Unlocking Customer Value: The Untapped Potential of Customer Data & Analytics

Customer data should unlock competitive advantage, yet many organizations find it only adds complexity. How can leaders shift from barely managing data to truly leveraging it?

Across industries, senior leaders are increasingly focused on building out core data, analytics, and AI capabilities. There is intense discussion about what these teams should look like: which roles and responsibilities are essential, what skills are needed, how the function should be structured, and who it should report to. 

Yet beneath this momentum lies a persistent uncertainty. Many executives are still asking: 

• Can such a team reliably deliver a return on investment? 

• If so, what does it take to capture that value in practice? 

• Does it make sense to start hiring and building these capabilities if the organization still struggles to turn insight into impact? 

Michael de Kare-Silver, Managing Partner at Signium in London, echoes these thoughts:

“Customer analytics is where these questions become very real. There’s huge potential to unlock new revenue and deepen customer engagement, but many organizations still struggle to get that value out in practice.” 

Why do customer analytics matter so much right now? 

Few can deny the importance of analysis and insight in driving better decisions through the current era of near-constant disruption.  

“Organizations face a genuine deluge of data,” says de Kare-Silver. “Leaders and their teams must sift through the ever-growing set of tools and AI capabilities to make sense of what information matters, and what doesn’t. The real task is turning that data into insights that drive better decisions and stronger performance.” 

The growing strategic weight of Data & Analytics is reflected in recent headlines: 

• Coca-Cola has publicly stated that it expects generative AI to have a high impact on both consumers and retail customers over the next three years. 

• GSK (GlaxoSmithKline) has announced a partnership with McLaren’s Formula 1 team to harness its expertise in big data analytics. 

• Bayer and ABB Electronics have positioned themselves as B2B pioneers in unlocking value from real-time analytics. 

• IBM has assembled a global team of around 20,000 people focused specifically on advanced analytics and AI. 

• American Express continues to find new revenue growth through deep analysis and research into customer behavior. 

• Uber, Amazon, and Netflix are widely cited as leaders in data science, machine learning, and predictive analytics. 

As Alexander Stojanovic, former VP at eBay, has observed: “If we truly understand the customer data, understand the journey from beginning to end, then we have the strategic horsepower to influence the entire organization.” 

The opportunity is there. The question is how fully it is being captured. 

The performance divide: “Customer data masters” vs Everyone else 

Despite sustained investment, many organizations remain dissatisfied with their progress. Salesforce’s 2024 State of Marketing report found that only 31% of marketers are fully satisfied with their ability to unify customer data, meaning the vast majority still struggle to translate data into meaningful action.  

At the same time, those who have mastered customer analysis and insight note striking results. These “customer data masters” can point to clear gains in: 

• Net new customer revenues 

• Revenues from customer referrals 

• Cross-sell and upsell performance 

• Reductions in annual customer service costs 

Success is not confined to large multinationals with vast budgets. Smaller, localized companies are also finding ways to leverage customer data effectively despite their limited resources. Real case studies illustrate how different organizations of varying sizes and sectors have approached the challenge. 

Adidas: From reactive to predictive marketing 

Lia Vakoutis, formerly Senior Director at Adidas, describes how the company has evolved its marketing approach. 

Historically, Adidas would plan promotions months in advance. Considerable time and budget were invested in campaigns that were then rolled out largely as planned. If a promotion did not perform as expected, there was limited ability to intervene or adjust in real time. 

To move beyond this rigid model, Adidas made a deliberate shift from “reactive marketing” to “predictive marketing.” 

To support this, the company deployed a suite of tools, including: 

• Salesforce Social Studio and Statusbrew for real-time social media and Marcomms monitoring 

• Meltwater for infographics and reports on competitive social performance 

• Brandwatch for large-scale social data management and analysis 

These tools work together to create a highly detailed picture of Adidas’ social media universe. 

Adidas focuses on five key platforms, specifically Twitter/X, YouTube, TikTok, Facebook, and Instagram. The organization tracks around 1,200 sports and football-specific message boards, blogs, and news sites. In one campaign, the team analyzed more than 4 million pieces of information across 17 markets and multiple languages. 

Because insights arrive in real time, Adidas can create adaptable campaigns that remain relevant as the market changes:  

• Adjust promotions as they run 

• Feature athletes and sports stars gaining traction 

• Change creative material and messaging 

• Respond swiftly to competitor moves 

Vakoutis has described it as “a brilliant way to test what works and what does not, and be able to react instantaneously.” In one recent effort, Adidas tested 300 concepts in a single 48-hour period – something that would have been impossible under its old, static model. 

Amazon: Real-time insight, unified action 

Amazon is often cited as a benchmark for data-driven decision-making, placing enormous importance on real-time customer analytics. 

Speaking with Amazon during his research, de Kare-Silver recalls the company emphasizing how central real-time customer analytics are to its model: “Of all the things we do today, we believe it’s our real-time customer analytics that make the difference.” 

A dedicated team monitors customer activity by the second. From this, Amazon can immediately see: 

• What is working and what isn’t 

• Where price changes might be effective 

• When webpage layouts should be adjusted 

• How product bundles should be constructed 

• How different customer segments are responding across the base 

Critically, insights do not sit unused in dashboards. Amazon has built a “virtual circle” of three groups – analytics, UX/conversion, and web development – that work together in a tightly integrated way. They are co-located, report to a single leader, and form a core commercial unit with shared accountability. This configuration allows Amazon not only to detect issues in real time but to act on them immediately. 

Some other businesses in fast-moving, fashion, or consumer categories, such as John Lewis and Next, have tried to organize similarly. Yet even when they invest in analytics, many still lack Amazon’s corresponding real-time change capability. Heavy approval processes and control layers can slow decisions and undermine the very advantage that real-time insight is meant to provide. 

Starbucks and Pret A Manger: Two paths to customer loyalty 

Customer loyalty can be built in many ways, and Starbucks and Pret A Manger offer two contrasting approaches that highlight how data strategy shapes the path forward. Although they begin with very different models, both offer useful insights into how loyalty evolves as an organization scales. 

1. Starbucks: Building a data-rich loyalty ecosystem 

Starbucks launched its customer loyalty program more than 15 years ago. What began as a simple payment card has evolved into My Starbucks Rewards, widely recognized as one of the most effective loyalty schemes globally, often summarized as “one sip gets you gold status.” 

Addressing shareholders at an AGM, Howard Schultz shared that: 

• More than half of the 31 million My Starbucks Rewards members are high-spending gold members. 

• Over 23 million customers use the mobile payments app. 

• In one quarter alone, members loaded more than $4 billion onto their cards. 

Starbucks has invested heavily in building out its customer database and analytics capability. The company uses Oracle customer relationship management (CRM) as the backbone of its loyalty system, tightly integrated with its Oracle ERP platform. This architecture combines transactional, analytical, and engagement features to manage customer data across channels, from in-store purchases to mobile interactions. 

All of this is tied into Oracle Exadata, a cloud-based data warehouse designed for scale. The result is a vast pool of data that must be continuously cleansed and analyzed. With more than four billion cups of coffee sold each year, Starbucks is still exploring how to unlock the full range of insights available from its customer base. 

2. Pret A Manger: From discretionary rewards to structured data 

Pret A Manger adopted a very different approach in its early years. Pret deliberately chose not to build a “clubcard-style” system. Leaders did not want to spend heavily on complex databases and large-scale loyalty programs. 

Instead, loyalty was left largely to the discretion of store employees. Staff were empowered to get to know their regular customers, build rapport, and give out free coffees and other rewards as they saw fit. The approach was intentionally “freestyle and fun”, relying on local recognition and human connection rather than technology. 

For many years, this model worked well. However, as Pret expanded to around 600 stores across 15 countries, the need for a more systematic approach to understanding and engaging customers grew stronger. 

The company eventually introduced its Pret Perks app, which: 

• Rewards customers with a free coffee after every 10 purchases 

• Captures valuable customer data that can be analyzed 

• Provides a foundation for more tailored engagement and offers 

“In effect, Pret has moved closer to the Starbucks model,” says de Kare-Silver. “Although they originally established customer loyalty on some really lovely sentiments and personalized service, they eventually had to recognize that at scale, customer loyalty and personalization are difficult to execute effectively without structure.” 

7 Reasons so many organizations still struggle 

Given these examples, why do so many executives remain frustrated with their own progress? 

Recent studies confirm how much work many organizations still have to do. A PwC survey found that 73% of business leaders now view customer experience as a top priority, yet most still struggle to turn insight into action. Gartner’s 2025 Planning Guide for Data Management reinforces this picture, stating that ongoing issues with data quality, governance, and system integration continue to limit how effectively teams can turn customer data into something usable, even in companies that prioritize customer experience. 

1. Proving ROI 

Companies may launch many customer initiatives, such as new digital journeys, service improvements, and loyalty experiments, but it’s often unclear which ones actually make a difference. Without knowing what drives results, leaders find it hard to justify new investments into the very customer experience programs that may be delivering the most value.  

2. Data deluge 

Organizations now have access to more customer data than ever before. De Kare-Silver notes the challenge this creates: “The recurring question is, once we’ve collected all this data, what do we do with it? Without structure, purpose, and prioritization, data becomes overwhelming. Instead of enabling growth, it can end up slowing progress.” 

3. Multi-channel complexity 

Customer data is spread across the many operational touchpoints that an organization may use: 

• Websites 

• Physical stores 

• Email campaigns 

• Social media platforms 

• tele-sales and call centers 

• Field sales teams 

• Mobile apps 

• Customer query handling and ticketing systems 

• Historic purchase and contact records 

Even assuming all of this data is captured accurately, which is not always the case, it remains hard to reconcile these different touchpoints into a single, coherent view of the customer. 

4. Fragmented data integration and standards 

Many organizations still operate with disconnected systems and siloed databases, making it difficult to create a consistent view of the customer. Gartner’s 2025 Data Management guidance reinforces this challenge, noting that gaps in governance, data quality, and system integration continue to limit analytics maturity and hinder effective use of customer data. 

In many companies: 

• Different departments maintain their own databases. 

• Teams draw conclusions from isolated sets of information. 

• Efforts to bring data together face resistance and technical barriers. 

“Fragmented data systems remain one of the most persistent barriers to progress,” says de Kare-Silver. “The result is a patchwork of insights that never adds up to a unified picture of the customer.” 

5. Gaps in skills and leadership 

Without clear leadership and ownership, analytics initiatives can stall or become fragmented. Even where a Chief Data Officer (CDO) role exists, responsibilities may be unclear. Leaders must ask: 

• Is the CDO deeply engaged in the data science itself? 

• Are they also responsible for driving insights into marketing, sales, and customer decision-making? 

• Is every stage in the customer value chain properly staffed and led, from raw data capture through to commercial action? 

• Is there a single senior Data/Analytics/Insight leader who brings all of this together, coordinates it, and champions it across the business? 

6. No unifying dashboard, metrics, or KPIs 

“Even when the data exists, many organizations still lack the structure to bring it together,” says de Kare-Silver. “Teams end up measuring different things, in different ways, with no shared picture of what success should look like.” 

This shows up in several ways: 

• No common definition of customer experience success 

• Limited benchmarking against competitors or external standards 

• Little visibility into where improvement gaps lie 

• No unified view of what full potential could be 

Without a shared dashboard and consistent KPIs, progress becomes difficult to track, and even harder to scale. 

7. Underuse of Net Promoter Score (NPS) 

Net Promoter Score (NPS), pioneered by Bain & Company and Satmetrix, is fast becoming a global standard for measuring customer service performance. Its value lies in: 

• Tracking scores over time 

• Benchmarking against competitors 

• Using it as a starting point for deeper analysis 

Organizations that embrace NPS often describe it as the catalyst for their customer analytics journey. It gives them an objective view of how they are performing, and prompts them to define what additional metrics and data they need. 

How did Countax/Ariens and Sainsbury’s overcome these challenges? 

To illustrate what progress looks like in practice, the experiences of Countax/Ariens and Sainsbury’s show how both smaller and larger organizations can address these challenges by unifying their data, establishing clearer metrics, and embedding insight into day-to-day operations. 

Countax/Ariens: From painful reporting to self-service insight 

Based in the UK, Countax/Ariens produces lawnmowers and other outdoor equipment, has a strong domestic base, and distributes worldwide – all with a team of around 120 staff. 

According to Darren Spencer, one of the former senior Directors, the business had long suffered from insufficient or hard-to-access data across operations, especially around its distributor and end-user customer base. “We had the data,” he says. “We just couldn’t use it, or it was very painful to get it.” 

Countax brought in Matillion, a Business Intelligence (BI) software firm, to design and implement a SaaS data warehouse solution. This new platform integrated the following: 

• The company’s ERP system 

• Other in-house bespoke systems 

• Core areas such as sales, inventory, supply chain, and customer data 

• Customer purchase and contact history 

Data inputs and definitions were standardized, and key dashboard metrics were agreed upon. A major effort went into unifying the data so that everyone could access a single, common view of insights. 

Crucially, the resulting BI environment was designed at multiple levels of detail, enabling even non-data-literate users to find and interpret what they needed. 

Spencer notes that this transformation had a profound impact: 

“The reporting ability that comes from this business intelligence has greatly helped. We now rely on these analyses to guide the strategic direction; we didn’t have this level of visibility before. And because we have a common dashboard, it makes our management meetings much quicker and easier. There’s no debate about the data, it’s now simply about what actions to take. We wish we had done this years ago.” 

Sainsbury’s: Toward an intimate understanding of every customer 

As CEO, Simon Roberts has been explicit about Sainsbury’s ambition. Speaking openly with de Kare-Silver, he says, “We are aiming for a future where we know every single customer on an intimate basis. We want to be able to predict what our customers will need, when they’ll need it, and how best to deliver that to them.” 

Over many years, Sainsbury’s has been collecting and building what it describes as a “vault of customer data.” However, in its early days, this information was accessible only to a small number of analysts and coders. Commercial teams in buying, merchandising, and marketing lacked the customer insights they needed to “break new ground.” 

To address this, Sainsbury’s partnered with Aimia, a specialist in customer analytics and loyalty, and subsequently acquired the business. Together, they developed a six-point strategy and change program: 

1. Identify key metrics 

Sainsbury’s defined the measures that indicate effective customer engagement and drive sales. These include average spend, basket size, purchase frequency, and customer lifetime value, as well as more emotional and sentiment-based indicators derived from research, online surveys, social media monitoring, and NPS. Wherever possible, these metrics are tracked not just at the store level, but at the individual customer level. 

2. Train staff continuously in customer service 

Customer data is only as powerful as the behaviors it informs. Ongoing training helps staff translate insight into better experiences. 

3. Build a dedicated team 

Sainsbury’s now has a team of more than 120 people responsible for customer data management, analysis, reporting, and insight. Reports are tailored to departmental needs. For example, the buying department receives its own dedicated analyses alongside a view of the bigger picture. 

4. Monitor use of data insights 

A key responsibility of senior managers is to ensure that insights are not left on the shelf. Leaders actively monitor whether data-driven recommendations are being followed through and actioned. 

5. Drive personalization 

The company uses its customer data to shape marketing and promotional initiatives with a high degree of individualization and personalization. 

6. Encourage supplier investment 

Sainsbury’s shares relevant customer insights with suppliers, helping them identify top-performing products, refine promotions, and innovate in line with customer demand. 

Roberts has reinforced the commitment to continue investing in this area. Plans include further investment in staff training, a new automated system to track product availability, and additional systems infrastructure to create an ever more robust single view of the customer, driving increasingly effective interactions. 

What leaders can learn: From data deluge to decision advantage 

Whether the greatest success stories in customer data and analytics come from IBM, Starbucks, or Netflix, or Countax and Sainsbury’s, the central lessons are consistent: 

1. It is no longer enough to simply collect data. 

2. Competitive advantage comes from building an integrated, insight-driven system that connects data to real commercial decisions. 

Successful organizations tend to share several characteristics: 

• Enterprise-wide commitment to understanding the customer 

• A clear focus on customer analytics as a strategic capability, not a back-office technical function 

• Strong data integration and standardization, reducing fragmentation and silos 

• A unifying set of metrics and dashboards that align leadership around what success looks like 

• Clear roles and leadership ownership, ensuring analytics is driven through to commercial action 

• Investment in people and skills, including training front-line staff informed by data and insight 

• A culture of experimentation and real-time adjustment, not one-off campaigns rolled out and left untouched 

“Leadership teams aren’t asking whether customer analytics matters. That debate is largely settled,” says de Kare-Silver. “The question is how quickly they can move from scattered initiatives and isolated dashboards to a fully integrated system that genuinely improves how the business operates.” 

Will future surveys still show that a vast majority of companies still struggle to build effective customer analytics platforms? Or will more organizations join the ranks of the “customer data masters” and unlock the full potential that already lies within their data?