Blocking ad blocking with Google

n the conflict between publishers that want to deliver ads to more eyeballs, and the possessors of those eyeballs who try to block them from appearing, Google is presenting itself as the gatekeeper of choice. It offers to serve as guard to a site's content, and only let in those who either allow the ads to run or who pay for their ad-free experience.

About a quarter of the US online population used ad blockers last year; and even higher percentages did so in other countries. That all adds up to an estimated loss of $42 billion globally, according to a report by OnAudience.com. With so much money at stake, it's no wonder that more and more sites now withhold their content from visitors using ad blockers, or at least make note of the use of ad blocking in the escalation of the ad blocker arms race.

For those companies that want to call in the really heavy guns, there's the possibility of getting Google on guard. In a blog post entitled “Helping publishers recover lost revenue from ad blocking,” Google announced it was expanding its Funding Choices solution, launched in beta last year.

Read more in Google On Guard Against Ad Blocking

Everybody lies with visualizations

Photo by Ashkan Forouzani on Unsplash

As I wrote here, before it became trendy to point out the problem of fake news, I explored how data visualizations can mislead people. I’ve noticed that in the last couple of years, data visualization has become a major focal point.  The old maxim of “Seeing is believing” is the real driving force behind visualizations of data.  While not all of us relate to spreadsheets, we tend to respond well to graphs, charts, and other visually appealing renderings of those numbers.  

While we can all fall for it, we can immunize ourselves to some extent with vitamin C. 

Back in 2016, I identified three key C's in a Baseline article, Data Visualization: You Must 'C' It to Believe It: Context, Correlatin, and Causation. 

Context: This includes contextual information for the graphs, which sometimes indicates that the results visualized represent outliers rather than typical results. Getting the context also requires getting the baseline for the survey, including timelines, locations, and the population size and type used to get the numbers.
As data visualization tools include ways to slice and dice your data, it is not all that difficult to zero in on just the segment that yields the results you want. So you need to know the larger context, as well as any added-in points that are outside that particular context.
Correlation: This is the supposed strongpoint of visualizations: showing up correlations. But they are easily manipulated and misleading, as there are many correlations of time that are not necessarily causally connected—though visualizations can make them appear that they are.
Causation: This is what real insight is all about: finding out what causes what. There is no substitute for thinking this through, no matter how seductive it may be to simply go with the correlations presented by the visualization.

In revisiting an argument offering data visualization as proof, I've come to add some additional C tests:

• Correspondence to reality. Just because someone claims expertise doesn't mean they are completely correct about their assertions. For example, when I was in labor with my first baby, the doctors and nurses at the hospital just dismissed my pains, claiming the contractions were "mild" and that the birth was far from imminent. I was not the expert; they were, but I knew that I felt the baby coming. As it turned out, the resident barely got to me in time. I learned from that experience that you should not be gaslighted by expert views that directly contradict not what you just think you know but what you do know and directly experience. 
• Convenience: This pertains to both means and ends. Convenience of means refers to using the data that is on hand or easily measured even if it's not necessarily the data that is the most relevant. It's rather like measuring how much snow fell on your windowsill because it's easy to reach rather than going out to get the measure on the street and in drifts to get a more accurate measurement. Convenience for ends is about selecting data that you can easily fit into the conclusion you wish to draw AKA cherry picking. 
• Confirmation Bias:In general, when you look for data on something, you have to bear in mind that absolute objectivity is rare. Many of us have deeply-seated values and beliefs that will not allow us to entertain the possibility that we are on the wrong track,which would skew our results because of what we allow and disallow in the data set. It is the equivalent to painting a bull's eye around where your arrow went. So ask yourself, does the person have some personal agenda that could be coloring the outcome? If so you should treat them with the same healthy skepticism you would treat cigarette tobacco studies sponsored by tobacco companies. 
• Certainty Camouflaging Contingencies: Few things are absolutes, so if someone states something without qualifiers, likely something is being hidden or glossed over -- like the fact that the data is out of date or taking searches of racist terms and jokes as proxies for the person being a racist and then shifting labels from what actually is measured to what the person says is signified by the measurement. This leads to a triple F: Fudging Figures and Facts.

All of these were inspired by an argument made in Seth Stephens-Davidowitz's book Everybody Lies. Read more about it in Sex, Lies, and Data Profiles

Related post: 

DATA VISUALIZATION: YOU HAVE TO C IT TO BELIEVE IT

Marketing for Mom's Day

Vintage mom image from
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A mother's love may be priceless, but there is definitely a price tag on Mother's Day. 

While Mother's Day does not have the status of a federal holiday like Easter, in fact, it occasions significantly more spending. The National Retail Foundation (NRF) forecasts that this year's Mother's Day spending will reach $23.1 billion.  For comparison, this year’s Easter spending was estimated to be $18.2 billion.  

Obviously, marketers have to seize the day for their brands, particularly if their brands feature jewelry. That's the top choice of gift for the day. According to the NRF's survey, 34% of shoppers intend to buy something in that category, bringing that total spend to an impressive $4.6 billion.

While many jewelry brands are, no doubt, sticking to the standard sentimental messages, some are breaking out of the box in their depictions of different types of mothers with strengths that go beyond the stereotyped image of a woman in an apron. Crimson Hexagon's data on what people are talking about the most and what garnered the most positive conversations. It uncovered some fresh takes in mother images in some jewelry campaigns, as well as some surprises.

Read more in Mother's Day Marketing

See some of the ads featured below:

Alex and Ani's “Symbolize Your Love” campaign includes the outtakes of commercials filmed with real people (which fits very well with increasing demands for authenticity in marketing)

Getting Women to Stay On in Tech

As more and more business and manufacturing processes revolve around technology, the demand for people with the necessary skills is growing. To assure the supply of qualified people filling those positions, we have to stop thinking in terms in terms of stereotypes and clear the way for women to get on board.

Image courtesy: Pixabay

The problem is not that women aren’t trained in science, technology, engineering, and mathematics (STEM) fields. “Women have earned 57% of all bachelor's degrees and about half of all science and engineering (S&E) bachelor's degrees since the late 1990s,” according to the latest figures from the National Science Foundation. The problem is that those percentages don’t translate into the same level of representation at work.

In fact, women are still far outnumbered at engineering positions at tech companies. You can see the numbers of engineers in actual companies updated regularly on a spreadsheet in Tracy Chou's Women in Tech list.  Though the numbers vary, the average representation for women engineers at the companies listed appears to be near 20% to 25%.

The gap between the sexes grows higher up the hierarchy. The Gender Divide in Tech-Intensive Industries put out in 2014 demonstrated that women with MBAs with tech qualifications were still far less likely to work in the industry than their male counterparts. Perhaps part of the reason is that women ae far more likely to be placed in entry level jobs, at the rate of 55% in contrast to the 39% for men.  Women MBAs also were more likely to leave the tech industry than their male counterparts at the rate of 53% to 31%.

Read more in 

Retaining Women in Tech Takes More Than Training

Gold Standard Tracking with Blockchain

Conflict minerals making their way into the electronic supply chain presents a challenge to companies

that want to act both legally and ethically. Tracing such minerals to their source is not straightforward or simple. Blockchain technology can solve that problem.

The Responsible Gold supply chain  is designed to track “responsibly sourced gold from mine, to refinery, to vault.” It’s put out by Emergent Technology, and has first been applied to gold mined by Yamana.

Read more in 

Blockchain & the Gold Standard for a Conflict-Free Supply Chain

AI Applied to Healthcare Marketing

DeepIntent's CEO and co-founder Chris Paquette came in with a background in healthcare, having worked as a data scientist for Memorial Sloan Kettering, using AI to find patterns predictive of patient outcomes. Prior to that he worked at a search company. DeepIntent's approach, is built on a combination of the two fields, as he explained in an interview.
Read more in 
Finding the Audiences for Healthcare Marketing