October 4, 2018

Beyond Personas: Creating Customer Doppelgangers

 

BlogPost#1

Dear smart marketers: it’s time to start dabbling in doppelgängers.

 

What does this mean, exactly? It means the future of substantial and effective consumer understanding relies on the intersection of behavioral analysis and Consumer Identity Strategy (CIS).

 

Although consumer research has always been a crucial component of advertising, CIS is a new, more comprehensive level of consumer research. Through CIS, brands establish an authentic and evolving portrait of a consumer and their purchasing journey informed through persistent evaluation of online and offline behavior coupled with demographics and psychographics.

 

By developing an identity strategy that layers behavioral data onto more traditional methods of qualitative and quantitative analysis, brands are able to truly identify their consumer.

 

Discovering how, when and where a consumer shops, seeing what brands are stealing closet or cart space, and understanding how customers connect on social channels — brands can even see how their customers behave when they aren’t shopping.

 

In essence, brands don’t just build personas, but create doppelgängers of their customers through Big Data.

 

It’s the creation of these doppelgängers that enable brands to attain a comprehensive understanding of how their consumers act, live and behave. With this knowledge, brands can make viable predictions of how particular consumers will shop and act based on similar consumers. This doppelgänger approach can even be applied to the smallest business all the way up through the big leagues. Even in big league baseball.

 

At age 32, Boston Red Sox slugger David Ortiz hit a career-threatening slump. But Nate Silver of fivethirtyeight.com used doppelgängers to refute the conventional wisdom that Ortiz was washed up.

 

>> Interested in taking a swing at the details of Big Papi’s doppelgängers? Download our white paper now to learn more.

 

Ortiz ultimately shook his slump and improved his game, just as Silver’s doppelgänger data suggested. With the right amount of the right data, brands can build more effective and accurate personas. They can design strategies to reach and serve their customers not only with the right messaging, but also the right timing and cadence.

 

Using behavioral data to create a consumer identity strategy is no longer for the Amazons, Walmarts and Googles of the world. It’s for every brand that has a physical, digital and mobile presence in their industry. Brands that don’t focus their marketing dollars on consumer identity strategies immediately will find themselves playing catch-up in the years to come.

 

Now is your chance to step up to the plate and make bold business moves. Get a deeper look into the power of behavioral analysis and AMP Agency here by downloading the white paper, or visit AMP’s website: www.ampagency.com.

October 1, 2018

Big Data's Impact on Consumer Research and Strategy

Customer Identity Strategy Blog Post

When it comes to the dynamic nature of marketing and advertising climates, stagnancy is rarely recommended.

 

That’s why it may seem unfathomable that consumer research tactics have seldom adapted since the folks at Arm & Hammer discovered that their customers were putting baking soda in their refrigerators to keep them fresh.

 

But now, marketers are no longer confined to surveys, interviews, and focus groups. Consumer research is finally following the lead of Arm & Hammer’s customers and freshening things up big-time.

 

Data scientists and smart data-led marketers today are creating methods that improve and expand upon the insights coming from traditional qualitative and quantitative research. As a result, consumer research as a whole is embracing a new wave of audience understanding thanks to the help of Big Data.

 

That’s right — Big Data just so happens to be the next big thing for consumer research.

 

By layering in Big Data, brands can develop a comprehensive Consumer Identity Strategy (CIS): an authentic and evolving portrait of a consumer and their purchasing journey informed through persistent evaluation of online and offline behavior coupled with demographics and psychographics.

 

The idea of observing people’s actions, habits and behaviors may not seem all that groundbreaking. But being able to observe consumers at scale and use data models based on behavior is, in fact, disruptive for marketers and is rapidly becoming the core of every identity strategy.

 

By augmenting self-reported surveys, behavioral data analysis builds a picture of a consumer based on their actual behaviors. These behaviors can range from what they purchase online and offline to behaviors as specific as what time of day they like to shop or how often they actually go to the gym.

 

To see how Big Data and CIS play out in real-life scenarios, just look at Netflix — a company who learned early on in its life cycle that actions speak much louder than words.

 

 

>> Read more about how this streaming giant succeeded in using Big Data-driven consumer identity strategy by downloading our complete white paper here.

 

 

Netflix grew their business by using behavioral data that showed true consumer behavior. On top of that, this data helps reveal counterintuitive results that may go against what society or individuals believe to be true.

 

When this behavioral data is layered onto more traditional methods of qualitative and quantitative analysis, brands are then able to truly identify their consumer in ways traditional research methods had not made possible before Big Data came into play.

 

Now’s the time to be bold and lead with the best tools available.Get a deeper look into the power of behavioral analysis and AMP Agency here by downloading the white paper, or visit AMP’s website: www.ampagency.com.

April 3, 2013

Facebook's Lookalike Audience Targeting in a Nutshell

FB Lookalike

"Mirror, mirror on the wall..."

While Facebook may not be answering "...who is the fairest of them all," its new Lookalike Audience targeting aims to help marketers answer the question of who "mirrors" the existing target audience.

Just a couple of weeks ago Facebook announced that it is now offering Lookalike Audience targeting. As you most likely know, lookalike models can be used to build larger audiences from smaller audience segments to create scale for advertisers at a premium price. Agencies and brands have been testing and successfully using lookalike modeling across networks for quite some time, but this is a new offering available on Facebook.

Taking a look back before looking ahead: Custom Audience database

In the fall of 2012, Facebook released its Custom Audience database. This allows advertisers to use existing information that they have compiled on their customers -- such as email addresses or phone numbers -- to match to the user's profile on Facebook. The new lookalike modeling takes the Custom Audience database to the next level by allowing advertisers to reach people who "look like" their Custom Audience database.

Lookalike Audience targeting on and off of Facebook

If we take a step back and look at how we have collected the data to use for lookalike modeling outside of Facebook, it is usually a combination of conversion data mixed with behavioral data. This combination allows advertisers to identify the consumer and what they were doing online before converting to the brand's site.

The difference with Facebook lookalike targeting is that the attributes that qualify the person to be a lookalike will be the "likes" and interests that they have stated in their profile versus actual behaviors they have shown online. If Facebook released the "likes" and interests being used to build this lookalike model, this would be extremely valuable information to advertisers to help build out targeting models across multiple channels, but this is currently not the case. Facebook will be conducting this lookalike modeling behind the scenes, and the advertiser will not be able to see what classifies the users as a lookalike.

What's the verdict?

It is important to keep in mind that a person may show on Facebook that they "like" BMW and The Four Seasons Hotels, but if you look at that same person's behavioral data, you would see that it indicates that they have spent time shopping online for Toyota and Best Western hotels. Case in point, just because they "like" something on Facebook doesn't necessarily mean they are in the market to buy it.

Keeping this in mind, we hope that Facebook will match many profile points to develop a precise lookalike model that will shape a target audience accurately. If this is the case, then the lookalike targeting on Facebook would be worth testing in addition to lookalike modeling outside of Facebook.

This article originally appeared in iMedia Connection on April 1, 2013. 

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