Why you always need the original source

Always, always go to the source cited to judge how relevant it is. That's my rule of thumb -- not just for my journalistic work but even for my content marketing. And it never ceases to amaze me how many people don't bother with that even while positioning themselves as authorities on the subject.

I knew I'd regret it, but I gave into temptation and clicked on an article with the title "How to Write Headlines That Grab Attention and Convert"

It gave the usual advice that most seasoned writers already know, including writing the title only after you've finished writing the article, being specific but not giving everything away, etc.

But it also sought to add insight taken) from "Data Driven Strategies for Writing Effective Titles & Headlines," the 28 page report put out by HubSpot and Outbrain.

Instead of putting in the title and link properly as you should do for anything you cite, it introduced the information this way:

Lessons from a 3-Million Headline Study

HubSpot and Outbrain analyzed more than 3 million paid link headlines from Outbrain’s network of 100,000+ publisher sites to find out what kinds of headlines can increase CTR, reader engagement and conversions, and this is what they found:

It then proceeded to share stats and insights from that study for the next 16 paragraphs. (I'm not exaggerating; I counted them). Despite drawing heavily on the study, the article never puts in a link to it.

In fact, it never even shares the title, which made it a bit more difficult to find. But I am nothing if not persistent when it comes to research and tracking things down.

I located the original source, which says that it was based on headlines in the time period of 2013-2014. That's right, the data is form nearly 10 years ago. In the world of online content, I wouldn't bank on anything more than two years old to still be current.

So why did the writer of an article published in August 2023 not include the link? It's possible that he deliberately intended to obscure that bit of historical context by not linking directly to the source. What's more likely, though, is that he came across another secondary source that cited those figures and takeaways and so didn't even know when the original study came out.

Unfortunately, that is often the case for writers who just go with the first Google result, which is more-often-than-not not the original source. You have to dig more to get the source in context.

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Today's targeted marketing is powered by data and automation

 Marketing is always more effective when it is more targeted. As a result of integrating data and algorithms, marketers are able to now deliver a personalized customer experience at scale. 

There are various ways to target specific customers, and approaches range from lumping customers into very broadly defined categories to getting a lot more fine-tuned about the segments and responsive to individual customer behavior .In collaboration with Google, Deloitte put out a Digital transformation through data: a guide for retailers to drive value with data that took a closer look at these gradations. 

It ranked them as follows:

• Limited segmentation: All users are analyzed in broad segments. 

• Basic segmentation: Uses standard characteristics (e.g., gender, geography) for segmentation.

• Detailed segmentation: Segments are based on personal and behavior

• Dynamic segmentation: The UX / UI can respond to a customer’s in-session behavior as he or she exhibits different segment characteristics.

Achieving the detailed level depends on much more data than the static kind that is used for basic segmentation, and advancing to the dynamic level requires a level of automation that will enable recommendations and responses to go out in real-time. 

 The coming AI revolution in retail and consumer products invoked the women’s clothing store,  Avenue Stores LLC as an example of dynamic segmentation. It explained that  it brings together “data across multiple touchpoints, including in-store activities and market trend analysis, to learn and reason about what customers want and when they want it.” On that basis it can reach out to customers with communication tailored to their situation in real-time, which makes it possible to capture their attention while in “‘shopping mode.” 

Marketing for loyalty

Being in touch with your customers to let them know you’re there for them without pressuring them to buy can pay off in winning their loyalty and business later. In this case, your automated messaging doesn’t have to respond to segment your audience, as you would be working off a general form of communication.

When you don’t have history

But what if you do need to sell your products now? Marketing recommendations can work even on the more basic level, not just for new customers for whom you have no history to flesh out a profile but for the type of marketing communication that depends on general trends. For example, a very broad segment of all people in the United States can work for promotions tied to events shared by all due to the calendar, whether it’s Mother’s Day, Memorial Day, July 4th, etc. 

You don’t need to know much about your customer other than that they’ll know what these days are because they are on their calendars due to living in the United States for the trending algorithm to work well. That makes using this approach ideal for customers for whom you don’t have first-party data.

It doesn’t matter so much what they are normally interested in or what they’ve bought before when you’re sending out a marketing message about buying their mother something before May 10. However, if you do have information about the customer, say you know they’ve ordered flowers for their mother last year, then you can combine the trending recommendation with what you know about their behavior.

Read more in

Advanced Segmentation and Automation Are Changing the Marketing Game

Data visualization: you have to C it to believe it

 credit https://c1.staticflickr.com/9/8075/8448339735_e6626c28ff_b.jpg

I wrote this blog a couple of months before everyone started decrying the proliferation of fake news. Notice just about every fake news piece is accompanied by some sort of visualization, whether it is a graph or photo or video. They all capitalize on the "seeing is believing" concept, and one has to be extra vigilant about the lure of visual evidence.

As a regular big data blogger for several years now, 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.  

As Brian Gentile, Senior VP and General Manager, TIBCO Analytics Product Group, TIBCO Software, wrote here there are business benefits to data visualizations.  They include making it easier to take in information, manipulating, data in various ways, and showing relationships.  On the latter, Gentile observes, that “finding these correlations among the data has never been more important.”

Indeed, the demand for that kind of instant insight that data visualizations can deliver is what drove Google to build its own data visualization product (currently in beta) called Data Studio. I saw a presentation of the features, including a report on the effectiveness of Olympics ads. It was that particular visualization that made me think of the danger inherent in relying completely on the story presented graphically.

In that analysis of the effects of ads on consumers, the report stresses that it asked people who saw the ads of particular brands what effect it had on their perception of them. Of course, the graphs are what grab your attention and that show that that 34.9% of viewers recall seeing the Coke ad. The graph does not show what the text admits that overall “only about 8% of viewers can recall both the brand and product in a specific advertisement.” So the graph here implies a much more positive effect for ad recall than the overall data actually shows.

 The next bar graph shows you that “Consumers who saw the ads were 18% more positive about the brand and were 16% more likely to find out more or purchase the product in the ad.” These are fairly modest numbers that don’t necessarily promise much bang for sponsor bucks. So this is followed by a third graph with the title “Which ads showed the greatest response?” That shows really impressive numbers ranging from 112%- 142% for the top 3 brands.

A mere glance would make you think that these show amazing results for the marketing efforts. Then when you read a bit, you realize that they merely reflect the increase in search.  In other words, the graph does not show that the McDonald’s commercial resulted in an increase of 42% in sales, merely an increase of that amount in online search that includes the brand. Still, you may say that is a positive metric that could possibly translate into improved sales down the road. But the chain of causation here is missing a few links. 

I got to speak to the Google people about Data Studio and asked if they had even determined if the people who were doing the search were the ones who had seen the ads as was the case for the first two graphical presentations. They had not.  True, it doesn’t say that the graph refers to the people who had seen the ads, but the context would make the viewer think that it does, and not everyone would even think to ask annoying questions like I do.

Ultimately, what makes data visualization so effective at conveying a point is that they don’t require much analysis on the viewer’s end because they’ve already done that kind of thinking for you. That’s both seductive and potentially misleading.

That’s exactly why we have to be careful about not merely accepting the visually expressed story at face value. Any data visualization should be subjected to a triple C test. 

Read about it here.

Also check out http://www.clickhole.com/article/greatest-all-time-statistical-portrait-babe-ruth-3983 

The one on the Babe versus the #12 may be my favorite example of the abuse of data visualization, and I'm not even a sports fan

Related post: 

EVERYBODY LIES WITH VISUALIZATIONS