What is *Sexy Little Numbers* by Dimitri Maex about?

*Sexy Little Numbers* explains how businesses can leverage existing data to drive growth, optimize marketing budgets, and improve customer engagement. It focuses on practical strategies like customer segmentation, predictive modeling, and ROI analysis, using real-world examples from author Dimitri Maex’s experience at Ogilvy & Mather. The book emphasizes turning raw data into actionable insights for smarter decision-making.

Who should read *Sexy Little Numbers*?

Marketing professionals, data analysts, and business leaders seeking to harness data for strategic growth will benefit most. It’s particularly relevant for those in customer-centric industries like retail, finance, or digital services, as well as entrepreneurs aiming to compete with larger firms through data-driven tactics.

Is *Sexy Little Numbers* worth reading?

Yes—it provides a clear roadmap for applying data analytics without requiring advanced technical skills. Readers praise its actionable frameworks, such as the “Customer Value Matrix,” and its focus on using accessible tools to uncover hidden opportunities in existing business data.

What are the key concepts in *Sexy Little Numbers*?

- **Customer Lifetime Value (CLV):** Prioritizing high-value customers to maximize ROI. - **Predictive Modeling:** Using historical data to forecast trends and behaviors. - **Micro-Segmentation:** Breaking audiences into hyper-targeted groups for personalized marketing. - **A/B Testing:** Optimizing campaigns through controlled experiments.

How does *Sexy Little Numbers* help with marketing budgets?

The book advocates for reallocating budgets based on data-driven insights rather than intuition. For example, Maex suggests shifting funds from broad campaigns to targeted micro-segments with higher conversion potential, supported by case studies from his Ogilvy career.

What critiques exist about *Sexy Little Numbers*?

Some argue the book focuses heavily on large corporations with abundant data, offering fewer solutions for small businesses with limited resources. Critics also note its 2012 examples may feel dated, though core principles remain applicable.

How does *Sexy Little Numbers* compare to *Competing on Analytics*?

While both emphasize data-driven decisions, Maex’s book is more tactical, focusing on immediate marketing applications. *Competing on Analytics* (Davenport & Harris) offers a broader organizational strategy perspective, making them complementary reads.

What famous quotes come from *Sexy Little Numbers*?

- *“People need a reason to listen to your sales pitch”* (on aligning messaging with customer needs). - *“Data doesn’t lie, but it needs a storyteller”* (highlighting the role of narrative in analytics).

性感数字

如何利用现有数据增长业务

迪米特里·马克斯

3.8(233 Reviews)

《性感数字》概述

解锁你已拥有的数据的隐藏力量。在《性感小数字》（Sexy Little Numbers）一书中，奥美（Ogilvy）的迪米特里·马克斯（Dimitri Maex）揭示了企业如何将现有指标转化为利润引擎。你业务中哪些被忽视的数字可能价值数百万？这本书让数据驱动营销变得极具吸引力且触手可及。

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第 1 章

When Data Transforms Marketing: The Power of Sexy Little Numbers

Have you ever wondered why Amazon seems to know exactly what you want to buy before you do? Or how Netflix recommends shows that become your new favorites? Behind these seemingly magical experiences lies a revolution that's reshaping marketing as we know it. In his groundbreaking book "Sexy Little Numbers," Dimitri Maex reveals how the most successful companies are turning the overwhelming deluge of customer data into precise, actionable insights that drive unprecedented growth. As the head of global data practice at Ogilvy, Maex has helped transform marketing from an art form dominated by creative "Mad Men" into a sophisticated science where every dollar spent can be measured and optimized. The book has become required reading at top business schools like Wharton and Columbia, while companies from Google to Procter & Gamble have adopted its methodologies. What makes this approach so powerful isn't just the technology-it's the fundamental shift in how businesses connect with customers by fishing where the biggest fish are and speaking to them about what truly matters.

第 2 章

The Evolution of Data-Driven Marketing

Numbers have always played a crucial role in marketing, evolving through distinct eras that have progressively refined how we measure effectiveness. The pioneers of direct response marketing-Montgomery Ward (1872) and Sears and Roebuck (1886)-revolutionized retail by meticulously tracking sales patterns, return rates, and customer preferences through their catalog operations. Their systematic approach to data collection laid the groundwork for modern marketing analytics. By 1923, Claude Hopkins had boldly declared advertising a science in his seminal work "Scientific Advertising," establishing fundamental principles for tracking campaign effectiveness that remain relevant today, including A/B testing, response tracking, and conversion analysis. While direct mail made measurement relatively straightforward through coupon codes and response cards, the emergence of mass media channels like radio and television required entirely new mathematical approaches. Nielsen ratings, launched in the 1950s, created the first systematic measurement of media consumption, while Gallup polls helped brands understand audience demographics and preferences. The 1950s saw the application of sophisticated operations research models to marketing challenges, introducing concepts like market segmentation, brand positioning, and media mix optimization. This was followed by the Customer Relationship Management (CRM) revolution of the 1990s, where powerful databases transformed how companies approached direct marketing, enabling personalized communications at scale. Today's digital era provides unprecedented measurement capabilities that earlier marketers could only dream about. Consider that Google processes over one billion searches daily, while social media platforms track billions of interactions, creating closed-loop systems where marketers can track customer journeys from initial exposure to purchase in real-time. Advanced analytics tools can now measure micro-conversions, attribute value across multiple touchpoints, and optimize campaigns automatically. This capability has fundamentally altered the marketing landscape, enabling real-time bidding, dynamic content optimization, and predictive analytics. The explosion of data collection has naturally raised valid privacy concerns, particularly regarding the depth and breadth of personal information being gathered. However, the marketing industry has begun self-regulating by implementing strict data protection protocols, not collecting personally identifiable information without explicit consent, and offering clear opt-out options through initiatives like the Digital Advertising Alliance. What's often overlooked in privacy discussions is how data collection actually benefits consumers through improved experiences. Advertising funds free content across the internet, estimated at over $100 billion annually, while personalized ads mean viewers see more relevant commercials and potentially fewer ads overall, as advertisers willingly pay premium rates for targeted placements. Studies show that properly targeted ads can achieve up to 5-10 times higher engagement rates than untargeted ones. The trade-off between privacy and personalization ultimately creates a better experience for everyone involved, though finding the right balance remains an ongoing challenge for the industry.

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第 3 章

The Value Spectrum: Finding Your Most Valuable Customers

The foundation of data-driven marketing is deceptively simple: focus your resources on the customers who matter most. This approach was powerfully demonstrated during my work with Cisco Systems in 2004, where we established an advanced analytics group despite initial skepticism from marketers who weren't naturally drawn to mathematics. Rather than overwhelming people with raw numbers, we created a simple Value Spectrum Model that segmented customers into four categories based on their value to Cisco. Unlike traditional segmentation approaches, our framework could identify specific individuals and their contact details-not just abstract demographic groups. We even developed statistical models showing how much customers were spending with competitors, which proved revolutionary for the sales team. The Value Spectrum is an elegant segmentation model that categorizes customers based on both their total market value and their loyalty to your brand. It reveals not just how much customers currently spend with you, but what share of their wallet you capture compared to competitors. The model divides customers into four quadrants: 1. Nuggets (high value/high loyalty) are your gold customers who require retention focus 2. Jackpots (high value/low loyalty) represent your greatest growth potential as they spend heavily but primarily with competitors 3. Acorns (low value/high loyalty) can be nurtured to grow their spending 4. Low Value/Low Loyalty customers warrant minimal investment This framework allows precise resource allocation, focusing marketing dollars where they'll generate the highest returns without increasing overall spending. When we presented this to Cisco's sales force with actual lists of high-value prospects in their territories, they were thrilled. The success earned us an audience with Cisco's global CMO, where we needed to prove our framework's effectiveness using historical data. Our analysis tracked how customers migrated between segments over time, creating simplified visualizations showing both positive revenue streams ($194 million from 5,274 companies) and negative ones (-$86 million from 2,640 companies) that clearly demonstrated where growth and losses were occurring. This data allowed us to make three specific recommendations: get more from existing customers (just 1% more of a Nugget's budget meant $8.6 million in revenue), convert more Jackpots to Nuggets, and prevent Nuggets from migrating downward.

第 4 章

Beyond Demographics: Understanding Customer Motivations

While knowing who to target is crucial, understanding what will resonate with them is equally important. Rather than focusing solely on what we want to sell, effective data-driven marketing requires deep insight into customer motivations, pain points, and aspirations. This understanding goes far beyond basic demographic data to uncover the emotional and practical drivers behind purchasing decisions. Working with British Telecom's small and medium business division, we combined hard data segmentation with needs-based research to create a comprehensive customer understanding framework. First, we created an exhaustive list of seventeen potential needs BT could fulfill, ranging from new digital channels and market expansion opportunities to advanced security solutions and operational efficiency improvements. Next, using rigorous quantitative research involving over 2,000 businesses, we determined which needs were most important to different customer segments. Finally, we employed sophisticated cluster analysis to create distinct customer segments based on their priority needs. This statistical technique allowed the data to form natural groupings of customers with similar needs, revealing patterns that might not be obvious through traditional segmentation methods. Interpreting these clusters requires both scientific precision and artistic intuition - looking for clear patterns like companies that prioritize supply chain management versus those focused on customer experience enhancement. The analysis revealed five distinct segments: Basic Needs (16%) seeking fundamental connectivity solutions, Customer Focus (33%) prioritizing customer service capabilities, Operational Efficiency (14%) emphasizing process optimization, Flexible & Secure (18%) requiring adaptable security solutions, and High Needs (19%) demanding comprehensive technology integration. This segmentation gave BT a powerful tool to tailor communications differently for each group. For example, security messaging could be positioned differently - emphasizing customer data protection for customer-focused businesses while highlighting operational continuity for efficiency-focused ones. When combined with the Value Spectrum approach, this created a comprehensive view of both who to target and what messaging would resonate most effectively. The power of this combined approach was dramatically demonstrated at Cisco, where we merged our Value Spectrum framework (based on detailed spending patterns and customer lifetime value) with marketing's existing attitude-based segmentation (advice driven, price driven, cutting edge, and enterprise-like). While some marketers initially saw these as competing approaches, they proved highly complementary - Value Spectrum provided precise targeting guidance, while attitude-based segmentation informed messaging strategy and content development. By overlaying these frameworks, we identified sixteen distinct segments which we then strategically consolidated into three actionable groups: Romance (high-value customers with perfect brand fit, requiring relationship deepening), Defense (high-value customers vulnerable to competitors, needing focused retention efforts), and Vanilla (all others, warranting standardized approaches). This simplified yet nuanced approach allowed us to quickly implement tailored marketing strategies that drove significant growth, with Romance segment customers showing 40% higher retention rates and Defense segment customers reducing churn by 25%.

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第 5 章

Finding Your Audience in a Fragmented Media Landscape

Once you've identified your target customers and what to say to them, the challenge becomes finding them. Unlike the predictable nature of dogs, customers behave more like cats-constantly shifting their media consumption and physical locations. Traditional targeting relied on identifying media channels your audience consumed and geographic locations where they lived. Today, we have sophisticated tools to find individuals rather than groups. Media planning matches your target audience profile with the media they consume. Using data from sources like Target Group Index (TGI) in the UK and Mediamark Research & Intelligence (MRI) in the US, you can identify which media your target audience disproportionately consumes. For example, in a video-on-demand case study, analysis showed that households with broadband, Visa credit cards, and DVRs were almost twice as likely to be heavy online users compared to the national average, while they were underrepresented among heavy TV viewers. Geographic targeting lets you find your audience based on where they live. For an online video provider, analysis of 81 major designated market areas (DMAs) identified which markets had the highest concentration of households with broadband, credit cards, and DVRs. This allowed identification of smaller DMAs with very high target concentration (like Las Vegas and Austin) for efficient targeting, and even drilling down to specific zip codes within DMAs like Honolulu. Search targeting capitalizes on the strongest indicator of interest: when someone actively looks for your product. When someone types "cucumber wet wipes" into Google, wet wipe manufacturers know they've found a potential customer. This makes search data incredibly powerful for targeting, whether through organic search optimization or paid search advertising. The most precise approach is individual targeting. While search is effective for reaching individuals at the moment of interest, it's limited to when consumers actively search. For more comprehensive individual targeting, marketers use three key data sources: 1. Internal databases storing customer interactions across stores, websites, and call centers 2. External databases containing information from magazine subscriptions, email lists, census data, etc. 3. Digital networks tracking browser interactions across digital properties Digital networks offer particularly powerful individual-level targeting online. Ad networks track user behavior across websites using cookies, building databases of what people visit, what ads they see, and what they search for. This allows precise targeting of ads to individuals most likely to convert. Companies calculate an "allowable" maximum price for showing ads based on a simple formula: A = (P(C) x MC) / ROI, where P(C) is conversion probability, MC is margin per conversion, and ROI is expected return on investment.

第 6 章

The Science of Marketing Budgets

The critical question of marketing budget allocation determines whether you'll get maximum return on investment. Most companies lack scientific rigor in determining marketing spend, whether they're investing thousands or hundreds of millions. With $154 billion spent on advertising in the US alone in 2012, even a 1% improvement in effectiveness would generate $1.5 billion in incremental value. Despite the high stakes, most companies rely on crude methods like "percentage of sales" rather than more sophisticated approaches. Harry Henry identified fifteen different budgeting approaches ranging from intuitive rules of thumb to sophisticated modeling, but thirty years later, most marketers still use simplistic methods rather than embracing data-driven decision making. The spend/get curve shows what you receive for your marketing investment. It typically shows a positive slope (more spending equals higher results) with decreasing marginal returns, meaning you eventually reach a saturation point where additional spending yields minimal results. While conceptually straightforward, the challenge lies in constructing accurate curves. Econometric modeling estimates how various factors impact demand for products or brands. Using mathematical functions like linear regression, econometricians determine the relationship between marketing efforts and sales outcomes. By plotting data points of marketing spend against sales results, patterns emerge showing whether spending significantly impacts results. Computers can calculate the best-fitting curve by minimizing the distance between data points and the line, allowing marketers to find optimal spending levels. For companies lacking complete data, David Coppock's hybrid approach combines econometrics with informed assumptions. This method requires answering four key questions: What would awareness be with zero spending? What's the maximum achievable awareness with unlimited budget? What are current spending and awareness levels? And what would awareness be if spending changed by X percent? Once you've determined your total budget, proper allocation can increase its value by up to 30%. The framework involves allocating across marketing tasks using the "Funnel Allocator" tool, which works in three stages: creating a revenue model, developing spend/get curves, and optimization. This approach helps determine optimal spending at each funnel level (awareness, consideration, purchase, loyalty) by mapping how changes in spending affect outcomes.

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第 7 章

Measuring What Matters: Beyond Vanity Metrics

Once you've targeted the right customers and built a plan with the right tactics, media, and budget, you need to measure which components are working. While overall metrics like sales increases or earnings drops provide aggregate information, they don't show the effectiveness of each dollar spent. Many organizations fall into the trap of focusing on easily accessible metrics like page views or social media followers, which may look impressive but don't necessarily correlate with business success. The fundamental problem in measurement is focusing too much on what can be measured rather than what should be measured. Working with Adrian Jarvis from Ogilvy's London office, we developed OgilvyEvaluate, a measurement framework that begins by asking "What is success?" The framework has become Ogilvy's standard measurement methodology worldwide. It emphasizes looking beyond surface-level metrics to understand the true drivers of business value, often requiring custom measurement approaches for each client's unique situation. When implementing this approach with a $35 billion electronics company, we discovered that despite months of campaign planning, the marketing executives had wildly different ideas about what success meant. The manufacturing head focused on moving product, marketing wanted customer insights, and finance cared only about short-term ROI. This common disconnect shows why clearly articulating objectives is critical before measurement begins. In subsequent workshops, we helped align these disparate views by creating a unified measurement framework that addressed all stakeholders' needs while maintaining focus on core business objectives. Real objectives must contain three components: a metric, a benchmark, and a timeframe. Without these, they're merely aspirations. For example, instead of "increase brand awareness," a proper objective would be "increase aided brand recall from 45% to 60% among target consumers within 12 months." The SMART framework (Specific, Measurable, Achievable, Realistic, Time-based) helps reformulate objectives, which automatically creates a list of key performance indicators (KPIs). Three essential categories of metrics for any measurement plan include: 1. Input metrics track resources invested in demand generation, such as marketing spend, staff hours, and content production costs 2. Output metrics measure immediate campaign impact through consumer engagement, including website visits, email open rates, and social media interactions 3. Outcome variables measure achievement against set targets, like sales growth, market share gains, or customer lifetime value increases Understanding how your actions affect performance requires going beyond simple tracking. Attribution is key-determining what portion of sales increases come from specific marketing actions. Two main approaches exist: econometric modeling, which uses statistical analysis to determine marketing effectiveness at an aggregate level, and individual level attribution, which tracks specific customer journeys across touchpoints. For Caesars, proper attribution analysis revealed banner ads had a 40% "view-through" effect lasting fifteen days, and increased search conversions by 12%-insights that prevented eliminating effective campaigns that initially appeared unsuccessful when measured by simplistic "last click" attribution. Similar analysis for a retail client showed that email marketing, while appearing ineffective in direct response metrics, actually drove significant in-store purchases within a two-week window. Modern measurement requires integrating multiple data sources and understanding the interplay between channels. Companies succeeding at measurement typically establish clear governance structures, invest in proper analytics tools, and maintain focus on metrics that truly matter to business success rather than those that simply look good in presentations.

第 8 章

The Continuous Optimization Cycle

Optimization isn't a one-time event but a continuous cycle of improvement: measure, analyze, optimize, then repeat. Companies that excel implement processes that institutionalize this approach, making their marketing progressively better with each iteration. The A2A framework consists of five bubbles: data, analyze, test (the "Analysis" phase) followed by execute and share (the "Action" phase). Knowledge sharing is critical-for one tech client, we instituted monthly calls where direct marketing teams worldwide discussed test results and reviewed the test pipeline. Digital channels offer nearly endless testing possibilities. For Kodak's online store, we created six different homepage versions, with the winning design generating an 11.3% revenue increase simply through layout changes. Testing eliminates subjectivity from decision-making-rather than debating based on opinion or taste, analytics determines what actually works. The Obama presidential campaign demonstrates testing's transformative potential. When Google employee Dan Siroker joined Obama's team, he applied multivariate testing to Obama.com. By changing just the visual and button text from "sign up" to "learn more," the new homepage outperformed the original by over 40%, resulting in 288,000 additional volunteers and $57 million in extra funds-more than 25% of what McCain raised online in total. TD Ameritrade exemplifies optimization at its finest. With a business model focused on account acquisition and cost per acquisition (CPA), and operating in a "closed loop" system where they can track individual customer journeys, TDA is perfectly positioned for analytics. Jim Dravillas implemented several groundbreaking tools for TDA. His automated frequency capping tool determined when showing additional ads to non-responding viewers became wasteful, redirecting those impressions to fresh prospects. This increased lead generation by 15% without increasing the marketing budget. His automated creative rotation tool analyzed real-time performance of different creative executions, automatically serving more of what worked and less of what didn't, boosting lead generation by 25-35%. Landing page optimization proved crucial for TDA. Using Memetrics technology, TDA tested 243 slightly different landing page versions by varying five elements: client login button, site link text, call-to-action button text, button color, and promotional offer layout. After fifteen days, the winning combination increased conversion by 15%-meaning 15 more account openings per 100 visitors, with a remarkable 43:1 ROI.

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第 9 章

The Future of Marketing Analytics

Analytics will fundamentally transform marketing, research, advertising and support functions within our lifetimes. Most functions will become automated: testing, creative rotation, targeting, real-time ad buying, and pricing decisions. The only jobs remaining will be for "technicians" who maintain the automation and "magicians" who leverage these tools to dramatically boost sales and earnings. We're experiencing a digital data deluge-by 2010, worldwide data storage approached 1,000 exabytes (compared to the 5 exabytes needed to store all words ever spoken by humans), with Google estimating 53 zettabytes by 2020. Soon, all media will become addressable, allowing precise targeting based on consumer value. The value of consumer data is driven by three factors: predictive power (how strongly it correlates with purchase intent), recency (real-time data showing immediate intent is vastly more valuable than older information), and exclusivity (unique data commands higher prices). Future innovation will focus on capturing real-time events and shortening the cycle between observation and targeting. Privacy concerns represent the "elephant in the room" for analytics. The industry faces a communication challenge about the economic reality: free content is funded by advertising, and effective targeting requires data collection. Without this exchange, free content would disappear. Two developments are likely: transparent regulations allowing consumers to opt out (with consequences for access to free content), and a shift toward consumers voluntarily sharing data for clear value, as seen with Foursquare, Mint.com, Nike Plus, and Pond's skin analyzer. Statisticians have become incredibly valuable, with Google calling their jobs "the sexiest" and IBM hiring thousands. Yet there's a severe shortage, particularly in the United States which ranks only thirty-fifth worldwide in math literacy. The rarest combination is someone with both advanced math skills and the ability to explain findings in marketing contexts to non-technical audiences-true "math marketers" are exceptionally scarce. Despite analytics becoming ubiquitous, there will still be crucial roles for creativity. The most basic analytic functions will become increasingly automated, with fewer people manually gathering and analyzing data. Instead, technicians will manage automated systems while "magicians"-creative thinkers who can translate insights into action-will become more valuable. The future demands close collaboration between these technical and creative specialists. Organizations must become adaptable to benefit from analytics. Most products fail because they aren't optimized-like the 50% of software functionality rarely or never used. Agile marketing, inspired by the 2001 Agile Manifesto for software development, offers a solution. Truly agile marketing is sensitive (customer-focused), adaptive (responsive to change), lean (focused on value creation), fast, and iterative. To prepare for the data-driven future: become and remain data literate; focus on what data should do for your business rather than getting lost in possibilities; and build or acquire the necessary skills through internal development or external partnerships. The companies that master this approach will be the ones that thrive in the increasingly data-driven marketing landscape of tomorrow.

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《性感数字》核心主题

marketing analytics
customer segmentation
predictive modeling
data-driven strategy
customer lifetime value

《性感数字》经典语录

Advertising funds free content across the internet.
Focus your resources on the customers who matter most.
Advertising [can achieve] 5-10 times higher engagement rates.
Convert more Jackpots to Nuggets.
Understanding what will resonate with them is equally important.

《性感数字》主要人物

Dimitri MaexAuthor and head of global data practice at Ogilvy
Montgomery WardPioneer of direct response marketing
Claude HopkinsAuthor of Scientific Advertising

关于作者

Dimitri Maex 是《Sexy Little Numbers: How to Grow Your Business Using the Data You Already Have》（中文译名常为《数据化营销》）一书的作者，他是全球公认的数据驱动营销和商业战略领域的领导者。作为奥美互动（OgilvyOne）纽约董事总经理及奥美集团（Ogilvy & Mather）全球数据业务负责人，Maex 的职业生涯致力于为财富 500 强企业将原始分析转化为可操作的洞察。他的著作将实用的商业指导与统计框架相结合，体现了他在帮助机构利用客户数据实现增长方面的专业见解——这一主题根植于他在 IPG 盟博（IPG Mediabrands）的开创性工作，他目前担任该公司旗下 Initiative 的全球首席执行官。

Maex 的权威性源于数十年的领导经验，包括执掌 Reprise Digital 的全球扩张，以及撰写被全球品牌广泛采用的业务框架。《Sexy Little Numbers》已被翻译成六种语言，至今仍是寻求连接数据科学与营销战略的专业人士的必备读物。

他的思想领导力还体现在频繁受邀参加广告周（Advertising Week）等行业论坛，并为《Entrepreneur》等刊物撰稿。该书被纳入商学院课程，并成为高管优化数据驱动决策的可靠资源，这些都彰显了其持久的影响力。

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"Instead of endless scrolling, I just hit play on BeFreed. It saves me so much time."

@Moemenn

"I never knew where to start with nonfiction—BeFreed’s book lists turned into podcasts gave me a clear path."

@Chloe, Solo founder, LA

117

"Perfect balance between learning and entertainment. Finished ‘Thinking, Fast and Slow’ on my commute this week."

@Raaaaaachelw

"Crazy how much I learned while walking the dog. BeFreed = small habits → big gains."

@Matt, YC alum

108

"Reading used to feel like a chore. Now it’s just part of my lifestyle."

@Erin, Investment Banking Associate , NYC

254

"Feels effortless compared to reading. I’ve finished 6 books this month already."

@djmikemoore

"BeFreed turned my guilty doomscrolling into something that feels productive and inspiring."

@Pitiful

4.5K

"BeFreed turned my commute into learning time. 20-min podcasts are perfect for finishing books I never had time for."

@SofiaP

"BeFreed replaced my podcast queue. Imagine Spotify for books — that’s it. 🙌"

@Jaded_Falcon

201

"It is great for me to learn something from the book without reading it."

@OojasSalunke

"The themed book list podcasts help me connect ideas across authors—like a guided audio journey."

@Leo, Law Student, UPenn

483

"Makes me feel smarter every time before going to work"

@Cashflowbubu

"Instead of endless scrolling, I just hit play on BeFreed. It saves me so much time."

@Moemenn

"I never knew where to start with nonfiction—BeFreed’s book lists turned into podcasts gave me a clear path."

@Chloe, Solo founder, LA

117

"Perfect balance between learning and entertainment. Finished ‘Thinking, Fast and Slow’ on my commute this week."

@Raaaaaachelw

"Crazy how much I learned while walking the dog. BeFreed = small habits → big gains."

@Matt, YC alum

108

"Reading used to feel like a chore. Now it’s just part of my lifestyle."

@Erin, Investment Banking Associate , NYC

254

"Feels effortless compared to reading. I’ve finished 6 books this month already."

@djmikemoore

"BeFreed turned my guilty doomscrolling into something that feels productive and inspiring."

@Pitiful

4.5K

"BeFreed turned my commute into learning time. 20-min podcasts are perfect for finishing books I never had time for."

@SofiaP

"BeFreed replaced my podcast queue. Imagine Spotify for books — that’s it. 🙌"

@Jaded_Falcon

201

"It is great for me to learn something from the book without reading it."

@OojasSalunke

"The themed book list podcasts help me connect ideas across authors—like a guided audio journey."

@Leo, Law Student, UPenn

483

"Makes me feel smarter every time before going to work"

@Cashflowbubu

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开启你的学习之旅，就是现在

第 7 章

性感数字

概述《性感数字》概述

一本书，多种聆听方式。

When Data Transforms Marketing: The Power of Sexy Little Numbers

The Evolution of Data-Driven Marketing

边听边看，每个词都清晰呈现

The Value Spectrum: Finding Your Most Valuable Customers

Beyond Demographics: Understanding Customer Motivations

从 《性感数字》中收获更多

Finding Your Audience in a Fragmented Media Landscape

The Science of Marketing Budgets

边听边看，每个词都清晰呈现

Measuring What Matters: Beyond Vanity Metrics

The Continuous Optimization Cycle

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The Future of Marketing Analytics

你已读完 《性感数字》

《性感数字》核心主题

《性感数字》经典语录

《性感数字》主要人物

关于作者

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关于本书的常见问题

What is Sexy Little Numbers by Dimitri Maex about?

Who should read Sexy Little Numbers?

Is Sexy Little Numbers worth reading?

What are the key concepts in Sexy Little Numbers?

发现更多

1,000,000+ curious minds.

1,000,000+ curious minds.

一本书，多种聆听方式。

When Data Transforms Marketing: The Power of Sexy Little Numbers

The Evolution of Data-Driven Marketing

边听边看，每个词都清晰呈现

The Value Spectrum: Finding Your Most Valuable Customers

Beyond Demographics: Understanding Customer Motivations

从 《性感数字》中收获更多

Finding Your Audience in a Fragmented Media Landscape

The Science of Marketing Budgets

边听边看，每个词都清晰呈现

Measuring What Matters: Beyond Vanity Metrics

The Continuous Optimization Cycle

从 《性感数字》中收获更多

The Future of Marketing Analytics

你已读完 《性感数字》

用户还喜欢

发现更多

《性感数字》概述

从《性感数字》中收获更多

从《性感数字》中收获更多

你已读完《性感数字》

从《性感数字》中收获更多

从《性感数字》中收获更多

你已读完《性感数字》