Tag Archives: digital marketing

Connecting Marketers to Machine Learning: A Traveler’s Guide Through Two Utterly Dissimilar Worlds

Artificial Intelligence for Marketing by Jim Sterne

There are people in the world who work with and understand AI and machine learning. And there are people in the world who work with and understand marketing. The intersection of those two groups is a vanishingly tiny population.

Until recently the fact of that nearly empty set didn’t much matter. But with the dramatic growth in machine learning penetration into key marketing activities, that’s changed. If you don’t understand enough about these technologies to use them effectively…well…chances are some of your competitors do.

AI for Marketing, Jim Sterne’s new book,  is targeted specifically toward widening that narrow intersection of two populations into something more like a broad union. It’s not an introduction to machine learning for the data scientist or technologist (though there’s certainly a use and a need for that). It’s not an introduction to marketing (though it does an absolutely admirable job introducing practical marketing concepts). It’s a primer on how to move between those two worlds.

Interestingly, in AI for Marketing, that isn’t a one way street. I probably would have written this book on the assumption that the core task was to get marketing folks to understand machine learning. But AI for Marketing makes the not unreasonable assumption that as challenged as marketing folks are when it comes to AI, machine learning folks are often every bit as ignorant when it comes to marketing. Of course, that first audience is much larger – there’s probably 1000 marketing folks for every machine learner. But if you are an enterprise wanting two teams to collaborate or a technology company wanting to fuse your machine learning smarts to marketing problems, it makes sense to treat this as a two-way street.

Here’s how the book lays out.

Chapter 1 just sets the table on AI and machine learning. It’s a big chapter and it’s a bit of grab bag, with everything from why you should be worried about AI to where you might look for data to feed it. It’s a sweeping introduction to an admittedly huge topic, but it doesn’t do a lot of real work in the broader organization of the book.

That real work starts in Chapter 2 with the introduction to machine learning. This chapter is essential for Marketers. It covers a range of analytic concepts: an excellent introduction into the basics of how to think about models (a surprisingly important and misunderstood topic), a host of common analytics problems (like high cardinality) and then introduces core techniques in machine learning. If you’ve ever sat through data scientists or technology vendors babbling on about support vector machines and random forests, and wondered if you’d been airlifted into an incredibly confusing episode of Game of Drones, this chapter will be a godsend. The explanations are given in the author’s trademark style: simple, straightforward and surprisingly enjoyable given the subject matter. You just won’t find a better more straightforward introduction to these methods for the interested but not enthralled businessperson.

In Chapter 3, Jim walks the other way down the street – introducing modern marketing to the data scientist. After a long career explaining analytics to business and marketing folks, Jim has absorbed an immense amount of marketing knowledge. He has this stuff down cold and he’s every bit as good (maybe even better) taking marketing concepts back to analysts as he is working in the other direction.  From a basic intro into the evolution of modern marketing to a survey of the key problems folks are always trying to solve (attribution, mix, lifetime value, and personalization), this chapter nails it. If you subscribe to the theory (and I do) that any book on Marketing could more appropriately have been delivered as a single chapter, then just think of this as the rare book on Marketing delivered at the right length.

If you accept the idea that bridging these two worlds needs movement in both directions, the structure to this point is obvious. Introduce one. Introduce the other. But then what?

Here’s where I think the structure of the book really sings. To me, the heart of the book is in Chapters 4, 5 and 6 (which I know sounds like an old Elvis Costello song). Each chapter tackles one part of the marketing funnel and shows how AI and machine learning can be used to solve problems.

Chapter 4 looks at up-funnel activities around market research, public relations, social awareness, and mass advertising. Chapter 5 walks through persuasion and selling including the in-store journey (yeah!), shopping assistants, UX, and remarketing. Chapter 6 covers (you should be able to guess) issues around retention and churn including customer service and returns. Chapter 7 is a kind of “one ring to rule them all”, covering the emergence of integrated, “intelligent” marketing platforms that do everything. Well….maybe. Call me skeptical on this front.

Anyway, these chapters are similar in tone and rich in content. You get the core issues explained, a discussion of how AI and machine learning can be used, and brief introductions into the vendors and people who are doing the work. For the marketer, that means you can find the problems that concern you, get a sense of where the state of machine learning stands vis-à-vis your actual problem set, and almost certainly pick-up a couple of ideas about who to talk to and what to think about next.

If you’re into this stuff at all, these four chapters will probably get you pretty excited about the possibilities. So think of Chapter 8 as a cautionary shot across the bow. From being too good for your own good to issues around privacy, hidden biases and, repeat after me, “correlation is not causation” this is Pandora’s little chapter of analytics and machine learning troubles.

So what’s left? Think about having a baby. The first part is exciting and fun. The next part is long and tedious. And labor – the last part – is incredibly painful. It’s pretty much the same when it comes to analytics. Operationalizing analytics is that last, painful step. It comes at the end of the process and nobody thinks it’s any fun. Like the introduction to marketing, the section on operationalizing AI bears all the hallmarks of long, deep familiarity with the issues and opportunities in enterprise adoption of analytics and technology. There’s tons of good, sound advice that can help you actually get some of this stuff done.

Jim wraps up with the seemingly obligatory look into the future. Now, I’m pretty confident that none of us have the faintest idea how the future of AI is going to unfold. And if I really had to choose, I guess I prefer my crystal ball to be in science fiction form where I don’t have to take anything but the plot too seriously. But there’s probably a clause in every publisher’s AI book contract that an author must speculate on the how wonderful/dangerous the future will be. Jim keeps it short, light, and highly speculative. Mission accomplished.

 

Summing Up

I think of AI for Marketing as a handy guidebook into two very different, neighboring lands. For most of us, the gap between the two is an un-navigable chasm. AI for Marketing takes you into each locale and introduces you to the things you really must know about them. It’s a fine introduction not just into AI and Machine Learning but into modern marketing practice as well. Best of all, it guides you across the narrow bridges that connect the two and makes it easier to navigate for yourself.  You couldn’t ask for a wiser, more entertaining guide to walk you around and over that bridge between two utterly dissimilar worlds that grow everyday more necessarily connected.

 

Full Disclosure: I know and like the author – Jim Sterne – of AI for Marketing. Indeed, with Jim the verbs know and like are largely synonymous. Nor will I pretend that this doesn’t impact my thoughts on the work. When you can almost hear someone’s voice as you read their words, it’s bound to impact your enjoyment and interpretation. So absolutely no claim to be unbiased here!

 

Gelato was the word I meant

I spent most of the last week on holiday in Italy. But since the holiday was built around a speaking gig in Italy at the Be Wizard Digital Marketing conference I still spent a couple of days talking analytics and digital. A couple of days I thoroughly enjoyed. The conference closed with a Q&A for a small group of speakers and while I got a few real analytics questions it felt more like a meet and greet – with plenty of puff-ball questions like “what word would use to describe the conference?” A question I failed miserably with the very pathetic answer “fun”.

I guess that’s why it’s better to ask me analytics questions.

The word I probably should have chosen is “gelato”.

And not just because I hogged down my usual totally ridiculous amount of fragola, melone, cioccolato, and pesca – scoop by scoop from Rimini to Venice.

Gelato because I had a series of rich conversations with Mat Sweezey from Salesforce (nee Pardot) who gave a terrific presentation on authenticity and what it means in this new digital marketing world. It’s easy to forget how dramatically digital has changed marketing and miss some of the really important lessons from those changes. Mat also showed me a presentation on agile that blends beautifully with the digital transformation story I’ve been trying to tell in the last six months. It’s a terrific deck with some slides that explain why test&learn and agile methods work so much better than traditional methods. It’s a presentation with the signal virtue of taking very difficult concepts and making them not just clear but compelling. That’s hard to do well.

Gelato because I also talked with and enjoyed a great presentation from Chris Anderson of Cornell. Chris led a two-hour workshop in the revenue management track (which happens to be a kind of side interest of mine). His presentation focused on the impact of social media content on sites like TripAdvisor on room pricing strategies. He’s done several compelling research projects with OTAs (Online Travel Agents) looking at the influence of social media content on buying decisions. His research has looked at key variables that drive influence (number of reviews and rating), how sensitive demand is to those factors, and how that sensitivity plays out by hotel class (turns out that the riskier the lodging decision the more impactful social reviews are). He’s also looked at review response strategies on TripAdvisor and has some compelling research showing how review response can significantly improve ratings outcomes but how it’s also possible to over-respond. Respond to everything, and you actually do worse than if you respond to nothing.

That’s a fascinating finding and very much in keeping with Mat’s arguments around authenticity. If you make responding to every social media post a corporate policy, what you say is necessarily going to sound forced and artificial.

That’s why it doesn’t work.

If you’re in the hospitality industry, you should see this presentation. In fact, there are lessons here for any company interested in the impact of reviews and social content and interested in taking a more strategic view of social outreach and branding. I think Chris’ data suggest significant and largely unexplored opportunities for both better revenue management decisions around OTA pricing and better strategies around the review ask.

Gelato because there was one question I didn’t get to answer that I wanted to (and somehow no matter how much gelato I consume I always want a little more).

Since I had to have translations of the panel questions at the end, I didn’t always get a chance to respond. Sometimes the discussion had moved on by the time I understood the question! And one of the questions – how can companies compete with publishers when it comes to content creation – seemed to me deeply related to both Mat and Chris’ presentations.

Here’s the question as I remember it:

If you’re a manufacturer or a hotel chain or a retailer, all you ever hear in digital marketing is how content is king. But you’re not a content company. So how do you compete?

The old-fashioned way is to hire an agency to write some content for you. That’s not going to work. You won’t have enough content, you’ll have to pay a lot for it, and it won’t be any good. To Mat’s point around authenticity, you’re not going to fool people. You’re not going to convince them that your content isn’t corporate, mass-produced, ad agency hack-work. Because it is and because people aren’t stupid. Building a personalization strategy to make bad content more relevant isn’t going to help much either. That’s why you don’t make it a corporate policy to reply to every review and why you don’t write replies from a central team of ad writers.

Stop trying to play by the old rules.

Make sure your customer relations, desk folks, and managers understand how to build relationships with social media and give them the tools to do it. If you want authentic content, find your evangelists. People who actually make, design, support or use your products. Give them a forum. A real one. And turn them loose. Find ways to encourage them. Find ways to magnify their voice. But turn them loose.

You can’t have it both ways. You can’t be authentic while you try to wrap every message in a Madison Avenue gift wrapping bought from the clever folks at your ad agency. Check out Mat’s presentation (he’s a Slideshare phenom). Think about the implications of unlimited content and the ways we filter. Process the implications. The world has changed and the worst strategy in the world is to keep doing things the old way.

So gelato because the Be Wizard conference, like Italy in general, was rich, sweet, cool and left me wanting to hear (and say) a bit more!

And speaking of conferences, we’re not that far away from my second European holiday with analytics baked in – The Digital Analytics Hub in London (early June). I’ve been to DA Hub several years running now – ever since two old friends of mine started it. It’s an all conversational conference modeled on X Change and it’s always one of the highlights of my year. In addition to facilitating a couple conversations, I’m also going to be leading a very deep-dive workshop into digital forecasting. I plan to walk through forecasting from the simplest sort of forecast (everything will stay the same) to increasingly advanced techniques that rely, first on averages and smoothing, and then to models. If you’re thinking about forecasting, I really think this workshop will be worth the whole conference (and the Hub is always great anyway)…

If you’ve got a chance to be in London in early June, don’t miss the Hub.