I've written a short book. It's called "Pragmalytics: Practical Approaches to Marketing Analytics in the Digital Age". It's a collection and synthesis of some of the things I've learned over the last several years about how to take better advantage of data (Big and little) to make better marketing decisions, and to get better returns on your investments in this area.
The main point of the book is the need for orchestration. I see too much of the focus today on "If we build It (the Big Data Machine, with some data scientist high priests to look after it), good things will happen." My experience has been that you need to get "ecosystemic conditions" in balance to get value. You need to agree on where to focus. You need to get access to the data. You need to have the operational flexibility to act on any insights. And, you need to cultivate an "analytic marketer" mindset in your broader marketing team that blends perspectives, rather than cultivating an elite but blinkered cadre of "marketing analysts". Over the next few weeks, I'll further outline some of what's in the book in a few posts here on my blog.
I'm really grateful to the folks who were kind enough to help me with the book. The list includes: Mike Bernstein, Tip Clifton, Susan Ellerin, Ann Hackett, Perry Hewitt, Jeff Hupe, Ben Kline, Janelle Leonard, Sam Mawn-Mahlau, Bob Neuhaus, Judah Phillips, Trish Gorman Clifford, Rob Schmults, Michelle Seaton, Tad Staley, and my business partner, Jamie Schein. As I said in the book, if you like any of it, they get credit for salvaging it. The rest -- including several bits that even on the thousandth reading still aren't as clear as they should be, plus a couple of typos I need to fix -- are entirely my responsibility.
I'm also grateful to the wonderful firms and colleagues and clients I've had the good fortune to work for and with. I've named the ones I can, but in general have erred on the side of respecting their privacy and confidentiality where the work isn't otherwise in the public domain. To all of them: Thank You!
This field is evolving quickly in some ways, but there are also some timeless principles that apply to it. So, there are bits of the book that I'm sure won't age well (including some that are already obsolete), but others that I hope might. While I'm not one of those coveted Data Scientists by training, I'm deep into this stuff on a regular basis at whatever level is necessary to get a positive return from the effort. So if you're looking for a book on selecting an appropriate regression technique, or tuning Hadoop, you won't find that here, but if you're looking for a book about how to keep all the balls in the air (and in your brain), it might be useful to you. It's purposefully short -- about half the length of a typical business book. My mental model was to make it about as thick as "The Elements of Style", since that's something I use a lot (though you probably won't think so!). Plus, it's organized so you can jump in anywhere and snack as you wish, since this stuff can be toxic in large doses.
In writing it amidst all the Big Data craziness, I was reminded of Gandhi's saying (paraphrased) "First they ignore you... then they fight you, then you win." Having been in the world of marketing analytics now for a while, it seems appropriate to say that "First they ignore you, then they hype you, then you blend in." We're now in the "hype" phase. Not a day goes by without some big piece in the media about Big Data or Data Scientists (who now have hit the highly symbolic "$300k" salary benchmark -- and last time we saw it, in the middle part of the last decade in the online ad sales world, was a sell signal BTW). "Pragmalytics" is more about the "blend in" phase, when all this "cool" stuff is more a part of the furniture that needs to work in harmony with the rest of the operation to make a difference.
"Pragmalytics" is available via Amazon (among other places). If you read it please do me a favor and rate and review it, or even better, please get in touch if you have questions or suggestions for improving it. FWIW, any earnings from it will go to Nashoba Learning Group (a school for kids with autism and related disorders).
Where it makes sense, I'd be very pleased to come talk to you and your colleagues about the ideas in the book and how to apply them, and possibly to explore working together. Also, in a triumph of Hope over Experience, my next book (starting now) will be a collection and synthesis of interviews with other senior marketing executives trying to put Big Data to work. So if you would be interested in sharing some experiences, or know folks who would, I'd love to talk.
About the cover: it's meant to convey the harmonious convergence of "Mars", "Venus", and "Earth" mindsets: that is, a blend of analytic acuity, creativity and communication ability, and practicality and results-orientation that we try to bring to our work. Fellow nerds will appreciate that it's a Cumulative Distribution Function where the exponent is, in a nod to an example in the book, 1.007.
Hi folks, I need a favor. I need 200 subscribers to this blog via Google Currents to get Octavianworld listed in the Currents catalog. If you're reading this on an iPhone, iPad, or Android device, follow this link:
If you are looking at this on a PC, just snap this QR code with your iPhone or Android phone after getting the Currents app.
Here's what I look like on Currents:
What is Currents? If you've used Flipboard or Zite, this is Google's entry. If you've used an RSS reader, but haven't used any of these yet, you're probably a nerdy holdout (it takes one to know one). If you've used none of these, and have no idea what I'm talking about, apps like these help folks like me (and big media firms too) publish online magazines that make screen-scrollable content page-flippable and still-clickable. Yet another distribution channel to help reach new audiences.
Here are some discussion questions we're considering. What's your "keep / change / drop / add" to this list? And, please take the poll at bottom!
1. How do you define "attribution analysis", and in particular, how do you distinguish it from media mix modeling? Where are the boundaries with "CRM"? Is it just a question of the fineness of the degree to which you can relate media effects to each other?
2. Everyone's got a poster child example or case study that illustrates the potential of multi-channel attribution analysis. For each of you, what's your poster child?
3. What are the practical limits of the utility of this analysis, and what's behind those limits? Put another way, at what point do you see diminishing returns to investments in attribution analysis, and why?
4. Let's talk techniques for a moment, since the session description promised we would. What particular approaches to data integration or to statistical analysis do you find robust enough for real-world use, and which ones are still too fragile to make work in most cases?
5. Some people approach attribution analysis as a capability-building exercise -- if we build it, they will come -- while others come at it from a tight, hypothesis-driven focus about where the value might lie, and in what sequence it might best make sense to explore connections. What do you need to know about a client to suggest which way, or what mix of the two, might make sense for them?
6. What are some principles and tactics for the governance of attribution analysis -- things like reconciling metrics and comp with the global optimization it implies -- that make sense here? Can you tell any stories about how these have been developed and applied?
7. What kind of experience, training, and topical education does it make sense for marketers to have, to fully take advantage of this?
8. How do you operationalize this analytic capability -- how do you make sure we move beyond insight and apply, either manually or even in automated ways, the directions the analysis suggests? And, how does this prospective operationalization [warning: mouthful!] affect the analysis you consider doing, in turn?
9. Let's build the ideal RFP for attribution analysis efforts, to help our clients out. What's in it? What shouldn't be?
10. What's your recommended reading list for learning about and following this field?
By this point, many of you will be familiar with some of the more interesting and exotic examples of "integrated cross-channel product experiences", such as the Nike+ product/ service/ community. But the approach has gone mainstream too. Here's a recent example I experienced:
I went with my family to the "99" Restaurant in Centerville,
on Cape Cod in Massachusetts (the one on Route 28). Lying on the table
was a pad of these forms:
I texted my information in, and 24 hours later this appeared in my
I clicked through:
to text to email to web to store, all nicely connected. Cool! Hope
we're back before it expires. Otherwise we'll have to sacrifice another
family member's phone. (Maybe do that anyway, and ask for separate
checks... Hey, times are tough!)
This program is run soup-to-nuts for the
"99" by an external service called Fishbowl Marketing. It's pretty good! I'm hoping to speak with them about experiences and results with it.
A few observations:
Instead of texting "99", they might have asked for "CapeCod", or
"Cville" to track the geographic location of the signup, allowing them
to compare signups to store traffic across regions. (Fishbowl might need
an SMS address unique to the "99" client for this, and that's more
I may not get back to the Cape soon. When I clicked through from
the email to the landing page from home, they could have recognized
where the request was coming from and returned a link to a map of nearby
locations under the coupon. I'm betting most folks won't explicitly
update their preferences, especially with the unremarkable landing page
copy exhorting them to do so (good testing opportunity!).
I sometimes participate in other offers like this. They could offer me an
opportunity to personalize my offer based on crossing my cell number
against other databases that also might have it, to see what other
information might be there that might have helped them -- and me
(analogous to caller ID).
Some sample of users could (may?) have been asked to participate in
an exit survey about their experience, perhaps for additional benefits.
maybe implement a "Share this coupon with three friends and get another
$5-off when they sign up" opportunity? (I did get follow up email
messages on holidays inviting me back, and suggesting I invite friends;
but when I clicked through to landing pages for inviting friends, the
forms asked me to tell them who I was, again, even though I hadn't
cleared cookies in the interim.)
Also, they might have added links to their social media presences
like Twitter and Facebook (for feedback, or to offer other promotions),
below the coupon.
Finally, and I'm sure this is on Fishbowl Marketing's agenda, they
might consider signing up with a mobile location-based service provider,
like Foursquare or Gowalla, either
as part of the vendor branded experience or via a "white label"
application developed off those vendors' APIs. That way the "99"
could vary loyalty rewards granted according to number of check-ins, and
take advantage of the viral marketing advantages of these services
(your friends get notified when you check in at the "99", so maybe they
stop by too). Of course you say, are "99" customers leading-edge tech
adopters with the latest 3G smartphones, with OC(I)D (Obsessive
Check-In Disorder)? Not yet, but what were we saying about Facebook a
couple of years ago?
What's your favorite example? Hope to see you tomorrow morning!
On the morning of April 29 I'll be moderating a MITX panel discussion titled "Integrating Cross-Channel Customer Experiences", in Cambridge, MA (Kendall Square). More here, more posts to follow. Hope to see you there!
Social marketing today is limited by the (un-)structure of social media. (And, most marketers accept the constraints of these media as they exist today, and "reasonably" adapt themselves to them.)
Applying "Structured Collaboration" principles to social media design ("unreasonably" adapting the medium) can expand and improve user engagement.
These same principles can inform the design of "engagement" for marketing analytics, to make segmentation and targeting easier and more effective.
The presentation included a survey and evaluation of the social marketing landscape today, and described two important shortcomings that limit what marketers can do with it:
"Off-the-rack" collaboration structures that don't align -- and often work against -- what marketers are trying to accomplish
Sample bias given the "1-10-90" nature of participation
The presentation then described and illustrated "Structured Collaboration" principles, and talked about how marketers like Nike are realizing significant results through initiatives that reflect them. Then, we discussed how the design of these initiatives can reveal segmentation and targeting insights much more readily than the typical application of marketing analytics to conventional social media.
Finally, the talk offered some existing examples for how to exploit these principles cheaply, as well as some ideas for how several firms could apply them to realize their own versions of what Nike, and more recently others like Fiat, have done.
Thanks once again to Kate, Ethan, and Irene, and to all their colleagues for participating!