Attracting and Retaining Customers

There are, of course, a variety of ways to attract and retain customers, and analytics can support most of them. One traditional means of attracting customers has been advertising. This industry has already been, and will continue to be, transformed by analytics. Two factors are most closely associated with the transformation. One is the econometric analysis of time series data to determine whether advertising is statistically associated with increased sales of a product or service. The other is the “addressable” and relatively easily analyzed nature of digital advertising, as exemplified by Google and other firms. We’ll describe each of these briefly.

Econometric analysis has begun to address the age-old problem with advertising in traditional media, as described around the turn of the twentieth century by department store pioneer John Wanamaker (or attributed in Europe to Lever Brothers founder Lord Leverhulme): “Half the money I spend on advertising is wasted; the trouble is, I don’t know which half.”

Sir Martin Sorrell, CEO of WPP plc, one of the world’s largest advertising agencies, calls econometrics the holy grail of advertising. He noted in an interview, “There is no doubt in my mind that scientific analysis, including econometrics, is one of the most important areas in the marketing-services industry.”⁴

Most large advertising agencies have created groups of econometrics experts to do such analyses for clients. These firms gather data for their clients, build data warehouses and Hadoop-based data lakes, and analyze the data to find answers to a variety of questions on advertising effectiveness. The questions include such issues as which medium is most effective, whether the additional cost of color is worthwhile in print advertising, and what days of the week are best to run ads. Typically, a great deal of data must be gathered to rule out alternative explanations of advertising lift.

The other dramatic change in advertising is the rise of digital ads. These are revolutionary, of course, because whether someone clicks on an ad can be tracked. There are a variety of web-based advertising approaches—banners, pop-ups, search-based, and more—and the effectiveness of each can be easily tracked. Analytical algorithms are also used to determine which digital ads are placed on which sites for each user. The ads are personalized (at least to some degree), and the decisions about placement are largely automated. The combination of digital and analytical advertising has led mainstream IT-oriented consulting firms like Accenture, Deloitte, and IBM to enter these businesses, largely through acquisitions.

Of course, one of the most powerful forms of online advertising is the search-based ad exemplified by Google. By having the industry-leading search engine, Google can serve ads that correspond to search terms (AdWords) used by a potential customer. Google also serves ads onto other companies’ online properties through its AdSense network. One of the primary reasons Google has been successful with advertisers is its extensive use of analytics. Because Google advertising is done for a large client base for small payment increments (a few cents per click-through), much of the analytics must be automated and highly scalable. Google employs self-learning algorithms that are constantly analyzing the efficacy (typically in conversion rates) of different keywords (the primary advertising medium on Google’s own properties), placement on the page, creative material, and so forth. The learning is input for an optimization engine that develops suggestions for advertisers without any human intervention. Advertisers can see the suggestions when they look at the reports for activity relative to their ads. The suggestions may differ for different types of sites, such as entertainment versus publishing. Large Google advertisers also have account managers who can work with the advertiser and provide analytics-based advice. Google’s philosophy is that analytics and metrics will make advertisers more successful in working with the company, so they try to provide as much analytical sophistication as advertisers can use. The current challenges in online advertising involve how to deliver personalized ads in an omnichannel environment and how to determine attribution for a sale across online and offline channels.

Other approaches to customer analytics primarily focus on retention and cross-selling. For example, the Norwegian bank DnB NOR has built analytics on top of a Teradata warehouse to more effectively build customer relationships. The bank uses “event triggers,” based on customer life events, in the data warehouse to prompt customer relationship analysts to offer one or more tailored services based on the event. For example, if a customer receives a substantial inheritance, a bank representative will call on the customer to offer investment products. DnB NOR has a set of automated tools that match customer profiles and events and then generate a set of suggested products. Based on customers’ past experience, DnB then chooses the most effective channel through which to contact a customer about the most appropriate products. Using these tools, the company has achieved a conversion rate on cross-selling between 40 and 50 percent and has halved its marketing budget while increasing customer satisfaction.⁵

Of course, organizations need to be careful that their event triggers don’t violate customer privacy. The best-known example of this issue is when Target analysts recognized that pregnant women were a “fertile” target for direct marketing, buying a wide variety of items in Target stores. They realized that they could identify a pregnant woman early on by her shopping habits. When one teenager received a targeted circular with pregnancy-oriented items, her father—enraged at the assumption that his unwed daughter was pregnant—complained to Target. Though the daughter turned out to actually be pregnant, Target rapidly discontinued this particular event trigger marketing approach.⁶

One of the most impressive users of analytics to retain customers is Tesco. Founded in 1924, Tesco is now the largest food retailer in the United Kingdom and one of the world’s largest retailers. Located in eleven countries, it operates in every form of retail food channel—convenience, specialty, supermarket, and hypermarket. Tesco’s spectacular transformation began in 1995, when it introduced its Clubcard loyalty program. The card functions as a mechanism for collecting information on customers, rewarding customers for shopping at Tesco, and targeting coupon variations for maximum return. Customers earn points that are redeemable at Tesco at a rate of 1 percent of purchase amounts. Tesco estimates it has awarded points worth several billon British pounds.

The results are impressive. While the direct marketing industry’s average response is only 2 percent, Tesco and its in-house consultant Dunnhumby achieve average redemption rates between 8 percent and 20 percent. The Tesco CEO who founded the program, Sir Terry Leahy, believes the Clubcard program is also responsible for the company’s success with its internet business. The world’s largest internet grocer, Tesco has delivered food to more than a million homes and serves four hundred thousand repeat customers. All online Tesco customers must have a Clubcard, so Tesco can know what they purchase and target online promotions accordingly. By analyzing Clubcard data, combined with a rigorous program of experimentation, Tesco’s internet business has seen sales surge for nonfood items including home furnishings, music downloads, and homeowner and automobile insurance. The company has also established a bank that makes use of Clubcard data.

Tesco uses the data it collects on purchases to group customers according to lifestyle. It has aggressively pursued a classification system to determine what products will appeal to customers with adventurous, healthy, or penny-pinching, etc., tastes. Some attributes, such as whether a product is frozen or what it costs per kilogram, can be pulled from its product databases. But others involving taste and lifestyle are more difficult to classify. When Tesco wants to identify products that appeal to adventurous palates, for example, it will start with a product that is widely agreed to be an adventurous choice in a given country—say, Thai green curry paste in the United Kingdom—and then analyze the other purchases made by people who bought the paste. If customers who buy curry paste also frequently buy squid or wild rocket (arugula) pesto, these products have a high coefficient of relatedness and so probably also appeal to adventurous customers.

Tesco says that it issues 12 million targeted variations of product coupons a year, driving the coupon redemption rate, customer loyalty, and ultimately financial performance to market-leading heights.⁷ When Leahy retired, Tesco’s performance deteriorated a bit, but there is no evidence that Clubcard and analytics were the problem. Kroger, which uses a similar approach (and the same consultant, Dunnhumby), has an industry-leading loyalty card usage rate and coupon redemption rate in the United States, and Kroger has had fifty-two straight quarters of positive sales growth.

Firms also use analytics to avoid bad customers while attracting the few customers who defy conventional measures of suitability or risk—an approach known as “skimming the cream off the garbage.” As we mentioned in chapter 3, Progressive and Capital One both eschew the traditional industry-standard risk measures. At Progressive, for example, instead of automatically rating a motorcycle rider as a high risk, analysts take into account such factors as the driver’s employment history, participation in other high-risk activities (such as skydiving), and credit score. A driver with a long record with one employer who is also a low credit risk and avoids other risky activities will be rated as a low-risk customer.

Capital One has improved on conventional approaches to attracting so-called subprime customers—those individuals who by their credit rating are considered to be a high risk for bankruptcy or default. Capital One employs its own proprietary consumer creditworthiness assessment tool to identify and attract those customers it sees as less risky than their credit scores would indicate.

Pricing Optimization

Pricing is another task that is particularly susceptible to analytical manipulation. Companies use analytics for a competitive advantage by pricing products appropriately, whether that is Walmart’s everyday low pricing or a hotelier’s adjusting prices in response to customer demand. Analytics also make it easier to engage in dynamic pricing—the practice of adjusting the price for a good or service in real time in response to market conditions such as demand, inventory level, competitor behavior, and customer history. This tactic was pioneered in the airline industry but now has spread to other sectors.

Retail prices, for example, have historically been set by intuition. Today, however, many retailers (and even business-to-business firms) are adopting analytical software as part of “scientific retailing.” Such software works by analyzing historical point-of-sale data to determine price elasticity and cross-elasticity (a measure of whether one good is a substitute for another) for every item in every store. An equation is calculated that determines the optimal price to maximize sales and profitability.

Retailers usually begin by using pricing analytics to optimize markdowns—figuring out when and by how much to lower prices. Some then move on to pricing for all retail merchandise and to analysis of promotions, category mix, and breadth and depth of assortments. Most retailers experience a 5 to 10 percent increase in gross margin as a result of using price optimization systems. Some yield even greater benefits. According to a Yankee Group report, “Enterprises have realized up to 20 percent profit improvements by using price management and profit optimization (PMPO) solutions. No other packaged software can deliver the same type of top-line benefits and address bottom-line inefficiencies. PMPO is the best kept secret in enterprise software.”⁸

Virtually all retailers today have adopted some version of analytical pricing software. Many adopted it initially for discount pricing, and later moved to all item pricing. Some companies, such as Macy’s, have combined it with in-memory analytics software to rapidly reprice merchandise based on factors like weather and competitors’ prices. The department store chain has been able to reduce the time to optimize pricing of its 73 million items for sale from over twenty-seven hours to just over one hour.

Analytically based pricing software is spreading to other industries as well. Many vendors offer price optimization software, and many industries are taking advantage of it. The San Francisco Giants, for example, pioneered the optimization of baseball game ticket prices. If the team is playing a popular rival or if the pitching matchup is considered a good one, prices can go up almost tenfold. In addition to professional baseball, pricing optimization is used by some professional football, basketball, hockey, and soccer teams.

It has also been successfully applied in casinos. Gary Loveman, the former CEO of Caesars Entertainment, wrote about it in the foreword to the first edition of this book:

One cautionary note: pricing changes are not always unobservable. Most consumers are used to the idea of dynamic pricing in the context of changing market conditions—resorts that lower room prices during the off-season and raise them during peak demand, for example—and probably find it fair. However, companies can face a backlash when they use demand elasticity (the fact that loyal customers will pay a higher price for something than fickle customers) to make pricing decisions. For example, for a time, Amazon priced its DVDs higher to people who spent more. When that practice became known to the public, Amazon was forced to retreat by the resulting outcry.

Brand Management

Just as analytics bring a heightened level of discipline to pricing, they also bring needed discipline to marketing activities as a whole. Leading companies have developed analytical capabilities that enable them to efficiently design and execute highly effective multichannel marketing campaigns, measure the results, and continually improve future campaigns. Many are using econometric modeling and scenario planning to predict performance outcomes depending on overall budget levels or how much is spent in different channels.

The great challenge for brand managers in the current age, however, is developing a closed loop of analytics describing how customers interact with a brand across multiple channels. With this information, brands can learn not only what ads and promotions customers see, but how they react in terms of click-throughs, conversions, and service. Most companies find it difficult to marshal all this data and make sense of it with analytics.

One company that does do it well, however, is Disney’s Parks and Resorts business unit. The business has long been highly analytical, optimizing hotel prices, ride times, and marketing offers. Now, however, due to a “vacation management” project called MyMagic+ that cost over $1 billion and began in 2008, it is able to close the loop on how all that marketing translates into a customer experience.¹⁰ The ambitious goal of MyMagic+ is to provide a more magical, immersive, seamless and personal experience for every single guest. From the beginning of planning a Disney park or hotels reservation, the customer is encouraged to register and to supply an email address. The customer can plan a family trip (and, at the same time, register all family members or friends participating in the trip) with the MyDisneyExperience website or app. Disney is then able to learn what activities the customer is considering and what web pages engage different family members. Customers are also encouraged to sign up for the FastPass+ service, which offers them shorter wait times; in exchange, they share information about the park attractions, entertainment options, and even greetings from Disney characters they intend to experience.

What really closes the loop for Disney, however, is the MagicBand. Rolled out in 2013, these wristbands are typically mailed to a family before its visit starts. From the customer’s standpoint, it allows access to the park and hotel rooms, FastPass+ entry to attractions at specific times, and in-park and hotel purchases. It also stores photos taken with Disney characters, and allows the characters to have personalized interactions with kids. From Disney’s standpoint, it provides a goldmine of data, including customer locations, character interactions, purchase histories, ride patterns, and much more. If customers opt in, Disney will send personalized offers to them during their stay and after they return home.

The scale and expense of the MyMagic+ system is reflective of the fact that the ante has been raised for competing on analytics. It may take a while for Disney to recoup its billion-dollar investment in this closed-loop system, but the company has already seen operational benefits in being able to admit more customers to parks on busy days. There is also a belief that the system will deter customers from visiting competitor parks. Key to the ultimate value of the program, however, will be extensive analytics on how marketing and branding programs translate into actual customer activity.

Converting Customer Interactions into Sales

The strategies described so far relate to marketing and branding, but it is also possible to use analytics to improve the face-to-face encounters between customers and salespeople. This process—previously one involving plenty of intuition—is becoming increasingly analytical. Many sales processes, such as lead streams, pipelines, and conversion rates, are now addressed analytically. In the early days of sales analytics, almost all methods were descriptive. A bar chart would be considered state-of-the-art. Today, however, companies such as Salesforce.com have embedded predictive and prescriptive analytics into their mainstream transactional systems. Instead of deciding which lead to address, for example, a salesperson might resort to a predictive lead scoring system.

Consider how Capital One Health Care, which sells financing services through medical practices for uninsured medical procedures (like cosmetic surgery), outsmarts competitors. Most financing firms market their credit services to doctors the same way many pharmaceutical reps do—known in the business as “pens, pads, and pizza.” By stopping by at lunchtime, representatives hope they can entice the doctor out for a quick lunch break and an even shorter sales pitch. At Capital One, however, reps don’t randomly chase down prospects and hope that a few freebies will clinch the deal. Instead, analysts supply the company’s reps with information about which doctors to target and which sales messages and products are most likely to be effective.

Boston-based publisher HMH, perhaps better known as Houghton Mifflin Harcourt, has been publishing books since the 1830s.¹¹ Now, however, much of its content is electronic or software-based, and the company’s leaders wanted to similarly transform sales processes. Like many companies, HMH used a transactional CRM system—one from Salesforce.com. But it had few analytics to inform and motivate salespeople and sales management.

HMH’s salespeople primarily call on school districts, and they used to track their sales opportunities and forecasts in Excel. But this approach didn’t facilitate communications about sales processes, and it was only descriptive analytics. But when sales became a key focus of the company’s executives, HMH began a collaboration between the marketing organization—responsible for lead generation—and sales, which has responsibility for converting them. Part of the initiative involved acquiring new software for descriptive sales analytics and a different system for lead scoring.

HMH improved the entire process for lead management, including capturing leads from events and webinars as well as salespeople. It launched a predictive lead scoring and routing system that prioritized leads for sales, which eventually reduced days leads outstanding from thirty days to six. The company also built an attribution model for leads across the sales funnel to determine the optimal channel mix for lowest cost-per-lead and cost-per-conversion. A series of new reports addressed such metrics as days leads outstanding, lead survival curve, estimated lead value, and funnel conversion velocity.

Sometimes sales analytics have little to do with a sales force. Caesars, for example, is able to use the information it collects to improve the experience of the customer while simultaneously streamlining casino traffic. Customers hate to wait; they may be tempted to leave. Worse, from the casino’s perspective, a waiting customer is not spending money. When bottlenecks occur at certain slot machines, the company can offer a customer a free game at a slot machine located in a slower part of the casino. It can also inform waiting customers of an opening at another machine. These prompts help redirect traffic and even out demand. According to Wharton School professor David Bell, Caesars is able to tell “who is coming into the casino, where they are going once they are inside, how long they sit at different gambling tables and so forth. This allows them to optimize the range, and configuration, of their gambling games.”¹²

Managing Customer Life Cycles

In addition to facilitating the purchases of a customer on a given day, companies want to optimize their customers’ lifetime value. Predictive analytics tools help organizations understand the life cycle of individual customer purchases and behavior. Best Buy’s predictive models enable the company to increase subsequent sales after an initial purchase. Someone who buys a digital camera, for example, will receive a carefully timed e-coupon from Best Buy for a photo printer.

Sprint also takes a keen interest in customer life cycles. It uses analytics to address forty-two attributes that characterize the interactions, perceptions, and emotions of customers across a six-stage life cycle, from initial product awareness through service renewal or upgrade. The company integrates these life cycle analytics into its operations, using twenty-five models to determine the best ways to maximize customer loyalty and spending over time.

Sprint’s goal is to have every customer “touch point” make the “next best offer” to the customer while eliminating interactions that might be perceived as nuisances. When Sprint discovered, for example, that a significant percentage of customers with unpaid bills were not deadbeats but individuals and companies with unresolved questions about their accounts, it shifted these collections from bill collectors to retention agents, whose role is to resolve conflicts and retain satisfied customers.

According to Sprint, the group responsible for these analytics has delivered more than $1 billion of enterprise value and $500 million in revenue by reducing customer churn, getting customers to buy more, and improving satisfaction rates.

Personalizing Content

A final strategy for using analytics to win over customers is to tailor offerings to individual preferences. In the mobile network business, for example, companies are vying to boost average revenue per user by selling subscribers information (such as news alerts and stock updates) and entertainment services (such as music downloads, ringtones, and video clips). But given the small screen on mobile devices, navigating content is a real challenge.

O2, a mobile network operator in the United Kingdom, uses analytics to help mobile users resolve that challenge. The company pioneered the use of artificial intelligence software to provide subscribers with the content they want before they know they want it. Analytical technology monitors subscriber behavior, such as the frequency with which users click on specific content, to determine personal preferences. The software then places desirable content where users can get to it easily.

The vast majority (97 percent) of O2’s subscribers have opted to use personalized menus and enjoy the convenience of having a service that can predict and present content to match their tastes. Today, O2 has more than 50 percent of the mobile internet traffic in the United Kingdom, and the company continues to explore new ways to use analytics; for instance, it is investigating new collaborative filtering technology that would analyze the preferences of similar customers to make content suggestions. Hugh Griffiths, formerly O2’s vice president of digital products, services and content, believes that “personalization is [O2’s] key service differentiator.”¹³

Personalization is also being applied to contexts like gaming and education. At the Strata + Hadoop World conference, online game developer Jagex Games Studio described its model that analyzes a decade of game content and 220 million player accounts to provide recommendations to its players in real time. One of its most popular games, RuneScape, is a free, massively multiplayer online role-playing game (MMORPG). By incorporating recommendations that point players to the most interesting and relevant content during the game, Jagex increased revenues (from advertising, paid subscriptions, and in-game purchases) while also improving player engagement and quest completion rates.¹⁴

The eLearning company Skillsoft is using big data to improve the effectiveness of its technology-delivered education solutions to its 6000 customers and 19 million learners worldwide. Customer learning recommendations and content are personalized by analyzing detailed data about how and when individuals use over sixty thousand learning assets, as well as other factors such as survey data and direct email response behavior. Though sophisticated personalization of both content and recommendations, Skillsoft has realized a 128 percent improvement in user engagement. According to John Ambrose, Senior Vice President, Strategy, Corporate Development and Emerging Business, “We’re building a powerful new big data engine that will enable us to optimize learning experiences and uncover new learning patterns that can be applied immediately so that the system is continually improving. This is the perfect application of big data—harness it and apply it to improve individual and organizational performance.”¹⁵

Connecting Customers and Suppliers

The mother of all supply chain analytics competitors is Walmart. The company collects massive amounts of sales and inventory data (over 30 terabytes as of 2015) into a single integrated technology platform. Its managers routinely analyze manifold aspects of its supply chain, and store managers use analytical tools to optimize product assortment; they examine not only detailed sales data but also qualitative factors such as the opportunity to tailor assortments to local community needs.¹⁶

The most distinctive element of Walmart’s supply chain data is not the sophistication of the analytics, but rather the availability of data and descriptive analytics to suppliers. Walmart buys products from more than sixty thousand suppliers in eighty countries, and each one uses the company’s Retail Link system to track the movement of its products—in fact, the system’s use is mandatory. In aggregate, suppliers run tens of millions of queries on the data warehouse every year, covering such data as daily sales, shipments, purchase orders, invoices, claims, returns, forecasts, radio frequency ID deployments, and more.¹⁷ Suppliers also have access to the Modular Category Assortment Planning System, which they can use to create store-specific modular layouts of products. The layouts are based on sales data, store traits, and data on ten consumer segments. Some suppliers have created more than one thousand modular layouts.

As Walmart’s data warehouse introduced additional information about customer behavior, applications using Walmart’s massive database began to extend well beyond its supply chain. Walmart now collects more data about more consumers than anyone in the private sector. Its marketers mine this data to ensure that customers have the products they want, when they want them, and at the right price. For example, they’ve learned that before a hurricane, consumers stock up on food items that don’t require cooking or refrigeration. The top seller: Strawberry Pop Tarts. We expect that Walmart asks Kellogg’s to rush shipments of them to stores just before a hurricane hits. In short, there are many analytical applications behind Walmart’s success as the world’s largest retailer.

Walmart may be the world’s largest retailer, but at least it knows where all its stores are located. Amazon’s business model, in contrast, requires the company to manage a constant flow of new products, suppliers, customers, and promotions, as well as deliver orders directly to its customers by promised dates.

Amazon is pretty quiet about all its analytics projects, but over the years, we’ve been able to glean a few details. The company is best known for its “collaborative filtering” analytics that recommend products to customers, but it has also worked diligently on supply chain analytics. It has integrated all the elements of its supply chain in order to coordinate supplier sourcing decisions. To determine the optimal sourcing strategy (determining the right mix of joint replenishment, coordinated replenishment, and single sourcing) as well as manage all the logistics to get a product from manufacturer to customer, Amazon applies advanced optimization and supply chain management methodologies and techniques across its fulfillment, capacity expansion, inventory management, procurement, and logistics functions.

For example, after experimenting with a variety of packaged software solutions and techniques, Amazon concluded that no existing approach to modeling and managing supply chains would fit their needs. They ultimately invented a proprietary inventory model employing nonstationary stochastic optimization techniques, which allows them to model and optimize the many variables associated with their highly dynamic, fast-growing business. An Amazon job description supplies some detail on the methods the company uses:

Amazon sells over thirty categories of goods, from books to groceries to industrial and scientific tools to home services, and its own electronic products Kindle, Fire, and Echo. The company has a variety of fulfillment centers for different goods. When Amazon launches a new goods category, it uses analytics to plan the supply chain for the goods and leverage the company’s existing systems and processes. To do so, it forecasts demand and capacity at the national level and fulfillment center level for each SKU. Its supply chain analysts try to optimize order quantities to satisfy constraints and minimize holding, shipping, and stockout costs. In order to optimize its consumer goods supply chain, for example, it used an “integral min-cost flow problem with side constraints”; to round off fractional shipments, it used a “multiple knapsack problem using the greedy algorithm” (if you know what that means, perhaps you should be working for Amazon). The company even obsesses over the optimized way to load a truck.

One of the Amazon’s more recent supply chain innovations was a patent it filed in 2012 for a “method and system for anticipatory package shipping.” That means that Amazon sometimes predicts what customers will order, and ships packages to a geographical area without knowing exactly where they will end up. It’s a unique combination of predictive sales and supply chain management.

Amazon is also planning to take over many aspects of its supply chain, bypassing cargo brokers and even shippers. Amazon would then be “amassing inventory from thousands of merchants around the world and then buying space on trucks, planes and ships at reduced rates,” according to Bloomberg Technology.¹⁹ The company has already received approval from China and the United States to act as a wholesaler for ocean container shipping. While the analytics implications of this are as yet unclear, it’s likely that Amazon will bring a new level of data-based insights to that traditional business.

Logistics Management

Sometimes a service company uses analytics with such skill and execution that entire lines of business can be created. UPS took this route in 1986, when it formed UPS Logistics, a wholly owned subsidiary of UPS Supply Chain Solutions. UPS Logistics provides routing, scheduling, and dispatching systems for businesses with private fleets and wholesale distribution.²⁰ The company claims to have over one thousand clients that use its services daily. This approach, captured in the “Don’t You Worry ’Bout a Thing” campaign, is enabling UPS to expand its reputation from reliable shipping to reliable handling of clients’ logistics value chains. UPS has also entered into the data product business, charging customers extra for the My Choice option to reroute, reschedule, or authorize delivery of packages en route.

Of course, UPS has been an analytical competitor in supply chains for many years. In 1954 its CEO noted, “Without operations research we’d be analyzing our problems intuitively only.”²¹ The company has long been known in its industry for truck route optimization and, more recently, airplane route optimization. Mike Eskew, UPS’s CEO from 2002 to 2007, founded UPS’s current operations research group in 1987. By 2003 he announced that he expected savings from optimization of $600 million annually. He described the importance of route optimization: “It’s vital that we manage our networks around the world the best way that we can. When things don’t go exactly the way we expected because volume changes or weather gets in the way, we have to think of the best ways to recover and still keep our service levels.”²² UPS has built on these capabilities over time to develop the ORION real-time routing application that we’ve described in detail in chapter 4.

FedEx has also embraced both analytics and the move to providing full logistics outsourcing services to companies. While UPS and FedEx both provide customers with a full range of IT-based analytical tools, FedEx provides these applications to firms that do not engage its full logistics services, leading one analyst to observe, “FedEx is as much a technology company as a shipping company.”²³ UPS and FedEx have become so efficient and effective in all aspects of the logistics of shipping that other companies have found it to their economic advantage to outsource their entire logistics operations.

Another company helping its customers manage logistics is CEMEX, the leading global supplier of cement. Cement is highly perishable; it begins to set as soon as a truck is loaded, and the producer has limited time to get it to its destination. In Mexico, traffic, weather, and an unpredictable labor market make it incredibly hard to plan deliveries accurately. So a contractor might have concrete ready for delivery when the site isn’t ready, or work crews might be at a standstill because the concrete hasn’t arrived.

CEMEX realized that it could increase market share and charge a premium to time-conscious contractors by reducing delivery time on orders. To figure out how to accomplish that goal, CEMEX staffers studied FedEx, pizza delivery companies, and ambulance squads. Following this research, CEMEX equipped most of its concrete-mixing trucks in Mexico with global positioning satellite locators and used predictive analytics to improve its delivery processes. This approach allows dispatchers to cut the average response time for changed orders from three hours to twenty minutes in most locations.²⁴ Not only did this system increase truck productivity by 35 percent, it also wedded customers firmly to the brand.²⁵ CEMEX has also used analytics to optimize other aspects of its business, including factory production, the size of its truck fleet, electricity consumption, and inventory management. Its disciplined and analytical approach to operations has made it one of the fastest-growing and most profitable cement companies in the world. And if you can be analytical about cement—one of the world’s oldest commodities—you can apply it to any business.