CMOs and CIOs need to be more aligned

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.

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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’.

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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.

Big Data (for Marketing) is Real!

MachineLearningWe’ve been hearing about big data driving marketing for a long time, and to be honest, most is purely aspirational.

Using third-party data to target an ad in real time does deploy some back-end big-data architecture for sure. But the real promise of data-driven marketing has always been that computers, which can crunch more data than people and do it in real time, could find the golden needle of insight in the proverbial haystack of information.

This long-heralded capability is finally moving beyond the early adopters and starting to “cross the chasm” into early majority use among major global marketers and publishers. 

Leveraging Machine Learning For Segmentation 

Now that huge global marketers are embracing data management technology, they are finally able to start activating their carefully built offline audience personas in today’s multichannel world.

Big marketers were always good at segmentation. All kinds of consumer-facing companies already segment their customers along behavioral and psychographic dimensions. Big Beer Company knows how different a loyal, light-beer-drinking “fun lover” is from a trendsetting “craft lover” who likes new music and tries new foods frequently. The difference is that now they can find those people online, across all of their devices.

The magic of data management, however, is not just onboarding offline identities to the addressable media space. Think about how those segments were created. Basically, an army of consultants and marketers took loads of panel-based market data and gut instincts and divided their audience into a few dozen broad segments.

There’s nothing wrong with that. Marketers were working with the most, and best, data available. Those concepts around segmentation were taken to market, where loads of media dollars were applied to find those audiences. Performance data was collected and segments refined over time, based on the results.

In the linear world, those segments are applied to demographics, where loose approximations are made based on television and radio audiences. It’s crude, but the awesome reach power of broadcast media and friendly CPMs somewhat obviate the need for precision.

In digital, those segments find closer approximation with third-party data, similar to Nielsen Prizm segments and the like. These approximations are sharper, but in the online world, precision means more data expense and less reach, so the habit has been to translate offline segments into broader demographic and buckets, such as “men who like sports.”

What if, instead of guessing which online attributes approximated the ideal audience and creating segments from a little bit of data and lot of gut instinct, marketers could look at all of the data at once to see what the important attributes were?

No human being can take the entirety of a website’s audience, which probably shares more than 100,000 granular data attributes, and decide what really matters. Does gender matter for the “Mom site?”Obviously. Having kids? Certainly. Those attributes are evident, and they’re probably shared widely across a great portion of the audience of Popular Mom Site.

But what really defines the special “momness” of the site that only an algorithm can see? Maybe there are key clusters of attributes among the most loyal readers that are the things really driving the engagement. Until you deploy a machine to analyze the entirety of the data and find out which specific attributes cluster together, you really can’t claim a full understanding of your audience.

It’s all about correlations. Of course, it’s pretty easy to find a correlation between only two distinct attributes, such as age and income. But think about having to do a multivariable correlation on hundreds of different attributes. Humans can’t do it. It takes a machine-learning algorithm to parse the data and find the unique clusters that form among a huge audience.

Welcome to machine-discovered segmentation.

Machines can quickly look across the entirety of a specific audience and figure out how many people share the same attributes. Any time folks cluster together around more than five or six specific data attributes, you arguably have struck gold.

Say I’m a carmaker that learned that some of my sedan buyers were men who love NASCAR. But I also discovered that those NASCAR dads loved fitness and gaming, and I found a cluster of single guys who just graduated college and work in finance. Now, instead of guessing who is buying my car, I can let an algorithm create segments from the top 20 clusters, and I can start finding people predisposed to buy right away.

This trend is just starting to happen in both publishing and marketing, and it has been made available thanks to the wider adoption of real big-data technologies, such as Hadoop, Map Reduce and Spark.

This also opens up a larger conversation about data. If I can look at all of my data for segmentation, is there really anything off the table?

Using New Kinds Of Data To Drive Addressable Marketing 

That’s an interesting question. Take the company that’s manufacturing coffee machines for home use. Its loyal customer base buys a machine every five years or so and brews many pods every day.

The problem is that the manufacturer has no clue what the consumer is doing with the machine unless that machine is data-enabled. If a small chip enabled it to connect to the Internet and share data about what was brewed and when, the manufacturer would know everything their customers do with the machine.

Would it be helpful to know that a customer drank Folgers in the morning, Starbucks in the afternoon and Twinings Tea at night? I might want to send the family that brews 200 pods of coffee every month a brand-new machine after a few years for free and offer the lighter-category customers a discount on a new machine.

Moreover, now I can tell Folgers exactly who is brewing their coffee, who drinks how much and how often. I’m no longer blind to customers who buy pods at the supermarket – I actually have hugely valuable insights to share with manufacturers whose products create an ecosystem around my company. That’s possible with real big-data technology that collects and stores highly granular device data.

Marketers are embracing big-data technology, both for segmentation and to go beyond the cookie by using real-world data from the Internet of Things to build audiences.

It’s creating somewhat of a “cluster” for companies that are stuck in 2015.