A quick interview about the integration of Krux into Salesforce covering use cases, data science, privacy, and cross-cloud applications for data.
In 1960, the US Navy coined a design principle: Keep it simple, stupid.
When it comes to advertising and marketing technology, we haven’t enjoyed a lot of “simple” over the last dozen years or so. In an increasingly data-driven world where delivering a relevant customer experience makes all the difference, we have embraced complexity over simplicity, dealing in acronyms, algorithms and now machine learning and artificial intelligence (AI).
When the numbers are reconciled and the demand side pays the supply side, what we have been mostly doing is pushing a lot of data into digital advertising channels and munching around the edges of performance, trying to optimize sub-1% click-through rates.
That minimal uptick in performance has come at the price of some astounding complexity: ad exchanges, third-party data, second-price auctions and even the befuddling technology known as header bidding. Smart, technical people struggle with these concepts, but we have embraced them as the secret handshake in a club that pays it dues by promising to manage that complexity away.
Marketers, however, are not stupid. They have steadily been taking ownership of their first-party data and starting to build marketing tech stacks that attempt to add transparency and efficiency to their outbound marketing, while eliminating many of the opaque ad tech taxes levied by confusing and ever-growing layers of licensed technology. Data management platforms, at the heart of this effort to take back control, have seen increased penetration among large marketers – and this trend will not stop.
This is a great thing, but we should remember that we are in the third inning of a game that will certainly go into extra innings. I remember what it was like to save a document in WordPerfect, send an email using Lotus Notes and program my VCR. Before point-and-click interfaces, such tasks were needlessly complex. Ever try to program the hotel’s alarm clock just in case your iPhone battery runs out? In a world of delightful user experience and clean, simple graphical interfaces, such a task becomes complex to the point of failure.
Why Have We Designed Such Complexity Into Marketing Technology?
We are, in effect, giving users who want big buttons and levers the equivalent graphical user interface of an Airbus A380: tons of granular and specific controls that may take a minute to learn, but a lifetime to master.
How can we change this? The good news is that change has already arrived, in the form of machine learning and artificial intelligence. When you go on Amazon or Netflix, do you have to program any of your preferences before getting really amazing product and movie recommendations? Of course not. Such algorithmic work happens on the back end where historical purchases and search data are mapped against each other, yielding seemingly magical recommendations.
Yet, when airline marketers go into their ad tech platform, we somehow expect them to inform the system of myriad attributes which comprise someone with “vacation travel intent” and find those potential customers across multiple channels. Companies like Expedia tell us just what to pay for a hotel room with minimal input, but we expect marketers to have internal data science teams to build propensity models so that user scores can be matched to a real-time bidding strategy.
One of the biggest trends we will see over the next several years is what could be thought of as the democratization of data science. As data-driven marketing becomes the norm, the winners and losers will be sorted out by their ability to build robust first-party data assets and leverage data science to sift the proverbial wheat from the chaff.
This capability will go hand-in-hand with an ability to map all kinds of distinct signals – mobile phones, tablets, browsers, connected devices and beacons – to an actual person. This is important for marketers because browsers and devices never buy anything, but customers do. Leading-edge companies will depend on data science to learn more about increasingly hard-to-find customers, understand their habits, gain unique insights about what prompts them to buy and leverage those insights to find them in the very moment they are going to buy.
In today’s world, that starts with data management and ends with finding people on connected devices. The problem is that executing is quite difficult to automate and scale. Systems still require experts that understand data strategy, specific use cases and the value of an organization’s siloed data when stitched together. Plus, you need great internal resources and a smart agency capable of execution once that strategy is actually in place.
However, the basic data problems we face today are not actually that complicated. Thomas Bayes worked them out more than 300 years ago with a series of probabilistic equations we still depend on today. The real trick involves packaging that Bayesian magic in such a way that the everyday marketer can go into a system containing “Hawaiian vacation travel intenders” for a winter travel campaign and push a button that says, “Find me more of these – now!”
Today’s problem is that we depend on either a small amount of “power users” – or the companies themselves – to put all of this amazing technology to work, rather than simply serving up the answers and offering a big red button to push.
A Simpler Future For Marketers?
Instead of building high-propensity segments and waiting for users to target them, tomorrow’s platforms will offer preselected lists of segments to target. Instead of having an agency’s media guru perform a marketing-mix model to determine channel mix, mar tech stacks will simply automatically allocate expenditures across channels based on the people data available. Instead of setting complex bid parameters by segment, artificial intelligence layers will automatically control pricing based on bid density, frequency of exposure and propensity to buy – while automatically suppressing users who have converted from receiving that damn shoe ad again.
This is all happening today, and it is happening right on time. In a world with only tens of thousands of data scientists and enough jobs for millions of them, history will be written by the companies clever enough to hide the math on the server side and give users the elegance of a simple interface where higher-level business decisions will be made.
We are entering into a unique epoch in our industry, one in which the math still rules, but the ability of designers to make it accessible to the English majors who run media will rule supreme.
It’s a great time to be a data-driven marketer! Happy New Year.
As Salesforce integrates DMP Krux, Chris O’Hara considers how proximity-based personalization will complement access to first-party data. For one thing, imagine how coffeemakers could form the basis of the greatest OOH ad network.
For years, marketers have been talking about building a bridge between their existing customers, and the potential or yet-to-be-known customer.
Until recently, the two have rarely been connected. Agencies have separate marketing technology, data and analytics groups. Marketers themselves are often separated organizationally between “CRM” and “media” teams – sometimes even by a separate P&L.
Of course, there is a clearer dividing line between marketing tech and ad tech: personally identifiable information, or PII. Marketers today have two different types of data, from different places, with different rules dictating how it can be used.
In some ways, it has been natural for these two marketing disciplines to be separated, and some vendors have made a solid business from the work necessary to bridge PII data with web identifiers so people can be “onboarded” into cookies.
After all, marketers are interested in people, from the very top of the funnel when they visit a website as an anonymous visitor, all the way down the bottom of the funnel, after they are registered as a customer and we want to make them a brand advocate.
It would be great — magic even — if we could accurately understand our customers all the way through their various journeys (the fabled “360-degree view” of the customer) and give them the right message, at the right place and time. The combination of a strong CRM system and an enterprise data management platform (DMP) brings these two worlds together.
Much of this work is happening today, but it’s challenging with lots of ID matching, onboarding, and trying to connect systems that don’t ordinarily talk to one another. However, when CRM and DMP truly come together, it works.
What are some use cases?
You might be one of those people who don’t open or engage with every promotional email in your inbox, or uses a smart filter to capture all of the marketing messages you receive every month.
To an email marketer, these people represent a big chunk of their database. Email is without a doubt the one of the most effective digital marketing channels, even though as few as 5% of people who engage are active buyers. It’s also relatively fairly straightforward way to predict return on advertising spend, based on historical open and conversion rates.
The connection between CRM and DMP enables the marketer to reach the 95% of their database everywhere else on the web, by connecting that (anonymized) email ID to the larger digital ecosystem: places like Facebook, Google, Twitter, advertising exchanges, and even premium publishers.
Understanding where the non-engaged email users are spending their time on the web, what they like, their behavior, income and buying habits is all now possible. The marketer has the “known” view of this customer from their CRM, but can also utilise vast sets of data to enrich their profile, and better engage them across the web.
When we think of the customer journey, it gets complicated quickly. A typical ad campaign may feature thousands of websites, multiple creatives, different channels, a variety of different ad sizes and placements, delivery at different times of day and more.
When you map these variables against a few dozen audience segments, the combinatorial values get into numbers with a lot of zeros on the end. In other words, the typical campaign may have hundreds of millions of activities — and tens of millions of different ways a customer goes from an initial brand exposure all the way through to a purchase and the becoming a brand advocate.
Understanding which channels go together, and which sequences work best, can add up to tremendous lift for marketers.
For example, a media and entertainment company promoting a new show recently discovered that doing display advertising all week and then targeting the same people with a mobile “watch it tonight” message on the night of it aired produced a 20% lift in tune-in compared to display alone. Channel mix and sequencing work.
And that’s just the tip of the iceberg — we are only talking about web data.
What if you could look at a customer journey and find out that the call-to-action message resonated 20% higher one week after a purchase?
A pizza chain that tracks orders in its CRM system can start to understand the cadence of delivery (e.g. Thursday night is “pizza night” for the Johnson family) and map its display efforts to the right delivery frequency, ensuring the Johnsons receive targeted ads during the week, and a mobile coupon offer on Thursday afternoon, when it’s time to order.
How about a customer that has called and complained about a missed delivery, or a bad product experience? It’s probably a terrible idea to try and deliver a new product message when they have an outstanding customer ticket open. Those people can be suppressed from active campaigns, freeing up funds for attracting net new customers.
There are a lot of obvious use cases that come to mind when CRM data and web behavioral data is aligned at the people level. It’s simple stuff, but it works.
As marketers, we find ourselves seeking more and more precise targeting but, half the time, knowing when not to send a message is the more effective action.
As we start to see more seamless connections between CRM (existing customers) and DMPs (potential new customers), we imagine a world in which artificial intelligence can manage the cadence and sequence of messages based on all of the data — not just a subset of cookies, or email open rate.
As the organizational and technological barriers between CRM and DMP break down, we are seeing the next phase of what Gartner says is the “marketing hub” of interconnected systems or “stacks” where all of the different signals from current and potential customers come together to provide that 360-degree customer view.
It’s a great time to be a data-driven marketer!
Chris O’Hara is the head of global marketing for Krux, the Salesforce data management platform.
Salesforce unveiled its Einstein AI platform this year, baking predictive algorithms, machine and deep learning, as well as other data analysis features throughout its Software-as-a-Service (SaaS) cloud. Einstein is essentially an AI layer between the data infrastructure underneath and the Salesforce apps and services on top. The CRM giant is no stranger to big money acquisitions, most recently scooping up Demandware for $2.8 billion and making a play for LinkedIn before Microsoft acquired it. The Krux acquisition gives Salesforce a new, data-driven customer engagement vector.
“We’re working to apply AI to all our applications,” said Eric Stahl, Senior Vice President of Marketing Cloud. “In Marketing Cloud, Krux now gives us the ability to do things like predictive journeys to help the marketer figure out which products to recommend. We can do complex segmentation, inject audiences into various ad networks, and do large-scale advertising informed by Sales Cloud and Service Cloud data.”
As Salesforce and Krux representatives demonstrated Krux and how it fits into the Marketing Cloud, the data management platform acted more like a business intelligence (BI) or data visualization tool than a CRM or marketing platform. Chris O’Hara, head of Global Data Strategy at Krux, talked about the massive quantities of data the platform manages, including an on-demand analytics environment of 20 petabytes (PB)—the entire internet archive is only 15 PB.
“This is our idea of democratizing data for business users who don’t have a PhD in data science,” said O’Hara. You can use Krux machine-learned segments to find out something you don’t know about your audience, or do a pattern analysis [screenshot above] to understand the attributes of those users that correlate greatly. We’re hoping to use those kinds of signals to power Einstein and do things like user scoring and propensity modeling.
The Einstein Journey Insights feature is designed to analyze “hundreds of millions of data points” to identify an optimal customer conversion path. In addition to its Krux-powered Marketing Cloud features, Salesforce also announced a new conversational messaging service called LiveMessage this week for its Salesforce Service Cloud. LiveMessage integrates SMS text and Facebook Messenger with the Service Cloud console for interactions between customers and a company’s helpdesk bots.
The more intriguing implications here are what Salesforce might do with massively scaled data infrastructure like Krux beyond the initial integration. According to O’Hara, in addition to its analytics environment, Krux also processes more than more than 5 billion monthly CRM records and 4.5 million data capture events every minute, and maintains a native device graph of more than 3.5 billion active devices and browsers per month. Without getting into specifics, Salesforce’s Stahl said there will be far more cross-over between Krux data management and Einstein AI to come. In the data plus AI equation, the potential here is exponential scale.
Clayton Christensen, the father of “disruptive innovation,” would love the ad technology industry.
With more than 2,500 Lumascape companies across various verticals chasing an exit, venture funding drying up for companies that haven’t made an aggressive SAAS revenue case and the rapid convergence of marketing and ad technology, the next few years will see some dramatic shifts.
The coming tsunami of powerful megatrends is driving ad technology relentlessly forward at a time when data is king and the companies that best package and integrate it into multichannel inventory procurement will be the rulers.
In a world where scale matters most, the big are getter bigger and smaller players are getting forced out, which is not necessarily good for innovation.
Data: Powering The Next Decade Of Ad Tech
Data, especially as it relates to “people data,” is and will be the dominant theme for ad technology going forward.
Monolithic companies with access to a people-based identity graph are leaning in heavily to identity management, trying to own the phone book of the connected device era. Facebook’s connection to Atlas leverages powerful and deeply personal deterministic data, continually volunteered on a daily basis by its users, to drive targeting. Google is attaching its massive PII data set garnered through Gmail, search and other platforms to its execution platforms with its new DMP, DoubleClick Audience Manager.
Both platforms prefer to keep information on audience reach safely within their domains, leaving marketers wondering how smart it really to tie the keys of user identity in a “walled garden” with media execution.
Will large marketers embrace these platforms for their consumer identity management needs, or will they continue to leverage them for media and keep their data eggs in another basket?
While some run into the arms of powerful cloud solutions that combine data management with media execution, many are choosing to take a “church and state” approach to data and media, keeping them separate. Marketers have to decide whether the risk of tying first-party data together with someone’s media business is worth having an all-in-one approach.
Agencies Must Adapt Or Die As Consultancies Edge Into Programmatic
Media agencies have also been challenged to provide more transparency around the way they procure inventory, the various incentive schemes they have with publishers and their overall methodology for finding audiences. With cross-device proliferation, agencies must be able to identify users to achieve one-to-one marketing programs, and they need novel ways to reach those users at scale.
That means a commitment to automation, albeit one that may come at the expense of revenue models derived through percentage of spend and arbitrage. Agencies will need new ways to add value in a world where demand-side players are finding closer connections to the supply side.
As media margins collapse, agencies need to act as data-driven marketing consultants to lift margins and stay relevant. They face increasing competition from large consultancies whose bread and butter has been technology integration. It’s a tough spot but opportunities abound for smart agencies that can differentiate themselves.
Zombie Companies Die Off But Edge-Case Innovation Continues
We’ve been talking about “zombie ad tech” for years now, but we are finally starting to see the end of the road for many point solution companies that have yet to be integrated into larger mar tech “stacks.”
Data-management platforms with native tag-management capabilities are displacing standalone tag-management companies. Retargeting is a tactic, not a standalone business, which is now a status quo part of many execution platforms. Fraud detection systems are slowly being dragged into existing platforms as add-on functionality. Individual data providers are being sucked into distribution platforms and data exchanges that offer customer exposure at scale. The list goes on and on.
This is an incredibly positive thing for marketers and publishers, but it is also a challenge. Cutting-edge technologies that give a competitive advantage are rarely so advantageous after they’ve moved into a larger “cloud.” Smart tech buyers must strike a balance between finding the next shiny objects that confer differentiating value, while building a stable “stack” that can scale as they grow.
That said, the big marketing technology “clouds” offered by Adobe, Oracle and Salesforce continue to grow, as they gobble up interesting pieces of the digital marketing “stack.”
Will marketers go all-in on someone’s cloud, build their own “cloud” or leverage services offerings that bring a unified capability together through outsourcing?
Right now, the jury is out, mostly because licensing your own cloud takes more than just money, but also the right personnel and company resources to make it work. Yet, marketers are starting to understand that the capability to build automated efficiency is no longer just a function of marketing, but a way to leverage people data to drive value across the entire company.
Today’s media targeting will quickly give way to tomorrow’s data-driven enterprise strategy. It’s happening now, and quickly
New Procurement Models Explode Exchanges, Drive Direct Deals
I think the most exciting things happening in ad technology are happening in inventory procurement.
Programmatic direct technologies are evolving, adding real audience enablement. Version 1.0 of programmatic direct was the ability to access a futures marketplace of premium blocks of inventory. Most buyers, used to transacting on audience, not inventory, rejected the idea.
Version 2.0 brings an audience layer to premium, well-lit inventory, while changing the procurement methodology. I think most private marketplaces within ad exchanges are placeholders for a while, as big marketers and publishers start connecting real people data pipes together and start to buy directly. It’s happening now – quickly.
I also can see really innovative companies leaning into creating a whole new API-driven way of media planning and buying across channels that makes sense. In the near future, the future-driven approaches of companies like MassExchange will bring to cross-channel inventory procurement a methodology that is more regulated, transparent and reminiscent of financial markets. It’s a fun space to watch.
Who will begin adding algorithmic, data-science driven automation and proficiency to the planning process, not just execution and optimization in the programmatic space?
Many of those in the ad technology and media game are here for the challenge, the rapid pace of innovation and the opportunity to change the status quo. We are all getting way more than we imagined lately, in a fun, exciting and fast-moving environment that punishes failure harshly, but rewards true market innovation. Stay safe out there.
[This post was originally published in AdExchanger on 6.16.15]
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:
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?
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.
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]