Q&A: Salesforce’s Chris O’Hara Wants Marketers to Capitalize on the Data Revolution

datadrivenIf you want to learn about data, Chris O’Hara is the right person to ask. O’Hara, who leads global product marketing for Salesforce Marketing Cloud’s suite of data and audience products, is a big believer in the data revolution—but first, marketers need to take stock of what data they actually have.

“Some marketers think they have way more data than they actually have, and others think they don’t have a lot of data but actually do,” O’Hara said.

Before joining Salesforce, O’Hara was at Krux, the data management platform that Salesforce acquired in 2016, working on data marketing. In October, O’Hara, along with Krux alums Tom Chavez and Vivek Vaidya, released a book, “Data Driven,” which dives into how marketers should think about using data to overhaul customer engagement and experience.

Before the book’s release, Adweek talked with O’Hara about the book and about how marketers can leverage the data they have while keeping data privacy and consumer trust in mind. A portion of that conversation, which has been edited and condensed for clarity, is below.

Adweek: A lot of marketers have talked about the importance of getting better at explaining to consumers what exactly is being collected and how exactly data is being used. Do you think it’s the responsibility of tech and advertising companies to explain that to the public?

Chris O’Hara: Marketing is better when you have the permission of consumers. Consumers are entitled to know exactly how their data is being used, and consumers are absolutely entitled to have control over their own data. As you talk about the opportunities to get more personalized with customers, you’re allowed to deliver great personalization if the customer has opted in for you to do that on their behalf. If you do that without their consent, it feels creepy and wrong, right?  It’s common sense. We’re always going to lead with the idea that trust comes first and that marketing is better with consent. Period.

You write in your book that the biggest risks of harnessing data are centered around privacy, security and trust. As concerns about data privacy grow, and as data breaches continue to occur, how does the industry best rebuild trust with the public? Where does the industry start with reestablishing trust and maintaining trust with consumers?

It’s all based on permission and an opted-in consumer. I like getting advertising messages that are relevant. When I am shopping for a car and I give Cars.com permission to introduce me to new models and send me an email every week, I appreciate it because I’ve asked for it. When I engage with certain sites on the web, like The Wall Street Journal, where I pay for content, I trust them with a certain amount of my data so they can make my reading experience better. That’s the way it should have been, always. Unfortunately, there are some companies in the space that have taken advantage of little oversight to do otherwise. But what we’ve seen in the market is that companies that are not leading with trust are not being valued as highly or perceived as more valuable than companies that do put trust at the center of their relationship with customers.

What’s the biggest misconception marketers have with data?

Something we write about in the book is that some marketers think they have way more data than they actually have, and others think they don’t have a lot of data but actually do. One of Pandora’s svps, Dave Smith, came to us and said, ‘I have one of the biggest mobile data assets in the world. Everyone who uses Pandora is logged in, so we know so much about our customers: what kind of cellphone they have, what kind of music they like, perhaps the ages of the kids in their home, when they listen.’ That’s a lot of data. Pandora probably has one of the largest data assets in the entire world. But Pandora doesn’t know when people are going to buy a car or people’s incomes, necessarily. They don’t know when you’re planning on taking a family vacation. So they turned to second- and third-party data to enrich their understanding of consumers.

Programmatic Direct is in the Top of the Second Inning

ScoreboardLately, I have been working on a whitepaper about the “programmatic direct” phenomenon. Part of the research involved surveying a bunch of influential people in the space, and asking them where they thought this new buying methodology was in terms of adoption. Their answers kind of surprised me.

If “programmatic direct” was a baseball game, we are in the top of the second inning.

The game has basically just started, and a few balls have been put into play, but the action is just getting started—and the big sluggers have yet to step up to the plate. If you are a regular AdExchanger reader, you would be justified in thinking that programmatic direct was quickly gaining steam by progressive agencies and publishers. After all, there has been a good deal of hype surrounding the idea of enabling programmatic access to higher classes of inventory, and it seems like almost every ad technology player in the display space is getting into the game.

Sure, some real innovations are happening in programmatic RTB that are enabling private marketplace transactions. Initiation-only auctions and fixed rate deals inside of exchanges are only the tip of the iceberg, though. New web-based technology and advanced ad server APIs are starting to provide real process automation—the tools that will make it easier to buy and sell the 70% of inventory currently procured through the “transactional RFP” process.

However, there are a few major things that need to happen before “programmatic direct” can really take hold:

A Directory: It may sound strange, but one of the biggest failings of digital media has been the lack of a directory for buyers. In direct mail, you can look up how many people get the L.L. Bean mailing list, add all kinds of criteria (males of a certain age that have purchased with a credit card in the past three months), find out exactly what it costs, and who to buy it from. No such thing exists in digital media. Hence, the RFP process, where buyers have to go through hoops just to get a sense of pricing and availability. This simple act of discovery adds time and complexity to every transaction. Today’s programmatic direct systems are being built from the ground up—starting with good information, and also with dynamic pricing and availability information thanks to API connections to DFP and other publisher ad servers.

Standards for Electronic Ordering: Another obvious thing that needs to happen before real process automation can happen in digital is that a set of standards have to be agreed upon. The IAB has known this since 2008, but five years later the “eBusiness Task Force” (now called the “Digital Automation Task Force”) seems no closer to its original mandate. Its stated mission: Updating the XML schema and implementation testing for the electronic delivery of digital advertising business document.” Those documents include Requests for Proposals (RFPs), insertion orders (IOs), and invoices—documents that must be standardized in order for adoption of programmatic direct buying to occur at scale. However, there is urgency like never before to get such standards implemented, and a source close to the action says that “we will see more movement in the next nine months in standards and protocols than has happened in ten years.” Let’s hope so. The wide adoption of a common set of standards and protocols opens up the door to the electronic IO—the key to achieving scale in programmatic direct.

Culture Change: While a directory can be created and standards adopted with lots of hard work, those things are actually easier than the real key to programmatic direct adoption: culture change among agencies and publishers.  Agencies must leverage technology to empower the “23 year old media planner” and give them a reason beyond sneaker parties to go to work. Technology will unleash their creativity and get them focused on solving real problems for clients. Likewise, publishers need to escape the “$200,000 a year salesman,” with his accompanying high T&E and schmoozy selling style. Publishers need data-driven sellers that understand how to drive programmatic adoption, and can sell based on the new “media investment” paradigm happening at agencies—understanding tactically how to spread digital dollars across a broad portfolio of channels. Agencies now they cannot remain stuck with the current cheap labor model. Publishers understand that they cannot keep their higher classes of inventory outside of programmatic channels. Change is hard, but it’s already here.

About a year ago, I said that 2013 would be the year of programmatic direct. It turns out that 2013 has been the year of programmatic direct hype, and a ton of valuable behind-the-scenes work on the technologies that will drive it in the future. But unlike the perennial “year of mobile” programmatic direct will become a reality quickly if some of the above building blocks come together.

[This post originally appeared in AdExchanger].

Programmatic Direct isn’t Just about Efficiency

When clients call, speed matters.

When clients call, speed matters.

When you are selling anything, it’s really easy to get caught up in pitching the benefits of your product, and ad technology is no different. Some of today’s new programmatic direct marketing solutions promise to change the very nature of how media buyers and sellers spend their time. Demand side systems are focusing on replacing Excel and e-mail with web-based, centralized systems that take the manual grunt work out of buying. Supply-side systems are tying into publisher ad servers to help create more streamlined access to inventory, without the hassles of secure it via paper insertion orders. While it’s easy to focus on all of the amazing efficiency benefits offered by today’s web-based solutions, it’s also critical to remember to ask your client what’s important to them.

On a recent sales call to a large agency, my old-school sales training kicked in. After showing off all of the neat bells and whistles of my software, I asked the company’s Chief Digital Officer why my ad technology was interesting to his agency. What he said was simple, but illustrative: “Our clients don’t ever come to us and ask what kind of tools we are using to do our jobs. They really couldn’t care less. But they do come to us and ask for huge media recommendations, due within several hours. And they definitely want to know why we are recommending what we are recommending.”

This made a lot of sense. Nobody wants to see the sausage get made, but it had better taste good once it’s cooked. Over the course of our conversation, I took away a few key nuggets that would be valuable for any technology company looking to sell programmatic solutions to marketers and publishers alike.

Clients Care about “Why,” not “How”

This statement is true for both agencies and publishers. An agency’s big client doesn’t care what tools the agency uses to create and execute its media plans (as long as the cost is transparent and within reason), but it does want to understand the overall strategy, rationale behind its vendor choices, and (of course) obtain measureable results. On the publisher side, the clients acquiring the inventory don’t care what kind of tags or datasets produce a targetable audience—they just want the publisher’s “auto intenders” to see ads for their cars.

In both cases, the “how” doesn’t matter—nor should it. Programmatic done right hides the way the sausage is made, and offers simple controls over complex processes. The best companies in the space will be able to turn a sound engineer’s control board (thousands of knobs and switches) into Avid’s Pro Tools. This is particularly important when trying to scale an organization; it is the difference between trying to turn dozens of people into technicians and having a technical system that everyone can use with little training. Companies with the right, scalable technology can grow…and grow fast.

For my agency client, being able to tell his client how he selected the programs on his media recommendation was critical. Using software that could help his planning team make choices based on past performance, alignment with demographic data, or even the client’s first party data was the key. When you have 40 20-something media planners spending millions of dollars, data-driven guidelines are essential, along with the platform to generate them. Likewise, on the publishing side, publishers need to tell their agency clients why certain programs were recommended, and have a systematic way to put together inventory packages that will perform well enough to avoid the dreaded out-clause.

Speed Matters

Another thing the agency CDO told me was how important speed was. They say efficiency doesn’t sell, but when your client is looking for a thoughtful media recommendation in two hours, being able to deliver a plan you can justify means having the tools to move fast, and move smartly. “It’s hilarious to me that our clients ask us for a completely unique, groundbreaking idea—at 6:30 PM—and expect something the next day.” This rolls down the hill to publishers, who are ultimately asked to help contribute to such plans on even shorter notice. Although there’s no cure for overly demanding clients, there is starting to be new programmatic direct solutions that help take some of the viscosity out of the transactional RFP funnel, increasing the speed to which proposals can come to market.

No Data, No Strategic Advantage

“Big Data” is all the rage, but even relatively small data can be the key to success when it comes to digital media buying and selling. “We know that every plan is going to have Facebook, AOL, and Yahoo on it. Access to their inventory and securing it is not the problem,” the agency CDO told me. “The real problem is, how do I know how much to allocate to each? What should my media channel mix be? That’s what we struggle with. Oftentimes, it comes down to gut instinct.”

Right now, data that can help with making those allocations is hidden all over the place: Excel-based media plans and performance reports, ad serving data that’s hard to report on, audience verification data from measurement tools, and in the brains of media supervisors. This structured data, centralized in the right place, can mean the difference between creating accessible insights—or being just another 10 gigabytes sitting on a computer’s hard drive. Agencies should be able to query all of the data available to them programmatically, and offer media choices chosen from algorithms that get smarter every time a campaign is run. Likewise, publishers should be able to systematically recommend inventory packages based on past performance, demographic and contextual relevance—and even whether or not they were re-purchased over time.

Programmatic direct solutions are starting to bring the type of data-driven efficiency once only found in RTB to both advertisers and inventory owners, creating a more “bionic” dynamic, where humans leverage technology to be better, faster, and smarter.

[This article originally appeared in AdExchanger on 10.28.13]

Smarter Video?

smartTVWill the rise of Smart TV Change Where Marketers Place their Ad Dollars?

Recently, the perfect storm of the oncoming football season and a broken TV sent me to Best Buy for a flat screen upgrade. I came home with a Vizio 60-inch “Smart TV.” I was pleasantly surprised by the great audio quality, high-definition picture, and the price (cheap, at $900). However, what really shocked me was its installed “app store.”

As a deeply committed believer in on-demand video, I immediately started enjoying my Amazon Instant Video and Netflix subscriptions—now accessed on a huge, 60-inch HD screen. YouTube videos now were available right from my remote control—as was Pandora, and other streaming music applications. All of the sudden, Verizon FIOS had a lot less to do with what I was watching in my living room.

As someone who is fairly up to date on advertising technology trends, I already realized that cable providers and broadcasters were being disintermediated by new technology—but seeing it in 60 high definition inches really convinced me that we are living in a new world, and the implications for advertisers are huge.

First of all, for those who haven’t played around with one of these new sets, let me tell you what works and doesn’t work at this early stage of the game:

What Works:

  • YouTube: It was simply amazing to watch YouTube videos on the big screen. I queued up some “Key and Peele” from Comedy Central, and some clips of comedian Louis CK and, before I knew it, an hour had gone by (a commercial-free hour, by the way). Smart TV might just take YouTube and other video-specific sites to a whole new level. Now, anyone can “broadcast themselves” right into your home.
  • Amazon Instant Video: It was great to watch all of the Prime content on my big TV, and I began immediately catching up with “Under the Dome” using Amazon’s elegantly designed UI. Amazon is one to watch in this space. They know how to do VOD.
  • Netflix: Again, video on demand was born for the Smart TV application. (I thought I might re-subscribe to binge-view House of Cards!) Watch for more streaming providers to produce more and more original content that can drive subscription sales.
  • Pandora: This one surprised me. With the right audio system, your TV may be the only thing you need to provide great sounding music with endless variety in your home.

What Doesn’t Work:

  •  Twitter: The news feed was quite limited, and at times expanding a tweet to access a link or video content did not work. Plus, do you want your individual Twitter feed broadcast to everyone in your home?
  • Facebook: Same thing as Twitter. I wonder if high-engagement applications—and specifically ones that promise an “embarrassment factor” – will succeed on the TV screen.
  • Skype: This app is convenient for users who depend on it for their primary communication, but typing on the remote (even with a full keypad) can be challenging.
  • Yahoo Fantasy Football: Great for checking stats, but hard to manage your team via the cramped interface and small remote control buttons.

I asked Tom Hespos of Underscore Marketing, who has tackled this topic before, what he thought, and he captured what I was thinking in a few sentences: “It seems pretty evident what’s going to succeed on app-enabled TV sets. Anything that’s ‘lean back’ in nature will likely do well. Things that require engagement or are subject to an embarrassment factor if projected for the whole family to see will not.”

For advertisers, Smart TV will prove challenging. Not only are subscription services like Amazon Instant Video and Netflix ad-free, but the amount of time-shifted and VOD viewing makes available eyeballs a scarce commodity. Look for CPMs for real “appointment viewing” shows such as NFL football and popular hits like Breaking Bad to rise dramatically. In my experience, I did not see any pre- or post-roll ads on YouTube, but they are coming. Recently-public companies like YuMe and Tremor are depending on an aggressive roll-out of interactive video, and their business models are 100% advertising-supported.  CPMs there will be high, considering the relatively low inventory volumes available.

So, if “lean-back” video applications make it more expensive to reach scarcer eyeballs on connected TVs, than what about the interactive social apps? Is there room for more display banner ads on Smart TV? I think the answer is probably yes—but only for so-called “native” advertising, like Twitter’s sponsored posts. Users will be more likely to “lean back” and access their social newsfeeds on connected TV, but will be less likely to post new content from their remote control. That means the tablet will still be in hand during viewing times. It’s early days, but look for more apps that exploit the trend in “double vision” viewing (as reported by Nielsen):

88 percent of tablet owners and 86 percent of smartphone owners said they used their device while watching TV at least once during a 30-day period. For 45 percent of tablet-tapping Americans, using their device while watching TV was a daily event, with 26 percent noting simultaneous TV and tablet use several times a day. U.S. smartphone owners showed similar dual usage of TV with their phones, with 41 percent saying their use their phone at least once a day while tuned in.

Those are big numbers, and it’s hard to see how they will diminish, even as more options are added to television. So, the big question remains: Where should advertisers stick their ads? If you believe that consumers will continue to “lean back” and enjoy Smart TV content just as they watch TV (but with a lot less ads), then the obvious choice for aligning brands with TV content is social media on the tablet. Twitter wins big here. If you believe that networks like YuMe and Tremor can leverage Smart TVs to make access to great HD content in your living room free, then put your money down on those stocks, and hope they find the right advertising model that makes “paying for” content with ad viewing worth it.

Either way, the maturity of connected, smart, app-enabled televisions means less ad inventory for advertisers—and the need for better channels to access fewer, but more addressable, eyeballs.

[This post originally appeared in eMarketingAssociation.com on 9/11/13].

The Hourglass Funnel Changes Everything

Hourglass_Branding_FunnelLately, I’ve been thinking a lot about the hourglass funnel. Most funnels stop at the thin bottom, when a customer “drops” out, having made the journey through awareness, interest, desire and action. After the “action,” or purchase, the customer gets put into a CRM to be included in more traditional marketing outreach efforts, such as calls, e-mails, and catalogue mailings. In the past, marketers often thought about how to turn customers into advocates, but couldn’t figure out how to do it at scale. Companies that were really good at multi-level marketing, like Amway, didn’t have easy-to-replicate business models.

Today, the situation has changed. Social-media platforms give marketers tools to engage customers in their CRMs and bring them back through the bottom of the funnel, turning them into brand advocates — and maybe even salespeople. This is why Salesforce has been snatching up social-media companies like Radian6 and Buddy Media, while Oracle bought Vitrue and Involver. These platforms can help get people talking about your brand– and, in turn, you get to listen to what they have to say. These platforms also can help you understand what it takes to get your customers to move from liking your page to actively sharing your content and to actually recommending your products and even selling them as an affiliate.

The ad-tech revolution of the last several years has supercharged our ability to drive people through this hourglass-shaped funnel. But instead of enabling this movement, we have instead – for the most part — focused  on wringing efficiency out of reaching the customers we’re already very close to getting. For example, programmatic RTB makes it easy to bid on people in the “interest” layer, who behave like existing customers. Additionally, it’s a no-brainer to retarget those customers who have already expressed “desire” by visiting a product page or your website. And technology also makes it increasingly easy to invite customers already in your CRM to “like” your Instagram page, or to offer them incentives to “recommend” products through social sharing tools.

But what about the very top of the funnel (awareness) and the very bottom (advocacy)? Those are the two most critical parts of the marketing hourglass funnel, but the two least served by technology today. While we have tools to drive people through the marketing process more quickly or cheaply, technology doesn’t create brands or turn social-media fans into brand advocates.

However, the right strategy for both ends of this funnel can still boost awareness and advocacy by creating a branding vortex that is a virtuous circle. Let me explain:

Awareness

You can’t start a customer down the sales funnel without making he or she aware of your product or service. Despite all of the programmatic promise in display, technology mainly emphasizes reaching our known audience most efficiently. It simply hasn’t yet proven that it can create new customers at scale. That’s why TV still gets the lion’s share of brand dollars. Cost-effective reach, pairedwith a brand-safe, viewable environment, is what TV supplies.

In my opinion, the digital answer for raising awareness is starting to look less and less like programmatic RTB and more like video and “native” formats, which are more engaging and contextually relevant. Also, new programmatic direct technologies are starting to make the process of buying guaranteed, premium inventory more measurable, efficient and scalable.

Programmatic RTB advocates will argue that you can build plenty of awareness across exchanges, but it’s hard to create emotion with three IAB standard units, and there still isn’t enough truly premium inventory available in exchanges today to generate a contextual halo for your ads. New “native” display opportunities, video and tablet advertising are where branding has the biggest impact. Adding those opportunities to social tools, such as Twitter and Instagram, would help you leverage your existing brand advocates and amplify your message.

Advocacy

Great digital branding at the “awareness” level of the funnel not only helps drive potential new customers deeper into the sales funnel, but also can help engage existing customers. This amplification effect is extremely powerful. Old-school marketers such as David Sarnoff understood that folks make buying decisions through their friends and neighbors. He also understood that, when you’re trying to sell the next big thing (like radio), you have to leverage existing media (print). Applied to digital marketing, this simply means leveraging awareness media — TV, video and “native” advertising — to stimulate word-of-mouth advertising, which is still the most powerful type. By using Facebook and other social sharing tools, the effect of any campaign can grow exponentially in a very short period of time. This virtuous circle of awareness media influencing brand advocates, who then create more awareness among their own social circles, is something that many marketers miss when they lead their campaigns with data rather than with emotion.

Everything In Between

I’m not saying that marketers can simply feed the top of the funnel with great branding and ignore the rest. That’s not true at all; the middle of the funnel is important too. I think it’s relatively easy, nowadays, to build a stack that also helps support all the hard work that brands are doing to create awareness. Most large marketers reinforce brand efforts with “always on” programmatic RTB that targets based on behavior, and all brands employ as much retargeting that they can buy. Once customers are in the CRM, it’s not hard to maintain a rewards/loyalty program, and messaging to an existing social fan base also is relatively simple.

But marketers are making a mistake if they think that this kind of programmatic marketing can replace great branding. With so many different things competing for customers’ attention, capturing it for more than a second is extremely difficult, and the challenge is only going to get harder.

The Datalogix Effect

So what does all this mean for for ad technology? The best way to think about this is to look at the Datalogix-Facebook partnership. Datalogix’s trove of customer offline purchase data essentially enables brands to measure whether or not  all their social-ad spending resulted in more online sales. A few studies have pretty much proven that media selling soap suds on Facebook created more suds sales at the local Piggly Wiggly. In fact, ROI turns out to be easy to calculate, as well as positive.

This type of attribution seems simple, but I don’t think you can overstate its impact. It’s the way we finally move from clicks and views to profit-optimization metrics such as those offered by MakeBuzz. And this method of tying online activity with offline sales is already having a vast impact on the ecosystem. It shows, beyond doubt, that branding sells product.

Getting the attribution right, though, means that brands are going to have to care about creative and content more than ever. It means big wins for video, “native” ad approaches, and big tentpole marketing campaigns. If quality premium sites can be bought programmatically at scale, then it may also mean big wins for large, traditional publishers.

It also likely means that many retargeters, programmatic RTB technologies and exchanges could end up losing in the long run. Don’t get me wrong: These technologies are needed to drive the “always on” machine that powers the middle of the funnel. But just how many DSPs and exchanges does the industry need to manage its commoditized display channel?

As marketers realize that they are spending money to capture customers that were going to convert anyway, they’re likely to focus less on audience targeting and more on initiatives to create new customers — and turn existing customers into advocates.

[This post originally appeared in AdExchanger on 7/31/13]

Does Driving Efficiency Drive Profit (A Contrarian View of RTB)

unicorn

Online display would be like this, if branding metrics took profit into account.

I’ve always loved the notion of programmatic RTB. As a data hound and an early adopter of Appnexus , the notion that advertisers can achieve highly granular levels of targeting and utilize algorithms to impact performance is right in my wheelhouse. Today’s ad tech, replete with 300 companies that enable data-driven audience segmentation, targeting, and analytics is testament to the efficiency of buying ads one impression at a time.

But what if driving efficiency in display actually does more harm than good?

Today’s RTB practitioners have become extremely relentless in pursuit of the perfect audience. It starts with retargeting, which uses first party data to serve ads only to people who are already deeply within the customer funnel. No waste there. The next tactic is to target behavioral “intenders” who, according to their cookies, have done everything BUT purchase something. Guess what? If I have searched 4 times in the last three hours for a flight from JFK to SFO, eventually you will get last view attribution for my ticket purchase if you serve me enough ads. Next? Finding “lookalike” audiences that closely resemble past purchasers. Data companies, each of whom sell a variety of segments that can be mixed to create a 35 year-old suburban woman, do a great job of delivering audiences with a predilection to purchase.

But what if we are serving ads to people that are already going to buy? Is efficiency really driving new sales, or are we just helping marketers save money on marketing?

It seems like online display wants to be more and more like television. Television is simple to buy, it works, and it drives tons of top funnel awareness that leads to bottom funnel results. We know branding works, and even those who didn’t necessarily believe in online branding need look no further than Facebook for proof. With their Datalogix offline data partnership, Facebook conclusively proved that people exposed to lots of Facebook ads tended to grab more items off of store shelves. It just makes sense. So why are we frequency capping audiences at 3—or even 10? I can’t remember the last time I watched network television and didn’t see the same car ad about 20 times.

The other thing that RTB misses out on is profit. RTB drives advertising towards lowering the overall cost of media needed to drive a sale. Even if today’s attribution models were capable of taking into account all of the top-funnel activity that eventually creates an online shopping cart purchase (a ludicrous notion), we are still just measuring those things that are measureable. TV ads, billboard ads, and word of mouth never get online credit—yet I believe they drive most of the online sales. Sorry, but I believe the RTB industry creates attribution models that favor RTB buying. Shocking, I know.

So, what is true performance and what really drives it? For most businesses, performance is more profit. In other words, the notion that a sales territory that has 100 sales a day can generate 120 sales a day. That’s called profit optimization. If I can use advertising to create those additional 20 sales, and still make a profit after expenses, than that’s a winner. RTB makes it cheaper to get the 100 sales you already have, but doesn’t necessarily get the next twenty. Getting the next batch of customers requires spending more on media, and driving more top-funnel activity.

The other thing RTB tends to fumble is how real life sales actually happen. Sure, audience buying knows what type of audience tends to buy, and where to find them online, but misses with frequency capping and a lack of contextual relevance. Let me explain. In real life, people live in neighborhoods. The houses in those neighborhoods are roughly the same price, the kids go to the same school district, the people have similar jobs, and their kids do similar activities and play the same sports. The Smiths drive similar cars to the Joneses, they eat at the same restaurants, and shop at the same stores. If the Smiths get a new BMW, then it’s likely the Joneses will keep up with a new Audi or Lexus in the near future. When neighbors get together, they ask each other what they did on February Break, and they get their vacation ideas from each other. That’s how life works.

What media most closely supports this real-life model, where we are influenced most by our neighbors?  Is it serving the Jones family a few carefully selected banners on cheap exchange inventory, which is highly targeted and cost effective? Or is it jamming the Smiths and Joneses with top-funnel brand impressions across the web? The latter not only gets Smith, the BMW owner, to keep his car top-of-mind and be more likely to recommend it—but also predisposes Jones to regard his neighbor’s vehicle in a more desirable light. That takes a lot of impressions of various types of media. You can’t do that and remain efficient. The thing is—you can do that and create incremental profit.

Isn’t that what marketers really want?

[This post originally appeared in AdExchanger on 5/20/13]

Underneath the Funnel

How Social Data Flips and Extends the Purchase Funnel

The traditional purchase funnel hasn’t changed much since its invention in 1898. Although there are many different versions of it, the basic “AIDA” model (awareness>interest>desire>action) remains the same:

Top_Funnel

  • Awareness: The traditional digital customer funnel starts at creating product awareness through impression-based display advertising and sponsorships.
  • Interest: The consumer continues down the purchase path when consumers demonstrate intent through behavioral and contextual signals. Those consumers can be targeted using a large variety of pre-packaged 3rd party segments.
  • Desire: Digital marketers capture a user’s desire, when they demonstrate affinity by clicking on an ad or visiting a product’s website. These consumers can be reached digitally through retargeting.
  • Action: Finally, the consumer purchases the product, at which point he “drops out of the funnel.”

Until recently, once the consumer entered the company’s CRM, he was marketed to in a more traditional way, via e-mail, postal mail, and telemarketing. In the case of digital media tactics, the consumer could reasonably be expected to be bombarded with retargeting ads for the remainder of his life (or, until he cleared his cookies), but that was the extent of things. Fast forward a few years, and all of the sudden Salesforce and Oracle are snatching up social media and measurement companies like they were going out of style. As I was writing my recent report on data management, I wondered:

Did they see this?

Bottom_Funnel

The perfect storm of advanced, extensible CRM platform technology, near ubiquitous availability and scale of social signals, and ability to activate first party data has extended the purchase funnel. Once the consumer “drops through” the real action starts.

  • Joins: Once in the customer database (CRM), the post-purchase journey starts with a commitment beyond the sale, when a consumer joins an e-mail list or signs up for special offers on the company’s site.
  • Likes: The next step is an expression of social interest, when the consumer agrees to make public his “like” for a company or brand by “friending” a company’s Facebook page, following a company’s Twitter account.
  • Recommends: Beyond the like or follow is true social activation, wherein the consumer actively (not passively) recommends the product or service, through commenting, sharing, or other active social behaviors, thus showing his brand affinity.
  • Sells: The final step is having the consumer sell on your behalf (directly via affiliate programs or, in the softer sense, as a “brand ambassador”).

To navigate the consumer from brand awareness, all the way through to actually selling on behalf of a brand takes an understanding of data and its application to each step in the journey. The most successful companies leveraging this new inverted funnel paradigm are aligning their first party CRM data with social affinity data to get a 360-degree view of their typical consumer—and modeling against that view to produce repeatable marketing outcomes.

What does that mean? It is not enough to understand your brand’s core demographic (e.g., male, aged 25-36, single family home, income >$125,000). That data is important, and you can certainly make somewhat efficient digital media decisions with it. Once that person expresses “desire” by visiting your website, you can certainly retarget him. And, once he finally purchases, you can pretend you “own” him, and deploy the various traditional CRM marketing tactics to create return purchases. All well and good.

The challenge is getting that person to like you back, and mutually engage with your brand. Once he is in your CRM, are you prepared to deliver new content to him via social media channels? Can you find the linkages between him and his internet friends, and get downstream of his activity via social affinity signals? Ultimately, can you create enough incentive, through affiliate programs, social gaming, couponing, or other active programs, to enable him to actually sell on your behalf? That is today’s digital marketing challenge—and it resides inside an integrated social CRM.

That’s why Salesforce bought Radian6 and Buddy Media, and why Oracle bought Vitrue and Involver. It will take some time for these new social data tools to get properly embedded into the traditional CRM, and even longer for marketers to get adept at leveraging them at scale—but we are now living in an inverted funnel world. Be prepared to turn your thinking about digital marketing upside down.

[This post originally appeared in ClickZ on 12/21/12]

LookSmart Adds Display

This is the first in a series of exciting additions to the LOOK portfolio. Stay tuned!

LookSmart Announces Display Capabilities 

Search Marketing Network Adds Display to Product Suite

SAN FRANCISCO, July 17, 2012 (GLOBE NEWSWIRE) — Today LookSmart (Nasdaq:LOOK) announced the addition of display advertising capabilities to its roster of performance-based search marketing offerings. By leveraging performance data from its  search network, LookSmart’s display offering will enable advertisers to extend the reach of their performance campaigns, and achieve higher ROI than typical display campaigns that do not benefit from deep conversion data. Advertisers can buy display advertising on a CPM or CPC basis, and leverage the full power of LookSmart’s managed services team to manage real-time bidding, and deep campaign optimization. Existing search network advertisers will benefit from having historical campaign performance data, which will enable LookSmart campaign managers to quickly optimize display campaigns towards performance goals. Advertisers can also buy display only, and benefit from LookSmart’s historical platform data to get
rapid results.

“We are truly excited about adding display to our advertising solutions,” stated LookSmart CEO Jean-Yves Dexmier. “Most of our advertisers rely on display advertising to influence search behavior, generate more queries, and get ROI lift. Now, our delivery team can run display and search campaigns simultaneously, which has the added benefit of starting with rich conversion data to create a higher probability that KPI goals are achieved more quickly.”

LookSmart’s plans on introducing its display capabilities to its existing advertising base, who can immediately take advantage of  historical data to target audiences with a high probability of conversion. With access to the majority of exchange inventory, extensive first party data, and a broad range of third party audience segments, LookSmart’s campaign optimization team will model its proprietary search performance data to achieve performance quickly.

“Adding display capabilities is the first of many exciting initiatives we have planned for the company,” added Dexmier. “With our robust performance platform, access to billions of daily search queries, and exceptional account and campaign management teams, we are well positioned to deliver performance at scale for direct response advertisers.”

Why We’re all Thinking Big Data (Jump Magazine Q+A)

Interview by Heather Taylor

One trend that is dominating conversation across marketing and wider business practices, is big data. How we measure it, how we store it and how we use it to inform the work that we do. We spoke to Chris O’Hara, domain expert on platform technology to find out about big data, why it’s important and how it is changing the marketing world.

Q Why is big data such a big deal and how did it get that way?

A: I think the term “Big Data” is getting thrown around a lot lately. There’s “data” that’s maybe too big for some companies to handle, and then there is truly “BIG DATA,” like you would find in the human genome, or Google search. The simple fact is that data has gotten a lot cheaper to store, and infinitely easier to access.

Big data is a big deal because people are leveraging technology to get insights from data they have never been able to get without spending more than those insights are worth. In short, understanding data makes money for those smart enough to leverage it, whether you are a digital agency, CPG marketer, or hedge fund. As the recent McKinsey report points out, “The volume of data that businesses collect is exploding: in 15 of the US economy’s 17 sectors, for example, companies with upward of 1,000 employees store, on average, more information than the Library of Congress does. New academic research suggests that companies using this kind of “big data” and business analytics to guide their decisions are more productive and have higher returns on equity than competitors that do not.”

Q: Why and how is big data moving us toward a more integrated marketing approach?

A: The largest change is not that data is being used to drive advertising creative and placement; it is that the data is available immediately, and that creates the opportunity for optimization. I think we are still in the early days, though. Most marketers and publishers are content to use off-the shelf 3rd-party segments to define and target audiences, rather than plumbing the (infinitely more valuable) depths of their own, first party datasets. Take large CPG companies who maintain databases all over the world. In one large company, you might have as many as 200 large databases, across dozens of operating companies all over the world. It is likely that those datasets have never been directly connected, and certainly it is highly unlikely that this data has never been stitched together and plumbed for insights.

The data equation in marketing is quite simple: the more an advertiser knows about you, the better you can be targeted. The real question is whether or not the effort and expense of such targeting is worth the incremental yield that targeting produces. As data gets cheaper and the cost of accessing diminishes, it is obvious that data starts to create real value for marketers.

Q: How is the era of big data changing the practice of digital marketing?

A: One of he biggest ways that data can help is in terms of avoiding waste. Before large amounts of data could be processed easily, there was no easy way to find out, as an example, what an advertisers’ unduplicated reach was across channels that include mobile, video, game consoles, and the Web.

The so-called “sciencification of marketing” is real. If you look at Terence Kawaja’s famous logo vomit slide of the digital display advertising landscape, it is clear that it is 100% driven by data. The underlying data is mostly audience-based, but there is also ad performance data, search data, engagement data, longitudinal data, and attitudinal data driving digital marketing these days.

On the direct marketing side, the transition from using mailing address data and surveys to target households to using IXI financial data to target online audience members via a cookie is not so different. Direct marketers can judge performance in real time with conversion data, and now brand marketers can leverage real-time engagement metrics to measure success.

Q: What are some examples of big data in integrated marketing?

A: The applications to use data in marketing are virtually unlimited. We are moving into a world where everything is interconnected, and we are surrounded by devices that transmit and store data constantly. These days, your supermarket partners with a brand to start a campaign on television, and that drives you to their website, to download a mobile coupon code that goes to your phone, and is used at the checkout line. Your purchase data is then stored, churned, and used to inform the next campaign. A better example of a big data approach to marketing (well, integrated digital marketing) is Google. Ingesting your search habits, video preferences, e-mail content, social network, mobile activity, and internet browsing habits takes a lot of expensive data storage, but it seems to be paying off for Google!

Q Which companies are jumping into the big data business and how will this help (and hinder) us?

A:  In the digital marketing space, you are going to see almost every progressive network, exchange, and data provider stake their claim to helping advertisers and publishers leverage their data. Some of them will be bigger than others. When it comes to managing truly huge amounts of audience data, there are very few companies that have managed to do it outside of the “big five” (AOL, Yahoo, Facebook, Google, and Microsoft). If a company truly has big data (petabytes, terabytes, or exabytes) then you need database software that can scale infinitely, and be able to query massive tables of data and return a result quickly. In marketing, that is starting to mean “real time,” which is not only a software challenge, but a hardware and logistical challenge as well. Marketers should look for a DMP that has actual experience working with massive data sets specifically for marketing applications.

Q What implications will digital marketers face with this big data trend?

A: Just because the data is there, doesn’t make it meaningful. These days it is possible to get a near real-time view of your audience at the creative level for digital display campaigns, but how many marketers can take advantage of the overwhelming amount of data that they receive every day beyond enabling a DSP to “auto-optimize a campaign, based on a single metric, such as conversion rate? Marketing insights that are driven by churning huge amounts of data are only as useful as the marketer’s ability to react to execute against them. That is why you are going to see the technology platforms that specialize in advertising execution team up with data platforms to try and get advertisers a true 360-degree view of the consumer that can be acted upon.

Q How can marketers leverage big data without being overwhelmed by it?

A: Try and learn what data is valuable and what is not. Even though I bought a new car 18 months ago, I am still bombarded with Volkswagen ads every time I check my email. Whoever is buying my “auto intender” cookie isn’t really getting their money’s worth, are they? My advice would be to perform a “data appraisal” that focuses on your own first-party data and see what you have. Even if your daily data is measured in gigabytes rather than petabytes, there is always something to leverage.

This appears in the current edition of eConsultancy’s Jump Magazine, which you can download here.

Choosing a Data Management Platform

“Big  Data”  is  all  the  rage  right  now,  and  for a good reason. Storing tons and tons of data has gotten very inexpensive, while the accessibility of that data has increased substantially in parallel. For the modern marketer, that means having access to literally dozens of disparate data sources, each of which cranks out large volumes of data every day. Collecting, understanding, and taking action against those data sets is going to make or break companies from now on. Luckily, an almost endless variety of companies have sprung up to assist agencies and advertisers with the challenge. When it comes to the largest volumes of data, however, there are some highly specific attributes you should consider when selecting a data management platform (DMP).

Collection and Storage: Scale, Cost, and Ownership
First of all, before you can do anything with large amounts of data, you need a place to keep it. That  place  is  increasingly  becoming  “the  cloud”  (i.e.,  someone  else’s  servers),  but  it  can  also  be   your own servers. If you think you have a large amount of data now, you will be surprised at how much it will grow. As devices like the iPad proliferate, changing the way we find content, even more data will be generated. Companies that have data solutions with the proven ability to scale at low costs will be best able to extract real value out of this data. Make sure to understand how your DMP scales and what kinds of hardware they use for storage and retrieval.

Speaking of hardware, be on the lookout for companies that formerly sold hardware (servers) getting into the  data  business  so  they  can  sell  you  more  machines.  When  the  data  is  the  “razor,”   the  servers  necessarily  become  the  “blades.”  You  want  a  data  solution  whose  architecture  enables the easy ingestion of large, new data sets, and one that takes advantage of dynamic cloud provisioning to keep ongoing costs low. Not necessarily a hardware partner.

Additionally, your platform should be able to manage extremely high volumes of data quickly, have an architecture that enables other systems to plug in seamlessly, and whose core functionality enables multi-dimensional analysis of the stored data—at a highly granular level. Your data are going to grow exponentially, so the first rule of data management is making sure that, as your data grows, your ability to query them scales as well. Look for a partner that can deliver on those core attributes, and be wary of partners that have expertise in storing limited data sets.
There are a lot of former ad networks out there with a great deal of experience managing common third party data sets from vendors like Nielsen, IXI, and Datalogix. When it comes to basic audience segmentation, there is a need to manage access to those streams. But, if you are planning on capturing and analyzing data that includes CRM and transactional data, social signals, and other large data sets, you should look for a DMP that has experience working with first party data as well as third party datasets.

The concept of ownership is also becoming increasingly important in the world of audience data. While the source of data will continue to be distributed, make sure that whether you choose a hosted or a self-hosted model, your data ultimately belongs to you. This allows you to control the policies around historical storage and enables you to use the data across multiple channels.

Consolidation and Insights: Welcome to the (Second and Third) Party
Third party data (in this context, available audience segments for online targeting and measurement) is the stuff that the famous Kawaja logo vomit map was born from. Look at the map, and you are looking at over 250 companies dedicated to using third party data to define and target audiences. A growing number of platforms help marketers analyze, purchase, and deploy that data for targeting (BlueKai, Exelate, Legolas being great examples). Other networks (Lotame, Collective, Turn) have leveraged their proprietary data along with their clients to offer audience management tools that combine their data and third party data to optimize campaigns. Still others (PulsePoint’s  Aperture  tool  being  a  great  example)  leverage  all  kinds  of  third party data to measure online audiences, so they can be modeled and targeted against.

The key is not having the most third party data, however. Your DMP should be about marrying highly validated first party data, and matching it against third party data for the purposes of identifying, anonymizing, and matching third party users. DMPs must be able to consolidate and create as whole of a view of your audience as possible. Your DMP solution must be able to enrich the audience information using second and third party data. Second party data is the data associated with audience outside your network (for example, an ad viewed on a publisher site or search engine). While you must choose the right set of third party providers that provide the best data set about your audience, your DMP must be able to increase reach by ensuring that you can collect information about as many relevant users as possible and through lookalike modelling.

First Party Data

  • CRM data, such as user registrations
  • Site-site data, including visitor history
  • Self-declared user data (income, interest in a product)

Second Party Data

  • Ad serving data (clicks, views)
  • Social signals from a hosted solution
  • Keyword search data through an analytics platform or campaign

Third Party Data

  • Audience segments acquired through a data provider

For example, if you are selling cars and you discover that your on-site users who register for a test drive are most closely  matched  with  PRIZM’s  “Country  Squires”  audience,  it  is  not  enough  to  buy   that Nielsen segment. A good DMP enables you to create your own lookalike segment by leveraging that insight—and the tons of data you already have. In other words, the right DMP partner can help you leverage third party data to activate your own (first party) data.

Make sure your provider leads with management of first party data, has experience mining both types of data to produce the types of insights you need for your campaigns, and can get that data quickly.  Data  management  platforms  aren’t  just  about  managing  gigantic  spreadsheets.  They  are   about finding out who your customers are, and building an audience DNA that you can replicate.

Making it Work
At the end of the day, it’s  not  just  about  getting  all  kind  of  nifty  insights  from  the  data.  It’s   valuable to know that your visitors that were exposed to search and display ads converted at a 16% higher rate, or that your customers have an average of two females in the household.  But  it’s   making those insights meaningful that really matters.
So, what to look for in a data management platform in terms of actionability? For the large agency or advertiser, the basic functionality has to be creating an audience segment. In other words, when the blend of data in the platform reveals that showing five display ads and two SEM ads to a household with two women in it creates sales, the platform should be able to seamlessly produce that segment and prepare it for ingestion into a DSP or advertising platform. That means having an extensible architecture that enables the platform to integrate easily with other systems.

Moreover, your DSP should enable you to do a wide range of experimentation with your insights. Marketers often wonder what levers they should pull to create specific results (i.e., if I change my display creative, and increase the frequency cap to X for a given audience segment, how much will conversions increase)? Great DMPs can help built those attribution scenarios, and help marketers visualize results. Deploying specific optimizations in a test environment first means less waste, and more performance. Optimizing in the cloud first is going to become the new standard in marketing.

Final Thoughts
There are a lot of great data management companies out there, some better suited than others when it comes to specific needs. If you are in the market for one, and you have a lot of first party data to manage, following these three rules will lead to success:

  • Go beyond third party data by choosing a platform that enables you to develop deep audience profiles that leverage first and third party data insights. With ubiquitous access to third party data, using your proprietary data stream for differentiation is key.
  • Choose a platform  that  makes  acting  on  the  data  easy  and  effective.  “Shiny,  sexy”  reports  are   great, but the right DMP should help you take the beautifully presented insights in your UI, and making them work for you.
  • Make sure your platform has an applications layer. DMPs must not only provide the ability to profile your segments, but also assist you with experimentation and attribution–and provide you with ability to easily perform complicated analyses (Churn, and Closed Loop being two great  examples).  If  your  platform  can’t  make  the  data  dance,  find  another  partner.

Available DMPs, by Type
There are a wide variety of DMPs out there to choose from, depending on your need. Since the space is relatively new, it helps to think about them in terms of their legacy business model:

  • Third Party Data Exchanges / Platforms: Among the most popular DMPs are data aggregators like BlueKai and Exelate, who have made third  party  data  accessible  from  a  single  user  interface.  BlueKai’s  exchange approach enables data buyers  to  bid  for  cookies  (or  “stamps”)  in  a  real-time environment, and offers a wide variety of providers to choose from. Exelate also enables access to multiple third party sources, albeit not in a bidded model. Lotame offers  a  platform  called  “Crowd  Control”  which  was  evolved  from  social   data, but now enables management of a broader range of third party data sets.
  • Legacy Networks: Certain networks with experience in audience segmentation have evolved to provide data management capabilities, including Collective, Audience Science, and Turn. Collective is actively acquiring assets (such as creative optimization provider, Tumri14) to  broaden  its  “technology   stack”  in  order  to  offer  a  complete  digital  media  solution  for  demand  side customers. Turn is, in fact, a fully featured demand-side platform with advanced data management capabilities, albeit lacking  the  backend  chops  to  aggregate  and  handle  “Big  Data”  solutions  (although  that  may   rapidly change, considering their deep engagement with Experian). Audience Science boasts the most advanced native categorical audience segmentation capabilities, having created dozens of specific, readily accessible audience segments, and continues to migrate its core capabilities from media sales to data management.
  • Pure Play DMPs: Demdex (Adobe), Red Aril, Krux, and nPario are all pure-play data management platforms, created from the ground up to ingest, aggregate, and analyze large data sets. Unlike legacy networks, or DMPs that specialize in aggregating third party data sets, these DMPs provide three core offerings: a core platform for storage and retrieval of data; analytics technology for getting insights from the data with a reporting interface; and applications, that enable marketers to take action against that data, such as audience segment creation, or lookalike modeling functionality. Marketers with extremely large sets of structured and unstructured data that go beyond ad serving and audience data (and may include CRM and transactional data, as an example), will want to work with a pure-play DMP

This post is an excerpt of Best Practices in Digital Display Advertising: How to make a complex ecosystem work efficiently for your organization All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording or any information storage and retrieval system, without prior permission in writing from the publisher.

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