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A Data Geek’s Take on Holiday Shopping and the Election

Posted by Brant Cruz

Fri, Dec 11, 2020

Brant Cruz Data Geek Holiday Shopping and Electon Dec 2020 Blog Opener

As someone who has spent nearly 25 years finding insightful truth in piles of data, I’ve accidentally trained my brain to be good at little else. For example, I’ve been in the top percent of dads when it comes to teaching my kids how to “estimate” in their early math classes, but could almost hear my brain crack when they needed help with geometry and its many obtuse angles

This is why, for nearly every topic I stumble upon, I immediately start analyzing and contextualizing the numbers. Instinctively, my brain takes me through the following sequence:

  1. Is that number in line with what I would have estimated?
  2. Can I contextualize it in terms of a number or change I am familiar with, and explain to someone less familiar “why” the number is what it is?
  3. If the answers to both #1 and #2 are “no,” is there other data can I use to reconcile the disconnect?
  4. If things still don’t line up, can I reasonably conclude that I am missing some important context that isn’t available publicly or in the data set that I am analyzing?
  5. If the answers to #3 and #4 are also “no,” have I or the author done something wrong (accidentally or intentionally through some bias)?

In my professional life, this is a perpetual stream, but all the best examples are proprietary. So, instead, I’ll illustrate with a couple of current newsworthy events: the 2020 Holiday Shopping Season to date, and the 2020 US Presidential Election.

Example 1: 2020 Holiday Shopping Season

This is a great CNBC article that features multiple data points, publicly available thanks to the power of Adobe Analytics, related to the US Holiday shopping season. Just picking one:

Holiday shoppers spent $10.8 billion on Cyber Monday, up 15.1% from 2019.”

Here’s an abbreviated recollection of my thought process:

  1. The number feels intuitively right in light of what I remember from past Cyber-Mondays, the overall trend of eCommerce, everything I have been reading about Brick & Mortar retail struggles…and, I have very high trust for Adobe’s data and the rigor of that team. Plus, there are other numbers in the same article that feel intuitive, e.g., $10.8 billion of a total ~$185 billion holiday season. My head-math says that current definitions of holiday season are likely around 50 days, meaning each day is 2% of the season. And Cyber Monday would be ~6% (3x average), which checks out.
  2. As far as the contextual “why” goes, it fits with my mental model of how the combination of headwinds and tailwinds for eCommerce net out in 2020:
    1. Headwinds: COVID-19 might be depressing total holiday spend across all channels given the economic struggles, short-term uncertainty, desire to save, and sad letters you can find (but also help!) through the USPS’s Operation Santa site.
    2. Tailwinds: eCommerce sales rose 18.8% in 2019 so this just continues that trend. Plus, perceptually, shoppers en masse feel far less “able” to shop of brick & mortar retail this year due to COVID-19. Rather than reinvent how my colleague Erica Carranza so aptly described the Fogg Model’s two axes of Motivation and Ability and possible implications for shopping months ago, I’ll point you to her blog.  

Given I feel so confident at this point, no need to continue with steps 3-5.

Example 2: President Trump says, “There is no way Joe Biden got 80 million votes”

Putting aside all other political issues leading up to, during, and since the election, this one stuck out to me as appropriately data-geek-worthy. President Trump may have made this claim multiple times, but I can say with certainty that he made it on a call with Fox News on November 29. Here’s how I processed this claim:

  1. I know that combined Trump and Clinton received 129 million votes in 2016, with Clinton winning the popular vote at just south of 66 million. And that Obama set the record in 2008 with 69.5 million votes. 80 million votes for Biden represents a ~21% lift over 2016 Clinton, and a ~15% over Obama’s record. Big jumps and certainly within the realm of possibility, but worth more investigation.
  2. There are lots of ways to contextualize a 15% lift, but I wanted to make sure I understood why.
    1. Anecdotally, people on both sides are more passionate about politics as evidenced by social media posts, strong passion for and against Trump, and media ratings.
    2. The candidates combined for >$14 billion in election spending, more than double what Trump and Clinton spent in 2016. That’s an increased spend of 100%+, for a 20% increase in turnout. Certainly believable.
    3. Back to the trusty Fogg Model: both Ability (in some neighborhoods, the need to wait in 9-hour voting lines due to closed polling locations was replaced with the ability to vote by mail) and Motivation (the aforementioned hyper-partisanship and Trump’s polarization) axes have seen big bumps since 2016.
  3. Is there other data available that I can reference? I don’t think so—and it seems like recounts and the courts agree.
  4. Could I be missing something? Likely not. (See above response to step #3.)
  5. Yes, I can see that President Trump may have some bias, given the prize and some historical context.

As you can see, this approach is pretty helpful in a job where I’m constantly involved in proving the rigor of my team’s data and analysis (and the resulting insights/business implications) to some of the world’s smartest and most passionate clients.

But you can imagine the faces I get from my daughters when statements like, “Dad, we need YouTube TV” are met with, “Oh yeah? Prove it.”


Brant CruzBrant Cruz is one of the many data geeks at CMB and is our VP: Platforms and Audiences Practice Leader.

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Topics: strategy consulting, business decisions, marketing science, marketing strategy, brand health and positioning, digital media and entertainment research, Market research, Election, retail, consumer psychology, ecommerce, COVID-19, mrx, Holidays

Personalization, Privacy and the Creep Factor

Posted by Julia Walker

Wed, Jul 25, 2018

online shopping

You’ve seen it before: a pair of shoes that follow you all the way from Zappos.com to Facebook, or even creepier, when you have a conversation about Patagonia and suddenly, Instagram serves you an ad for their latest down jacket. Today’s marketers don’t have to guess where to place their ads anymore. Instead, they track online behavior to tailor ads, offers, products, and experiences to the specific consumer.

Leveraging online consumer behavioral data for personalization is now a standard marketing strategy, but what are the implications for brands and consumers?

Personalization drives consumer behavior. In fact, 80% of people are more likely to do business with a company if it offers them a personalized experience. Amazon revolutionized personalization when they rolled out their product recommendation algorithm—a feature some attribute to their huge sales increase (29% in the second fiscal quarter) in 2012. And it’s only advanced since then. With the help of AI and big data, brands can deliver highly custom experiences to consumers. Now, personalization spans devices, following you from your tablet to your desktop, and can recommend your next TV binge or anticipate an unmet need.

Personalization can also inspire loyalty, which means a greater customer lifetime value and possible advocacy. With forty-four percent of consumers saying they will likely make additional purchases after a personalized shopping experience, this is a tremendous opportunity for brands to break through the clutter with tailored messaging and offers.

But is there such thing as too much personalization? As brands continue to collect data to better understand and serve their customers, where does the line between service and invasion of privacy begin to blur? InMoment's 2018 CX Trends Report found that 75% of consumers find most forms of personalization at least somewhat “creepy”. And while half of consumers admitted they’d still shop with the brand after a creepy experience, almost a quarter reported it would drive them to a competitor.

The stakes are high for companies collecting customer data: 70% of consumers would stop doing business with a company that experienced a data breach. And this data is exactly what enables brands to personalize their offerings.

So, we’ll continue to see this tension play out across industries—while consumers continue to expect more personalization, brands must deliver tailored experiences without risking the creep factor.

Julia Walker is a Project Manager at CMB and an avid online shopper whose decisions are often influenced by algorithm recommendations.

Topics: retail research, Artificial Intelligence, ecommerce, data privacy

What Amazon Can Teach Us About Delivering on Customer Loyalty

Posted by Ashley Harrington

Wed, Apr 25, 2018

 

amazon packages (resized)

I live in a historic Boston neighborhood rich in restaurants and charm, but poor in parking. If you have a car, you have two choices: spend a fortune on a dedicated monthly parking spot or drive in circles until you find free street parking.  I don’t like to waste money or time, so I don’t own a car.

I don’t own a car, but I do have two kids. So, I need stuff and I need it all the time. Enter, Amazon.

I use Amazon on all my devices and have multiple apps. I use it for both planned purchases and impulse buys. I Subscribe and Save for everything from baby wipes to granola bars. I order groceries from Amazon Fresh. I try on clothes with Amazon Wardrobe. I buy e-books. I watch movies and TV shows on Prime. I use Now to get emergency toddler bribes delivered in an hour.

On top of all that, I pay Amazon for the privilege of buying things with them with an annual Prime membership. If that’s not the ultimate sign of customer loyalty, I don’t know what is.

If I was responding to an Amazon loyalty study, I would certainly make it into the “Super User” group, checking all the boxes for how we might define loyalty: frequency of purchases, cross shopping, willingness to try new categories, likelihood to recommend, etc. 

My “Super User” status didn’t happen all at once—it was gradual thanks to the “Amazon Effect.” Over time, Amazon plucked one more category of our household expenses from another retailer.

I work with clients every day to help measure, understand, and improve their customer loyalty. While few companies have the infrastructure and the sheer breadth of product and services in such a frictionless way, there are lessons any brand can learn from Amazon’s excellence in curating a faithful customer base.

 Here’s how Amazon keeps me loyal:

  • Anticipates my needs: I wasn’t actively thinking about how great life would be with a paper towel subscription. But, I gave Subscribe and Save a shot and now we never run out and I can't imagine my household without it.
  • Gives me back my time: With Fresh, I can enjoy time with my family instead of spending it in the grocery store (if you enjoy taking your children to the grocery store, I nominate you for a Parent-of-the-Year Award!)
  • Provides me with flawless execution and problem resolution: Amazon’s apps and website are easy, fast, and intuitive. Once I order something, I know exactly when it’ll arrive on my doorstep. If there is an error, Amazon’s customer experience team is polite and fair in resolving an issue.

While I am a loyal customer, there are certain things I don’t buy on Amazon. Some because they aren’t sold (yet) (e.g., wine) and others because I enjoy shopping elsewhere. And there are Amazon services that aren’t for me. For example, I don’t need to tell Alexa to turn on my lights.

 So, even for this Super User, loyalty has its limits.

 Ashley Harrington is a Research Director at CMB who recently starting using “Amazon” as a verb and probably has goldfish crackers in her bag.

Topics: brand health and positioning, customer experience and loyalty, retail, ecommerce