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?<\/p>\n\n\n\n
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. <\/p>\n\n\n\n
Yet beneath this momentum lies a persistent uncertainty. Many executives are still asking: <\/p>\n\n\n\n
Michael de Kare-Silver<\/strong><\/a>, Managing Partner at Signium in London<\/a data-aos=\"fade-up\">, echoes these thoughts: <\/p>\n\n\n\n \u201cCustomer analytics is where these questions become very real. There\u2019s\u00a0huge potential\u00a0to unlock new revenue and deepen customer engagement, but many organizations still struggle to get that value out in practice.\u201d\u00a0<\/p>\n<\/blockquote>\n\n\n\n Few can deny the importance of analysis and insight in driving better decisions through the current era of near-constant disruption. <\/p>\n\n\n\n \u201cOrganizations face a genuine deluge of data,\u201d says de Kare-Silver. \u201cLeaders 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\u2019t. The real task is turning that data into insights that drive better decisions and stronger performance.\u201d <\/p>\n\n\n\n The growing strategic weight of Data & Analytics is reflected in recent headlines: <\/p>\n\n\n\n As Alexander Stojanovic, former VP at eBay, has observed: \u201cIf we truly understand the customer data, understand the journey from beginning to end, then we have the strategic horsepower to influence the entire organization.\u201d <\/p>\n\n\n\n The opportunity is there. The question is how fully it is being captured. <\/p>\n\n\n\n Despite sustained investment, many organizations\u00a0remain\u00a0dissatisfied with their progress.\u00a0Salesforce\u2019s 2024 State of Marketing<\/strong><\/a data-aos=\"fade-up\">\u00a0report\u00a0found 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.\u00a0\u00a0<\/p>\n\n\n\n At the same time, those who have mastered customer analysis and insight note striking results. These \u201ccustomer data masters\u201d can point to clear gains in: <\/p>\n\n\n\n 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. <\/p>\n\n\n\n Lia Vakoutis, formerly Senior Director at Adidas, describes how the company has evolved its marketing approach. <\/p>\n\n\n\n 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. <\/p>\n\n\n\n To move beyond this rigid model, Adidas made a deliberate shift from \u201creactive marketing\u201d to \u201cpredictive marketing.\u201d <\/p>\n\n\n\n To support this, the company deployed a suite of tools, including: <\/p>\n\n\n\n These tools work together to create a highly detailed picture of Adidas\u2019 social media universe. <\/p>\n\n\n\n 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. <\/p>\n\n\n\n Because insights arrive in real time, Adidas can create adaptable campaigns that remain relevant as the market changes: <\/p>\n\n\n\n Vakoutis has described it as \u201ca brilliant way to test what works and what does not, and be able to react instantaneously.\u201d In one recent effort, Adidas tested 300 concepts in a single 48-hour period \u2013 something that would have been impossible under its old, static model. <\/p>\n\n\n\n Amazon is often cited as a benchmark for data-driven decision-making, placing enormous importance on real-time customer analytics. <\/p>\n\n\n\n Speaking with Amazon during his research, de Kare-Silver recalls the company emphasizing how central real-time customer analytics are to its model: \u201cOf all the things we do today, we believe it\u2019s our real-time customer analytics that make the difference.\u201d <\/p>\n\n\n\n A dedicated team monitors customer activity by the second. From this, Amazon can immediately see: <\/p>\n\n\n\n Critically, insights do not sit unused in dashboards. Amazon has built a \u201cvirtual circle\u201d of three groups \u2013 analytics, UX\/conversion, and web development \u2013 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. <\/p>\n\n\n\n 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\u2019s 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. <\/p>\n\n\n\n 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. <\/p>\n\n\n\n 1. Starbucks: Building a data-rich loyalty ecosystem <\/p>\n\n\n\n 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 \u201cone sip gets you gold status.\u201d <\/p>\n\n\n\n Addressing shareholders at an AGM, Howard Schultz shared that: <\/p>\n\n\n\n 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. <\/p>\n\n\n\n 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. <\/p>\n\n\n\n 2. Pret A Manger: From discretionary rewards to structured data <\/p>\n\n\n\n Pret A Manger adopted a very different approach in its early years. Pret deliberately chose not to build a \u201cclubcard-style\u201d system. Leaders did not want to spend heavily on complex databases and large-scale loyalty programs. <\/p>\n\n\n\n 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 \u201cfreestyle and fun\u201d, relying on local recognition and human connection rather than technology. <\/p>\n\n\n\n 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. <\/p>\n\n\n\n The company eventually introduced its Pret Perks app, which: <\/p>\n\n\n\n \u201cIn effect, Pret has moved closer to the Starbucks model,\u201d says de Kare-Silver. \u201cAlthough 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.\u201d <\/p>\n\n\n\n Given these examples, why do so many executives remain frustrated with their own progress? <\/p>\n\n\n\n Recent studies confirm how much work many organizations still have to do.\u00a0A\u00a0PwC survey<\/strong><\/a>\u00a0found that 73% of business leaders now view customer experience as a top priority, yet most still struggle to turn insight into action. Gartner\u2019s\u00a02025 Planning Guide for Data Management<\/strong>\u00a0<\/a data-aos=\"fade-up\">reinforces this picture,\u00a0stating\u00a0that 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.\u00a0<\/p>\n\n\n\n 1. Proving ROI <\/p>\n\n\n\n Companies may launch many customer initiatives, such as new digital journeys, service improvements, and loyalty experiments, but it\u2019s 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. <\/p>\n\n\n\n 2. Data deluge <\/p>\n\n\n\n Organizations now have access to more customer data than ever before. De Kare-Silver notes the challenge this creates: \u201cThe recurring question is, once we\u2019ve 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.\u201d <\/p>\n\n\n\n 3. Multi-channel complexity <\/p>\n\n\n\n Customer data is spread across the many operational touchpoints that an organization may use: <\/p>\n\n\n\n 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. <\/p>\n\n\n\n 4. Fragmented data integration and standards <\/p>\n\n\n\n Many organizations still operate with disconnected systems and siloed databases, making it difficult to create a consistent view of the customer. Gartner\u2019s 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. <\/p>\n\n\n\n In many companies: <\/p>\n\n\n\n \u201cFragmented data systems remain one of the most persistent barriers to progress,\u201d says de Kare-Silver. \u201cThe result is a patchwork of insights that never adds up to a unified picture of the customer.\u201d <\/p>\n\n\n\n 5. Gaps in skills and leadership <\/p>\n\n\n\n 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: <\/p>\n\n\n\n 6. No unifying dashboard, metrics, or KPIs <\/p>\n\n\n\n \u201cEven when the data exists, many organizations still lack the structure to bring it together,\u201d says de Kare-Silver. \u201cTeams end up measuring different things, in different ways, with no shared picture of what success should look like.\u201d <\/p>\n\n\n\n This shows up in several ways: <\/p>\n\n\n\n Without a shared dashboard and consistent KPIs, progress becomes difficult to track, and even harder to scale. <\/p>\n\n\n\n 7. Underuse of Net Promoter Score (NPS) <\/p>\n\n\n\n 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: <\/p>\n\n\n\n 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. <\/p>\n\n\n\n To illustrate what progress looks like in practice, the experiences of Countax\/Ariens and Sainsbury\u2019s 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. <\/p>\n\n\n\n Based in the UK, Countax\/Ariens produces lawnmowers and other outdoor equipment, has a strong domestic base, and distributes worldwide \u2013 all with a team of around 120 staff. <\/p>\n\n\n\n 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. \u201cWe had the data,\u201d he says. \u201cWe just couldn\u2019t use it, or it was very painful to get it.\u201d <\/p>\n\n\n\n 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: <\/p>\n\n\n\n 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. <\/p>\n\n\n\n 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. <\/p>\n\n\n\n Spencer notes that this transformation had a profound impact: <\/p>\n\n\n\n \u201cThe reporting ability that comes from this business intelligence has greatly helped. We now rely on these analyses to guide the strategic direction; we didn\u2019t have this level of visibility before. And because we have a common dashboard, it makes our management meetings much quicker and easier. There\u2019s no debate about the data, it\u2019s now simply about what actions to take. We wish we had done this years ago.\u201d <\/p>\n\n\n\n As CEO, Simon Roberts has been explicit about Sainsbury\u2019s ambition. Speaking openly with de Kare-Silver, he says, \u201cWe 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\u2019ll need it, and how best to deliver that to them.\u201d <\/p>\n\n\n\n Over many years, Sainsbury\u2019s has been collecting and building what it describes as a \u201cvault of customer data.\u201d 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 \u201cbreak new ground.\u201d <\/p>\n\n\n\n To address this, Sainsbury\u2019s 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: <\/p>\n\n\n\n Sainsbury\u2019s 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. <\/p>\n\n\n\n Customer data is only as powerful as the behaviors it informs. Ongoing training helps staff translate insight into better experiences. <\/p>\n\n\n\n Sainsbury\u2019s 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. <\/p>\n\n\n\n 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. <\/p>\n\n\n\n The company uses its customer data to shape marketing and promotional initiatives with a high degree of individualization and personalization. <\/p>\n\n\n\n Sainsbury\u2019s shares relevant customer insights with suppliers, helping them identify top-performing products, refine promotions, and innovate in line with customer demand.\u00a0<\/p>\n\n\n\n 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. <\/p>\n\n\n\n Whether the greatest success stories in customer data and analytics come from IBM, Starbucks, or Netflix, or Countax and Sainsbury\u2019s, the central lessons are consistent: <\/p>\n\n\n\n Successful organizations tend to share several characteristics: <\/p>\n\n\n\n \u201cLeadership teams aren\u2019t asking whether customer analytics matters. That debate is largely settled,\u201d says de Kare-Silver. \u201cThe 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.\u201d <\/p>\n\n\n\n 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 \u201ccustomer data masters\u201d and unlock the full potential that already lies within their data? <\/p>\n","protected":false},"excerpt":{"rendered":" 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 […]<\/p>\n","protected":false},"featured_media":12143,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","region":[],"news_type":[19],"acf":[],"yoast_head":"\n\n
Why do customer analytics matter so much right now? <\/h2>\n\n\n\n
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The performance divide: \u201cCustomer data masters\u201d vs Everyone else\u00a0<\/h2>\n\n\n\n
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Adidas: From reactive to predictive marketing <\/h3>\n\n\n\n
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Amazon: Real-time insight, unified action <\/h3>\n\n\n\n
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Starbucks and Pret A Manger: Two paths to customer loyalty <\/h3>\n\n\n\n
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7 Reasons so many organizations still struggle <\/h2>\n\n\n\n
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How did Countax\/Ariens and Sainsbury\u2019s overcome these challenges? <\/h2>\n\n\n\n
Countax\/Ariens: From painful reporting to self-service insight <\/h3>\n\n\n\n
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Sainsbury\u2019s: Toward an intimate understanding of every customer <\/h3>\n\n\n\n
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What leaders can learn: From data deluge to decision advantage <\/h2>\n\n\n\n
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