping the global workforce. From AI-driven cybersecurity to automation-powered analytics, AI-augmented roles are growing faster than the available talent supply. Enterprise talent teams face a critical challenge daily: How do we reskill and upskill our existing workforce to stay competitive?   

Traditional workforce planning models are no longer sufficient – they are struggling to address the ever widening skill gap. Enterprises must move toward skills-based workforce planning, leveraging skills gap analysis and data-driven insights to identify skill adjacencies within the existing / accessible talent pool and create structured reskilling pathways.   

The urgency: AI adoption is moving way faster than talent supply  

The rise of AI-driven roles is not a future problem—it’s a “now” problem. According to industry reports:   

• By 2025, nearly 85 million jobs will be disrupted due to automation, while 97 million new roles will emerge. (World Economic Forum)  
• Demand for AI and machine learning specialists has grown by 74% in the last five years. (LinkedIn)
• Enterprises are struggling to find AI-ready talent, with a lot of executives citing skills shortages as a key barrier to AI adoption.   

Despite this growing demand, many companies overlook their biggest talent pool: their existing workforce. Employees with adjacent skills can be reskilled for high-value AI-powered roles—but identifying the right talent and pathways requires a data-driven approach to skills gap analysis.   

Why traditional talent strategies fail in the AI era   

HR and talent leaders often face these roadblocks when addressing AI talent shortages:   

• Lack of visibility into emerging skills – Traditional job descriptions do not capture evolving AI skill requirements, making skills gap analysis difficult. 
• Rigid job architectures – Organizations often prioritize job titles over skills-based workforce planning, without conducting proper skills gap analysis. 
• Inefficient reskilling efforts – Without data-backed insights, skills gap analysis is incomplete, and reskilling programs often miss the mark. 

• Talent leakage – High-skilled employees leave due to a lack of internal mobility opportunities, which skills gap analysis could help prevent.   

To close these gaps, enterprises must adopt a proactive, AI-powered talent intelligence approach that prioritizes skills gap analysis. 

How Enterprises Can Close the AI Skills Gap  

Powered by skill gap analysis, enterprises are taking a skills-first approach to workforce planning by:   

• Mapping skills adjacencies – Identifying employees with transferable skills for AI-driven roles. 
• Creating internal talent marketplaces – Enabling employees to explore AI-based career transitions.
• Investing in continuous learning – Shifting from one-time training to ongoing AI upskilling programs.
• Leveraging AI-powered talent intelligence – Using real-time data to track emerging AI skills. 
• This shift not only ensures talent readiness but also reduces hiring costs and improves employee retention.   

How Draup’s talent intelligence platform helps  

Draup empowers enterprises to navigate AI-driven workforce transformation with real-time, skills gap analysis-backed insights.   

• Real-time skills intelligence – Identifies emerging AI skills gaps before they impact business performance through skills gap analysis. 
• AI-powered talent mobility – Maps skills adjacencies to create targeted reskilling pathways informed by skills gap analysis. 
• Workforce planning for AI transformation – Helps CHROs align talent strategy with AI adoption goals, leveraging skills gap analysis.   

The future: Build AI talent from within  

Closing the AI skills gap is not just an HR challenge—it’s a business imperative.  

Enterprises that fail to act now risk falling behind in innovation and competitiveness.   

By leveraging AI-powered talent intelligence and skills gap analysis, CHROs can future-proof their workforce, improve retention, and drive AI-led business growth.  

The next move is yours: Are you ready to transform your workforce for the AI era? Write to us! 

Sales Intelligence to Map Organization Hierarchy in Complex B2B Sales: Identify the Right Decision Makers

 

Finding the right decision-maker isn’t about job titles—it’s about knowing who truly takes the deal forward. Influence stems from budget ownership and direct alignment of KPIs— and not just seniority. Yet, sales teams often make three critical mistakes that stall deals: 

1. Attributing decision-making authority to job titles – Senior executives may show interest, but true decision-making authority often lies elsewhere. 
2. Fail to multithread within influencing ecosystem– Deals often do not materialize through a single POC; multiple stakeholders and teams need to be engaged meaningfully and simultaneously.  
3. Wasting time on manual research – Sales leaders eventually reach there, but often later than their competitors do. 

Leveraging AI-powered sales intelligence solves such challenges by instantly mapping decision-making processes, identifying key influencers, and ensuring early engagement with the right stakeholders. 

Unlocking the Right Decision-Makers in Tech Sales with Sales Intelligence 

Consider the following scenario: 

A cybersecurity software provider was pitching its solution to a large enterprise technology company. The sales team identified a senior IT Operations executive as the key decision-maker. This executive: 

• Showed strong interest 

• Attended multiple demos 

• Provided positive feedback 

Yet, weeks later, the deal stalled. Communication slowed, and approvals never came through. 

By leveraging sales intelligence, the team realized three critical mistakes: 

1. Attributing Decision-making authority to job titles 

The sales team assumed the senior IT Ops executive had the authority to approve the purchase. However, while this executive happened to be one of the stakeholders, the actual evaluation, shortlisting and purchase were being driven by some other teams.  

• The engineering team was responsible for vetting compatibility and integrations. 
• The CISO’s team was assessing compliance risks. 
• The CFO and his team needed to justify the investment, factoring in the business priorities and annual objectives. 

By not engaging these stakeholders early, the sales team failed to build technical and financial alignment, causing delays in approval. 

 

2. Fail to multithread within influencing ecosystem –

Chasing one contact in an account is a classic sales trap. If that person goes dark, changes roles, or leaves the company, your deal is dead in the water. Yet, too many sales reps rely on a single POC, only to realize too late that: 

• They don’t have enough internal pull to drive the deal. 
• Their priorities shift, and your solution gets deprioritized. 
• They leave, forcing you to rebuild momentum from scratch. 

Multithreading—engaging multiple stakeholders across the buying committee—keeps your deal moving, reduces risk, and ensures you’re not left stranded when a single contact drops off. 

3. Wasting time on manual research – 

You can gather all the data manually, but by the time you’re done, your competitor has already had the key conversations and moved the deal forward. While the sales team was stuck in research mode, buyers were already: 

• Engaging with competitors who delivered insights faster. 
• Progressing through internal discussions without their input. 
• Prioritizing solutions that moved at their speed. 

How Leveraging Sales Intelligence Could Have Prevented This 

Sales intelligence fixes this by giving reps real-time data on key stakeholders, account activity, and buyer intent—so you can engage the right people at the right time and stay ahead of the competition. 

Sales intelligence platforms like Draup helps Microsoft, Salesforce, Accenture, CapGemini and 260+ others fast track enterprise sales by revealing market/account-level signals, buyer behavior, decision-making patterns, existing tech stack and channel partner ecosystems. 

Draup empowers sales teams to navigate intricate organizational hierarchies with confidence. Draup’s AI sales intelligence instantly maps the entire decision-making processes, identifies key influencers beyond surface-level connections, and ensures early engagement with the stakeholders who truly drive deals forward. 

This strategic advantage translates into accelerated market penetration, enhanced ABM effectiveness, and ultimately, the ability to secure and expand into high-value accounts. 

Discover how Draup’s sales intelligence gives your team the edge in complex B2B sales. 

Book a demo now!!