Reward Tracking and Product Recalls

booth branding business buy
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Stores like for us as consumers to give them a customer ID to track what it is that we are buying. Many have phone number or a card or an email address. They use this information to track our purchases and personalize their nudges for us to buy products.  There is one way they might improve customer loyalty: Recall notices.

I pay attention to the news, so I see recalls every week. But, I doubt I am seeing them all. And, I doubt that I can reliably say whether I have the recalled item. But, the store where I bought it probably does.

A couple years ago, I was in a grocery aisle mulling over what to select when a manager came through to take off the shelf something nearby. He had a scanner which told him the information about the recalled item.

What would be really cool is if the system that is telling the stores what to pull from their shelves, looks through the customer purchases and informs the customers. They could pass along the recall notice and let the customer identify the lot number the same as the store. (I knew the manager was working a recall notice because he was talking to himself.)

Thinking maybe this already exists as an opt-in, I checked the stores where we have web site accounts. Nothing. (Given these places tend to go with an opt-out model, I was not surprised.)

Personalization modes

hacker screen
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In shopping for Mother’s Day the algorithms now think I am female. Obviously, they took the items I looked at for this quest and incorporated them into my profile’s records and are basing new recommendations on them. They are fresher. And they have left over inventory they want to move. So, I get it.

This shopping for another persona has to be a relative common phenomenon since personalization became a buzzword, so I don’t get why this hasn’t been solved over a decade later. People shop for others’ birthdays all the time. And maybe my solution below doesn’t exist because people impulse buy for themselves and others based on getting things suggested later. And, one can go into the recommendations and delete off items to restore them to normalness.

This other persona influence to recommendation must have happen so much that I am surprised that such companies that use it have not created shopping modes.

  1. Allow users to say they are shopping for another person. Associate the personalization that that profile. Based on what is bought for that person, the suggestions can get better.
  2. With some sort of confirmation from the person being shopped for, they might make recommendations based on their wishlists. Although mine are sorely out of date.
  3. If the user is looking for things that seem… uh… out of character or in character for the subject of an upcoming holiday like mother’s day or father’s day, then prompt the user if they ought to change modes.

 

 

TED Talk: How to take a picture of a black hole | Katie Bouman

A talk on how the process would work presented a couple years ago. Interesting how closely the actual image matches the reconstruction before they did it.

At the heart of the Milky Way, there’s a supermassive black hole that feeds off a spinning disk of hot gas, sucking up anything that ventures too close — even light. We can’t see it, but its event horizon casts a shadow, and an image of that shadow could help answer some important questions about the universe. Scientists used to think that making such an image would require a telescope the size of Earth — until Katie Bouman and a team of astronomers came up with a clever alternative. Bouman explains how we can take a picture of the ultimate dark using the Event Horizon Telescope.

Outraged? Don’t share

Our attention is the product for Facebook and Twitter. They make money by selling advertising. The more time we spend on the site, the more ads they put in front of us, the more money they make.

Outrage makes them the most money. We are more likely to share what outrages us. We have tribalized our social groups such that our friends are most likely going to be outraged too and more likely to share. So the outrages go viral.

The most effective things to make us share are also probably fake or misleading. We get so upset that we do not bother to check until maybe someone not so outraged fact-checks and points out the problem. So fake items go viral.

The synergy of fake outrageous news is powerful. It is manipulative. We train the social media algorithms that we WANT to be manipulated. We spend more time on these sites because we are addicted to being manipulated.

Crowdsourcing Bias Identification

Zittrain’s set of tweets was interesting reading.

Lots more on it: best1, 2, 3.

There is an interesting plug-in called Media Bias/Fact Check which will help show how a news site skews. Before you rush off to use it, you should like I did review their methodology to ensure you can reasonably trust it.

My one issue with it is a crowdsourcing component where each site they review has a PollDaddy widget. PollDaddy like any other similar thing tries to prevent multiple voting, but that security relies on cookies, so if I wanted to skew the poll and say make Snopes appear extreme right, it is not all that difficult to vote, delete the cookie, vote, delete, etc. A possible example is MBFC marks Palmer Report as “left center” while the poll shows Extreme Left=76, Left=36, Left Center=44, Least Biased=26, Extreme Right=2. There appears to be some disagreement between those polled and the reviewers. The polls are not pulled into the plug-in database and instead used by humans to review whether they should revisit a prior determination. So MBFC cannot be directly manipulated through the polls.

Back around 2003 spam was really, really bad. Work had not yet devised an anti-spam solution, so I turned to an interesting client plug-in where users marked messages as spam. If enough users marked a message as spam, then the sender was blacklisted and anything from them sent to a junk folder. The dark side to this model? Some people interpreted “spam” as any email they did not want to receive. Several email lists or legitimate advertisers with easy and functional unsubscribe tools were blacklisted. It was easier for people to hit spam than do the right thing.

The wisdom of crowds has very narrow applications.

But a look at recent cases and new research suggests that open-innovation models succeed only when carefully designed for a particular task and when the incentives are tailored to attract the most effective collaborators.

So I appreciate that MBFC has a firewall between the crowdsourcing and their reviews. But, that also means their method is very, very labor intensive. Sites will be very slow to be added.

Facebook is talking about removing fake news. Some are calling for them to so something like MBFC and help users understand what they are reading. Back in May they removed their editors who were in charge of doing essentially the MBFC of thing of reviewing and ensuring what ended up in Trending was good material and writing summaries. These editors were accused of bias. When Facebook replaced them, fake news immediately started showing up in Trending. Removing fake news could help, but how they go about it could be interesting. Their editor debacle could push them in the algorithm route, but their algorithm debacle could push them back toward editors. Maybe some mix of the two?

TED Talk: Don’t like clickbait? Don’t click

Fake clickbait like The Onion is good. ALWAYS click on The Onion. I don’t care if you dislike their fake news stories. I enjoy them. 🙂

The algorithms choose which stories we see. If you dislike what you see, then you need to change what you click. My Facebook feed? It is chock full of science, soccer, TED talks, baby photos, wedding photos, and of late Star Wars. I rather like my feed, but it took discipline not to send messages about my interest in fear mongering, gossip, and hate. Tough, I know. But the results were so worth it. I’m no longer thinking of declaring bankruptcy on Facebook.

As Twitter and other social media succumb to algorithms to display stories, apparently I am going to have to use the same discipline avoiding clickbait elsewhere. I wonder about the mental discipline required to achieve and maintain the Internet experience I desire. Hopefully, in achieving it, I develop good habits I can maintain.

Anyway, Sally Kohn discusses how to get the social media we want by being smart on what we click.

 

Ad Fails

An advertisement for a Porsche plug-in hybrid really fails. First, Porsche was old and lame by high school. Lotus, Lamborghini, Ferrari, and so many other car companies come ahead. Second, I do not have a job where an ostentatious car helps me. Third, I cannot keep my mobile phone properly charged. A plug-in hybrid is not the car for me.

Given how much activity I have online and all the tracking data collected about that activity, I feel that advertisements delivered to me ought to be fantastic. There should only be advertisements delivered on the pages I visit that confirm my desires or make me suddenly desire it.

Certainly looking up this car put plenty of data out there supporting the advertiser’s algorithms pushing this ad at me. Probably I will see more of it. Perhaps it is better, though, than the ads of the last item I checked out on Amazon. Reminding me that I did not buy it probably will not trick me into actually buying it.

UPDATE: Perhaps the ad had more to do with the page I visited than data about me? It was a piece critical of the Chegg IPO by comparing it Twitter as a success. I visited it because I heard a stock doubling after the IPO like Twitter’s did should be considered a failure. (The gains go to investors not Twitter, so Twitter should have set a higher price since other valued it more.)

Algorithmic Random

Mac Keyboard
Mac Keyboard

If you are out on the Internet or around academics long enough, then you will run across the rant about random designed by humans not being really random. It might be the iTunes shuffle. It might be random sampling of an experiment. It might be a complaint of you using the word for how you spend your time online.

OK. I took that last one a bit too personal.

If a human is performing the random, then there probably is a pattern. But then in nature, things we call random typically have a pattern too. DNA mutations involve changed molecules at a position and chance that it has no bearing, disables the bearer, or gives the bearer an advantage. The lack of true randomness is a sign of intelligent design to some. And a sign that it is natural to others. Quantum mechanics. Encryption. Stock trading. Prediction. Truly random is unnatural. Well… It just means we have not yet figured out the pattern. Give us time.

Since random is the wrong word, how about algorithmic random or a-random for short. It just means a pattern-based approximation of random that is good enough for the purpose of acting random.

Extending Gmail Addresses

Surprised I have not posted prior about this. Gmail allows one to use username+anything@gmail.com and have it delivered to username@gmail.com. Use it to sign up for web sites or things and filter later. Should this address be compromised, you can create a filter to delete anything sent through just that address.

Keep in mind…

    1. Though I would expect pretty good spammers or hackers to remove the +anything. 
    2. Some web sites use algorithms that consider these addresses not real.

So your results may vary.

Dashboard vs Feed

John Pavlus in Ghost’s Blogging Dashboard Doesn’t Need to Exist fell hook line and sinker for Anil Dash’s All Dashboards Should Be Feeds false dichotomy. The better argument is dashboards only tell the past with all the noise where the more useful information is an accurate future. People ultimately want to know what is going to happen. The feeds would do that.

However, to accomplish that feeds take the same data, apply criteria, and report a prediction of value to the user. That’s fantastic stuff. You know… Fantasy.

Someone has to decide how to produce the signal out of all the noise. Probably that is a quant or a wannabe who teases out of the data the important predictions. So unless you are beholden to someone like Anil, you want to be able to manipulate the data by looking at something like a dashboard to build feeds.

Not everyone is like me, I get that. Simple users want a magic number or an easy indicator of what is going on. Think of an alert that a site is going to break in 15 minutes. Power users like me want to know if components of those web sites are going to break 15 minutes from now. You know, so I can go fix it. But I would not mind being able to allow others to subscribe to my feeds where appropriate.

I’ve never had a problem taking dashboard data and projecting from them trends. A good one, like Yaketystats will even graph the prediction lines for me. I often work with the data to see how this line changes in order to get a sense if the prediction has biases built into it. But then, I enjoy being hands on and manipulate the graphs to see what I want to know. Predictions are only as good as the algorithm. Any why should we trust other’s when we can build our own? I could see YS with alert feeds for directors and above letting them know about upcoming milestones. It would be great for them, but that high level view is not so interesting to me. I want the details and build the things that produce the signal from the noise.