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A few weeks ago, I flew to Houston, Texas. It was great–a short, comfortable flight. And, there were enough vacant seats to give our youngest her own. After an hour, I picked up the shopping magazine in the pouch in front of me, despite having never bought anything throughout decades of flying. I couldn’t help it.

After flipping through page after page of seemingly useful — yet, unconventional — products, I came to the same conclusion that I’ve come to for years: Not one product had a valuable use case for me. All of them were solving problems I didn’t have.

As marketers, we sometimes slip into this trap of spamming our consumers with products that solve problems they don’t have. Oftentimes, it’s not that we don’t have the right product, but rather, we don’t have the right context. A single-device understanding of consumers doesn’t provide enough context to deliver truly personalized offers, especially considering that the average consumer owns more than three devices.

People-based marketing solves this problem. People-based marketing means that you don’t treat a device as a person. Instead, you can link devices that are used by the same person and then treat that person as an individual across all their devices.

Top Four People-Based Marketing Use Cases
With the correct components in place, people-based marketing can have a major impact on everything marketing — from reporting and analysis to attribution and experience. In this post, we’ll walk through the top four use cases for people-based marketing.

1. People-Centric Reporting and Analysis
Historically, digital-marketing measurement was built on a foundation that was intended to understand people but designed to understand devices. When a device graph is integrated with an advanced analytics solution, the device graph can transform the context of reports from being device-centric to being people-centric. This means that — for the first time — marketers can understand how many people (rather than phones and tablets) visited their site, or how many people (rather than laptops and Apple watches) interacted with their brand across multiple domains, apps, or even a brand’s offsite advertising.

To illustrate this point, let’s imagine a marketer launches two campaigns. The first campaign reports $10 of revenue per visitor. The second campaign reports $20 of revenue per visitor.

Using a device-centric metric (like revenue per visitor) creates the impression that the second campaign, costs being equal, is more effective. However, using a people-centric metric to analyze these campaigns, the same marketer might see that the first campaign touched one person across three devices, making the revenue per person $30; compared to the second campaign that touched one person on one device, giving the campaign a $10-per-person revenue. From this insight, an analyst could build a compelling case for promoting the first campaign to hit their quarterly revenue target.

People-based marketing gives marketers insights on people — not devices.

2. Seamless Cross-Device Experiences
Personalization tools strive to deliver meaningful experiences, but these tools alone can only deliver the right experiences to familiar devices. What happens with unfamiliar devices your customers use to interact with your brand? What do they see then?

For example, if an avid reader made it halfway through a riveting article on her tablet, and then visited the same website from her phone to finish reading the article, what kind of experience would she have? She’d likely fumble over the keys to enter a search, scroll down the page, and meticulously comb through each line of copy until she found exactly where she left off — far from ideal.

A people-enabled personalization tool offers a drastically different story. The reader would get halfway through the article on one device, pick up another device that she has never visited the publisher’s website from, and still be brought to the exact article she was reading on the first device.

People-based marketing makes seamless, cross-device experiences possible — experiences that are continuous, consistent, and compelling.

3. Cross-Device Efficiency for Advertising
Traditional advertising platforms, just like analytics and personalization platforms, are susceptible to the same device-centric limitations.

For the display advertiser, keeping tabs on his ROI is a relentless top priority. A common way to assure the return on ad spend (RoAS) is maximized is to apply a frequency cap on the number of times someone sees the same ad. Unfortunately, frequency caps apply to devices — not people. So, a frequency cap of five impressions per person can quickly become 20 impressions per person if each person uses an average of four devices. This leads to wasted ad dollars and perturbed customers.

People-enabled advertising platforms apply frequency caps that span the various devices used by a person. A cap of five impressions means a cap of five impressions for a person. Using the previous example, a people-enabled advertising platform could have delivered a 75-percent higher ROI than the non-people-enabled advertising platform.

4. Holistic Attribution
Attribution is constantly evolving to include more touchpoints, more-accurate weighting, and machine learning. So, it’s ironic that one of the most important factors in understanding the impact of marketing on the buying behaviors of people has always been missing.

Traditional attribution algorithms only analyze pre-login information from a single device. And, considering that the average person owns three devices, it’s likely that a big part of the attribution story is missing.

For instance, if a consumer visited company A’s website from her laptop, was retargeted with a display ad, and then finally converted on the website; a linear-attribution model would yield something like this:

But, in reality, this same consumer also conducted a search on her phone, read an article from company A’s site, and then went back to her laptop to make the purchase. Using the same linear-attribution model that’s being fed information from a device graph would yield this:

After a month, the marketer sees that this same article is among the top-ten, most-valuable traffic sources according to the linear-attribution model. As a result, the marketing team surfaces the article’s dialogue on all product pages (which truncates the customer journey to three touches), decreases their search spend on the keywords that point to the article, and all-in-all delivers a better experience to consumers as well as a stronger bottom line for the business.

With holistic attribution, marketers can now prove the value of all their marketing within the context of people — not devices.

A Very Real Payoff
Marketing has always been about understanding consumers to the extent that our messages and offers deliver solutions for which they are willing to pay. But, it’s only recently that nailing these key ingredients alone won’t necessarily convince consumers to make purchases. Consumers want consistent, continuous, and compelling brand experiences. Device-centric marketing can’t deliver this — but people-based marketing can!

While I haven’t personally purchased anything from in-flight magazines (like SkyMall), they’re successful business endeavors, and their magazines have been my go-to source for in-flight entertainment for many years. With a captive audience of people looking for a distraction while their devices are stowed, SkyMall may not need people-based marketing. For the rest of us, people-based marketing is like setting your tray table and seat in the upright position — you simply have to do it.

The post Top Four People-Based Marketing Use Cases appeared first on Digital Marketing Blog by Adobe.

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As I’ve been writing about tools and tactics quite a bit lately, I thought for this month’s column I’d take a step back and share some ideas on how you can become a better analyst.

And improving our analysis skills as marketers goes beyond broadening our career options and helping us be better at our craft.

It should actually improve all areas of your life as a byproduct of nurturing our critical thinking skills. Some ideas follow that I apply in my own life and hope you’ll consider too.

Find a passion outside work which involves developing hypotheses

The scientific method, as you know, is a body of techniques for investigating phenomena, acquiring new knowledge, or correcting and integrating previous knowledge.

You’re already applying this to your marketing and analytics practice by putting it to work for testing and optimization efforts (for example, having a hypothesis that a new landing page with less clutter will convert better, which you then test).

But beyond work, you should also, in free time, be involved in something which flexes your prediction muscle.

Whether this manifests as fantasy sports, investing in startups or some other activity which involves future predictions (and cool datasets!), this can be a fun and rewarding way to sharpen your mind and will help you see analysis problems in a new light.

Learn to fill in the missing pieces, be comfortable working with imperfect data/information

100% perfect data is really only possible in a controlled lab setting with expensive and fine-tuned equipment. While, of course, we should ensure our analytics implementation is setup correctly to keep our own data as clean as possible, we must also get comfortable working with a “good enough” information.

This is necessary in order to be agile in how we work and keep projects moving forward. A great analyst will work out the way to fill in the missing pieces and make effective projections (while of course providing a rationale/caveats where needed).

You want to get confident enough to make recommendations and create analysis’ based off “minimum viable data.”

Have a sandbox project to test new tools

If you are truly serious about improving your skills, doing analyst work in your live business environment isn’t enough.

The reason being you can’t test and tinker with any new tool without permission or change settings at whim, you likely have compliance and managers to work through.

But a sandbox project such as your own site, app or side business provides a place you can test, tinker and experiment in a no-stress setting.

Bonus: our team at Google recently launched an Analytics Demo Account for this very purpose.

Live and breathe your company and sector metrics (beyond what you’re accountable for)

Being a great analyst isn’t about just running reports and delivering insights that are your remit.

Rather, the best analysts have their finger on the pulse of the bigger pictures and are deftly able to put their own work into context with the larger organization and sector as a whole.

The analysts I talk to that leave a lasting impression are the ones who can speak articulately about various areas of the business and how they make impact across teams and functions.

Be a part of the industry, network and collaborate with peers

I’m personally a big believer in educating others about digital marketing and since starting my career well over a decade ago I’ve spent time both at and outside of work helping others learn our craft.

Our industry is tight knit and so being an active participant who helps others is of great benefit (not to mention fulfilling).

For you, whether this takes the form of speaking/attending events (such as ClickZ Live), starting your own local analytics meetup, or even making friends with other analysts near you to talk shop this is a valuable use of time.

Adam Singer will be speaking at ClickZ Live San Francisco in August. 

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I’m often asked where do you start with Analytics? How do you go about doing it? The simplest answer to that question is start with your customers needs and work back from there. To illustrate lets take a simple example … Read more › The post Measuring the Good, the Bad and the Ugly – How to get started with Analytics appeared first on Blackbeaks Blog, Digital Analytics and marketing optimisation blog .

Source: Measuring the Good, the Bad and the Ugly – How to get started with Analytics