People Analytics is Reinventing Hiring – Ignore It, and You’ll Lose Top Talent

 

75% of global HR leaders are struggling to fill job vacancies. This challenge isn’t arising because there is a lack of talent. It is the result of rigid hiring models filtering skilled candidates (due to narrow job descriptions, experience benchmarks, etc) and limiting access to untapped talent pools. 

People analytics – the practice of using data-driven insights to understand workforce trends, employee behaviour, and skill gaps – is changing that. By extending beyond internal data to include insights from the external labor market – such as talent supply-demand trends and skills mapping – HR leaders gain a clearer view of where talent lies and how to access it. 

With this Talent Intelligence, HR leaders can get expanded visibility, break free from traditional hiring constraints, uncover overlooked talent, and build stronger, future-ready teams. 

Which Hiring Roadblocks Is People Analytics Breaking Down? 

Let’s explore three key hiring roadblocks that HR leaders can break down by combining people analytics with talent intelligence: 

1. Talent Supply-Demand Heatmaps: Identifying Where to Hire and How to Stay Competitive

Companies looking to expand their workforce often focus on traditional talent hubs, overlooking regions that offer both skilled candidates and cost advantages. This narrow focus leads to longer hiring cycles, higher costs, and missed opportunities to secure top talent. 

People analytics solves this by offering data-driven insights into: 

• Emerging talent hubs with a strong supply of skilled professionals. 
• Regions with talent shortages to manage hiring expectations. 
• Location-based salary benchmarks to build competitive compensation strategies. 

For example, a semiconductor company faced hiring challenges for chip design engineers in Silicon Valley due to high competition and rising salaries. Talent Intelligence identified Toronto as a cost-effective alternative with a strong talent pool. By opening a new office in Toronto, the company reduced hiring costs, improved time-to-fill, and gained access to a rich talent pool to support its growth.

2. Strategic Workforce Planning: Preparing for Future Hiring Needs

Many enterprise businesses struggle with last-minute hiring, rushing to fill roles only when employees leave, or new projects demand it. This reactive approach often results in missed opportunities, higher costs, and talent gaps. 

People analytics combined with Talent Intelligence helps HR leaders shift to a proactive strategy by providing insights into: 

• Predicting future hiring needs based on industry trends and business growth plans. 
• Identifying at-risk skills that may become outdated, allowing businesses to focus on upskilling. 
• Developing succession pipelines by assessing internal talent readiness for leadership roles. 

For example, a global pharmaceutical company using Talent Intelligence forecasted emerging demand for biostatisticians and AI specialists. By investing in university partnerships and internal training, they filled key roles efficiently while strengthening their talent pipeline and being prepared in advance.

3. Skills Adjacency Mapping: Unlocking Hidden Talent Pools for Better Role Fit

Many skilled professionals are overlooked simply because their job titles don’t match traditional hiring criteria. However, roles evolve, and skills often transfer across industries and functions – meaning enterprise businesses could be missing out on high-potential talent just because they aren’t looking in the right places. 

Talent Intelligence helps HR leaders uncover skills adjacency by providing insights into: 

• Which roles have overlapping skills and where hidden talent pools exist. 
• How employees from adjacent roles have successfully transitioned into new positions. 
• Which internal employees can be reskilled based on their current capabilities. 

For example, a manufacturing company struggling to hire data analysts discovers through talent intelligence that quality control specialists within their workforce already use data visualization and predictive modelling – key analytics skills required for the role. By identifying this overlap, they create personalized upskilling paths, improving retention while reducing hiring costs and time-to-fill. 

Unlock the Power of Talent Intelligence with People Analytics 

The future of hiring lies in adaptability. As industries evolve, businesses must move beyond reactive hiring and embrace talent intelligence that anticipate workforce needs. 

Combining people analytics with talent intelligence is enabling HR leaders to make smarter decisions – not just about where to hire, but how to build resilient, future-ready teams. By focusing on skills, emerging talent hubs, and workforce agility, organizations can stay competitive in a rapidly shifting landscape. 

Looking ahead, the most successful companies will embrace flexible hiring models, prioritize continuous learning, and invest in internal mobility. Those that leverage talent intelligence to guide these efforts won’t just fill roles – they’ll create a workforce capable of driving innovation and long-term success. 

Draup helps companies like Pepsico, Randstad, Vodafone, Paypal, Pfizer, Intuit and many more to understand such trends and plan early – factoring in the futuristic software engineering roles required to drive Gen AI led innovation and growth.  

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Account Expansion Made Simple: Using AI to Unlock Cross-Selling and Upselling Opportunities

Cross-selling or upselling generates 42% more revenue than solely focusing on acquiring a new account. However, enterprise sales teams often miss these opportunities due to a lack of real-time account intelligence such as business priorities, executive movements, tech stack changes, partnerships, and more. 

Having access to these AI-powered sales intelligence insights helps sales teams unlock new revenue streams by: 

• Tracking real-time account changes to identify cross-selling opportunities 
• Identifying the right decision-makers aligned with those opportunities 
• Building the right messaging that will resonate with the decision-makers

Track Real-Time Account Changes to Identify Cross-Selling Opportunities 

The first step in enterprise account expansion is identifying the right business unit by tracking real-time changes within the customer’s organization that align with your solution. Since up to 50% of sales time is often wasted on unqualified leads, identifying the right account becomes crucial for successful cross-selling and upselling. 

Tracking real-time account changes includes mapping parent companies, subsidiaries, and business units with their respective business priorities, pain points, and account-level changes. With an AI-powered sales intelligence platform, enterprise sales team can leverage real-time data to: 

• Pinpoint high-value target business units within the customer ecosystem. 

• Track budget shifts, product launches, partnerships, and organizational changes that signal new priorities. 

• Monitor executive movements, restructures, and hiring trends for potential changes. 

For example, an IT service provider specializing in cloud cybersecurity uses AI sales intelligence to analyze public records and discovers that a multinational account is opening a new practice area in the Eurozone. Real-time tracking further reveals that the company is hiring a CIO with a track record of launching business units aligned with cloud cybersecurity. Armed with this insight, the enterprise sales team—now having identified an opportunity—can strategically focus on upselling and cross-selling, maximizing the opportunity.  

This approach reduces the research efforts and enables the sales team to identify and pinpoint the right account and their business intention with precision—eliminating the need for cold outreach. 

Identify the Right Decision-Makers Aligned with Those Opportunities 

The next step in account expansion is identifying key stakeholders and decision-makers within the target business unit. With an average of 7.4 decision-makers involved in a typical B2B purchase, targeting the right individuals is essential for successful upselling and cross-selling. 

With an AI-powered sales intelligence platform, the enterprise sales team can pinpoint the right decision-maker within the target organization by: 

• Identifying the target decision-makers based on the business function they are responsible for and the tech stack to ensure it aligns with the solution ecosystem. 
• Weighing the hierarchy-level tag to understand their area of responsibilities, decision-making abilities, and potential influence within their organization’s structure. 
• Determining the budget control level to identify executives with the authority to approve deals. 
• Assessing their deal conversion history, location, and outsourcing size.  

For example, a records management solutions provider serving a US-based healthcare company leverages the sales intelligence platform to identify key decision-makers within a target business unit. The platform reveals that the newly appointed Chief Compliance Officer (CCO) has a strong track record of engaging with vendors prioritizing HIPAA-compliant PHI cybersecurity. 

This insight helps the enterprise sales team confirm that the new CCO—with a history in PHI cybersecurity—is a key stakeholder aligned with their solution ecosystem. This sales intelligence also validates the company’s commitment to stronger compliance and healthcare records management. 

As a key decision-maker, the CCO plays a critical role in overseeing the organization’s compliance-focused records management strategy. Recognizing this, the enterprise sales team can strategically engage them, emphasizing records management upgrades. This targeted approach increases the chances of securing an upsell before competitors step in. 

Build the Right Messaging That Will Resonate with the Decision-Makers 

Generic or irrelevant solution recommendations are known to frustrate the target customers and wear down trust. That’s why the final and most critical stage of account expansion is crafting the right messaging using sales intelligence—tailored to the priorities of the target business unit and the decision-makers. 

To build the right messaging, the AI-powered sales intelligence platform enables enterprise sales teams to: 

• Map offerings to customer pain points by evaluating investment initiatives, current operations, and financial metrics. 
• Focus on value propositions that align with regulatory and shareholder concerns while linking solutions to key programs and leaders. 
• Use a data-driven approach, leveraging intent data for account segmentation and personalized campaigns. 
• Execute targeted marketing campaigns using micro-cadences to nurture stakeholder relationships. 
• Implement personalized omnichannel marketing campaigns for initiatives like product upgrades, mergers and acquisitions, and transformation programs. 

For example, an ML solutions provider to a robotics engineering company using sales intelligence discovers that the target business unit is rapidly investing in its robotics operations in the Eurozone. Real-time tracking reveals recent hires for compliance specialist roles and the appointment of a Chief Robotics Officer (CRO) who prioritizes regulatory compliance. These insights indicate the company’s strong commitment to growth in the Eurozone while ensuring adherence to GDPR and industry-specific standards with the help of the new CRO. 

Armed with these insights, the enterprise sales team can craft tailored messaging for the new CRO, highlighting their expertise in compliant and secure robotics solutions. This targeted approach ensures maximum impact and increases conversion rates. 

Leveraging AI-powered account intelligence isn’t just an advantage today, it’s a necessity. Businesses that embrace AI-driven sales intelligence will build stronger relationships, drive more revenue, and unlock long-term account growth.

Using AI-driven Account Planning to Identify High-Potential Enterprise Sales Opportunities

 

A key challenge in account planning for enterprise ABM is not just finding accounts from your Serviceable Obtainable Market (SOM), but also shortlisting the ones with high potential, identifying the correct time to engage – thus edging out your competitors chasing the same set of accounts. 

AI-driven account planning makes this possible by: 

• Prioritizing accounts based on real-time market/account-level signals (executive movements, business expansion plans, new product launches etc) to engage at the right time. 

• Tracking competitor activity to ensure timely reach outs, with relevant messaging highlighting your edge over competitors. 

• Analyze account’s current installed base to uncover integration opportunities, target competitor weaknesses, and position your solution for strategic displacement and expansion. 

This data-driven account planning approach helps sales teams engage smarter, prioritize better, and gain a competitive edge.  

AI-Driven Account Planning: A Step-by-Step Approach for Maximum Impact 

Account planning is a continuous, intelligence-driven approach that enables sales teams to focus on the right accounts at the right time. Here’s how to pick up the right accounts: 

Step 1: Prioritizing accounts based on real-time market/account-level signals 

Focus on those showing strong real-time buying signals, such as: 

• Use hiring & technology investments as a predictor of upcoming purchases – Enterprises ramping up hiring in cloud security, AI, or analytics are preparing for large tech investments. Ignoring these signals means engaging too late in the buying cycle. 

Fig: Following strategic and tactical signals for emerging opportunities 

• Track M&A activity to identify companies actively reassessing vendors – Most enterprises undergoing mergers reassess vendor contracts. Ignoring these shifts means missing a prime engagement window. 

Step 2: Tracking Competitor Movements for Strategic Positioning 

Competitor engagement can make or break a deal. Monitoring which accounts competitors are targeting helps sales teams proactively position their solutions where they have the strongest advantage –  

• Track Competitor-Engaged Accounts – Accounts evaluating alternative solutions may still be open for engagement. Lack of visibility into competitor engagements can lead to missed opportunities. 

• Identify Weak Spots in Competitor Offerings – If existing vendors fail to meet expectations, accounts become more open to switching. Research shows that 68% of customers leave due to perceived indifference from vendors. 

• Time Your Pitch Strategically – Stay ahead by tracking competitor pricing shifts, contract renewals, and market positioning. 

Step 3:  Analyze account’s current installed base 

Understanding an account’s existing tech partnerships and outsourcing landscape helps uncover expansion opportunities and optimize engagement strategies. Here’s how to leverage installed base insights effectively: 

Pinpoint Expansion and Upsell Potential Through Outsourced Workloads – Analyze outsourced IT workloads to identify investment areas and position your solution for expansion, cost optimization, or strategic advantage. 

Map Partner Relationships to Unlock Strategic Engagement Paths – Asses an account’s tech and service provider partnerships to identify co-sell opportunities, competitive positioning, or differentiation strategies. 

The Way Forward: Transforming Account Planning into a Competitive Advantage 

Winning enterprise sales isn’t about chasing every account—it’s about knowing which accounts to pursue, when to engage, and how to outpace competitors. AI-driven sales intelligence makes this process sharper, faster, and more strategic. 

By leveraging data-driven insights to track real-time business signals, monitor competitive movements, and prioritize high-value opportunities, sales teams can shift from reactive selling to proactive engagement. This isn’t just about efficiency—it’s about transforming account planning into a competitive advantage that drives revenue growth.  

AI-powered Sales Intelligence isn’t the future of account planning—it’s the present. The teams that harness it today will be the ones closing the biggest deals tomorrow.  

Ready to transform your account planning process? 

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How Labor Market Data Helps Identify and Develop High-Quality Talent Pipelines

In 2025, 73% of companies face challenges in building high-quality talent pipelines due to widening skill gaps, high competition, and rising talent costs. A major factor contributing to this is a lack of access to up-to-date talent intelligence. 

Having access to such data on talent intelligence can help workforce planning teams extract insights to: 

• Study industry and workload-level emerging skill trends  
• Capture location-level workforce insights  
• Extract role/skill-level peer compensation insights  

These insights help organizations build resilient talent pipelines that drive mission-critical business outcomes. 

Study Industry and Workload-Level Emerging Skill Trends  

With 39% of core skills expected to change by 2030, HR leaders developing a talent pipeline need to first study the industry’s skill pathway based on emerging and declining trends. This helps determine the most in-demand technical and soft skills across peers and competitors. 

Using advanced AI talent intelligence platforms, organizations can analyze real-time global labor market data to: 

• Track skill emergence velocity across industry segments with greater accuracy than traditional methods 
• Predict skill half-life timelines to prioritize reskilling and upskilling investments 
• Quantify skill adjacencies to identify high-potential internal mobility candidates 
• Measure skill premium values in compensation packages across competitor talent markets 
• Detect early signals of disruptive skill shifts 6-18 months before mainstream adoption 

For example, HR leaders at a cloud technology company using predictive labor market analytics identify an emerging demand for specialized NLP and generative AI skills fourteen months before competitors. Their analysis reveals not just isolated skills but entire skill clusters forming around AI engineering roles, indicating a significant increase in hiring velocity within their competitive landscape. 

This talent intelligence empowers HR leaders to build a high-quality talent pipeline by proactively upskilling and reskilling their workforce to align with future skill demands. Simultaneously, they can develop strategic recruiting campaigns for specialized NLP and generative AI talent, securing top candidates ahead of the competition. 

Capture Location-Level Workforce Insights Using AI Talent Intelligence 

With 74% of employers facing or anticipating talent shortages in traditional markets, HR leaders are turning to AI-driven labor market data to assess talent availability, hiring competition, and cost-efficiency at a location-specific level. These insights help organizations identify and develop talent pipelines strategically.  

By leveraging AI-driven talent intelligence, enterprises can: 

• Map talent density heat zones with neighborhood-level precision for specialized skills. 
• Identify “hidden gem” markets with high-quality talent at 30-45% lower acquisition costs. 
• Measure remote work adoption rates by role and industry to refine distributed workforce strategies. 
• Track competitor hiring trends at the role and skill level to tap into high-quality talent pipelines. 

 For example, a San Francisco-based cloud IT service provider primarily serving EU-based clients discovered that Warsaw’s cloud engineering talent pool is 81% larger and 34% less utilized than in the Bay Area. This insight into talent availability and hiring competition makes developing a high-quality talent pipeline significantly easier. 

By integrating real-time AI-powered talent intelligence, HR leaders can develop a data-driven talent pipeline and make strategic hiring decisions. These insights provide a competitive edge in talent acquisition while optimizing workforce strategies based on location-specific trends. 

Extract Role-to-Skill-Level Peer Compensation Insights  

Just as competitive compensation is key to attracting and retaining talent, role-to-skill-level compensation intelligence enables data-driven benchmarking for a competitive edge. By benchmarking pay and benefits against industry peers, HR leaders can refine their talent pipeline strategies to attract top candidates while maintaining cost-efficiency, ahead of their peers.  

Role and skill-level compensation intelligence enables HR leaders to analyze median base pay, stock options, and other benefits at a location-specific level. Unlike traditional compensation models tied to job titles, skill-based compensation rewards employees for their expertise, competencies, and specialized skills. By leveraging real-time talent intelligence, organizations can refine salary structures to ensure fair, competitive, and strategically positioned pay packages strengthening their talent pipeline.  

For example, AI-powered labor market data reveals that the median base pay for cloud engineers in Warsaw is just 12% of the San Francisco Bay Area cost. This insight enables HR leaders to benchmark compensation strategically, attracting high-quality talent in cost-effective locations. By offering competitive pay, the company strengthens its talent pipeline and enhances its position as a top employer in key markets. 

By combining workforce-level talent intelligence, location intelligence, and compensation insights, HR leaders can identify new talent pools and explore cost-efficient locations for remote or hybrid teams. This data-driven approach ensures competitive hiring decisions while optimizing workforce costs.

Market Report: Digital Shift and Opportunities in BFSI – IT Investment Trends, Key Players & Market Growth

 

The Banking, Financial Services, and Insurance (BFSI) industry is rapidly evolving as financial institutions accelerate IT investments to enhance customer experience, security, and operational efficiency. 

With growing reliance on AI-driven automation, cloud computing, and digital payments, BFSI players are transforming their operational models to stay competitive. 

To navigate this shift, enterprises must focus on three critical areas: 

• Tracking Digital Transformation in Retail – AI, cloud computing, and supply chain optimization are shaping IT investments in BFSI. 
• Monitoring Competitor Tech Adoption – Leading players are leveraging predictive analytics, AR/VR, and digital payments for a competitive edge. 
• Identifying Growth Regions for Retail IT – Asia-Pacific, North America, and Europe are key hubs for BFSI innovation and IT spending. 

This blog explores the critical trends and insights for BFSI published by Draup, offering a deep dive into market size, outsourcing trends, and top investment areas in the industry. 

Tracking Digital Transformation in Retail Banking 

BFSI companies are accelerating IT investments to enhance retail banking experiences, optimize operations, and strengthen fraud prevention.  

By 2028, BFSI IT spending is projected to reach $44.9 trillion, driven by AI-driven personalization, omnichannel banking, and cloud adoption​. 

Figure: Projected IT Spending Growth with a 9.65% CAGR by 2028 

Key Areas Driving Digital Transformation –  

1. AI-Driven Personalization

• Enhancing customer engagement – AI is powering real-time financial recommendations and automated advisory services.
• Major players – JPMorgan Chase, HSBC, and Citi use AI to offer personalized credit offerings, chatbot-assisted banking, and fraud detection​. 
• For example, Wells Fargo’s AI-driven personalization led to a 50% increase in digital product adoption rates among its customers. 

2. Omnichannel Banking and Cloud-Based Transactions

• The digital banking market is projected to reach $19.89 trillion by 2026, growing at a CAGR of 3% 
• Seamless digital interactions – Cloud-based platforms unify mobile apps, online banking, and in-branch experiences. 
• Major players – Citibank and Goldman Sachs are expanding hybrid cloud infrastructure for real-time banking. 

3. Supply Chain Optimization

• AI-powered risk management – Predictive analytics detects fraud patterns in real-time payments & lending. 
• Major players – Visa & Mastercard use AI to automate supply chain finance and enhance transaction security​. 

Banks are enhancing security with voice banking and biometric authentication, enabling seamless access while reducing fraud. Blockchain adoption is securing cross-border transactions, ensuring faster settlements, transparency, and compliance. 

Monitoring Competitor Tech Adoption 

BFSI firms are increasingly tracking competitor IT strategies to gain an edge in predictive analytics, AR/VR-driven banking, and next-gen payment solutions.   

Key Technologies Competitors Are Adopting –  

1.Predictive Analytics and AI 

• Enhancing risk management – AI-powered predictive analytics detects fraud patterns and optimizes credit risk models. 
• Major players – JPMorgan Chase, Citibank, and Bank of America use AI to analyze customer spending, automate lending decisions, and prevent fraud.​ 
• Market impact – Predictive analytics has improved fraud detection rates by 30-40%, leading to faster and more secure transactions​. 

2️. AR/VR-Driven Customer Experiences 

• Transforming customer engagement – AR/VR is being tested for immersive financial planning and virtual advisory services. 
• Major players – HSBC and Citi are experimenting with AI-powered virtual assistants and VR-based financial advisory to enhance digital banking. 
• Emerging trend – Virtual reality banking could soon be used for remote collaboration in high-net-worth wealth management.

Figure: Leading BFSI Players Driving Innovation Through Key Technologies 

3️. Next-Gen Payment Technologies 

• Blockchain & AI in transactions – BFSI firms are integrating blockchain for secure payments and AI for real-time fraud detection. The market is expected to grow to $10.85 billion in 2025, at a CAGR of 55.3%. 
• Major players – Citigroup and Deutsche Bank are deploying blockchain-powered payment networks to reduce cross-border transaction costs​. 
• Industry shift – Visa and Mastercard have launched AI-driven fraud prevention tools to enhance contactless and digital transactions. 

BFSI leaders are investing heavily in predictive analytics, immersive banking experiences, and blockchain-powered payments to stay ahead of evolving customer expectations.  

Identifying Growth Regions for Retail IT 

BFSI enterprises are strategically expanding IT investments in regions with high digital adoption, strong fintech ecosystems, and regulatory support for digital banking.  

Key Regions Driving BFSI IT Innovation –  

1️. Asia-Pacific: Fastest-Growing Market for BFSI IT 

• Market Growth: The Asia Pacific IoT in BFSI market is expected to reach nearly $116.2 billion by 2026, growing at a CAGR of 28.6%. 
• High fintech adoption & digital banking expansion – APAC leads in mobile payments, AI-driven risk assessment, and digital wallets. 
• Major players – India, China, and Vietnam are integrating AI-powered lending platforms and real-time payments to accelerate financial inclusion​. 

Figure: Key Regions Driving Retail IT Growth in the BFSI Sector 

2️. North America: IT Investment Hub for Security & Payments 

• Strong focus on cybersecurity & cloud adoption – U.S. and Canada BFSI firms are prioritizing AI-driven fraud detection and cloud migration. 
• Major players – Fintech disruptors like Stripe and Square are redefining digital transactions and embedded finance solutions​. 

3️. Europe (EMEA): Open Banking & Blockchain Expansion 

• Regulatory-driven digital transformation – Europe is leading in open banking initiatives, blockchain-based transactions, and AI-powered credit scoring. 
• Major players: Germany, UK, and France are investing in cross-border digital payments and AI-based lending models​. 

Asia-Pacific, North America, and Europe are leading BFSI IT growth by adopting AI-driven banking, cloud-native financial services, and secure digital payment ecosystems. As financial institutions scale digital transformation efforts, tracking high-growth markets will be critical for long-term IT investment strategies. 

Conclusion 

BFSI IT investments are surging, driven by AI-powered personalization, omnichannel banking, and cloud security enhancements. Financial institutions must: 

• Prioritize AI and cloud adoption to enhance customer experience & streamline banking operations. 
• Monitor competitor adoption of predictive analytics, AR/VR, and blockchain to stay ahead of innovation. 
• Expand IT investments in high-growth regions where BFSI digital transformation is scaling rapidly. 

With digital banking, fintech partnerships, and next-gen payment solutions shaping the BFSI landscape, tracking IT trends and strategic investments will be key to sustaining growth in the evolving financial ecosystem. 

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. 

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