How CRM and a DMP can combine to give a 360-degree view of the customer

360-degree-gif-01For 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?

Targeting people who haven’t opened an email

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.

Combining commerce and service data for journeys and sequencing

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.

How can you automatically discover the top 10 performing journeys?

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.

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What is Data Science?

A Conversation with Ankur Teredesai, Data Scientist, nPario

These days, the term “bid data” is all the rage and over a dozen data management platforms are competing for the right to manage audience segmentation, targeting, analytics, and lookalike modeling for advertisers and publishers. I recently sat down with noted data scientist Ankur Teredesai to help understand data science, and how ad technology companies are using data science principles to help publishers and marketers understand audiences better. Ankur is the head of data science for nPario, a WPP portfolio company focused on data management, and he is also a professor at the University of Washington.

You hear lots of digital advertising people talk about “data science.” Does their perception differ from the broader, academic understanding of what data science is? Is the notion of utilizing data science in digital marketing applications new?

Ankur Teredesai (AT): It’s very interesting to see that the digital advertising folks have started deep conversations with data scientists. Data science is a very interesting space these days where data mining and database management technology is now helping variety of disciplines in establishing a scientific approach to decision making. Data science dealing with the problems of finding patterns in large amounts of data is not a new concept for digital marketing. What is new is the advent of technologies that now support finding useful patterns in large variety and velocity of data in addition to volume; thereby advancing the state of the art in marketing analytics.

Do ad technology companies really rely on data science? What does being a data-driven organization really mean?

AT: No ad-tech company can afford to NOT rely on data science in some shape or form. The power of predictive modeling is quickly differentiating the players who are making quick inroads by using the low-hanging fruits of data science for these domains from the ones that are treating data science as a passing buzzword. My advice to all ad-tech companies is to get at least one data scientist in their ranks; even if they don’t like the term data science for some or the other reason. Machine Learning, data mining and big data analytics are all equally acceptable today.

Describe the concept of data modeling for the non-academic user. What kind of models are being built for digital marketing applications?

AT: A variety of problems in digital marketing are being addressed using predictive modeling. Some examples of the work we are doing at nPario are (a) look-alike modeling that helps find targetable audiences to “rightsize” or expand a particular segment, (b) recommend cost-aware segments that are similar to desired audience segments for targeting, (c) provide comparative analytics for exploring the unique properties of a given segment, (d) enabling real-time audience classification to reduce the time to target in an efficient and effective manner.

Is the concept of lookalike modeling legitimate? How does this work? Is LAM a scalable targeting practice?

AT: Given customer behavior data, the lookalike model estimates and exploits the variations and similarities in behavior across various segments. Once the model figures out which similarities or differences are robust enough in the audience to be useful for predicting future behavior, it exploits these attributes in the data to expand a particular segment’s size by including those customers that are similar to the base segment but were not included because they did not meet the segment definition criteria. This allows fairly restrictive criteria to be relaxed using data science methods such as association mining and regression analysis to expand the segment size accurately, confidently, and in a scalable manner.

The entire digital advertising ecosystem is driven by data. What are the most valuable types of data for targeting? How do you see the future for the ecosystem? Will those with the most data win?

AT: This is central to success of the entire digital advertising world and the benefit of end consumers in finding the right and useful advertising. If we have to understand the user and focus on making digital advertising useful without making it adversarial, we have to focus carefully on the types and granularity of the data being collected. At both nPario and the Institute of Technology, University of Washington Tacoma where I hold an academic appointment, we stress the need for developing an ecosystem of data collection, management and mining that is customer centric with highest regards for security through robust multitenancy, cryptography and privacy aware practices.  The entire technology stack at nPario is for example, data agnostic. We decided very early on in the company to not be supplies of data but to be data pool neutral to allow our clients to bring their own first, second and third party data. Our platforms help clients derive value from the variety of big datasets while at the same time ensuring that customer and end-user privacy is preserved and not compromised through our actions in any manner whatsoever. To address your question if those with most data win I would like to quote that : Everybody has some data and some just have lots of data. It is the ones that have the right tools at the right time that will monetize their data the best.

This interview, among many others, appears in EConsultancy’s recently published Best Practices in Data Management by Chris O’Hara. Chris is an ad technology executive, the author of Best Practices in Digital Display Media, a frequent contributor to a number of trade publications, and a blogger.