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Cesar A. Brea bio at Force Five Partners

     

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27 posts categorized "ecommerce"

June 14, 2010

OMMA Metrics & Measurement "Modeling Attribution" Panel SF 7/22: Hope To See You There

I'll be moderating a panel at the OMMA Metrics & Measurement Conference in San Francisco on July 22.  

The topic of the panel is, "Modeling Attribution: Practitioner Perspectives on the Media Mix".  Here's the conference agenda page.

The panel description:

How do you determine the channels that influence offline and online behavior and marketing performance?  

How should you allocate your budget across CRM emails, display ads, print advertising, television and radio commercials, direct mail, and other marketing sources? 

What models, techniques, and technologies should you use develop attribution and predictive models that can drive your business? 

Do you need SAS, SPSS, and a PhD in Statistics? 

Does first click, last click, direct, indirect, or appropriate attribution matter – which is best?

What about multiple logistic regression? 

What is the impact of survey and voice-of-the-customer data on attribution? 

Hear from experts who have to answer these questions and tackle these tough issues as they work hard in the field every day for their consultancies, agencies, and brands.

So far, Manu Mathew, CEO from VisualIQ, and Todd Cunningham, SVP Research at MTV Networks, will be participating on the panel as well.

Hope to see you there.  Meanwhile, please suggest questions you'd like to ask the panelists by commenting here.  Thanks!

April 28, 2010

MITX Panel: "Integrating Cross-Channel Customer Experiences" (April 29, 2010 8-10a) Part II

We've assembled a terrific panel for tomorrow's event:

  • SmartDestinations' Rob Schmults is also a Creative Good Council Leader;
  • Judah Phillips is a leader at the cutting edge of analytics in his role at Monster;
  • At Staples, Colin Hynes plays a leading role in figuring out store / digital integration, and is heavily focused on mobile's role in that;
  • VisualIQ's Manu Mathew sees a broad assortment of situations in facilitating his customers' efforts to develop a cross-channel perspective and optimize based on it.

Here are some of the questions we thought to cover:

  • What integrated experiences do you look to as best practice models?
  • What are you doing in your organizations (or your clients') to better integrate experiences?
  • Where do you believe the greatest opportunities for better integration still lie for you?
  • How are you addressing the organizational and technical challenges required for better integration?
  • How far down the path toward a more integrated, globally-optimized analytic perspective do you see yourself today?
  • What's your favorite integrated experience story, for good -- or not so good?
  • What resources have you found helpful for learning more / tracking what's going on in this area?
  • What advice would you have for folks trying to push further down this path?

Suggestions for questions welcome -- just email me via the link at left.

----------

Postscript:  a recap of the panel on the MITX blog

----------

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:

99 Restaurant Loyalty Coupon Form0001

  I texted my information in, and 24 hours later this appeared in my inbox:

99 email

I clicked through:

99 coupon



Store 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 expensive.)
  • 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!

March 13, 2010

Fly-By-Wire Marketing, Part II: The Limits Of Real Time Personalization

A few months ago I posted on what I called "Fly-By-Wire Marketing", or the emergence of the automation of marketing decisions -- and sometimes the automation of the development of rules for guiding those decisions.

More recently Brian Stein introduced me to Hunch, the new recommendation service founded by Caterina Fake of Flickr fame.  (Here's their description of how it works.  Here's my profile, I'm just getting going.)  When you register, you answer questions to help the system get to know you.  When you ask for a recommendation on a topic, the system not only considers what others have recommended under different conditions, but also what you've told it about you, and how you compare with others who have sought advice on the subject.

It's an ambitious service, both in terms of its potential business value (as an affiliate on steroids), but also in terms of its technical approach to "real time personalization".  Via Sim Simeonov's blog, I read this GigaOm post by Tom Pinckney, a Hunch co-founder and their VP of Engineering.  Sim's comment sparked an interesting comment thread on Tom's post.  They're useful to read to get a feel for the balance between pre-computation and on-the-fly computation, as well as the advantages of and limits to large pre-existing data sets about user preferences and behavior, that go into these services today.

One thing neither post mentions is that there may be diminishing returns to increasingly powerful recommendation logic if the set of things from which a recommendation can ultimately be selected is limited at a generic level.  For example, take a look at Hunch's recommendations for housewarming gifts.  The results more or less break down into wine, plants, media, and housewares.  Beyond this level, I'm not sure the answer is improved by "the wisdom of Hunch's crowd" or "Hunch's wisdom about me", as much as my specific wisdom about the person for whom I'm getting the gift, or maybe by what's available at a good price. (Perhaps this particular Hunch "topic" could be further improved by crossing recommendations against the intended beneficiary's Amazon wish list?)

My point isn't that Hunch isn't an interesting or potentially useful service.  Rather, as I argued several months ago,

The [next] question you ask yourself is, "How far down this road does it makes sense for me to go, by when?"  Up until recently, I thought about this with the fairly simplistic idea that there are single curves that describe exponentially decreasing returns and exponentially increasing complexity.  The reality is that there are different relationships between complexity and returns at different points -- what my old boss George Bennett used to call "step-function" change.

For me, the practical question-within-a-question this raises is, for each of these "step-functions", is there an version of the algorithm that's only 20% as complex, that gets me 80% of the benefit?  My experience has been that the answer is usually "yes".  But even if that weren't the case, my approach in jumping into the uncharted territory of a "step-function" change in process, with new supporting technology and people roles, would be to start simple and see where that goes.

At minimum, given the "step-function" economics demonstrated by the Demand Medias of the world, I think senior marketing executives should be asking themselves, "What does the next 'step-function' look like?", and "What's the simplest version of it we should be exploring?" (Naturally, marketing efforts in different channels might proceed down this road at different paces, depending on a variety of factors, including the volume of business through that channel, the maturity of the technology involved, and the quality of the available data...)

Hunch is an interesting specific example of the increasingly broad RTP trend.  The NYT had an interesting article on real time bidding for display ads yesterday, for example.  The deeper issue in the trend I find interesting is the shift in power and profit toward specialized third parties who develop the capability to match the right cookie to the right ad unit (or, for humans, the right user to the right advertiser), and away from publishers with audiences.  In the case of Hunch, they're one and the same, but they're the exception.  How much of the increased value advertisers are willing to pay for better targeting goes to the specialized provider with the algorithm and the computing power, versus the publisher with the audience and the data about its members' behavior?  And for that matter, how can advertisers better optimize their investments across the continuum of targeting granularity?  Given the dollars now flooding into digital marketing, these questions aren't trivial.

March 09, 2010

Filtering The Collective Preconscious: Darwin Ecosystem

More and more, people agree that filtering the flood of information that's coming at us is supplanting publishing, finding, and connecting as the problem of the Information Age.  Today, the state of the art for doing this includes several approaches:

  • Professional filters: we follow people whose jobs are to cover an area.  Tom Friedman covers international issues, Walt Mossberg covers personal technology.
  • Technical filters: we use services like Google Alerts to tell us when there's something new on a topic we're interested in
  • Social filters: we use services like Digg, Reddit, and Stumbleupon to point us to popular things
  • Tribal filters: we use Facebook, Twitter, LinkedIn, and (Google hopes) Buzz to get pointed to things folks we know and trust think are important

In addition to what gets through, there's how it's presented.  RSS readers for example offer a huge productivity boost to anyone trying to keep up with more than a few sources of information.  However, once you get several hundreds items in your RSS reader, unsorted by anything other than "last in", it's back to information overload.  To solve this, innovative services like Newsmap provide multi-dimensional visual displays to try to push your information awareness productivity even further.  But so far, they've seen only modest adoption.

One limitation of today's filtering and productivity tools is that they pick items up either too early, before it's clear they represent something meaningful, or too late, once the advantages of recognizing a trend have passed.

Yesterday, I visited the team behind a new service called Darwin Ecosystem  that takes a different and potentially more powerful and useful approach to helping you "filter the collective preconscious"  -- that is, to identify emergent signals in the vast noise of the Internet (or any other body of information you might point to -- say, for example, customer service call logs).  Co-founder and CEO Thierry Hubert is a veteran of the knowledge management world going back to senior technical roles at Lotus and IBM; his partner Frederic Deriot shares similar experiences; and, my friend Bill Ives -- formerly head of Accenture's KM client practice -- is also involved as VP Marketing.

Briefly, the service presents a tag cloud of topics that it thinks represent emergent themes to pay attention to in the "corpus" filled by sources you point it to (in the demo, sources run to hundreds of news sources and social media).  The bigger the font, the more important the theme.  Hover your mouse over a theme, and it highlights other related themes to put them all into a collective context.  The service also provides a dynamic view of what's hot / not with a stock-ticker-style ribbon running at the top of the page.  You can view the cloud of emergent themes either in an "unfiltered view", or more usefully, filtered with "attractor" keywords you can specify.

This interface, while interesting, will likely not be the eventual "user/use-case" packaging of the service.  I can see this as a built-in "front page" for an RSS reader, for example, or, minus the tag cloud, as the basis for a more conventional looking email alert service.

The service is based on the math behind Chaos Theory.  This is the math that helps us understand how the proverbial beating of a butterfly's wings in China might become a massive storm.  (Math nerds will appreciate the Lorenz-attractor-plot-as-butterfly-wings logo.)  The service uses this math to tell you not only what individual topics are gaining or losing momentum, but also to highlight relationships between and among different topics to put them into context  -- like why "underwear" and "bomber" might be related. 

Now in beta, with a few large organizations (including large media firms) as early adopters, the service has had some early wins that demonstrate its potential.  It told users, for example, that Lou Dobbs might be on his way out at CNN a week before his departure was reported in the mainstream press.  It also picked up news of UCLA's planned tuition hikes 48 hours in advance of this getting reported in popular mainstream or social media.

It strikes me that a service like Darwin is complementary to that of  Crimson Hexagon, a sentiment analysis firm based on Prof. Gary King's work at Harvard (here's the software that came out of that work), with a variety of marketing, media, and customer support applications.  Darwin helps tell you what to pay attention to -- suggests emergent themes and their context; Crimson Hexagon can then tell you how people feel about these issues in a nuanced way, beyond simple positive / negative buzz.

The current business model has Darwin pursuing enterprise licensing deals with major firms, but depending on partners that emerge, that may not be the last stop on the adoption / monetization express.  For example, it seems to me that a user's interaction with a tool like Darwin represents highly intentional behavior that would be useful data for ad / offer targeting, or personalization of content generally.  This potential use as a marketing analytics input makes it especially interesting to me.

Bottom line: if you are responsible for syndicating and helping users usefully navigate a highly dynamic information set collected through a multitude of sources -- say, a news organization, a university, a large consumer products or services firm -- and are evaluating monitoring technologies, Darwin is worth a look.

February 09, 2010

Beyond #A/B and #MVT #Testing: Optimizing Experiences

I caught up recently with Jeff Eckman and Geordie McClelland of BGC to learn more about their service for deploying, testing, and optimizing direct-response page flows.  The easy-to-use service (I saw a live demo), which is based on ion Interactive Pres & CTO Scott Brinker's LiveBall platform, combines a template-based authoring tool, CMS platform, and testing engine.  A marketing organization creates a subdomain on its web domain, and then works with BGC to develop different landing "experiences" (sequences of pages arrayed along a logical path) on it, instead of a single landing page or static, product-focused microsites.  Then the system tracks conversion rates along and through these experiences and, with full transparency to the marketers, shifts a greater share of requests to the winner.  Meanwhile, the marketers develop new page elements, pages, and paths based on what the more granular feedback about those suggests might make sense.

Contrary to popular wisdom about how fewer clicks drive increased conversion rates, BGC has found that an experience that asks for an "optimum" amount of information in an "optimum" series of screens delivers far higher results: on average 300% higher and as much as 3000+% higher in the 3+ years this practice has been in use.  I asked if their experience across multiple clients had yet suggested an overall rule of thumb, such as, "For products in this price range, three pages and three questions to each page work best", to help jump-start the creative process and save on initial creative costs, but Jeff and Geordie hadn't seen one emerge yet, and were somewhat skeptical that it could / would be worth it to pursue.  I struggled with that answer for a bit, but it's occurred to me that maybe I need to break out of my heuristic-seeking box and accept that in 2010 and beyond, the Process is the Answer (see my earlier post "Personalization Is A Process").  Jeff and Geordie are putting their money where their mouths are on this point:  The service can be offered on an easy-to-try-and-buy CPA basis (after a very modest setup fee).

We discussed technical directions that could broaden the span of the experiences they can engineer and test.  For example, you might imagine that an experience might start within a Facebook application for a couple of screens, and then make the jump to your site for the rest of the process and, hopefully, a conversion.  While they haven't done one of these yet, since a url is a url and its contents can be rendered ecumenically, this should be straightforward.

I haven't seen all the direct and indirect competition yet, but BGC's solution seems fairly unique and easy to use.  In theory you could cobble a GA-based solution together that combines their funnel analysis with their Web Optimizer A/B testing tool, but that would be clunky and likely brittle, perhaps a false economy.  And, not too long ago I wrote about Sitespect's url tunneling capability, but it strikes me that while somewhat similar, the two are currently focused on different parts of the "attract-engage-convert" process.

Bottom line: have a look, especially if you use form-based landing pages to qualify and convert leads for higher-price-point products and services.

January 29, 2010

Ecommerce On The Edge In 2010 #MITX

Yesterday morning I attended MITX's "What's Next For E-Commerce" Panel at Microsoft in Cambridge.  Flybridge Capital's Jeff Bussgang moderated a panel that included Shoebuy.com CEO Scott Savitz, CSN CEO Niraj Shah, Mall Networks CEO Tom Beecher,and Avenue 100 Media Solutions CEO Brian Eberman.

The session was well-attended and the panelists didn't disappoint. Across the board they provided a consistent cross-section of the sophistication and energy that characterizes life 2 SDs the right on the ecommerce success curve.

My notes and observations follow. But first, courtesy of Jeff, a quiz (answers at the end of the post):

1. Name the person, company, and city that originated the web-based shopping cart and secure payment process?

2. Name the person, company, and city that originated affiliate marketing on the web?

3. Name the largest email marketing firm in the world, and the city where it's headquartered?

Jeff opened by asking each of the panelists to talk about how they drive traffic, and how they try to distinguish themselves in doing so.

Brian described (my version) what his firm does as "performance marketing in the long tail", historically for education-sector customers (for- and non-profit) but now beyond that category. What that means is that they manage bidding and creative for 2 million less-popular keywords across all the major search engines for their customers. Their business is entirely automated and uses sophisticated models to predict when a customer should be willing to pay price X and use creative Y for keyword Z to reel in a likely-profitable order. The idea is that the boom in SEM demand has driven prices way up for popular keywords, but that there are still efficient marketing deals to be mined in the "long tail" of keyword popularity (e.g.,structured collaboration").

Niraj noted that there's an increasing returns dynamic in the SEM channel that raises entry barriers for upstarts and helps firms like CSN preserve and expand their position.  Namely, as firms like his get more sophisticated about conversion through scale and experience, they can afford to pay higher prices for a given keyword than smaller competitors can, and can reinvest in extending their SEM capabilities.  CSN now has a 10-person search marketing team within its total staff of 500. Since SEM is, to some degree, a jump-starter for firms that don't yet have a web presence sufficient to drive traffic organically, this edge is a powerful competitive weapon.  CSN is up to $200 million in annual revenues, and now manages the online furniture stores for folks like Walmart.

Scott sounded a different note, with similar results.  Shoebuy has focused more on cultivating its relationship with its existing customers and on Lifetime Value -- including referrals.  This focus has had a salutary effect on SEO, allowing them to rely less on SEM as it gets pricier.  Last year Shoebuy experienced double-digit top line growth and hit 8M uniques for December's shopping season, while realizing its lowest marketing expense as a percentage of sales since 2002.  They've continued to plow the savings into a better overall customer experience.  One way Shoebuy guides this reinvestment is through extensive use of Net Promoter-based surveys.  They keep the surveys brutally simple:  1)"Were you satisfied?" 2)"Whould you shop with us again?" 3)"Would you recommend us?".  Then they calculate the resulting NP scores to different things they try in their marketing mix, to give them a more nuanced insight than the binary outcome of an order can provide.

Tom described how while Mall Networks' traffic is "free" -- it all comes from their loyalty program partners' sites (e.g. Delta Skymiles website awards redemption page) -- they still have to jockey for Mall Networks' placement on those pages. (Though Tom was too polite to say so, the processes for deciding who goes where on popular pages is often a blood sport and ripe in most organizations for a more structured, rational approach.)

Former Molecular founder and CEO Ralph Folz asked about display -- is that making a comeback?  Brian indicated the lack of performance and the lack of placement control through ad networks made that a highly negative experience.  He did note that they are now experimenting with participation in real-time-bidding through ad exchanges for inventory that ad networks make available, sometimes for time windows only a hundred milliseconds long.  Jeff reinforced the emergence of "RTB" and mentioned MIT Prof. Ed Crawley's Cambridge-based DataXu (which Flybridge has invested in) as a leader in the field.

Affiliate marketing came up next.  Tom explained the basics (in response to a question): each of the 600 stores in Mall Networks stable pays Mall Networks, say for example, a 10% commission on orders that come through Mall Networks.  Mall Networks gives a chunk to the members of various loyalty programs that shop through it -- say 3-5% of the value of the order; some goes to the loyalty programs themselves, as partial inducements for sending traffic to Mall Networks, and the rest goes to Mall Networks to cover costs and yield profits.

All the other panelists include affiliates in their marketing mix, and all appeared satisfied to have them play a healthy role.  Niraj specifically mentioned the ShareASale and Google Affiliate networks.  Jeff asked about everyone's frenemy Amazon; the answers were uniformly respectful: "they're a tough competitor, but they build general confidence and familiarity with the ecommerce channel, and that's good for everyone."  Niraj noted the 800 lb. gorilla nature of their category dominance: "They're at $20m and NewEgg is the next biggest pure play at $2B.  They're a fact of life. We just have to be better at what we focus on."

Someone in the audience raised email.  All of the panelists use it, with lists ranging from millions to hundreds of millions of recipients in size.  They noted that this traditional pillar of online marketing has now gotten very sophisticated.  In their world, they look well beyond top line metrics like open- and clickthrough rates to root-cause analysis of segment-based performance.  Re-targeting came up, and Niraj noted that for them, email and re-targeting weren't substitutes (as some have seen them) but in fact played complementary roles in their mix.  (Jeff explained re-targeting for the audience: using an ad network to cookie visitors to your site, and then serving them "please come back!" ads on other sites in the network they go to after they've abandoned a shopping cart or otherwise left your site.  A twist: serving ads inviting them to *your* site after they've abandoned one of your competitors' sites.  Hey, all's fair in love, war, and ecommerce...).  A common theme:  unlike most of the rest of the world, email teams at these leading firms are tightly integrated with other channels' operators to better integrate the overall experience, even to the point of shared metrics.

What about social?  Scott: "Building community is key for us.  We run contests -- "What are you hoping will be under your tree this Christmas?" -- to stimulate input from our customers.  And, while we have social media coordinators, many people here participate in channels like Twitter in support of our efforts."  Niraj: "Our PR team came up with a 'Living Room Rescue' contest which we did in partnership with [a popular] HGTV host [whose name escaped me -- C.B.].  We got six thousand entries; we used a panel of professional decorators to narrow the list to a hundred, and then used social voting to choose a winner.  We publicized the contest, and it took on a life of its own, as local papers tried to drum up support for their local [slobs -- my word, not Niraj's].  While we couldn't / didn't measure conversion directly from this campaign, our indirect assessment was that it had a great ROI."  Jeff observed that social's potential seems greater when the object of the buzz is newsworthy.

It was a short leap from this to a question about attribution analysis, the simultaneous-dream-and-nightmare-du-jour for web analytics geeks out there.  Brian was surprisingly dismissive.  In his experience (if I understood correctly), he's seeing only up to 20%, and usually only 5-10% of order-placing customers touch two or more properties they source clicks from, across the broad landscape they cover, across a time frame ranging from a day to a month long.  "In the end, only a couple of dollars would shift from one channel to another if we did attribution analysis, so in general it's not worth it."  We chatted briefly after the panel about this; there are large ticket, high-margin exceptions to this rule (cars).  I need to learn about this one some more, it surprised me.

Mobile!  Is it finally here?  Scott reports that 6-9 months ago *customers* finally began asking for it (as opposed to having it pushed by vendors), so now they have a Shoebuy.com iPhone app.  Jeff noted that customers are rolling their own mobile strategies -- some folks are now going into (say) Best Buy, having a look at products in the flesh, then checking Amazon for the items and buying them through their iPhone if the price is right.  So, your store is now Amazon's showroom.  If you can't find something, or didn't even know you wanted it, but happen to stray near a store carrying it, location-based services will push offers at you -- and the offers may come from competitors.  (Gratuitous told-you-so here.)  Niraj:  "Say you're in Home Depot.  You want a mailbox.  Their selection is 'limited' [his description was more colorful]. We have 300 to choose from.  Wouldn't you want to know that?" Jeff:  Soon we'll also see the death of the checkout line: you'll take a picture of the barcode on the object of your desire, your smartphone will tell the store's POS system about it, and the POS system will send back a digital receipt you can show someone (or in the future, something) on your way out of the store. 

With all these channels in use, I asked how often they make decisions to reallocate investments across (as opposed to within) them -- say from search to email, as opposed to from keyword to keyword.  Brian: "Every day, each morning.  Some things -- like affiliate relationships -- may take 3-4 days to unwind.  But the optimization is basically non-stop."  Later we talked about the parallels with Wall Street trading floors.  For him, the analogy is apt.  Effectively he's a market-maker, only the securities are clicks, not stocks.  It's now reflected in their recruiting: many recent hires are former Wall Street quants.

A final note: The cultures in these shops are intensely customer-focused, flat, and data-driven.  Scott reads *every one* of the hundreds of thousands (yes you read right) of customer survey responses Shoebuy gets each year.  He also described the enthusiasm with which their customer service team embraced having all company communications to customers end with an invitation to email senior management with any concerns.  Niraj described CSN's floor plan:  500 people, no offices.  Everyone in the company takes a regular turn in customer service.  Everyone has access to the firm's data warehouse.  Brian told us about a digital display they have up in their offices showing hour-by-hour, source-by-source performance.  They also recently ran a "Query Day" in which everyone in the company -- including sales, finance, HR -- got training in how to use their databases to answer business questions.  Tom described that they “watch the cash register every minute, hour, day during the Christmas shopping season.”

This was a terrific session, and I've only captured half of it here.  Further comments / corrections / observations very welcome.

Quiz Answers:

1. MIT Prof. David K. Gifford, Open Market, Cambridge

2. Tom Gerace, BeFree, Cambridge

3. Constant Contact, Waltham

January 26, 2010

What's NYT.com Worth To You, Part II

OK, with the response curve for my survey tailing off, I'm calling it.  Here, dear readers, is what you said (click on the image to enlarge it):

Octavianworld nyt com paid content survey

(First, stats: with ~40 responses -- there are fewer points because of some duplicate answers -- you can be 95% sure that answers from the rest of the ~20M people that read the NYT online would be +/- 16% from what's here.)

90% of respondents would pay at least $1/month, and several would pay as much as $10/month. And, folks are ready to start paying after only ~2 articles a day.  Pretty interesting!  More latent value than I would have guessed.  At the same time, it's also interesting to note that no one went as high as the $14 / month Amazon wants to deliver the Times on the Kindle. (I wonder how many Kindle NYT subs are also paper subs getting the Kindle as a freebie tossed in?)

Only a very few online publishers aiming at "the general public" will be able to charge for content on the web as we have known it, or through other newer channels.  Aside from highly-focused publishers whose readers can charge subscriptions to expense accounts, the rest of the world will scrape by on pennies from AdSense et al

But, you say, what about the Apple Tablet (announcement tomorrow! details yesterday), and certain publishers' plans for it?  I see several issues:

  • First, there's the wrestling match to be had over who controls the customer relationship in Tabletmediaworld. 
  • Second, I expect the rich, chocolatey content (see also this description of what's going in R&D at the Times) planned for this platform and others like it to be more expensive to produce than what we see on the web today, both because a) a greater proportion of it will be interactive (must be, to be worth paying for), but also because b) producing for multiple proprietary platforms will also drive costs up (see for example today's good article in Ad Age by Josh Bernoff on the "Splinternet"). 
  • Third, driving content behind pay walls lowers traffic, and advertising dollars with it, raising the break-even point for subscription-based business models. 
  • Fourth, last time I checked, the economy isn't so great. 
The most creative argument I've seen "for" so far is that pushing today's print readers/ subscribers to tablets will save so much in printing costs that it's almost worth giving readers tablets (well, Kindles anyway) for free -- yet another edition of the razor-and-blade strategy, in "green" wrapping perhaps.

The future of paid content is in filtering information and increasing its utility.  Media firms that deliver superior filtering and utility at fair prices will survive and thrive.  Among its innovations in visual displays of information (which though creative, I'd guess have a limited monetization impact) is evidence that the Times agrees with this, at least in part (from the article on Times R&D linked to above):

When Bilton swipes his Times key card, the screen pulls up a personalized version of the paper, his interests highlighted. He clicks a button, opens the kiosk door, and inside I see an ordinary office printer, which releases a physical printout with just the articles he wants. As it prints, a second copy is sent to his phone.

The futuristic kiosk may be a plaything, but it captures the essence of R&D’s vision, in which the New York Times is less a newspaper and more an informative virus—hopping from host to host, personalizing itself to any environment.

Aside from my curiosity about the answers to the survey questions themselves, I had another reason for doing this survey.  All the articles I saw on the Times' announcement that it would start charging had the usual free-text commenting going.  Sprinkled through the comments were occasional suggestions from readers about what they might pay, but it was virtually impossible to take any sort of quantified pulse on this issue in this format.  Following "structured collaboration" principles, I took five minutes to throw up the survey to make it easy to contribute and consume answers.  Hopefully I've made it easier for readers to filter / process the Times' announcement, and made the analysis useful as well -- for example, feel free to stick the chart in your business plan for a subscription-based online content business ;-)  If anyone can point me to other, larger, more rigorous surveys on the topic, I'd be much obliged.

The broader utility of structuring the data capture this way is perhaps greatest to media firms themselves:  indirectly for ad and content targeting value, and perhaps because once you have lots of simple databases like this, it becomes possible to weave more complex queries across them, and out of these queries, some interesting, original editorial possibilities.

Briefly considered, then rejected for its avarice and stupidity: personalized pricing offers to subscribe to the NYT online based on how you respond to the survey :-)

Postscript: via my friend Thomas Macauley, NY (Long Island) Newsday is up to 35 paid online subs.

January 17, 2010

What's NYT.com Worth To You?

Via Chris Schroeder's  (@cmsschroed) RT of Henry Blodget (@hblodget), the news of the NYT's decision to start charging (again) for content.

Blodget's prior analysis suggested this might be worth ~$100 million per year  (my deduction based on his math) to NYT Co.  If a tenth of its 130M monthly unique visitors end up being "heavy users" that pay, 4 bucks a month gets them ~$600 million annually (13m * $4 * 12 months = $624 million).  (Seems high; better data anyone?)

What's it worth to you?  See what some folks had to say in the chart below.  Please take this survey to add your perspective, and let your friends know about it:

Note: I removed one response of "1 million articles for free, willing to pay $0 thereafter" because it messed up the display, but am mentioning it here for full disclosure. And to the respondent, thank you for participating!

Postscript: conclusions and analysis

January 02, 2010

Looking for a Web Marketing Analyst

Force Five Partners is looking for a web marketing analyst.  See http://www.forcefivepartners.com/opportunities.html for more details.  Referrals appreciated!

November 18, 2009

@Chartbeat: Biofeedback For Your Web Presence

Via an introduction by my friend Perry Hewitt, I had a chance yesterday to learn more about Chartbeart, the real-time web analytics product, from its GM Tony Haile.

Chartbeat provides a tag-based tracking mechanism, dashboard, and API for understanding your site's users in real time.  So, you say, GA and others are only slightly lagged in their reporting.  What makes Chartbeat differentially useful?

I recently wrote a post titled "Fly-By-Wire Marketing" that reacted to an article in Wired on Demand Media's business model, and suggested a roadmap for firms interested in using analytics to automate web publishing processes. 

After listening to Tony (partly with "Fly-By-Wire Marketing" notions in mind), it occurred to me that perhaps the most interesting possibilities lay in tying a tool like Chartbeat into a web site's CMS, or more ambitiously into a firm's marketing automation / CRM platform, to adjust on the fly what's published / sent to users.

Have a look at their live dashboard demo, which tracks user interactions with Fred Wilson's blog, avc.com.  Here's a question: if you were Fred -- and Fred's readers -- how would avc.com evolve during the day if you (as Fred or one of Fred's readers) could see this information live on the site, perhaps via a widget that allowed you to toggle through different views?  Here are some ideas:

1. If I saw a disproportionate share of visitors coming through from a particular location, I might push stories tagged with that location to a "featured stories" section / widget, on the theory that local friends tell local friends, who might then visit direct to the home page url.

2. If I saw that a particular story was proving unusually popular, I might (as above) feature "related content", both on a home page and on the story page itself.

3. If I saw that traffic was being driven disproportionately by a particular keyword, I might try to wire a threshold / trigger into my AdWords account (or SEM generally) to boost spending on that keyword, and I might ask relevant friends for some link-love (though this obviously is slowed by how frequently search engines re-index you). 

(Note: pushing this further, as we discussed with Tony, we'd subscribe to a service that would give us a sense for how much of the total traffic being driven to Chartbeat users by that keyword is coming our way, and use that as a metric for optimizing our traffic-driving efforts in real time.  Of course such a service would have to anonymize competitor information, be further aggregated to protect privacy, and be offered on an opt-in basis, but could be valuable even at low opt-in rates, since what we're after is relative improvement indications, and not absolute shares.)

4. If you saw lots of traffic from a particular place, or keyword, or on a particular product, you might connect this information to your email marketing system and have it influence what goes out that day.  Or, you might adjust prices, or promotions, dynamically based on some of this information.

Some of you will wonder how these ideas relate to personalization, which is already a big if imperfectly implemented piece of many web publishers' and e-retailers' capabilities.  I say personalization is great for recognizing and adjusting to each of you, but not to all of you.  For example, pushing this further, I wonder about the potential for "analytics as content".  NYT's "most-emailed" list is a good example of this, albeit in a graphically unexciting form.  What if you had a widget that plotted visitors on a map (which exists today of course) but also color-coded them according to their source, momentarily flashing the site or keyword that referred them?  At minimum it would be entertaining, but it would also hold a mirror up to the site's users showing them who they are (their locations and interests), in a way that would reinforce the sense of community that the site may be trying to foster otherwise. 

Reminds me a bit of Spinvision, and by proxy of this old post