Brands in the digital age face a paradox when it comes to customer data. On the one hand, they are inundated with an ever-expanding deluge of possible data points across every interaction, transaction, and touchpoint. At the same time, regulatory bodies are increasing scrutiny and restrictions over how that data is collected, combined, and used.
To thrive in this complex landscape, brands must adopt new strategies and technologies that enable them to harness the power of their data while maintaining the highest standards of compliance and integrity. In other words, it’s time for a new approach to customer intelligence.
What is customer intelligence?
Customer intelligence (CI) is the process of getting to know your customers on a deeper level, by gathering, combining, and analysing consumer data from a wide range of sources. The better you know their needs, preferences, and behaviours, the better you can develop and market the right products and services. While market research has traditionally held a back-office or reactive function, today it’s taken on a strategic role in driving all kinds of decision-making.
What are the benefits of customer intelligence?
With countless ways to leverage customer intelligence, many companies run into the challenge of option paralysis. One 2024 CI survey found that while 87% of executives agree that analytics provide an edge in improving customer experiences, only 26% are using insights to actively drive product and service innovation. Here are a few top customer intelligence use cases to consider.
Personalise customer experiences
Today’s consumers don’t just appreciate personalisation – they expect it. As many as 76% get frustrated (and are less likely to purchase) when they aren’t delivered a tailored experience, according to a 2021 McKinsey study. Customer intelligence empowers you to build individual customer profiles, serve more relevant product and content recommendations, and provide a seamless experience across channels and touchpoints.
Increase revenue
Customer intelligence helps businesses boost sales in many ways, from more precise campaign targeting and optimised pricing to refined go-to-market strategies and product feature roadmaps. These insights can also drive upsell and cross-sell opportunities – for example, by using a customer’s order history to suggest add-on items at check-out, or looking at regional sales trends to inform store merchandising decisions.
Improve customer retention and satisfaction
Robust customer intelligence empowers you to increase retention and minimise churn. By leveraging CI, you can provide a smooth, tailored experience for each customer, and proactively identify and address any issues before they become account risks. Customer feedback also facilitates the creation of more impactful loyalty programs to keep shoppers engaged.
Discover conversion opportunities
Analysing customer data is invaluable in helping refine your sales funnel. By looking at trends across channels, demographics, or past behaviour, you can pinpoint audience segments that are most likely to convert, then prioritise these high-value prospects with targeted marketing efforts. Cross-screen measurement and analytics also illuminate gaps or friction points in the customer journey, revealing opportunities to optimise the path to purchase.
Data-driven decision making
Maybe the most transformative benefit of customer intelligence is the ability to drive smarter decision-making at every level. Customer intelligence turns data points into insights that can help maximise marketing impact or create predictive AI/ML models that anticipate market trends. According to a 2024 Winterberry report, US spending on data-driven marketing has skyrocketed over the past two decades, from $85.7 billion in 2004 (focused on direct mail and infomercials) to over $270 billion in 2023 (spanning paid social, video, connected TV, and more).
The vast majority (87%) of 200 surveyed marketing, data, analytics, and technology leaders are leveraging first-party data, or information collected directly from consumers. Only roughly half are tapping into the power of second-party data (41%) – other companies’ first-party insights – and third-party data (45%), or audience segments purchased via data providers.
Types of customer intelligence
With such a vast and growing landscape of data sources, what types of customer intelligence should be on your radar? Let’s explore some of the most common and valuable categories.
Demographic Data
Basic factual attributes about individual consumers (age, gender, income, education, etc.) fall under the demographic data umbrella. While some of this information may be collected as first-party data directly from consumers, companies can also access custom audience segments through a data marketplace, tapping second- and third-party providers to expand the reach, targeting, and measurement of campaigns.
Psychographic Data
A level deeper than demographics, psychographic data reflects customers’ personality traits, values, attitudes, interests, and lifestyle preferences. These insights can get to the heart of why consumers make the choices they do, and often reveal a broader addressable market for products or services that may be associated with specific age groups or genders.
Transactional Data
From items purchased and order frequency to average order value and subscription status, transactional data serves as a crucial piece of the customer intelligence puzzle. There’s no better way to understand what your customers need and want than seeing what they actually buy, and how that activity varies by season, geography, or other key variables.
Behavioural Data
Tracking how users engage across your brand’s many touchpoints (website, social channels, mobile apps, etc.) generates a wealth of actionable behavioural data for sales, product, and executive teams. To connect the dots beyond your owned channels, brands can tap into second-party data collected by business partners.
For example, CPG brands and retailers might share shopper insights, or airlines and credit card companies could co-design travel loyalty programs. In these cases, data is not sourced directly, but thoughtfully procured through allied partnerships, and analysed within privacy-enhancing data clean rooms.
Attitudinal Data
While the data types above are largely quantitative, attitudinal data lends a qualitative lens by capturing customers’ sincere thoughts and feelings about your brand, products, or services. Methods like surveys, focus groups, and social listening give customers a proverbial microphone to share unfiltered feedback.
How to collect customer intelligence
Just as there are many types of customer intelligence, there are many tactics for capturing all of those valuable insights, while still respecting consumer privacy. Some key data collection channels include:
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Of course, the process of gathering and activating this trove of customer data is not without its challenges. A slew of landmark laws and policies have emerged in recent years to protect personal data, from Europe’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) to industry-specific regulations like HIPAA.
The third-party cookie may be the most famous casualty of the privacy-first era, but it’s likely not the last. Identifiers such as mobile ad IDs and IP addresses could soon face similar fates, requiring alternative approaches to durable, future-ready targeting and customer identification.
Beyond privacy concerns, there is also the barrier of vast fragmentation and complexity of the data itself. A 2024 Wakefield survey found that marketers in the United States now consult an average of 28 different data sources to measure program impact. With countless internal systems (e.g. CRM, POS, ERP) and external partners collecting complementary pieces of the customer puzzle, brands need the right process and technology to unify those pieces into clear customer profiles and actionable next steps for personalisation.
5 steps to customer intelligence
Ready to reap the benefits of a more customer-centric marketing strategy? Getting started is easier than you might think. Follow these five steps to customer intelligence success:
1. Establish your objectives for customer intelligence
Before diving into the tactical steps of collecting and analysing data, zoom out to define what customer intelligence means for your organisation. What specific questions are you trying to answer? What business problems could better data help solve? By starting with a clear and focused set of objectives, you can work backwards to build a consumer data strategy that efficiently delivers on what the business needs.
2. Gather customer data across multiple channels
Connecting data from more sources than ever, while preserving customer privacy, is no easy feat. Teams need to invest in both internal and external data collaboration, or the combining of diverse data sets, which requires either hefty IT resources or purpose-built technology. Increasingly, brands are making use of data clean rooms (DCRs)—controlled, privacy-centric spaces that enable data collaboration within and across brands, publishers, retailers, and social networks. According to a recent IDC Spotlight report, the top five use cases for DCRs include customer attribute analysis, marketing audience building, and attribution tracking.
3. Integrate your customer data into a unified view
The next step is to bring all that disparate data together into a single source of truth—a unified view of each customer. This process involves ingesting raw data from the various source systems, consolidating identities, joining attributes to profiles, standardising fields and formats, and mapping a consistent schema. According to a 2024 CI survey, as many as half of brands are still using manual processes and legacy in-house systems for data integration, rather than opting for the speed of an identity resolution or data collaboration solution.
4. Examine your customer data for deeper insights
With unified customer profiles in place, the real magic of customer intelligence can begin – uncovering actionable insights to inform both strategic and tactical decisions.
Advanced analytics and data science techniques (like clustering models, propensity scoring, and next best action) can be applied to trustworthy data sets to:
- Define key audience personas and microsegments
- Predict customer lifetime value and churn risk
- Detect behavioural and attitudinal patterns
- Identify cross-sell and upsell opportunities
- Prescribe hyper-personalised offers and experiences
This type of granular optimisation has become table stakes for modern marketers. But without a solid foundation of accurate, accessible data, and trusted customer profiles, even the most sophisticated analytics and AI will succumb to the age-old axiom of “garbage in, garbage out.”
5. Take action on your customer insights
The true test of customer intelligence lies in your ability to activate data and take action. Only by pushing insights to the front lines of customer engagement can you deliver more seamless experiences and build lasting relationships. Here’s how you can take a holistic, omnichannel approach to data activation:
- Inform both real-time decisions (like dynamic web content and next best offers) and long-term strategies (like product development and market expansion).
- Deploy data-driven campaigns across popular outbound channels like email and paid social, as well as under-utilised tactics such as video, CTV, and mobile (as ranked by a 2024 Winterberry survey).
- Empower business teams and data scientists with self-serve access to reporting and analytics, so they can ask and answer their own questions.
- Test and learn continuously, using findings and feedback to optimise performance.
Most importantly, never lose sight of the end goal – creating more meaningful moments and interactions with your customers.
Examples of customer intelligence use cases
Now that we’ve covered the basics of customer intelligence, let’s dive into some examples of powerful use cases that drive real-world marketing effectiveness and business outcomes.
Customised customer journeys
Customer intelligence makes it possible to connect the dots across every touchpoint –online and offline, across channels and devices –to build a cohesive end-to-end journey. By understanding a customer’s unique path from initial ad exposure to active consideration and final purchase, you can optimise each interaction based on where they are in the funnel. For example, when snack brand Mondelēz International partnered with Albertsons and Pinterest on a new charcuterie board campaign, it combined first-party and retailer data to unlock powerful consumer insights, driving a 16% incremental sales lift and more meaningful experiences for customers at every stage of their shopping journey.
Customer lifetime value analysis
Predictive lifetime customer value (LCV) models consider factors like purchase frequency, average order size, and retention rates to determine which segments are driving the greatest long-term profitability. This approach helps prioritise marketing efforts and investments. NBCUniversal, for instance, unified fragmented data across its vast portfolio of films, TV programming, theme parks, and more to develop a proprietary customer ID. This enables consistent matching for over 200 million individuals across 1,000+ attributes – the first step to building a more holistic view of each customer and identifying the most valuable segments.
Churn prediction
On the flip side of lifetime value analysis is churn risk assessment. A robust customer intelligence program can predict which customers are most likely to disengage or defect, so you can take proactive steps to re-engage them before it’s too late. This process analyses data points like past transactions, support interactions, and customer feedback. Early and frequent alerts around shifts in sentiment and interest are critical for consumer brands that prioritise humanisation and personalisation in their customer experience, as heard in our RampUp 2024 panel with leaders from LVMH, NBA, and United MileagePlus.
Demand forecasting
Looking beyond individual customers to broader market trends, customer intelligence helps forecast demand and inform smart inventory decisions. Granular insights around topics like purchase seasonality, regional preferences, and competitive activity can be integrated with real-time signals like economic indicators and weather patterns to fine-tune predictive models. Advanced analytics and real-world testing also shed light on demand elasticity, and how factors like price changes or promotions impact sales volume.
Personalised marketing
As many as 90% of organisations are deploying some level of personalisation today, according to a 2024 Forrester study. Customer intelligence is the fuel that powers tailored experiences at scale. Just look at CPG leader Danone, which leaned on identity resolution to link disaggregated customer data and redesign its customer segments. By targeting the creative and bidding to these new groups across Facebook and Google Display & Video 360, Danone’s test campaigns drove a 25% incremental lift in sales and 22% increase in web traffic.
Sentiment analysis
While most customer data is quantitative in nature, qualitative insights are equally essential. Surveys, reviews, social listening, and other voice-of-customer inputs capture valuable attitudinal data that brings the customer story to life. By layering these unstructured data points with structured behavioural and transactional data, brands can detect meaningful correlations between what customers say and what they ultimately do. For example, mining customer support logs could surface a product issue that’s creating a negative sentiment, even before it shows up in sales or retention metrics.
Real-time product recommendations
At least two-thirds of consumers now expect brands to provide relevant product recommendations, according to a 2021 McKinsey study. Customer intelligence can hyper-personalise these suggestions in real-time based on an individual’s purchase history, browsing behaviour, and inferred preferences. AI-powered engines can further surface adjacent items that consumers are likely to enjoy based on sophisticated lookalike modelling, and those predictions only get better with more time, engagement, and data inputs.
Identifying cross-sell and upsell opportunities
Customer intelligence also illuminates new revenue opportunities through smart cross-selling and upselling. Predictive models can determine which complementary items a customer is most likely to buy based on their past behaviour and similar shoppers’ actions. These insights allow you to present tailored recommendations at the right moment, boosting attachment rates and average order values, and maximising the potential of every customer interaction.
Transform your customer intelligence strategy with LiveRamp
These use cases are just the tip of the iceberg when it comes to customer intelligence. To truly harness the power of your first-party data and transform it into business growth, you need a trusted partner with deep expertise in data collaboration and identity resolution. With an extensive network of over 3,000 data partners and 550+ activation destinations, all underpinned by the highest standards of privacy and security, LiveRamp offers an end-to-end suite of solutions to elevate your customer intelligence efforts.
Ready to unlock the full potential of your data? Let’s talk about how we can help.