A quick interview about the integration of Krux into Salesforce covering use cases, data science, privacy, and cross-cloud applications for data.
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.
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.
A survey of both senior marketing and IT professionals has revealed that there are significant differences between these two core business functions in their perception of organizational priorities and the quality of digital infrastructure. Governance frameworks to ensure better alignment between the CMO and CIO are often lacking.
The Backbone of Digital report, freely available from ClickZ (registration required), has also found that, compared to their colleagues in marketing, IT professionals have a much rosier view of the customer experience their companies are delivering across digital channels.
Below I have outlined more detail around three key findings from the research which is sponsored by communications infrastructure services company Zayo.
IT pros have exaggerated view of the quality of their companies’ current infrastructure
According to the research, 88% of IT respondents describe their company’s infrastructure as ‘cutting-edge’ or ‘good’, compared to only 61% of marketing-focused respondents, a massive difference of 27 percentage points.
The research also looks at the ability of tech infrastructure to deliver across a range of marketing communications channels, with IT respondents and marketers both asked to rate performance.
Both marketers and IT professionals felt that the best engagement and experience is delivered across desktop, cited as ‘excellent’ or ‘good’ by 71% and 93% of these groups respectively, but trailed by other channels including mobile website, mobile app, desktop display, mobile display, social and push messaging.
Across the board it is evident that those working in IT have a much more optimistic view of how well they are delivering across the full gamut of digital channels compared to their IT counterparts.
It seems likely that those working in more customer-facing departments, i.e. marketers (generally), are much more likely to be aware of deficiencies impacting customer experience which can adversely affect business performance and brand reputation (and often their own bonuses).
A lack of co-operation is undermining excellence in digital delivery
Just 19% of marketers strongly agree with the statement “marketing and IT work closely together to ensure the best possible delivery of product/service”, and only 11% strongly agreed that they “have a clear governance framework to ensure that CIOs/CTOs and CMOs work together effectively”, suggesting a lack of alignment around marketing and IT business objectives.
This compares to 45% of IT professionals who strongly agreed that “marketing and IT work closely together to ensure the best possible network performance”, and a similar percentage (46%) who strongly agreed that they “have a clear governance framework to ensure that front-end business applications and back-end infrastructure work together effectively”.
While there are differing perceptions about the extent of marketing and IT co-operation, the report concludes that business objectives need to be much better aligned to ensure closer harmony across these core business functions. If a framework to facilitate this is not put in place at the top of the organization, it becomes exponentially more difficult to implement lower down.
Speed of data-processing is crucial – real-time means real-time
Marketers are increasingly aware that the proliferation of data sources at their disposal is only of use to their businesses if they can analyse that information at high speed and transform it into the kind of intelligence that can then manifest itself as the most relevant and personalized messaging or call to action for any given site visitor.
According to Mike Plimsoll, Product and Industry Marketing Director at Adobe:
“A couple of years ago the marketing leaders at our biggest clients typically expected that data could be processed within 24 hours and that was fine.
“Now when we talk to our clients the expectation is that data is processed instantly so that when, for example, a customer engages with them on the website, the offer has been instantly updated based on something they’ve just done on another channel. All of a sudden ‘real-time’ really does mean ‘real-time’.”
The ability to harness ‘big data’ has become a pressing concern for IT departments as their colleagues in marketing departments seek to ensure they can take advantage of both structured and unstructured data and ensure the requisite speeds for real-time optimization of targeting, messaging and pricing.
More than half of IT respondents (56%) said that the ability to manage and optimize for big data was currently a ‘very relevant’ topic for their organization, in addition to 37% who said it was ‘quite relevant’.
According to Chris O’Hara, Head of Global Data Strategy at Krux Digital:
“Today, consumers that are used to perfect product recommendations from Amazon and movie recommendations from Netflix expect their online experiences to be personal, email messages to be relevant, and web experiences customized.
“Delivering good customer experience has the dual effect of increasing sales lift, and also reducing churn by keeping customers happy. Things like latency, performance, and data management are all part and parcel of delivering on that concept.”
Please download our Backbone of Digital research which, as well as a survey of marketing and IT professionals, is also based on in-depth interviews with senior executives at a number of well known organizations.