Engineered

The end of the circus

I'm so glad that Amazon.com didn't pick Dallas as its HQ2. Mostly I'm glad out of self-interest, because I didn't want the increase in real estate prices and traffic that such a decision would have brought, but I'm also glad because the whole spectacle of cities abasing themselves to try to attract oh-so-important Amazon.com was disgraceful. Given that Amazon.com thinks it's ok (and perhaps even entertaining, for whoever came up with the idea) to let local governments prostrate themselves at its feet with enormous tax credits that would be better spent on its local workforce, there's just no way that this company - which has been in the news for its poor working conditions at its warehouses and caps salaries at $160k a year with the rest in stock options that naturally have to vest before employees can exercise them, trapping staff in a vicious circle but starkly aligning everyone's interest with the increase in the fortune of billionaire Jeff Bezos - cares about its workers or even its customers. The "customer obsession" motto is just a convenient way to try to get some pressure off its back by pretending to care while Amazon.com is busy enriching itself. 

Not that it has an easy way of it. If Amazon.com was making so much money, it wouldn't have to resort to tricks to get more money from its customers. For instance, whenever you (or at least I) click on the MP3 version of an album, Amazon.com redirects you to the "Streaming Unlimited" page even if you didn't click on it. Amazon.com also spends considerable amounts of time getting customers to sign up for Amazon Prime. Let's be clear: if Amazon.com was losing money on it, it wouldn't push it so hard. Membership services are priced so that a few users come out with a net gain and many lose, because they just don't use the service enough to make the membership worth it. I'd love to try Audible if I could listen to recordings of books on a one-by-one basis, the way you select Kindle editions individually, but the only way to get audiobooks is to sign up for a membership, and of course Amazon.com says you can cancel any time, but they know people tend to have inertia once they've signed up for something. 

I've also been puzzled by many of Amazon.com's tactical decisions. In Dallas the same-day delivery option is priced way too low compared to standard shipping. (I suspect one day they will jack up the prices so that you can't get your item fast unless you sign up for Amazon Prime.) This leads to a lot of last-minute orders, lack of visibility for the local warehouses and delivery issues. I've had several times an Amazon carrier pretend he couldn't deliver to my address due to some non-existent access code that I supposedly hadn't provided (although Amazon has successfully delivered thousands of packages to my building), which just so happened to be at the end of the carrier's delivery window. Surprise! The carrier can't deliver within the time window and comes up with an excuse to avoid taking the blame. So much for customer obsession. Do you think someone at Amazon.com is looking into this to try to tease out signs the pricing and delivery routes are suboptimal? Think again. But then by now, you've probably noticed that Amazon.com's recommendation system at the bottom of every Amazon.com page just lists what some other random people have bought, without any attempt of filtering items that clearly have no connection whatsoever with the book. (This is particularly obvious for books with low inventory turnover.)

Even Jeff Bezos knows he might have pushed too hard on that one, because after it was announced that the HQ2 would be split (apparently the tax incentives were too good to pass up, so it looks like someone came up with the brilliant idea of getting them twice) between Queens in New York (does that make anyone dream?) and Crystal City, VA outside D.C. he said something along the lines of "one day Amazon will fail." He's doing a phenomenal job making Amazon the most hated company in America, a spot that used to be Microsoft's, and we all know how that story ended up. (Short memory? Two words: antitrust lawsuit.)

Another hint that he might be realizing it is that people started talking about how DC and NYC had the most tech workers. As if people would've moved to Seattle if it weren't for Amazon. Seattle is really far out of the way from a continental U.S. perspective, and while it has some good entertainment opportunities and decent seafood restaurants, it is hard to escape the feeling it is a backwater town with a gorgeous view of the Pacific Ocean. Now given the hours of Amazon employees, being a backwater town isn't a bad thing: there are plenty of outdoors activities anyway. But anyone who says Seattle is a foodie town hasn't lived in cities with superior restaurants. (And again, the usefulness of top restaurants for the Amazon workforce is open for debate. Good options to eat at your desk might be just as useful.)

Sad also is the spectacle of naive journalists babbling along on the fact that the most valuable info Amazon.com got was the data from each city that competed from HQ2. When you look at how little they use their own data for their operations (recommender systems, delivery routes), you can be left with no doubt whatsoever that they don't care about the data they collected and only wanted to make the maximum number of cities humiliate themselves. 

I hope one of those cities that competed for HQ2 will announce that they will spend what they would have given to Amazon.com on incentives for their local workforce, such as free community college education or college loans forgiveness for residents. It is time to make something good out of that sorry spectacle of American capitalism at its worst.

HBR IdeaCast: The Future of (MBA) Education

Over the past several years, there's been a lot of talk about transforming education with breakthrough ideas such as using online courses -- often Massive Online Open Courses -- to gain knowledge or bypassing college altogether, but sometimes the change that lasts comes from more incremental innovation. I was reminded of that while I was listening to a HBR IdeaCast podcast from earlier this year (HBR IdeaCast is the podcast of Harvard Business Review) on The Future of MBA Education.

In this podcast, the Dean of the University of Michigan's Ross School of Business talks about how his program aims at bringing together the school world of learning business fundamentals in a classroom and the work world of learning by doing into one integrated framework of experiential learning. This, to me, is also the future of higher education in general. I've always been struck by the missed opportunity (from a university perspective) of having students do summer internships without even debriefing them at the end of the summer on what their courses should have better prepared them for, let alone mentoring and advising them in the process. For instance, I started teaching Tableau (a visualization software useful for descriptive analytics) to my students after I learned from students returning from internships that Tableau had become extremely popular in the consulting world. If students hadn't bothered mentioning it to me, I would never have known I could give my students a competitive advantage in the workplace by introducing all of them to Tableau before graduation. As students seek better value for their tuition money, it is critical that all educational experiences build upon each other. 

Yet, integrating such experiences take time and effort. Someone must coordinate courses and supervise students. In effect, students almost need a coach as their academic advisor, to define with them growth experiences and go over what worked and what didn't in previous assignments. (This is not the same role as the direct internship supervisor at the company, with whom an intern might be more reluctant to share difficulties in completing his tasks, since he might hope for a job offer at the end of the summer.) At a time where people worry about tuition costs, we have to be careful not to add more costs to the college experience. But my observations in my own line of work, especially of the finalist teams for the UPS George D Smith Award recognizing programs best preparing tomorrow's practitioners in operations research and management science, suggest that top programs these days implement such an integrated approach. It is easier to do at the graduate rather than undergraduate level, whether for Masters of Science programs in Business Analytics or Masters of Engineering in Operations Research or related disciplines, because of the more focused nature of these programs compared to Bachelor's degrees. It certainly shouldn't be limited to MBA programs.  

As a starting point, it would be useful for Master's programs to have a debriefing process at the beginning of the second year of study, especially to understand how students could have been better prepared for their summer internship if they did one. It would also be important to review degree plans to better understand how the courses are supposed to fit with each other. Instructors can easily lose sight of the other courses students are taking concurrently and the ones they have already taken, creating overlaps or assuming knowledge that hasn't been acquired yet. Of course, some overlap is sometimes necessary and even welcome in providing a different look on the same topic, and the directors of programs that give students a lot of choices about their course of study can't keep track of the "big picture" for every single student. But presenting a solid integrated program rather than a disjointed collection of courses can only be a good thing, especially for programs without a capstone requirement to tie everything together.

This quote by the Dean of the Ross School of Business particularly resonated with me. "[N]ow the content is largely commoditized in that it’s accessible to the world. And so, now what that does is it frees us up to create experiences that can truly transform how people think and how they act, how they connect with others, how they actually engage in the doing of business...And so, having these types of experiences that are going to better prepare you and ready you for not only day one on the job of today, but actually, give you the mindset and the skillset to thrive in the career of tomorrow."

Another great idea in the podcast is the "7-year MBA" (an expression due to Dartmouth), where seven years refers not to the time it takes to complete all the MBA requirements but the two years of the degree and the first five years following that degree. Similar ideas include Stanford's open-loop university, where "once you start you can always loop back and come back into the university and your learning never stops" and Michigan's Alumni Advantage for executive education. Whatever the future of education, surely continuing education and lifelong learning will be a part of it. 

Jeff Bezos at SMU

Last week I attended Jeff Bezos's talk at SMU when he visited Dallas. (The talk was hosted by the George W. Bush Presidential Center's Forum on Leadership.) Bezos didn't drop any hint about Amazon's HQ2, but he still shared some interesting tidbits. The most important one for me, as an analytics professional, was (I paraphrase) "when the anecdote and the data tell a different story, believe the anecdote". This resonated for me because not too long ago I ordered something on Amazon using one-day delivery and at the end of the day I got a message saying that because the access codes to my residence had changed, the carrier had been unable to deliver my package that day and would attempt again the same day. Now, I get plenty of packages delivered from Amazon without any problem. There is no need for access codes to deliver packages, and so the access codes definitely hadn't changed, since they don't exist.

When I first read that email I found that statement outrageous and wanted to contact Amazon to get a refund on the delivery fee. (It is, after all, easy enough to check I've had plenty of packages delivered without an issue in the past.) But I came to realize it was the end of the day and the courier had probably run out of time in his shift and so he told the Amazon system it was my fault he couldn't deliver my package to avoid getting penalized. From a data perspective, my package doesn't get counted as late. From an anecdote perspective, it's a good thing my Keurig coffee machine, which was giving signs of imminent demise, decided to survive one more day so that I could face the world my usual cheerful self, and so I didn't report the issue to Amazon. But you have to wonder, what on-time delivery metrics mean if they can be gamed so easily.

I also liked Bezos's attitude toward memos and meetings: the idea is that each meeting starts with half an hour where meeting attendees read the memo in silence, because otherwise people might be tempted to pretend having read the memo when they were actually too busy to find the time to. He also had a great anecdote about how he thought, when Amazon was just starting out and he was on the floor on his knees packing people's orders, it'd be excellent to buy kneepads, at which point someone else in the room looked at him as if this was the dumbest thing he'd ever heard and replied "what we need is packing tables". Also, people thought he was crazy when he decided to publish all customers' reviews instead of just the positive ones, but he realized early Amazon was in the business of helping people make purchase decisions, rather than make people buy something they won't necessarily like (thus making them more likely to shop elsewhere next time).

I wasn't as interested in his discussion about space and I would've wanted some more deep insights, but overall it was an enjoyable talk. You can watch it here. 

Talk by Google Cloud COO at INFORMS WORMS event

 Diane Bryant, COO of Google Cloud after spending 32 years at Intel, gave a phenomenal talk at the INFORMS Analytics conference, invited by Women in OR/MS and sponsored by Intel (a big thank you to Intel for the drink tickets!) From the moment she mentioned joining a jazz band in high school in the 1970s because her friend didn’t want to be the only woman in the band, Bryant managed to hit that sweet spot combining insights and wit that kept her audience engaged and laughing.

Her talk was structured around sponsorship, confidence and inclusion. Starting with inclusion, she mentioned that only 6% of Intel VP at first were women, and then Bryant and her fellow female VPs started a formal advocacy program that brought the number of female VPs to 21%. Another take-away message was the importance of projecting confidence – a skill men appear to be much better at than women. Regarding sponsorship, Bryant underlined the importance of advocacy not mentorship. Mentorship is good when you need advice from someone not in the line of command, but according to research sponsored by Intel, men are sponsored and women are mentored, and guess who have the most successful careers? Bryant told an insightful story there about the days when she was a waitress for Sunday brunch and a Distinguished Engineer at the local company (biggest employer in Sacramento where she lived) only wanted to sit at her table with his wife for Sunday brunch because she was great at refilling his and his wife's cup of coffee and getting their order out of the kitchen fast. Ultimately, he recommended her for a job at his company – she got offers from two different groups and she didn’t even have to interview, thanks to the strength of his recommendation. Bryant also put forward the Amber principle (the female equivalent to the Peter principle): “women rise to their level of self doubt”. She doesn’t believe in luck to explain people’s career success - allies are needed. My favorite statement of hers was: “You can’t ask the marginalized population to fix the marginalization,” leading her to conclude: “We have to engage the men.”

I also liked the moment when she said: “Diversity is a fact but inclusion is a choice”. She also showed a good quote about Warren Buffett talking about his sister, who he said was smarter than he was, and (WB) stating that he only had to compete against half the talent. Bryant mentioned China where 40% of the talent in STEM is female, vs 23% in the U.S. Finally she pointed out the need to worry not about the hard decisions (the reason you can’t decide is that both outcomes are usually similar) but the easy one where everyone is in agreement, sometimes leading to very bad decisions, such as PR nightmares that came from very poorly thought-out ads.

For me the most touching moment came toward the end when Bryant disclosed that her father, who did time at San Quentin's maximum security prison, kicked her out of the house when she was 18, although she still had 4 months to go before graduating from high school. She didn’t say, although she could have: and yet, look at her now. Bryant’s resilience in spite of such adversity was an inspiration to everyone in the room. To finish, Bryant had this great anecdote at the end about her son who went to a playdate at a new friend’s house and, seeing that his friend had an amazing house, exclaimed “what does YOUR mother do?” Welcome to the 21^st century, where hopefully more women will get the jobs they deserve thanks to the advice of women like Diane Bryant.

PhD in Analytics

The issue that most animated the audience at the Meeting of Analytics Program Directors in Baltimore a few days ago came up last during the day-long event, during the session about industry perspectives: the idea of a PhD in Analytics. Because this was part of a panel discussion with industry experts and because analytics is applied to real-life data almost by definition, the impression I got was that such a PhD would be mostly geared toward producing doctoral-level industry practitioners, although a PhD is supposed to produce new knowledge beyond the deliverables of an industry project.

The discussion turned into a very lively exchange because an attendee from a business school mentioned that PhD graduates who go to industry are a fail for his program, and one of the industry panelists, with a little smirk on her face that she probably shouldn't have had, replied that she'd like to challenge that notion.

Of course that was  easy for her to say, since she gets highly-trained PhDs who, as soon as they graduate, lose any interest in publishing the research papers they have in the pipeline and focus on solving their companies' problems to achieve the non-minor goal of remaining gainfully employed. But from the standpoint of that other faculty member in a business school whose program is tuition-funded, the fact that research papers won't be able to be revised in a timely fashion and may not have the impact they should have because the lead student left for industry is not a successful outcome, both from the viewpoint of the dean who funded that student and from the viewpoint of the faculty member who needs those research papers to get promoted.

I do love that in the United States, people with PhDs are not limited to teaching jobs in universities but can have their skills recognized and sought after by top companies, and I am particularly proud of my own PhD students' placement record at companies such as Amazon, IBM, Marriott, JP Morgan and Ernst & Young, but it is a fact that faculty's research output suffers from their doctoral students going to industry because a lot of research papers are then put in limbo. When that happens, the business programs that funded those students don't get the best return for their money, and the federal agencies that funded research assistantships for doctoral students through grants don't get the best return for their money either.

Should we all refuse to let our students graduate until the revision of the last paper has been submitted, even if that means the student has to start working without having her PhD yet? This is a very different situation than that of a Master's student in analytics, who gets a grade for her capstone project before graduation. Yet, there is no doubt that Master's programs are becoming too short to completely train students in the full scope of data analytics, from descriptive analytics to predictive analytics to prescriptive analytics, and from small data to big data.

In fact a lesson I learned from another session at the MAPD is that traditional operations research skills such as optimization (prescriptive analytics) risk getting shortchanged because companies understand predictive analytics best and don't yet understand what they can do with the more advanced prescriptive analytics. If we only teach to what companies want, we won't produce graduates with the skills they will need to succeed in the workforce 5 years from now when companies start seeing the benefits of prescriptive analytics and decide to leverage those as well. We do have to be careful not to teach reactively to what companies think they want today. As academics, we also have to protect our students' interests and also our own careers in ensuring the concept of a PhD is not denatured through industry pressures.

So what is the solution? Multiple Master's in various branches of analytics? A Master's and a continuing education program? A Master's and a training program held at the companies themselves for its data scientists? A PhD? Or a more applied degree with a new name that would sit between the Master's and the PhD and be geared toward industry placement?

Meeting of Analytics Program Directors: Disney keynote

 This year I represented my department at the Meeting of Analytics Program Directors in Baltimore. First I have to give a big shout out to Bill Griffin of INFORMS for organizing such an excellent meeting full of insightful discussions and wonderful networking opportunities. (It was funny that I had to go all the way to Baltimore to meet faculty from University of Texas at Arlington and Texas A&M). You can see the agenda here.

The plenary by Mark Shafer, SVP of Revenue Management and Analytics at Disney was absolutely outstanding: just the perfect mix of high-level insights into Disney's transformation into an analytics powerhouse (and 2017 INFORMS Prize co-winner) and broad takeaway messages applicable to any industry. Shafer worked for People Express in the early 1980s when it grew to the 5th largest US airline in 5 years thanks to bottom-low prices - until American introduced the Ultimate Super Saver fare in January 1985 (pioneering revenue management as we know it). People Express faced such a sharp change in its outlook it had to be bought out by Continental in February 1986: one year was all it took for it to tumble from grace. Shafer told this story to illustrate the cost of doing nothing. He believes in the importance of evangelizing analytics ("evangelitics") and also emphasized that analytics doesn't guarantee that you'll win every time: sometimes you can take the right decision but have the wrong outcome. 

He mentioned some of the skills he believes graduates should have to be successful in the workforce as analytics practitioners. One is storytelling, which has become such a cliche in industry. (You need to make a story out of your numbers.) The other one is that graduates should seek to "become a thermostat, not a thermometer". Thermometers just tell you the temperature, thermostats do something about it. Graduates should not only run the reports that they're told to run but also show initiative and come up with solutions to problems they identify. Successful graduates harness ambiguity and ill-defined problems to their advantage to demonstrate problem-solving skills.

Shafer also touched upon the importance of integration (produce results vs consume results) and two important lessons: (1) recognize potential biases in data or systems, and (2) the flaw of averages, which is a lesson he learned from Sam Savage's book. He illustrated his point with a toy example from Disney about upgrading in a hotel with two types of room. He also commented on the "man vs machine" attitude ("show me your computer can mimic my decisions") which should be revised into "man with machine". The point is for the machine to help the user spend more time gaining insights and doing value-adding tasks. The machine helps man get a better starting point for his analysis.

Shafer discussed Disney's onboarding procedure, which includes a traditional mentor for strategic thinking but also a "friend", who can answer the more day-to-day or tactical questions of the new hire. Also, Disney put the new hires right away on a project and has all new hires go through the same "Traditions" course.

Unsurprisingly given the recent Facebook scandal, ethics came up in the discussion. Disney's standard question is "does this improve the trust with our guests?" There was also a question about analytics displacing jobs. Shafer replied that he couldn't think of a single situation where analytics had led to a decrease in the number of people needed. Talent does sometimes need to become more sophisticated. Another question was how to disperse analytics in an organization. Disney's model is rather centralized with a hub-and-spoke model. But every organization can measure what it does and publish those measures as a first step. Then it can implement descriptive analytics (visualizations etc) and protect itself against the law of averages. 

For further insights, check out this interview of Shafer in the Journal of Revenue Management and Pricing.

More about the MAPD in my next post!

How much advertising does Grammarly need?

I admit it: at home I sometimes like watching YouTube videos when I need to take a break from grading. Not too long ago I noticed I keep getting over and over AND OVER the same Grammarly advertisement. Grammarly is a "cloud-based English-language writing-enhancement platform" (according to its Wikipedia description) positioning itself as a "writing assistant" improving proofreading but also targeting plagiarism-detection. Its business model falls within the class of freemium models, where a watered-down version is available for free and a full version is available at a fee, sometimes paid by universities for their students.

The monthly subscription rate is of $30/month, while longer subscriptions lead to smaller prices (for instance the annual subscription of $140/year is equivalent to about $12/month). Frankly, when I see the extreme prevalence of Grammarly ads on YouTube, I have to wonder how much money they have to spare to spend such egregious amounts on advertisement. When does it all become too much advertising, i.e., a signal that a company is desperate for market growth?

At least vary the ads, for heaven's sake. This is becoming almost a joke. 95% of the time I see the very same ad on YouTube. ("If you write anything, you need to get Grammarly!" says the cheerful co-ed sitting on a sofa before the first 5 seconds are up and I can skip the ad.) The other 5% of the time I see the one other ad they seem to have, which starts with a stressed-out young co-ed trying to write a paper late at night. I guess cheerful co-eds did better than stressed-out co-eds in beta testing. What a surprise.

Why don't companies realize it makes them look desperate when they inundate the market with the very same ads? It just signals they don't think the product can do well with the average amount of advertising they'd spend on other products. Frankly, they'd be better off using some of the money they spend on such intense advertising to give some students access to the full version of Grammarly and encourage them to tell their friends. When I read through some of my students' essays, it looks like quite a few SMU students don't yet know they can have access to a watered-down version for free. Maybe in the not-so-distant future, business professors will use Grammarly as one of their cases of how not to run an advertising campaign, unless the goal is to look desperate and faintly ridiculous.

HBR on Advertising Analytics 2.0

This is another article from the HBR OnPoint issue on The Data-Driven Manager. It makes interesting points on the fact that ads increasingly interact, and on the difficulty in properly attributing a sales to a specific ad or a customer's action online, although historically marketers have attributed customers' purchase behavior to their most recent actions (online, since it is impossible to track their actions offline). Silo-based thinking and "swim-lane measurement" mean that marketers often misattribute specific outcomes to their marketing activities.

Questions that marketers ask themselves these days include: how does a combination of ad exposures interact to influence this consumer? is this company investing the right amounts at the right points in the customer-decision journey to spark her to action? Of key interest is to quantify the cross-media and cross-channel effects of marketing. For instance, what is the assist (a marketing term for impact) of ads that consumers don't actively engage with, but that may have brought them closer to ultimately buying the item?

Advertising analytics 2.0 refers to three core activities:

• attribution is the process of quantifying the contribution of each element of advertising.
• optimization is maybe not quite what we OR/MS professionals would call optimization, but instead is something between scenario planning, sensitivity analysis and local optimization, to which the author gives the engaging name of "war gaming" (first the decision maker determines elasticities of key business drivers and then answers questions such as: what if we increase our TV ad marketing budget by 10%?)
• allocation is the distribution of resources across marketing activities.

The author makes the following observations: 

1. advertising analytics 2.0 must be championed by a C-level executive sponsor.
2. it is important to assign an analytics-minded director or manager to become the point person for the effort.
3. analytics advocates must conduct an inventory of data throughout the organization.
4. the organization should start small with proofs of concepts and build limited-scope models.
5. the organization should then test aggressively and feed the results back into the model.

Marketing 4.0

Marketing 4.0 by Philip Kotler, Hermawan Kartajaya and Iwan Setiawan is a good but not great book. At first I even thought it would never rise above the level of a puffed-up magazine piece for Harvard Business Review because the early chapter on the influential digital subcultures was so underwhelming, but it turned out to be ok. Kotler, a professor at Northwestern’s Kellogg School of Management, is widely regarded as the father of modern marketing so I suppose he can sell any marketing book with his name on it. My final rating for this book is somewhere between 3.5 and 4.0 out of 5.0, so my one-sentence advice would be to buy it on Amazon.com if you really want to own a copy of it.

I read it during my recent trip to Phoenix, AZ with the INFORMS Meetings Committee (we went and checked the venue of the 2018 annual meeting and decided on plans for future conference locations as well as other business matters; more about that some other day). The book is about 150 pages long so it was never supposed to be a long read to begin with. If I were to pinpoint what is missing in it, I’d venture that it should have included more detailed examples or mini-case studies. More substance, basically. But then I got it for cheap on Amazon.com, so perhaps I shouldn’t complain. (Unless the fact that the book is available at a 44% discount on Amazon.com already tells you it didn’t quite measure up to Wiley’s expectations? Wiley, like all academic publishers, is not usually known for giving customers deep discounts.)

Anyway, I liked most in Marketing 4.0: (a) the discussion of the new customer path: Aware, Appeal, Ask, Act and Advocate, which takes into account the influence of customer communities on customer paths and (b) the Purchase Action Ratio (PAR) and Brand Advocacy Ratio (BAR) metrics. They go as far as discussing median and range for BAR and what those mean for a company.

The authors have an interesting discussion on the conversion rates along the customer path. The four major industry archetypes p.94 were also particularly insightful, in the way the authors visualize the conversion rates (attraction, curiosity, commitment and affinity levels) across the 5 A’s. The authors give those archetypes the following names: door knob (low curiosity and affinity, e.g., consumer-packaged goods), goldfish (high curiosity, e.g., B2B), trumpet (high affinity, e.g., luxury cars and designer handbags, where the number of advocates exceeds the number of buyers) and funnel. The authors describe the ideal customer-path pattern as a bow tie. (I can’t resist pointing out that what I liked most about this book could, indeed, fit into a HBR magazine piece.) They also have valuable although somewhat superficial discussions on content marketing and omnichannel marketing.

And what does the Marketing 4.0 in the title stand for, you will ask? I wish people put an end to that habit of using 2.0 and 3.0 and 4.0, but here goes: Marketing 1.0 was for product-driven marketing, Marketing 2.0 was for customer-centric marketing, Marketing 3.0 was for human-centric marketing (“whole human beings with minds, hearts and spirits,” what a program). Marketing 4.0 is now about the rise of digital marketing in an increasingly connected world. I hope that no one will get tempted to define Marketing 5.0. It sounds far too gimmicky by now. 

Kotler has co-authored a number of textbooks in marketing and maybe those would be better choices if new copies didn't sell for about $200.