How Do We Leverage Data to Drive a Faster Economic Recovery?

As growth leaders, we will be waking to a world with fewer resources and businesses desperate to grow again once we get past the coronavirus pandemic. However, in our struggle to regain our financial footing we will have a very valuable resource that previous generations did not: data and data science.

As growth leaders, we will be waking to a world with fewer resources and businesses desperate to grow again once we get past the coronavirus pandemic. And despite the global hardships that will be felt by many, in our struggle to regain our financial footing we will have a very valuable resource that previous generations did not: data and data science.

When used well, data science will help direct scarce resources to the right opportunities and efficiently drive growth. I am convinced this will be a big differentiator versus previous recoveries of this magnitude.

Over my career, I have consistently encountered inefficient and counter-productive practices in data-driven decision management and have written about them often. They are paralleled in the crisis today. Below are three issues I would like us all to think about when we leverage data science to rebuild the national and world economy.

1. Customer Data Hoarding

Companies collect so much data that many are “drowning in data.” If you have no idea of the value of what you are collecting, then it is digital garbage.

We were led to believe that AI and data mining would help make sense of the data. It does to some extent, but more often it leads to head-scratching conclusions. We can’t leverage what we can’t understand.

As a data-driven consultant, I am amazed at how much time is spent sifting through data just trying to make sense of it all before any valuable insights can be generated. Going forward we cannot afford this luxury. If there are 10 gallons of fuel in the tank, we can’t spend five gallons trying to figure out if the engine works. However, when it comes to mining company data, we often do.

2. It’s About Qualitative, Not Just Quantitative

We can’t be slaves to the data we have. Collecting the right data is often cheap and easily done, if time is taken to plan. This means that measurement strategy can’t be a retrospective exercise. Too often, I have engaged clients in the post-mortem analysis of very important projects. In many cases, my team is often limited to the data that is available and not the data that was needed. Critical answers are sometimes left unanswered. This is a waste of time, resources and most importantly, valuable information.

3. Data Is Not the Solution, It’s the Tool

We must regularly remind ourselves that data does not solve problems or create opportunities. Rather, brave decision making solves problems and creates opportunities. Data is a valuable tool that can only inform the decisions we need to make. It can help lower the risk and provide valuable insights. Sometimes, collecting more data before acting can be wise. Other times it can also be the delay in action that leads to disaster.

What is happening today has no parallel in recent memory. While the 1918 flu pandemic had similar infection rates, the world was a different place then. Today, we have advanced tools and technology to aid our recovery.

Data science will be one of those important tools, especially if we collectively decide to use it to its true potential. As a result, I am hopeful that we can come out of this faster than we realize.

The Data-Inspired Big Idea: Why That Matters in the Ad Business

We are amid an age where consumers are royalty — and it’s the brands that serve them. Yes, data science is required to uncover insights and inform the creative strategy, for both prospecting and retention. But that big idea still lies in the creative execution.

I just got schooled this past week at the Association of National Advertisers Masters of Marketing Conference in Orlando, along with 3,000-plus industry colleagues.

You see, I’m a data- and direct marketing- junkie. Advertising is worthless if it’s not accountable and measurable (check and check). As I was reminded repeatedly this week it also must be memorable (not always checked).

What does this mean? That in today’s always-on but distracted consumer marketplace, the ad message must tell a story. It needs compelling creative, a message that resonates, and a big idea that’s transparent and authentic and unique to a brand.

We are amid an age where consumers are royalty and it’s the brands that serve them. Yes, in the customer experience mix, data plays a pivotal role. Yes, data science is required to uncover insights and inform the creative strategy, for both prospecting and retention. But that big idea still lies in the creative execution that’s the clincher. If it doesn’t hook, then it’s not going to stick.

Brand-Building Requires Purpose and Perspective

Consider some of these executions showcased at the conference, and look for how the brand creates an emotional connection:

Disney | The Little Duck

Target | Design for All

Chipotle | Bee For Real

Ally | Banksgiving

Dunkin | Fuel Your Destiny

https://youtu.be/31A1EsTZlHA

The Data Play in ‘Brand Crave’

Then ask yourself, what role does data play in these brand stories?

At the conference, there were plenty of CMOs discussing first-party data, customer journey mapping, personas, net promoter scores, operational data, transactional data, and sentiment scoring among other metrics and inputs. Even second- and third-party data were mentioned (albeit briefly here) about how to expand reach, discover new customers, and deepen understanding with existing customers. These data points also inform the creative brief, as well as shape the media strategy.

Researchers still report that consumers still base many of their buying decisions on impulse, and on emotion. According to Kirk Perry, president of global client and agency solutions at Google, as much as 70% of advertising success depends on creative; and Kai Wright, lecturer at Columbia University, reported on how emotion weighs into consumer consideration and purchase behavior (see Image 1).

Image 1:  Emotion & Experiential Data Motivate Consumer Behavior, Perhaps More Than Audience Data

Data-Inspired big idea image
Credit: Kai Wright, Columbia University, ANA Masters of Marketing Conference, 2019.

SAP CMO Alicia Tillman reports that humans experience (and act upon) 27 emotions (Image 2). “Any one can make or break a brand or category.”

Image 2: Lots of Sentiment Scoring

Data-Inspired big idea sentiment scoring
Credit: Alicia Tillman, SAP, at ANA Masters of Marketing Conference, 2019

“Nobody can differentiate on data! It’s data-inspired storytelling that is going to win the future,” said Rishad Tobaccowala, chief growth officer at Publicis Groupe.

We are great at curating audience data. For a next-generation data ecosystem, what are we doing to help create more effective marketing through finding innovative ways to score emotion, at-scale?  What are we doing to include these consumer motivators in our business rules, algorithms and to help enhance creative prowess in authentic ways? You solve for these opportunities and there are many brand leaders and CMOs likely ready to talk to you.

It’s time to help brands tell their data-inspired stories.

 

Winner of the 2012 Presidential Election: Data

Now that the contentious 2012 election has finally ended, we get a chance to look back and assess what happened and why. Regardless of who you voted for, it’s impossible not to acknowledge that the real winner of the 2012 election was data.

Now that the contentious 2012 election has finally ended, we get a chance to look back and assess what happened and why. Regardless of who you voted for, it’s impossible not to acknowledge that the real winner of the 2012 election was data.

For the first time in history, this election demonstrated the power of using analytics and numbers crunching for politics. What I find most remarkable is the rapid evolution of this change. If you look back just a few years ago, Karl Rove was widely regarded as the political mastermind of the universe. Rove’s primary innovation was the use of highly targeted direct mail campaigns to get out the evangelical and rural vote to win the 2004 election for George W. Bush. Fast-forward a few short years, and not only did Rove’s candidate lose, but the master strategist was reduced to challenging his network’s numbers geeks live on the air, only to be rebuffed.

In every way, the old guard was bested by a new generation of numbers crunchers, nerds and data geeks who leveraged data science, analytics, predictive modeling and a highly sophisticated online marketing campaign to poll, raise money and get out the vote in an unprecedented manner.

On the subject of polling, I was intrigued by Nate Silver’s incredibly accurate FiveThirtyEight blog that used a sophisticated system to synthesize dozens of national polls in a rolling average to predict the actual election results. In the run-up to the election, he even received a lot of flak from various pundits who claimed he was wrong basing on their perception on voter “enthusiasm,” “momentum” and other non-scientific observations. At the end of the day, however, data won out over hot air and punditry big time. Silver’s final tally was absolutely dead on, crushing most other national polls by a wide margin.

I especially love his Nov. 10 post in which Silver analyzes the various polls and shows which ones fared the best and which ones weren’t worth the paper they were printed on. It’s shocking to see that the Gallup Poll—in many people’s mind the oldest and most trusted name in polling—was skewed Republican by a whopping 7.2 points when averaged across all 11 of their polls. Ouch. For an organization that specializes in polling, their long-term viability must be called into question at this point.

One thing I find highly interesting when looking at the various poll results is that when you examine their methodologies, it’s not too surprising that Gallup fell flat on its face, relying on live phone surveys as the primary polling method. When considering that many young, urban and minority voters don’t have a landline and only have a cellphone, it doesn’t take a rocket scientist to conclude any poll that doesn’t include a large number of cellphones in its cohort is going to skew wildly Republican … which is exactly what happened to Gallup, Rasmussen and several other prominent national polls.

Turning to the Obama campaign’s incredible Get Out The Vote (GOTV) machine that turned out more people in more places than anyone could have ever predicted, there’s no doubt in anyone’s mind that for data-driven marketers, the 2012 U.S. election victory was a watershed moment in history.

According to a recent article in Time titled “Inside the Secret World of the Data Crunchers Who Helped Obama Win,” the secret sauce behind Obama’s big win was a massive data effort that helped him raise $1 billion, remade the process of targeting TV ads, and created detailed models of swing-state voters that could be used to increase the effectiveness of everything from phone calls and door-knocks to direct mailings and social media.

What’s especially interesting is that, similarly to a tech company, Obama’s campaign actually had a large in-house team of geeks, data scientists and online marketers. Composed of elite and senior tech talent from Twitter, Google, Facebook, Craigslist and Quora, the program enabled the campaign to turn out more volunteers and donors than it had in 2008, mostly by making it it simpler and easier for anyone to engage with the President’s reelection effort. If you’d like to read more about it, there’s a great article recently published in The Atlantic titled “When the Nerds Go Marching In” that describes the initiative in great detail.

Well, looks like I’m out of space. One thing’s for sure though, I’m going to be very interested to see what happens in coming elections as these practices become more mainstream and the underlying techniques are further refined.

If you have any observations about the use of data and analytics in the election you’d like to share, please let me know in your comments.

—Rio