A game-changer for businesses, AI agents allow them to boost productivity, deliver personalised customer experiences , and drive topline growth. But what’s next? 

What else can businesses in ASEAN expect, and how can they leverage AI agents to grow their business and develop their workforce further? 

We got Sujith Abraham, SVP and General Manager for Salesforce ASEAN, and Gavin Barfield, Vice President & Chief Technology Officer, Solutions, Salesforce ASEAN, to share their top 10 predictions that will shape the course of ASEAN’s dynamic business landscape in 2025. 

Sujith leads the Salesforce ASEAN business and brings more than 20 years of leadership experience in enterprise technology to his role. He’s passionate about ensuring the success of our ASEAN customers, partners, employees and community, and transforming good ideas and people into impactful businesses built on strong culture and high levels of engagement. Gavin also has over 20 years of experience, with a solid technology background that includes IT infrastructure, enterprise architecture, cybersecurity, and program management across a variety of industries. 

Given their expertise and authority in the field, they foresee ASEAN businesses transitioning from AI experimentation to full-scale implementation in 2025, as businesses work toward a future where humans and agents drive customer success together with AI, data and action. Here are the key trends identified.

1. AI progressing beyond the experimentation stage, driven by autonomous agents

When Generative AI launched in 2022, there was huge excitement around the technology’s potential to revolutionise operations, boost productivity, and enhance customer experiences. Many businesses invested significantly in developing a generative AI strategy.

Despite initial excitement, few organisations have moved beyond Proofs of Concept (POCs) and limited trials to full-scale implementation. In some cases, Generative AI has failed to deliver accurate and useful outputs due to incomplete data. In others, solutions are disconnected from workflows, making them clunky and inefficient. Many applications of Generative AI, such as copilots and chatbots, were created as “solutions looking for problems”, focusing on experimentation rather than solving actual business issues.

Unlike chatbots and copilots, AI agents can autonomously navigate tasks and make real-time decisions directly in the flow of work — moving from mere assistance to taking action based on live data and context, marking a major step forward in enterprise AI.

In 2025, purpose-driven AI agents designed to address specific workflow needs and provide measurable benefits will help organisations move beyond experimentation to achieve tangible outcomes. For this to happen, generative AI needs to be grounded in the right data and delivered in the flow of work to offer meaningful impact.

2. Autonomous agents providing opportunity for topline growth

In the past two years, businesses have focused on cost-cutting measures in response to global economic uncertainties and slowing growth. The availability of autonomous agents today has now surfaced more opportunities for businesses to drive topline growth. 

How, you ask? These agents will deliver on the promise of AI, succeeding where solutions such as copilots fall short. One limitation of earlier innovations was their siloed focus on unstructured data. For example, copilots could only act based on data within emails or presentations. This misses critical transactional context, such as customer purchase history or product details. 

Copilots could only see part of the customer story, causing them to fall short on providing actionable insights. Businesses, in turn, missed out on opportunities to foster deeper and more compelling customer relationships that generated new revenue streams.

Autonomous agents can significantly impact a company’s growth trajectory. Take a bank that works with thousands of business clients as an example. An initial analysis of spend may lead the bank to think that most of their customers are SMEs with small spends. But a deeper look reveals that these businesses are spreading their spend across banks. 

It’s extremely difficult to turn the workforce around to deepen engagements with all customers. Imagine if they implemented autonomous agents to maintain consistent customer engagement without constant human oversight. And agents operate 24/7 – think of how much coverage across customers is now possible. This enables the bank to increase its revenue base, which might otherwise be lost to competitors.

AI agents also allow sales teams to automatically pre-qualify leads before handing them over to human agents. This way, human agents don’t waste time on unresponsive prospects, basic inquiries, or low-engagement leads, which can significantly improve productivity and the bottom line.

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3. Out-of-the-box AI/agentic solutions and unified data will underpin AI success

In the race to operationalise AI, the winners will be those who forgo DIY solutions in favour of out-of-the-box solutions that offer superior speed, deployment, and accuracy. Businesses that adopt out-of-the-box solutions can focus on AI deployment and achieve immediate impact and value. In contrast, those who attempt to “DIY” their AI often face setbacks in the form of hidden costs and a slow realisation of AI capabilities. 

Having the right data foundation is also key to maximising ROI from AI investments. Businesses need to consolidate structured data, such as customer transaction records, and unstructured data, such as customer emails, product information, and corporate policies, to build a unified view of their customers. 

Without it, AI cannot deliver accurate, contextualised, and trusted outputs. Zero-copy capabilities are needed to ensure companies maximise their existing assets while minimising data preparation costs.

4. An organic AI ecosystem emerging within the region

AI is ushering in one of the biggest technological shifts of our generation, creating new services, roles, and industries. Just as the invention of smartphones and mobile applications created a thriving ecosystem of app developers, the growth of AI platforms is fostering a new generation of AI developers. 

This drives innovation in ASEAN and opens the pathway for local talent to develop AI tools tailored to meet the region’s unique needs — whether it is Small Language Models (SLMs) that support native languages like Singlish or Taglish, or advanced models that tackle specific challenges like anti-money laundering. 

With a combined population of over 650 million (including individual markets with over 100 million people such as Indonesia and the Philippines), and a combined GDP comparable to major economies, there’s a massive opportunity for AI developers in ASEAN. 

The growth of the AI industry in the region will not only attract established global tech giants to set up operations and company headquarters locally, but also catalyse the birth of home-grown startups. 

With that, we’ll see a migration of strategic roles typically available in the West to this part of the world, creating new opportunities for the future workforce.

5. AI agents disrupting traditional service models in ASEAN with scalable capacity, intelligence, and personalised experiences

In ASEAN, businesses often hire additional service staff as a quick fix for improving customer experience, especially with lower labour costs in the region. However, increasing headcount alone doesn’t necessarily improve problem resolution or overall customer satisfaction.

AI agents provide a fundamentally different approach by autonomously handling requests and enhancing customer interactions in ways that go beyond scaling capacity. This isn’t about efficiency alone but delivering high-quality customer service. AI agents leverage real-time data to provide context-aware support, making decisions and taking action as customer needs arise.

With AI agents embedded directly into workflows, businesses can reimagine customer service, delivering faster and more accurate responses without increasing complexity or requiring extensive training. 

6. AI agents redefining jobs, empowering employees to focus on strategic work

AI agents are transforming the workforce by automating repetitive and time-consuming tasks, freeing employees to focus on higher-value work that drives innovation and growth. 

This presents an opportunity for the workforce to transform their skill sets and take on more strategic roles. As AI agents become increasingly integrated into the workforce, employees will need to develop new skills to manage and optimise them. They’ll also have to leverage their industry knowledge to train these agents so that they can deliver the desired business outcomes. 

This transformation mirrors the 1980s shift in banking, when staff moved from routine tasks like producing bank statements to customer service and financial advisory roles as automation took over.

Fast-forward to today — a telco that relies on AI agents to handle routine customer inquiries at scale will be able to empower its employees to focus on strategic tasks like optimising AI deployment and enhancing customer experiences. 

This not only creates new career opportunities for the telco’s staff, but also allows them to save on operational expenses and infrastructure costs. With virtual AI agents running routine operations, the telco can easily scale operations, without necessarily building new offices or stores for its workforce to service the expanded client pool. 

By blending human expertise with AI, companies can create a more agile workforce focused on driving growth and preparing employees for roles that require creativity, problem-solving, and strategic thinking.

7. New AI skill sets required for building and testing agents, including defining guardrails

As AI agents become central to business operations, there’s a greater need for professionals with specialised skills to guide these systems effectively. These skills will include being able to define agent instructions, craft prompts, and set guardrails.

Writing prompts may seem straightforward because they are written in natural language. However, crafting and refining these instructions and establishing clear guardrails to ensure an AI model performs as intended requires expertise.

While prompt engineering for LLMs is common, writing instructions and setting guardrails for reasoning engines will become critical skills. As more organisations integrate AI agents into their workflows, the demand for professionals with the skills to build and test agents in real-world scenarios will increase.

8. New types of AI models will push the boundaries of what AI can deliver

In 2025, we’ll see new, highly specialised AI models that go beyond text generation to drive complex, autonomous actions. Salesforce’s xLAM (Large Action Model) is at the forefront of this evolution. Unlike traditional Large Language Models (LLMs), which excel at generating responses, xLAM models are designed for action and decision-making, allowing AI to autonomously execute tasks and manage workflows without requiring explicit instructions.

These Large Action Models add a new dimension to CRM, enabling AI agents to handle tasks like function-calling, reasoning, and planning, adapting actions to fit real-world business contexts. By managing entire workflows proactively, like an autonomous sous chef that prepares each step, xLAM models can streamline operations and enhance decision accuracy across various environments.

As businesses adopt these models, xLAM can operate across multi-agent systems, coordinating actions between specialised AI agents to tackle increasingly complex, customer-focused processes. This innovation will make AI a powerful partner in business, delivering efficiency, context-aware responses, and automated actions that drive customer success with accuracy and reliability.

We’ll also see a proliferation of Small Language Models designed for particular industries or purposes.  These models are trained on smaller but more reliable datasets and are effective at performing certain tasks.  They’re cheaper to run, train and often more accurate than ‌Large Language equivalents.

9. Agents building agents, agents talking to agents becoming commonplace

Just as organisations have employees specialised in specific functions, AI agents will soon be assigned unique roles within a network. These agents will work alongside human employees, communicate with other agents, and create new agents as business needs evolve. Each agent will have a defined function, allowing the network to handle a wide range of tasks efficiently.

In this agent network, meta-agents will be crucial, coordinating actions across other agents to keep workflows seamless. For example, a concierge agent might interact with users, guiding them on tasks it can help with and providing updates on task progress. 

An orchestration agent would assess user needs and route requests to the proper agent, ensuring tasks are managed effectively. This setup enables collaboration on platforms like Slack, where human employees and AI agents can interact as a unified team, improving responsiveness and coordination.

10. Robotics driving the next wave of AI innovation

Robotics, the fourth wave of AI will emerge, transforming how businesses and customers connect. Beyond agents, robotics will see interactions evolve from text and voice systems to immersive experiences with physical robots and virtual avatars with lifelike, dynamic, and highly interactive engagements.

Picture virtual avatars powered by AI agents, with heads that move, lips that smile, and expressions that react naturally during interactions. This evolution will create more personal and engaging experiences in physical settings like a concierge in shopping malls, where customers can hold a real-time conversation with a lifelike avatar rather than typing queries into a screen.

At the same time, physical robotics will bring AI agents into the tangible world, unlocking new opportunities in environments such as F&B. Imagine a robotic barista powered by an AI agent that can offer personalised drink recommendations based on a customer’s past orders‌ — ‌including details like sugar preferences‌ — ‌and prepare the drink instantly.

Robotics will empower businesses to deliver natural, lifelike, and highly personalised interactions, redefining customer experiences that are powered by AI agents

Take your business to the next level with Agentforce

Now that you know what’s in store for ASEAN businesses in 2025, why not welcome Agentforce into your business so it can unlock unlimited potential with your team? 

Agentforce is the agentic layer of the Salesforce platform for deploying autonomous AI agents across any business function — enabling you to scale your workforce. Build and customise autonomous AI agents to support your employees and customers 24/7, and access a library of ready-to-use skills for any use case across sales, service, marketing, commerce, and more.

Our handy ROI calculator can help you measure Agentforce’s value for your business, and you can take a closer look at how agent building works here.

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Read more:

• Everything You Need to Know About AI in Customer Service
• What is Data Transformation and How Can it Help ASEAN Unlock AI’s Potential?
• The Sales AI Playbook: 7 Ways AI Can Help You Close More Deals

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In Singapore, 96% of business leaders feel their data holds untapped potential, yet many are struggling to translate this into meaningful business outcomes. 

To explore how ASEAN businesses are overcoming hurdles and blazing new trails for an AI-driven future, Salesforce gathered insights from hundreds of Singaporean analytics, IT, and business leaders. Their perspectives, alongside those from 10,000 global leaders, are featured in our State of Data and Analytics Report.

Here are key takeaways from Singapore’s leaders — including practical tips on how you can make the most of your data and seize the AI opportunity.

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Takeaway #1: Without trusted data, your AI can’t deliver

It’s no surprise that 9 in 10 analytics and IT decision-makers in Singapore agree that trustworthy data is more important than ever. Every AI use case — from predictive analytics to IT management — runs on data. But your AI solutions won’t produce the results you’re looking for if you aren’t working with quality, trusted data. Simply put: flawed data leads to flawed results.

Yet, only 59% of analytics and IT leaders in Singapore are fully confident in the accuracy of their data. This gap shows there’s still plenty of work to do, which is why improving data quality emerged as a top priority for many businesses in the region.

So, how do you make sure your data is “trustworthy”? As data volumes grow, what are the best practices to ensure quality and consistency? Here are a few things to ask yourself:

• Do you have a clear and consistent data collection and data entry process? 
• Has your data been cleaned and normalised?
• Is your data protected and secure? 
• Do you have a governance framework addressing all aspects of the data life cycle, including access and control, management, privacy, security, compliance and regulatory requirements? 
• Is the data fit for the purpose? Does it follow ethics and integrity expectations?

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Takeaway #2: Getting more value from your data is easier when your data and business strategies are aligned

Data generation is exploding worldwide, and ASEAN is no exception. The digital ecosystem in this region is expected to triple in size by 2030, jumping from US $300 billion to nearly US $1 trillion. Data usage per user in Southeast Asia is set to skyrocket from 9.2 GB per month in 2020 to 28.9 GB by 2025.

Amid all this growth, 98% of Singaporean business leaders agree that data and analytics improve decision-making. However, two major challenges are holding many back: the sheer volume of data and the time it takes to turn that data into insights.

Our survey found that 82% of analytics and IT leaders in Singapore say their organisations struggle to drive business priorities with so much data. Only 63% feel their data strategy is fully aligned with business goals, making it clear why their second-highest priority — after improving data quality — is aligning data strategies to better harness the volume for meaningful business outcomes.

Understanding your business objectives is crucial when planning your data strategy. Start by clearly defining your goals, then map out the specific data needed to achieve them. With high volumes of data, it’s even more important to be selective — focus on the data that will have the greatest impact, and ensure you have the right tools in place to leverage it effectively. This targeted approach helps to drive much-needed insights across the organisation and reduce data silos that impede collaboration and decision-making.

The results? Better financial performance, higher productivity, and a stronger competitive edge. Research from McKinsey Global Institute shows that data-driven companies are 23 times more likely to acquire customers, 6 times more likely to retain them, and 19 times more likely to be profitable.

Takeaway #3: Higher data maturity opens doors to growth

When an organisation is data mature, we mean it’s well advanced on its data transformation journey — using data to drive decisions, fuel innovation, and plan for the future. But according to our research, only 28% of Singaporean businesses describe their data maturity as best-in-class.

Why does this matter? Because data maturity brings clear rewards. McKinsey’s research shows that data-driven companies achieve goals faster and see at least 20% higher contributions to earnings before income taxes.

Singaporean IT and analytics leaders report that a well-developed data culture — where everyone has the insights needed to be data-driven — leads to outcomes like higher cost savings, better productivity, improved customer service, more innovation, and faster decision-making.

And as AI becomes more integral to business strategies, data maturity is also a key indicator of AI success. Globally, organisations with higher data maturity are twice as likely to have the quality data needed to use AI effectively and recognise the growth benefits that come with a well-executed AI strategy

Unified data is the foundation that enables AI — first predictive, then generative, and now agentic — to work smarter and more autonomously. Agentforce taps into this unified data to deliver automated, intelligent responses and actions across your business. For example, when a Service Agent is connected to your order management system, knowledge articles, policies, and purchase history in Service Cloud, it can autonomously handle refunds, exchanges, and inquiries — boosting customer satisfaction while freeing your team for higher-value tasks.

With the right data foundation, agents streamline processes and unlock growth by helping businesses scale efficiently, improve retention, and generate new revenue through cross-selling and loyalty-building initiatives. Humans with agents can transform business growth — but it all starts with data maturity.

AI and Data Transformation in Singapore

Businesses that thrive are the ones that adapt, and today, data transformation is one of the most important adaptations a company can make. With AI poised to be as pervasive as the internet, companies must be ready to unlock its potential — but great AI starts with great data.

Yet, 84% of Singaporean business leaders are worried about missing out on the opportunities that generative AI presents. With AI set to revolutionise industries, Singaporean businesses that focus on improving data maturity, strengthening governance, and aligning data strategies with business goals will be the ones best positioned to succeed in the AI era.

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• How To Build a Data Culture: What Leaders Need To Know
• 5 Key Behavioural Elements that Build Successful Data Cultures
• The 2024 Connectivity Benchmark Report: Key Trends Shaping APAC’s Digital Landscape

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Whether that’s helping sales reps nurture leads, brainstorming campaign ideas for product marketers, or deflecting customer service calls, these purpose-built agents are focused on one specific job, and doing it exceedingly well. Even better, they boast a superpower most GPT-based tools can’t match: the ability to take action and get real work done. 

“LLMs are extremely powerful, but they are part of a much more complex and nuanced AI architecture,” said Itai Asseo, senior director of incubation and brand strategy, AI research at Salesforce. “Sometimes you need a big multipurpose LLM, but in many cases a specialized agent will be a much more efficient solution.” 

Business leaders clearly see the potential. A Capgemini survey found 82% of large companies plan to implement agents by 2027, heralding a new way of working that demands a new technological approach. 

Autonomous agents will revolutionise work

If you’ve ever used generative AI to help draft an email or brainstorm a campaign idea, you’ve probably been impressed by the technology’s potential, while simultaneously feeling frustrated by some of the hard limitations of GPT-based tools in the workplace.

They’re trained on “general” data, so they don’t know your business or customers, and can’t generate outputs that reflect your day-to-day reality. They can’t tell you, for example, about open sales opportunities or provide year-to-date campaign performance. 

Forward-thinking organizations are already beginning to bridge that knowledge shortfall with an innovative new type of data platform that aggregates and harmonizes data, and connects the dots between every data point. But for AI to truly be effective in an enterprise setting, a second requirement must be met: it needs to have the ability to take action on your behalf. 

Agents deliver this capability by combining the language and reasoning powers of LLMs with large action models (LAMs). A LAM is a type of language model that specializes in “function-calling,” which is the ability to execute actions in other systems and apps. LAMs are trained on datasets specifically curated for performing tasks, enabling agents to autonomously trigger a wide range of actions. 

“Large action models are ideal for agentic AI systems because they are specifically designed to take action by invoking functions directly in applications. This opens up a world of possibilities for autonomous applications,” said Asseo. “Salesforce’s xLAM, a family of large action models designed for AI agent systems, breaks down each portion of a request, mining several different places to find the data it needs and to take action.” 

LLMs and LAMs: the backbone of autonomous AI

How do LLMs and LAMs work together? Imagine asking an AI agent to send a personalized email to the first 100 people who purchase a new product, with a discount for future orders. 

On its own, an LLM would be hard pressed to pull this off.  Sure, it could generate the copy, but segmenting 100 buyers and sending each a personalized message requires action: That’s where the LAM takes over. Through its function-calling capabilities, the LAM would send requests to execute individual tasks, whether that’s a call to an internal database to pull customer and product information, or an API call to an external system. 

“Trying to get an LLM to do the same thing would involve a lot of prompting and engineering work,” said Asseo. 

In healthcare, an agent could help patients identify the right doctor based on their symptoms, needs and location. It finds an available time and books the appointment, streamlining what’s often a frustrating and time-consuming process.

In retail, an agent could handle simple queries like “where’s my package?” as well as deliver highly targeted sales recommendations, customer service, or marketing promotions exactly when the customer is most receptive.

In financial services, an agent could analyze a client’s spending habits, investment history, and financial goals to suggest adjustments to their investment portfolio, factoring in risk tolerance and potential returns. This saves investment managers time analyzing data, allowing them to focus on delivering high-value client service. 

Organizations that lead with AI agents can greatly expand the capabilities of their workforce. Imagine zero hold queues for service calls, websites that update themselves based on user engagement, and agentic sales coaches who never tire of helping salespeople close deals. 

Autonomous agents work alongside you

We’re rapidly approaching a future where employees work with agents to deliver better outcomes for businesses and customers alike. These systems promise to boost efficiency and free humans to focus on innovation and creation. 

With AI managing the details, individuals can delve into complex problem-solving and strategic thinking, pushing the boundaries of what’s possible and sparking breakthroughs across various fields. This partnership between human ingenuity and AI marks a new era of productivity, efficiency, and creative potential.

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Digital transformation isn’t just a trend. It’s a core shift to the business landscape, with IT leaders refining their strategies, headcounts and budgets to cater to growing customer expectations and project demands. The 2024 Connectivity Benchmark Report sheds light on the state of digital transformation worldwide, with the latest trends in AI, integration, automation and API management. So how do businesses in the APAC region measfure up in the evolving digital landscape?

IT leaders are optimistic about AI

The report reveals that 88% of organisations in APAC are already using AI, and adoption is continuing to grow. IT leaders in the region foresee an 89% increase in their usage of Large Language Models (LLMs) over the next three years, exceeding the 69% increase expected globally. Moreover, 86% are confident that AI will increase developer productivity at their organisations in that timeframe.

This optimism is a good thing, considering they’re simultaneously reporting a 39% increase in IT requests. AI will play an essential role in sustaining productivity under these demands, helping IT teams to manage growing workloads and expectations both efficiently and cost-effectively.

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Organisations need to get their data ready for AI

Despite the optimistic outlook, 69% of APAC IT leaders say their organisation is ill-equipped to harmonise data systems to fully leverage AI, with 82% pointing the finger at data silos as hindering digital transformation efforts. While this figure is lower than the 81% global average, organisations across APAC still have a way to go to break down silos and better integrate data across the business. 

Integration hurdles are blamed by IT leaders for stalling digital transformation for 82% of APAC organisations. Kurt Anderson, Managing Director and API Transformation Leader at Deloitte Consulting LLP explains, “A lack of integration is the top barrier to adopting emerging technologies, especially AI. And as demand grows for seamless, personalised customer experiences, the interoperability of systems is crucial for harnessing the full potential of data, AI, and automation. That’s why integration should be the cornerstone of every IT leader’s digital transformation efforts in 2024.”

The potential of AI is limited only by the data that organisations can connect it to, and the outcomes they can drive from it. The report shows that IT leaders across APAC are increasingly aware of these integration and automation challenges, and underscores the need for a robust data strategy, with a focus on data currency, reuse and access.

IT teams are under pressure, but workflow automation can help

With 98% of APAC IT teams struggling to integrate efficiently, workflow automation emerges as a solution. Robotic Process Automation can drive efficiency and reduce the workload on IT teams. As automation is demanded across businesses, IT often plays a gatekeeper role, but workflow automation permits other teams to self-serve. The global investment in RPA is now 31%, up significantly from 13% in 2021, as IT teams realise its potential.

Singapore Institute of Management (SIM) underwent its digital transformation with Salesforce and MuleSoft, integrating multiple back-end systems to streamline the end-to-end experience for its learners and administrators. Learners can now access courses with a single sign-on and automated processes encourage self-service and more efficient case management.  

APIs become a strategic lever for growth

APIs are now a staple in the digital ecosystem, with 99% of organisations using them to streamline data access and fuel growth. In APAC APIs and API-related offerings contribute to 33% of all revenue. Furthermore, APIs have contributed to increased revenue for 41% of APAC respondents and cut operational costs for 27%.

M1, Singapore’s most dynamic communications company, found its legacy on-premise API gateway was too labour-intensive and slowed down the delivery of new offerings. Supported by MuleSoft Professional Services, it migrated to a more agile solution in just 9 months and is now completing 13% more projects a year through API reuse, while saving 15 man-days per project. 

“I’m really excited about the scalability we have with MuleSoft. In a fast-paced industry, we now have the confidence that we can stay ahead of the game with a future-proofed environment and delight our customers as we grow our business,” says Chiam Chee Kong, Deputy Head of Software Engineering & Architecture at M1.

With outlets in Thailand and Malaysia, popular retail brand Lotus’s chose Salesforce and MuleSoft to unify its systems and data so it can provide more personalised and streamlined customer experiences. MuleSoft’s API reuse and prebuilt assets helped it complete its digital transformation in just 14 months – half the time it allotted. 
“We knew that with MuleSoft as our API gateway and integration layer we would be able to be more agile and transform more quickly,” says Wiphak Trakanrungsi, Head of Technology Software Development and Innovation at Lotus’s.

Digital transformation is the competitive advantage

An enterprise API strategy that facilitates data integration across applications will empower leaders to accelerate innovation and operationalise AI to drive business value and growth for the future. Through revenue generation, operational cost reduction and the promotion of self-service, integration, automation and APIs will help businesses maintain their competitive edge in 2024 and beyond.

Read the full 2024 Connectivity Benchmark Report for a comprehensive understanding of the digital transformation landscape in APAC and beyond.

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Regardless of where your data comes from — internal apps, systems, channels, external data lakes, and even emails, images, or PDFs — Data Cloud can bring it all together, creating a unified customer profile that helps your team better understand what your customer needs today, and anticipate what they may need tomorrow. This helps teams across your organisation use all of your company’s data to boost productivity and deliver deeper customer value.

Using Data Cloud, your business can consistently provide personalised customer experiences that lead to increased customer satisfaction, loyalty, and business growth. Data Cloud ensures that every employee has the right information at the right time to deliver what customers need, in near real-time, across the entire customer journey.

Examples of Data Cloud

Here are a few examples that show how organisations use Data Cloud to improve customer experiences:

• Formula 1 delights fans with personalisation based on location, content preferences, and favourite driver. They’ve created near real-time fan journeys with one-of-a-kind experiences and exclusive offers, providing meaningful interactions to turn new fans into loyal ones and fuel sustained growth worldwide. Results include 88% fan satisfaction, 86% first contact resolution, and 99.6% email delivery rate.
• Air India has achieved faster case handling, efficient routing, and personalised customer experiences using Data Cloud and Einstein. With AI-powered reply recommendations and predictive AI, agents provide quick assistance and offer personalised recommendations.
• Turtle Bay, a luxury vacation destination on O‘ahu, has invested in Data Cloud to help segment customers into ideal personas, and Einstein Copilot will make tailored recommendations. For example, if they are segmented into a family category, family-friendly activities will be recommended by Einstein Copilot.

The powerful combination of data and CRM makes these personalised customer experiences possible. And for today’s “need-it-now” consumer, milliseconds matter. The cost of not keeping up with your customer could be lost sales opportunities, poor social media reviews, a disconnect in healthcare delivery, and much more.

Is this a customer data platform (CDP)?

Historically, customer data platforms (CDP) have been a solution used primarily by marketing teams. Data Cloud not only provides the traditional benefits of a CDP but also offers a comprehensive data solution for all other lines of business, including Sales and Service. With Data Cloud, these teams can effortlessly activate their data to automate workflows, personalise customer interactions, and build smarter AI.

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New and innovative Data Cloud features

Data Cloud is unlike any data solution in the market today. 

1. Data Cloud is natively integrated with the Salesforce metadata framework  

This allows your organisation to turn data from any source into standard objects and fields that teams using Salesforce already know how to work with. This means companies can more easily use all their data that lives outside of Salesforce within everyday apps, like Sales Cloud and Service Cloud, without having to invest in and maintain messy, expensive data pipelines.

2. Data Cloud is designed to put data to work through low-code, no-code tools 

With all your data harmonised within the Salesforce metadata framework, you can put your data to work using low code tools unique to Salesforce, such as Flow, and generative AI solutions like Einstein Copilot and Prompt Builder. This helps business teams more easily use data to power all their workflows, automations, AI, and analytics without relying on IT.

3. Data Cloud is fully open and extensible

Your company has already dedicated substantial resources to implement data solutions, such as data lakes or data warehouses. With Data Cloud, you can maximise the ROI on all of these investments. That’s because Data Cloud is designed with an open, extensible architecture that employs zero-copy integrations, allowing seamless connections to top platforms like Snowflake or Databricks — without the need to move or copy data. This approach provides unparalleled flexibility and control for managing data within Data Cloud, making it easier than ever to bring data in and send it out as needed.

How does Data Cloud work?

Data Cloud is part of the Einstein 1 Platform, which helps you and your team gain a complete understanding of your customer, based on data. Data Cloud has three key capabilities that make this possible:

1. Data Cloud connects to every data source

First, Data Cloud lets you easily combine your data on Salesforce with data from any other external source to create a trusted, comprehensive view of your customer. Use pre-built connectors or our zero-copy integrations to quickly pull in data from across your enterprise that’s trapped in platforms like AWS, Snowflake, and Google Big Query.

2. Data Cloud harmonises your data

Next, since Data Cloud is purpose-built on Salesforce, it helps you take advantage of integrating all your data to our standard metadata model. Once integrated into the model, companies can access and use any of this data directly inside Salesforce applications — like Sales Cloud and Service Cloud. Unlike other data solutions that are difficult to use when harmonising disparate data into a singular model, Salesforce makes data harmonisation incredibly easy through point-and-click mapping and pre-configured data bundles that automatically map your data for you.

3. Data Cloud makes it easy to activate your data

Finally, with Data Cloud, teams can transform messy, hard-to-use data that’s scattered across your enterprise into a unified resource. Data Cloud makes it easy to use your data to build data-driven automations and business processes.

With seamless integrations to both Salesforce applications and popular destinations, like telemetry data or purchase invoicing systems, teams can activate experiences powered by Data Cloud in nearly any environment where their work takes place.

How does Data Cloud keep data secure and what data ethics standards are in place?

The Salesforce vision for Data Cloud is to lead the industry on privacy and data ethics via best-in-class consent management, policy automation, and more. In collaboration with our Office of Ethical and Humane Use, Data Cloud is designed with privacy and data ethics best practices from the ground up. Most importantly, the Einstein 1 Platform adds a layer of data policy management to Data Cloud to make it possible for customers to keep their data safe and meet regulatory compliance requirements globally.

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Companies are collecting more data about their customers than ever before, yet that data sits in siloes and is disconnected from the customer experience. Data Cloud integrates information from various sources, which helps all teams provide a personalised customer experience at every touchpoint, leading to increased customer satisfaction, loyalty, and ultimately, business growth.

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That’s why many organisations are investing a lot into becoming more data-driven, and using their new data centricity to drive analytics transformations. 

The benefits of doing so are clear. Increased close rates, faster customer response times, increased employee engagement, and improved customer retention leading to revenue growth are just a few examples of the pot of gold that lies at the end of the analytics rainbow.

However, there is a known gap between ambition and reality: According to a Hewlett Packard Enterprise (HPE) survey, only 13% of organisations in Singapore said their data strategy is a key part of their corporate strategy. And an EY study revealed that only 16% of businesses in the Asia-Pacific region said that they are data-centric.

Barriers to data-informed business decisions

Despite the availability of solid technology choices and solutions, it is behaviour and practises that are creating barriers to data-informed business decisions. This affects data quality, which negatively impacts an organisation’s ability to leverage reliable, insightful and trustworthy analytics, artificial intelligence (AI) and generative AI.

For example, organisations that create data via separate applications risk creating unresolved duplications, which may cloud analytics insights. And a lack of consideration of the applied use of data beyond a single team’s needs compounds the problem. 

This can create a situation where an organisation has plenty of data, but doesn’t know what to do with it. Undocumented data sources, inconsistencies across reports, no clear data ownership, and a lack of accountability are all indicators of an absent data culture.

This lack of accountability is leading to poor data leadership in organisations across the region.

These organisations risk losing skilled analysts to better jobs elsewhere, and, at the same time, can see the cost of data sourcing, movement and development increase due to poor economies-of-scale activities that could otherwise simplify or reduce data costs.

5 key behavioural elements all successful data cultures share

All digital transformation is data transformation, and this starts with people and their relationships with each other. These relationships – between employees, and with customers and partners –  are a critical ecosystem of information exchanges that are central to the success of all organisations. That’s why taking a human-centred approach that focuses on experiences your people have with data and technology is the vital first step on the road to effective data transformation. 

But changing human behaviour often requires a massive change management effort. So, what are these behaviours and practices that lead to good data cultures?

Becoming truly data-driven requires changing mindsets, attitudes, and habits that support embedding data quality into the identity of the organisation. Ultimately, people have to want to use data and encourage others to do the same.

As such, successful data cultures typically focus on these five key behavioural elements:

1. Trust

Effective leaders trust their people, and high-performing people are empowered with trusted data to make confident decisions.

However, this trust in data requires a solid data governance model that supports secure, widespread access to a single source of truth, and balances centralised data governance with decentralised self-service analytics. This approach breaks down silos across teams, builds trusting, collaborative relationships, and shares data across the organisation to identify impactful solutions.

Reflect on your organisation. Is there disagreement on whose data is correct? Is your data still siloed? Are metrics linked across your organisation to assess and promote data value, performance, and innovation?

2. Committment

Successful data cultures have full commitment to realising the value of their data assets. That means not just storing and collecting data, but helping people use data to make better decisions. 

This commitment should be evident in all aspects of your organisation – from your organisational structure to day-to-day processes. It also requires an assigned executive that is accountable for your organisation’s data use and ensuring that analytics projects tie back to critical business efforts.

This is what it means to treat data as a strategic asset, and demonstrates why business outcomes must inform data collection and processes, and why support – including funding for long term data maturity programs – is a must.

Still, HPE research suggests that 34% of Singaporean companies do not have an overarching data and analytics architecture.

Ask yourself: Is there a commitment in your organisation to treating data as a strategic asset? Is there a transactional attitude towards data? Is your data strategy operationally solid, however still lacks business relevance? Are you leading by example?

There is an important role for an executive champion to lead good data practices that focus on the critical practice of continually improving your data strategy and execution.

3. Talent and skills

If people don’t understand how to work with data, they can’t be data-driven. Everyone in the organisation should feel confident finding the right data, applying analytical concepts to their work, and developing data curiosity.

It’s also important to note that Identifying data champions within your business requires an organisation-wide approach. They often are not data analysts, but people with a deep understanding of the company processes and challenges across departments. Equipped with the right tools and empowered by their leadership, they can become role models for others and eventually train other people.

The results show that an overwhelmingly concerning majority of businesses (Global: 88%, APJ: 88%) have yet to progress either their data technology and processes and/or their data culture and skills. 

This is a problem for the 88% of HPE survey respondents in Asia-Pacific and Japan that said their companies have yet to progress their data culture and skills. Successful organisations prioritise data literacy skills when recruiting, developing, and retaining talent, and executives must prioritise data skills as part of their talent strategy.

Job descriptions should clearly outline the data literacy skills required for all roles across the organisation, along with tailored training and incentives offered for data literacy improvement.

How is your organisation prioritising data literacy skills? Is analysis ad-hoc and done by specific experts? Are there efforts to formalise information requirements? Do your executives model data literacy? Is data literacy understood across various roles, supported through internal activities, and informing career development plans?

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4. Sharing 

Your people must have a shared purpose – to use data to better the organisation and amplify the impact they can have. To achieve this, your organisation needs to foster a sense of community. This can be displayed through meetups, messaging groups, and portals, and formalised into active internal communities around data and analytics. 

That’s critically important because most problems worth solving with data depend on vital data inputs from multiple systems and collaboration across many teams. This requires a level of organisation-wide data access and control that is not present in many ASEAN organisations. In Singapore, for example, only 16% of HPE survey respondents said their companies have implemented a central data hub that provides unified access to real-time data across their organisation.

People need to be supported to actively share best practices across the organisation through incentives and time availability for learning, coaching, and documenting. This includes collaboration between data analysts and departmental heads to share the nuts and bolts of good data processes, and what they are delivering for the organisation.

Consider how people are supporting each other and building a sense of belonging within your organisation. Is your strategy understood by everyone in your organisation, and is your data strategy clearly aligned to achieving specific business outcomes? Or is organisational misalignment and siloed data creating barriers? 

5. Mindset 

When people are curious and willing to challenge their own assumptions with data, experimentation and innovation comes to the fore. Teams refocus and measure business outcomes, not only operational metrics. Data-led decision making is viewed as a source of personal growth and career development.

This mindset is just as important as developing data skills, and it is shared across your organisation, open discussions will generate ideas that lead to exploration and innovation. 

Do you use data as a catalyst for holistic improvement and organisational evolution? If not, you may find yourself reactively firefighting spreadsheets, and progress could be dampened by inconsistent incentives and short-term investment.

The DNA of successful data cultures

Being ‘data driven’ means having access to timely and trusted insights that help your people deliver on business and service goals, and establishing a virtuous loop of testing and learning from your organisational data. 

This kind of data framework can transform the DNA of your organisation. Ashish Braganza, Director of Global Business Intelligence at Lenovo experienced this power after embracing an organisation-wide data transformation driven by Tableau. 

But ultimate success comes down to the strength of your data culture. Successful data cultures have foundational trust and accountability; are committed to realising organisational value from data; set high expectations on talent for a wide range of data activities; share through incentivised collaboration and reduced silos; and have a mindset shift that encourages data exploration and curiosity.

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It’s the kind of question that companies are increasingly using artificial intelligence (AI) to answer quickly. But consider these two potential, AI-generated responses your agent could send to this customer.

Response 1:

Thank you for your inquiry. What is your name, email, and order number? Where did you place your order?

Response 2:

Jon, thanks for being a Gold Level loyalty member! How can I help? Do you have an issue regarding the mug in your cart, the messenger bag you’re currently browsing on our website, or something else?

There is a huge difference in how those replies make your customer feel. And that difference comes down to data.

The data difference

Generative AI promises to significantly reshape how you manage your customer relationships, but it requires data that is accurate, updated, accessible, and complete.

The Trailblazer view

“You might have data available but is it current, authoritative, and complete?” said Carl Brundage, a certified technical architect specialising in data and analytics at Odaseva. “If not, you might not have a complete picture of your customer.”

• Having the complete picture — knowing what, when, why, and how your customer does something – is the key to adding valuable context to AI.  

What your company can do now

While some of the most impactful AI tools for businesses are still being developed, there are some key steps your business can take now to get its data house in order:

• Ensure the quality of your data. Remove duplicates, outliers, errors, and other things that can negatively affect how you make decisions. 
• Connect your data sources — marketing, sales, service, commerce – into a single record, updated in real time, so the AI can make the best recommendations.  

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Lay the groundwork for data-centric AI

Customer data is at the heart of delivering great experiences. Your data does not need to be perfect to build an effective AI program, but it needs to be clean. That means free of errors, incorrect formats, duplicates, or mislabelings. 

The data experts at Tableau offer these steps on how to clean your data, an important first step in unifying data sets for AI projects:

Remove duplicate or irrelevant observations

Duplication happens when you combine data sets from multiple places, and duplicate entries are created. Irrelevant observations happen when data (say, on older consumers) doesn’t fit into a problem you’re trying to analyse (say, millennial shopping habits). Removing these makes analysis more efficient, useful, and accurate for an AI system. 

Fix structural errors

This happens when data includes typos, incorrect capitalisation, or mislabelings. For example, “N/A” and “not applicable” mean the same thing, but are not analysed the same way because they’re rendered differently. The entries should be consistent to ensure accurate and complete analysis by the AI system. 

Filter unwanted outliers

There are often one-off observations that don’t appear to align with the data you’re analysing. That might be the result of incorrect data entry (and should be removed) but sometimes the outlier will help prove a theory you’re working on. In any case, analysis is needed to determine its validity.  

Handle missing data

Missing or incomplete data is a very common problem in data sets, and can reduce the accuracy of AI models. There are a few ways to deal with this: 

• Eliminate observations that include missing values; however, this will result in lost information.
• Input missing values based on other observations; however, you may lose data integrity because you’re operating from assumptions and not actual observations
• Consider altering the way the data is used to effectively navigate the missing values.

Validate

After cleaning the data, you should be able to answer these questions: 

• Does the data make sense? 
• Does the data follow the appropriate rules for its field? 
• Does it prove or disprove your theory, or surface any insight?
• Can you find trends that help inform the next theory? If not, is that because of continued data quality issues? 

Data-centric AI + CRM = killer combo

AI has already begun to transform CRM and the way companies connect with and serve their customers. AI is useless without good data that is integrated, accurate, and real-time. At the same time, making sense of your mountains of data is impossible without AI. 

The winning approach is combining the two practices. Doing so will help you identify and foresee trends, challenges, and opportunities across all lines of business — and serve your customers better.  

A unified customer profile, enabled by Data Cloud, gives you a comprehensive view of your users, whether they are visitors, customers, prospects, or subscribers. Historically, marketing data has been locked inside marketing systems, service data in service systems, etc., which doesn’t give you the complete picture of your customer’s activity. 

“There’s absolutely a need to have a trusted unified customer profile, in one place and updated in real time,” said Brundage. “Something you did last month, you may do differently this month, based on the data. And if you have outdated data, that’s what AI will use.” 

There’s a famous pyramid which demonstrates the hierarchy of knowledge management. Data sits at the bottom, representing everything we collect, progresses to information, then knowledge, and finally wisdom, which sits at the top. 

Data and information provide little context or answer the “why” of anything. But with integrated, real-time data layered with AI, you can see patterns, predict trends, and make connections between things that on the surface might not seem to go together. 

“Knowledge is knowing that a tomato is a fruit,” Brundage said. “Wisdom is knowing it does not go into a fruit salad.”

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Getting familiar with these essential terms will help you and your teams, regardless of technical ability, feel confident talking about data and understanding how to use it to create business value.

Generative AI Terms by Topic

Batch processing

Batch processing is when a computer automatically runs a repetitive task or group of tasks on a large amount of data, processing it as a single unit rather than a series of separate jobs. Certain processor-intensive tasks can be inefficient to run individually; with batch processing, the data jobs are run together, often at an off-peak time to conserve computer resources.

• What it means for customers: When jobs like order processing are run as a batch, customers experience quicker turnaround times than when those tasks are handled individually, as well as more consistent and accurate results. 
• What it means for teams: Teams save time by minimising the overhead required for individual tasks, and gain more consistent quality control by using standard business rules across a batch process.

Business analytics

Business analytics is the practice of using data to test hypotheses and make predictions or more informed decisions, often around future performance. Business analytics is predictive, which means you model and analyse data to identify new insights and anticipate trends.

• What it means for customers: Customers get improved experiences across the board, including personalised product recommendations and just the right marketing messages, delivered at just the right time.
• What it means for teams: Teams get ahead of the curve with business analytics, using it to help them create more accurate predictions, and make smarter decisions about resource planning, demand forecasting, risk assessment, and more.

Business intelligence

Business intelligence is the practice of bringing together large amounts of data to view a current snapshot of performance, and pulling actionable insights to drive decisions. Business intelligence is descriptive, which means it “describes” what’s happening at a particular moment in time.

• What it means for customers: When organisations can see how their past or current efforts have (or haven’t) worked, and use these insights to make improvements, they’re better able to serve their customers, which leads to an increase in customer satisfaction and loyalty.
• What it means for teams: Teams use business intelligence for internal efforts, like tracking key performance indicators (KPIs), and external efforts, like spotting business risks within departments or teams, such as monitoring customer satisfaction (CSAT) scores.

Customer data platform (CDP)

A CDP helps businesses collect, organise, and use customer data from sources like websites, mobile apps, emails, and social media to build unified profiles of their customers.

• What it means for customers: With a CDP, companies can better anticipate customer needs for more meaningful brand interactions that help solve customer problems.
• What it means for teams: With a unified view of their customers, teams can create more meaningful and targeted experiences, campaigns, and products. They’re also better able to track, measure, and improve as more data comes in.

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Dashboard

A dashboard is a visual display of data used to monitor conditions or facilitate understanding. Dashboards generally include multiple interactive charts describing important business processes and KPIs.

• What it means for customers: organisations that can monitor their processes effectively can produce targeted insights that better serve customers’ needs.
• What it means for teams: By monitoring progress towards key business goals, dashboards let people see positive and negative trends, and drill into the reasons that cause them, allowing them to take action.

Data and big data

Data is the raw facts, figures, and other information, like customer names and contact details, that organisations collect, store, and analyse. Data can come from different sources, like customer interactions, surveys, sensors, and social media. Big data means large and complex amounts of information. The five V’s of big data — volume, velocity, veracity, value, and variety — describe the challenges of storing, governing, and analysing it in structured, unstructured, and semi-structured forms.

• What it means for customers: When companies use big data, customers not only receive more tailored and relevant messaging. They also benefit from increased security and trust, since big-data analysis spots patterns that identify fraudulent behaviour.
• What it means for teams: Teams use data to create better customer interactions. They collect and analyse data related to past purchases, browsing behaviour, and other data points to recommend specific products or services. That enhances customer experience and increases the odds of a purchase.

Data analytics

Data analytics is the science of examining raw data to draw conclusions. It includes tools and technologies that make it easier to understand, aggregate, and visualise data.

• What it means for customers: Customers experience the benefits of data analytics when they engage with improved products and services.
• What it means for teams: Teams use data analytics for continuous improvement across key functions like customer service, product development, marketing, and more.

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Data culture

A data culture is the shared behaviours and beliefs of individuals who advocate for and prioritise using data to enhance decision-making. A data culture empowers everyone, not just data analysts, to unlock and create business value with data.

• What it means for customers: When every person in the organisation is empowered to use data, everyone can make smarter decisions about what the customer needs.
• What it means for teams: Teams solve problems faster. With data at the forefront, they get ahead of trends, create more tailored offerings, improve forecasting, and more.

Data governance

Data governance is the framework organisations use to define the rules and responsibilities for effective handling of data throughout its lifecycle to ensure its reliability and relevance. These rules define processes and protocols to maintain usability, quality, policy compliance, privacy, and security.

• What it means for customers: Having reliable and relevant data is essential to creating quality customer experiences. Plus, customers are more likely to trust an organisation that demonstrates respect for their privacy rights and sensitive personal information.
• What it means for teams: Teams have more peace of mind knowing that data is reliable and relevant, and that clear standards and practices exist to protect data to reduce the likelihood of a breach.

Data harmonisation

Data harmonisation is the process of bringing together data from multiple sources to create a unified dataset that functions as if it were a single data source. It involves aligning data elements, formats, and structures to eliminate inconsistencies and make the data easier to compare and analyse.

What it means for customers: Customers get a consistent experience across departments because organisations can access data, like customer preferences and purchase history, from various sources as if it were a single source.

What it means for teams: Teams have a more holistic view of customers and can access and analyse information more quickly, without having to access multiple systems.

Data insights and real-time insights

Data insights are key findings, like data patterns and trends, that you get from data analysis. Real-time insights are the immediate and up-to-date information from data analysis that comes in the moment an event occurs, such as sales through an ecommerce site. You can use these insights to guide decision-making and strategies.

• What it means for customers: Data insights lead to key customer benefits across all brand interactions, including more tailored products and services, and proactive support. With real-time insights, organisations can create real-time personalisation, more targeted marketing, and nearly immediate responses to issues.
• What it means for teams: Teams build a competitive advantage in the market, using data insights to gain a deeper understanding of customers, improve processes, and fuel smart decisions.

Data lake

A data lake is a centralised storage repository of raw data. It’s a vast, flexible, and low-cost storage system organisations use to collect and store large volumes of structured, unstructured, and semi-structured data in its original format. Data lakes capture a wealth of unstructured data like social media posts, sensor logs, and location data.

• What it means for customers: With the immense information available in a data lake, brands can anticipate customer wants and needs.
• What it means for teams: Teams access a huge amount of data in a single space, allowing them to move faster and keep up (or get ahead of) competitors.

Data lakehouse

A data lakehouse has the scalability and flexibility of a data lake, and the structure and governance of a data warehouse — the best of both worlds. Because of this hybrid quality, organisations can quickly and easily extract insights from all their data, regardless of format or size.

• What it means for customers: Customers benefit from unified omnichannel experiences, quicker response times, and improved data security.
• What it means for teams: Teams eliminate the need for separate data storage and processing structures, which allows them to unify historical and real-time data all in one place.

Data literacy

Data literacy is the ability to explore, understand, and communicate with data.

• What it means for customers: Customers want to know brands understand them and can help them solve their problems. organisations that embrace data literacy can have this kind of in-depth knowledge across customer touchpoints.
• What it means for teams: Teams with strong data literacy skills build personal, professional, and organisational growth, increasing critical thinking, career opportunities, and data-driven success.

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Data masking

Data masking is the process of replacing sensitive data with fictitious or anonymised data to protect sensitive or private information and to comply with privacy requirements. Data masking is used in training or testing scenarios when real data is not needed, or when sharing data with third parties. You can also use masking to ensure you’ve eliminated all personal data when writing AI prompts or training an AI model.

What it means for customers: Customers feel more confidence when companies protect sensitive and personally identifiable information.

What it means for teams: Teams can easily follow privacy requirements while still having functional data to use in testing, training, or development.

Data mining

Data mining is the process of discovering patterns in large datasets. It uses techniques like machine learning, statistics, and database systems to turn raw data into useful information.

• What it means for customers: Your customers get predictive recommendations about what they want and need, often before they know they need it. Customised recommendations, reminders, and add-on product offerings are all powered by data mining.
• What it means for teams: A deeper understanding of customer behaviour keeps all your marketing and sales strategies efficient and effective.

Data science

Data science is a field that combines scientific methods, statistics, algorithms, and data mining techniques to generate insights from structured and unstructured data.

• What it means for customers: Customers experience faster service and improved personalisation with data science tools like recommendation algorithms, which provide tailored suggestions and machine learning algorithms that automate specific support tasks.
• What it means for teams: Teams use data science to continually improve and iterate on service and product offerings to create more relevant, efficient, and satisfying customer experiences.

Data security

Data security refers to the measures and practices used to protect an organisation’s data, like user permissions and role-based access, to ensure only authorised individuals have access to specific data.

• What it means for customers: Customer trust is everything. When customers know that an organisation takes great care in protecting their data and privacy, it builds relationships and loyalty.
• What it means for teams: When teams have data security measures in place, they protect themselves from data breaches, maintain customer trust and reputation, ensure they comply with regulatory standards, and even safeguard intellectual property.

Data storytelling

Data storytelling is the use of data, visualisations, and narratives to communicate insights and convey a compelling story to an audience. You can create stories to tell a data narrative, provide context, demonstrate how decisions relate to outcomes, or simply make a compelling case.

• What it means for customers: organisations use data storytelling to create a deeper, more meaningful understanding of their customers.
• What it means for teams: Teams use data storytelling to simplify complex information, and share it in an engaging way across their organisation. This improves understanding and buy-in of key data concepts and related projects.

Data visualisation

Data visualisation is the practice of creating detailed charts, graphs, and maps to make information easier to understand. This helps organisations better spot trends and patterns in data, and allows nontechnical people to understand and make sense of data.

• What it means for customers: Customers have more connected interactions with a brand when organisations are on the same page about data insights.
• What it means for teams: Teams enrich their understanding of data and uncover hidden insights with rich visualisations.

Data warehouse

A data warehouse is a large, organised storage space for processed data, where an organisation collects and stores information from different sources in a structured way.

• What it means for customers: Customers expect their interactions with a brand to be seamless. organisations meet this expectation better when they have all their data organised in a single place.
• What it means for teams: Teams have a central hub for data, which gives them quick access whenever they need it for reporting, decision-making, and more.

Predictive analytics

Predictive analytics uses statistical techniques (including machine learning) to predict future events or outcomes based on historical data. In the context of CRM, this might involve predicting which customers are most likely to churn, or which are most likely to respond to a certain promotion.

• What it means for customers: With predictive analytics, customers receive the information and promotions that are most interesting and relevant to them.
• What it means for teams: Teams can use predictive analytics to forecast demand, identify trends, make proactive decisions, and inform business strategies.

Structured, unstructured, and semi-structured data

Structured data is well-defined data in a fixed format, such as a spreadsheet or customer database, with rows for each customer and columns for name, address, phone number, and email. Structured data is easily understandable, searchable, and machine-readable by traditional analytics tools.

Unstructured data is information that doesn’t have a predefined format or specific data model, and requires specialised tools to create insights. Examples of unstructured data include emails, social media posts, audio and video recordings, images, and web pages. Because unstructured data is growing at a higher rate than structured data, big data technologies that can seamlessly analyse it will be crucial to businesses.

Semi-structured data has some organisational structure but isn’t easy to analyse as-is; it needs some organising or cleaning to be imported into a relational database like structured data.

• What it means for customers: Brands that take advantage of different data types can better serve their customers by deriving insights from more quantitative structured data and more qualitative unstructured data.
• What it means for teams: Teams can use all three data types for analysis, with a combination of solutions like Hadoop for ingesting unstructured data, and Tableau for analysing and visualising structured and semi-structured data.

Take the next step with data

Data is more important now than ever, and the ever-expanding flow of data is a huge management and governance responsibility. But data holds great power. The more you expand data access and data literacy for individuals throughout your organisation, the greater the potential for business insights that can guide decision-making and create incredible customer experiences. When you combine real-time, actionable data with AI and CRM, it can drive intelligent actions and deliver personalised experiences at scale.

That’s why it’s important to understand the data essentials. When data literacy spreads throughout your company culture, anyone can gain insight with data and create value.

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They’re also 1.6x more likely to rate themselves as effective at automating processes. Customers who have used Salesforce Customer 360 have seen automation of business processes speed up by 27%. 

When you have your customer data on one trusted platform, with automated processes handling your day-to-day operations, you can focus on your customer and grow your business.

Automation is critical in the drive for efficiency

Automation allows you to create a more efficient and productive workforce. 

In a recent Salesforce Success Metrics study, companies across industries and regions reported seeing, on average, a 26% increase in employee productivity* using Salesforce automation, plus an estimated 25% savings on IT costs*.

Other benefits of automation are described in the State of Service report — top of the list is time saving, with 98% of those surveyed describing it as a benefit, and 50% saying that it’s a major benefit to their business. 

The report also includes benefits such as connection with other departments, reduction of errors, and time for new projects. All of these factors contribute to a more productive organisation — and that’s not all. Forty-seven percent of service professionals also said that automation helped them focus on their customers.

Lower your ordering costs

To meet new challenges and keep customer satisfaction at the forefront, businesses need to streamline operations to reduce costs, improve customer experience, and earn loyalty. That means streamlining order management and automating repetitive and tedious processes. 

Order management system in Commerce Cloud Genie enables businesses to grow revenue with intelligent insights and build trusted relationships with real-time data. We’ve seen companies achieve a reduction of 26% in ordering costs* and boost productivity with Einstein AI within the Commerce Cloud. With commerce automation, 89% of Salesforce customers achieve positive ROI in only nine months.*

Customers demand personalised digital experiences

Your customers are more online than ever. As reported in our State of the Connected Customer report, customers worldwide expect more than 60% of their interactions with companies to be online this year. 

The report also highlights three important considerations:

• 88% of consumers say that the experience a company provides is just as important as the product or service it provides
• 73% of customers also say that they expect companies to understand their unique needs and expectations 
• 56% of customers still think that companies treat them as a number, rather than an individual.

The State of Commerce report told us that, after revenue growth and expanded customer base, the greatest concern for most industries is deepening customer relationships. 

Leaders in the world of commerce are nearly 1.7x more likely to rate themselves as ‘effective’ when it comes to personalised experiences. They’re also 1.5x more likely to be effective when it comes to understanding customer behaviour.

For your business to thrive, you need to create smoother journeys with highly personalised and intelligent shopping moments that make your customers feel truly understood. These insights present an opportunity for any company to offer a personalised digital service to wow your customers with great experiences and drive business growth. And this can be done with a comprehensive understanding of your customers’ needs – which comes from a complete set of customer data.

New channels = more opportunities

One of the keys to digital success is expanding into new channels. As shown in the State of Commerce report, 69% of digital commerce leaders are already investing in new digital channels. These new channels include mobile apps, online marketplaces, and social media platforms like TikTok. 

Fifty-seven percent of customers prefer to engage with companies through digital channels — that number rises to 65% for younger generations. Online comes out on top even when it comes to the final purchase — 63% of millennials and 58% of Gen Z prefer buying online over going into a store.

The time to start automating is now

The stakes for not automating the way you handle data are high — even among leaders. In fact, leaders who report that they aren’t effective at using their data are 37% more likely to report not being prepared to handle rising inflation. 

More than 60% of customers are already telling us that the majority of their interactions with companies will be online in the coming years. From Salesforce Cyber Week 2022 shopping insights, global online sales and digital traffic broke records. The numbers hit an all-time high of USD$281 billion, up 2% compared to 2021, and USD$68 billion, up 9% compared to 2021. If the trend continues, that number will only get higher. 

Prepare your automation strategy now, and you’ll be ready to grow in the future. The second edition of the State of Commerce contains analysis of buying data from over 1 billion customers worldwide. Download the report today to find out how you can handle your data to grow your commerce business more efficiently.

*Source: 2022 Salesforce Success Metrics Global Highlights study.

Data is from a survey of 3,706 Salesforce customers across Singapore, the US, Canada, the UK, Germany, France, Australia, India, Japan and Brazil conducted between June 8 and June 21, 2022. Results were aggregated to determine average perceived customer value from the use of Salesforce. Respondents were sourced and verified through a third-party B2B panel. Sample sizes may vary across metrics.

The post Automate Your Customer Data and Help Your Commerce Business Grow appeared first on Salesforce.

Let’s look at some of the exciting features in this release:

• With Live Prediction on Snowflake, you can make real-time predictions using external Snowflake data
• Accelerate time to insights with new enhancements in UI
• With the new enhancements in CRM Analytics Home, unify all your analytics assets in one place and easily discover insights using contextual capabilities
• Get a deeper understanding of your model with new and improved metrics and variable importance with Model Inspector

Make more informed predictions with your live Snowflake data

Within CRM Analytics, you can now deploy models to make real-time predictions with live Snowflake data. 

Salesforce and Snowflake share native integration that lets you access, combine, and analyse your data to grow your business. This release takes that integration to the next level, allowing you to create real-time predictions in Einstein Discovery — integrating live data outside of Salesforce with data from Snowflake.

One advantage of CRM Analytics and Einstein Discovery is that no other machine learning platform can combine incredible Salesforce Customer 360 data with external datasets to boost your predictive models. You can join live data seamlessly, like any other traditional dataset, and boost predictions with minimal effort.

In the demo below, see how you can get real-time predictions by creating a story from a deployed model that uses live product usage data and training data from Snowflake.

The insights you want — where you want them

Another major upgrade in the Winter 2023 release: you can unlock insights faster and within a more user-friendly experience. 

You can now move between pages with a single click — no more multi-selection is needed. It’s as simple as creating a flow and selecting that action. You can easily navigate to any detailed page that you want.

You will find a new Interaction tab in the widget panel that allows you to create a selection interaction and add a navigation action. These updates transform a static dashboard into a powerful asset. You can also link a page, component, URL, and lens. 

You can also bring in filters for deeper explorations to integrate your content together, allowing you to do more with just one click.

For example, you can access your team member’s profile by clicking on their image and navigating to an opportunity page by selecting it from the account. You can also drill down from a country to a state to a city to get granular insights.

In future releases, we will add more widgets and actions to the interaction framework. Till then, learn more about our user interface improvements in the overview demo video below.

Unify your CRM Analytics assets and content

We’ve completely redesigned CRM Analytics Home to give you one central place for creating, finding, and sharing Dashboards, Lightning Reports, and all your CRM Analytics assets. 

We’ve enhanced the user analytics experience with powerful contextual search capabilities, such as browse and find to uncover insights faster and to manage your content easily.

You can easily narrow your search to a specific dashboard type, report, or lens that was last modified by you with new filter capabilities. Using filters, you can manage all CRM Analytics assets, Lightning reports, and dashboards in one place.

And the good part is if someone does not have CRM Analytics, they can still find, access, and manage all their operational analytics content through the Analytics tab.

Check out the demo video to learn more.

Analyse your machine learning models better with Model Inspector

We’ve added Model Inspector to Einstein Discovery to help you better understand machine learning model quality and to analyse models more easily. 

Model Inspector allows you to easily evaluate your model and quickly identify which variables are influencing the model’s predictions with an importance score.

Previously, only correlation scores were displayed. Now, we can also look at importance scores. In terms of predicting an outcome, importance shows how much the model relies on the variable. 

With new and improved model metrics, you can analyse your model’s performance by comparing it with “No model” or “Theoretically perfect model” from the Gains chart. Additionally, we’ve included predicted versus actual and new residual charts to better understand the model. The predicted versus actual chart is available for all regression models.

The confusion matrix is now much easier to interpret as it shows the labels of the two classes in the outcome variable. This eliminates the need to remember what is positive and what is negative. 

You can see these new enhancements to the model evaluation feature in action below. Explore a Binary Classification model that predicts win probability for sales opportunities and a Regression model that predicts quantity sold daily in stores.

The CRM Analytics Winter ’23 release is available now. To learn more about all the new features in this release, visit the CRM Analytics Winter ‘23 release notes.

This post originally appeared on the U.S.-version of the Salesforce blog.

*Source: 2022 Salesforce Success Metrics Global Highlights study.

Data is from a survey of 3,706 Salesforce customers across the US, Canada, the UK, Germany, France, Australia, India, Singapore, Japan and Brazil conducted between June 8 and June 21, 2022. Results were aggregated to determine average perceived customer value from the use of Salesforce. Respondents were sourced and verified through a third-party B2B panel. Sample sizes may vary across metrics.  

The post Get Insights Faster With Winter ’23 CRM Analytics Release appeared first on Salesforce.