Joe Biden said he's spoken to Sen. Bernie Sanders and former President Barack Obama about selecting a running mate — and that he wants to build "a bench of younger, really qualified people" who can lead the nation over the course of the next four presidential cycles.

Driving the news: Biden spoke about the state of the 2020 race during a virtual fundraiser on Friday night that was opened to pooled coverage.

• He said he'll announce a committee in mid-April to oversee the vice presidential selection process.
• The former VP has a near-insurmountable lead over Sanders, but has not yet secured the number of delegates needed to claim the Democratic nomination.
• "I don’t want him to think I’m being presumptuous, but you have to start now deciding who you’re going to have background checks done on as potential vice presidential candidates, and it takes time."

Between the lines: Biden also acknowledged the coronavirus has overshadowed coverage of the race and given President Trump an opportunity to dominate messaging via his task force briefings.

• “I got a lot of people who are supporters getting very worried," Biden said. "‘Where’s Joe? Where’s Joe? The president’s every day holding these long press conferences.’”
• “For a while there, I kept getting calls — people saying, ‘Joe, the president’s numbers are going way up and he’s every day on the news. What are you going to do about it?’”
• “You can’t compete with a president. That’s the ultimate bully pulpit," Biden said, but added, "Those numbers aren’t going up anymore" because "the things he’s saying are turning out not to be accurate and people are getting very upset by it.”

Biden, 77, also said he's begun outreach to assess who he could bring into the administration if elected.

• He said "one of the ways to deal with age" is "to build a bench of younger, really qualified people who haven’t had the exposure that others have had but are fully capable of being the leaders of the next four, eight, 12, 16 years to run the country."
• "They’ve got to have an opportunity to rise up."

Editor’s note: this is part of our investigation into analytic models and best practices for their selection, deployment, and evaluation.

We all know that a working predictive model is a powerful business weapon. By translating data into insights and subsequent actions, businesses can offer better customer experience, retain more customers, and increase revenue. This is why companies are now allocating more resources to develop, or purchase, machine learning solutions.

While expectations on predictive analytics are sky high, the implementation of machine learning in businesses is not necessarily a smooth path. Interestingly, the problem often is not the quality of data or algorithms. I have worked with a number of companies that collected a lot of data; ensured the quality of the data; used research-proven algorithms implemented by well­-educated data scientists; and yet, they failed to see beneficial outcomes. What went wrong? Doesn’t good data plus a good algorithm equal beneficial insights?

The short answer: evaluation. You cannot improve what you improperly measure. A misguided evaluation approach leads us to adopt ineffective machine learning solutions. I have observed a number of common mistakes in companies trying to evaluate their predictive models. In this series, I will present a variety of evaluation methods and solutions, with practical industry examples. Here, in this first piece, I’ll look at accuracy evaluation metrics and the confusion between a good prediction and a good nudge.

Good predictions? Good nudges.

Let’s take the retail industry as an example. Many retailers believe if they can accurately predict their customers’ future purchasing preferences, they can increase sales. After all, everyone has heard the stories of Target identifying a pregnant teen and Nextflix’s success with its recommendation system.

This supposedly flawless assumption that accurate predictions can increase sales is easily overthrown, however, when we implement the accuracy evaluation metrics in reality. In machine learning, a metric measures how well a predictive model performs, usually based on some pre­defined scoring rules. Different models can then be compared. For instance, in recommender systems research, metrics such as recall/precision, Root Mean Square Error (RMSE), and Mean Average Precision (MAP) are frequently used to evaluate how “good” the models are. Roughly speaking, these metrics assume that a good model can accurately predict which products a customer will purchase or give the highest rating.

What’s the problem then? Let’s say I want to buy cereal and milk on Google Shopping Express. Once I open the app, let’s assume it accurately predicts that I will buy cereal and milk and shows them to me. I click and buy them. That’s great, right? But wait, the retailer originally expected the predictive model to increase sales. In this case, I was going to buy cereal and milk anyway, regardless of the accuracy of the prediction. Although my customer experience is probably improved, I do not necessarily buy more stuff. If the aim is to increase sales, the metric should, for example, focus on how well the model can predict and recommend products that will nudge me to buy much more than just cereal and milk.

Oftentimes, the true objective is to nudge customers toward some choice or action.

Researchers and businesses have a vested interest in nudging. For instance, Lowe’s grocery store in El Paso  successfully conducted an experiment that nudged customers to buy more produce. How? Simply by adding huge green arrows on the floor that pointed toward the produce aisle. Another successful example of nudging is “checkout charity”: retailers raise millions of dollars for charity by asking customers for small donations at the checkout screens.

Applying the predictive power of machine learning techniques to nudge, if done right, can be extremely valuable. Many bright statistical minds are, however, confused by the subtle difference between a good prediction and a good nudge. If we mix up the two in the evaluation process, we may end up choosing a model that does not help us achieve our goal. For example, Facebook’s emotional contagion experiment, despite its controversy, not only shows us how data influence users’ emotions, it also gives us a vivid example of when the goal of a metric should be to measure influence (or nudge) rather than predict.

The best metric is one that is consistent with your business goal. Oftentimes, the true commercial objective is to nudge customers toward some choice or action. Perhaps it is time for data practitioners to increase their awareness of metrics that reflect this kind of psychological impact of machine learning — sometimes the most effective result requires more than just good prediction.

Country-pop star Taylor Swift penned an optimistic essay in Tuesday’s Wall Street Journalabout the lasting bonds between performers and their fans, and why she thinks the music industry is “just coming alive.” You can think what you want about Swift’s songs, but her take on the business is a welcome change from the doom-and-gloom we normally read.

In her essay, Swift is upfront about what everyone knows: CD sales fell off a cliff and, while streaming and digital sales have grown dramatically, they have not plugged a shortfall that has seen overall music revenue fall from $15 billion in million IN 2003 to $7 billion million today.

Often, such numbers are a cue for a musician to launch into a long harangue about piracy and the need for Congress to expand copyright. Instead, Swift does something different. She offers some new insights into about the evolving relationship between musicians and fans. Here’s what she says about autographs, for instance:

There are a few things I have witnessed becoming obsolete in the past few years, the first being autographs. I haven’t been asked for an autograph since the invention of the iPhonewith a front-facing camera. The only memento ‘kids these days’ want is a selfie. It’s part of the new currency, which seems to be ‘how many followers you have on Instagram.’

And here is how Swift sees social media changing traditional music deals:

A friend of mine, who is an actress, told me that when the casting for her recent movie came down to two actresses, the casting director chose the actress with more Twitter followers. I see this becoming a trend in the music industry … In the future, artists will get record deals because they have fans—not the other way around.

Swift’s essay also makes a heartfelt plea for the album as art, and expresses a belief that fans will always pay for those special albums that change their lives: “I think the future still holds the possibility for this kind of bond, the one my father has with the Beach Boys and the one my mother has with Carly Simon.”

A way forward

It’s easy to be snarky to Swift. Indeed, others have already pointed outthat her faith in revived album sales is misguided, and that the economics of digital distribution mean that only a lucky few, like Swift or Justin Bieber, have the celebrity klout to sell records in this day and age.

That might be true, but it doesn’t mean that Swift’s other observations aren’t helpful — if only the music industry would act on them. Alas, the industry is instead expending its legal and lobbying power on trying to wring more money from 50-year-old music. Just look at the current efforts to squeeze the likes of Pandora dry with ever-higher royalty rates and ill-considered class action suits.

Imagine if the industry directed more of its energy to finding new revenue sources amid all those selfies and Twitter followers surrounding Swift. As my colleague Mathew Ingram has explained in the context of news, new business ideas based on “membership” may offer more promise than attempting to replace past product sales.

Yes, many of the details are still fuzzy. But new user-based companies like Twitter and Instagram and are still developing their business models, and in coming years they will no doubt offer Swift and others a range of money-making ideas that we have yet to to imagine. Meanwhile, YouTube, despite contract scuffles, is already offering millions in ad revenue to famous acts and upcoming ones alike.

In the future, there will also continue to be a growing range of web and app platforms – everything from games to disappearing messaging apps — that offer both music licensing opportunities, and new ways for fans and performers to connect. The money may take time to emerge but, for cynics and the music industry, this is the way forward. Or in Swift’s words, “This is a love story, just say yes.”

This story was updated at 12:30pm ET to reflect that the music industry figures are in billions not millions.

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