This article compares Bob Brinker’s S&P 500 revenue quotes for 2018 and 2019 with those of one of my favorite economists, Dr. Ed Yardeni. 163 with a PE ratio of 17 to 18. As my desk below shows, this is approximately the highest PE percentage Brinker has thought acceptable since 2008 predicated on the Marketimer notifications I surveyed to make the table below. Email Alerts for New Articles: Click “Follow” on the right hand side of this blog and it (Google Blogger) should send you a FREE alert via email after I publish a fresh article here.

I am confident it generally does not send email notifications after I make improvements to the articles so I will try to create “check back” if I plan to enhance the article. Of course, you need to consider this numerology with large grains of sodium. I highlight in this desk.

I’ll try to provide regular improvements of that table here because it has some interesting calculations and it offers a good historical record. Is an example from his May 3 Here, 2017 Marketimer how he frames the data with words. From over 20 years of following Brinker, I’ve observed he usually begins his S&P 500 revenue estimates less than the consensus of the average of most analysts tracked by tracking services. Then if the entire year goes well, Brinker raises his estimates such that by the end of the year they are near to consensus.

This also allows him to improve his quotes for the market price before it goes “closer to the period when investors will discount” another year’s earnings quotes. Dr. Ed Yardeni’s Estimates: This is exactly what one of the best economists, Dr. Ed Yardeni, publishes. 155 and 166, respectively. Subscribe and get the May 2018 Concern for FREE NOW!

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To counter a shrinking customer foundation, a European bank or investment company attempted a number of retention techniques concentrating on inactive customers, but without significant results. Then it turned to machine-learning algorithms that predict which currently active customers are likely to reduce their business with the lender. This new understanding provided rise to a targeted marketing campaign that reduced churn by 15 percent.

A US bank or investment company used machine understanding how to study the discounts its private bankers were offering to customers. Bankers claimed that they were provided by them only to valuable ones and more than made up for them with other, high-margin business. The analytics showed something different: patterns of unneeded special discounts that could easily be corrected.

After the machine adopted the changes, profits rose by 8 percent within a few months. A top consumer bank or investment company in Asia loved a sizable market share but lagged behind its rivals in products per customer. It used advanced analytics to explore several units of big data: customer demographics and key characteristics, products held, credit-card statements, transaction and point-of-sale data, online and mobile exchanges and obligations, and credit-bureau data. The bank uncovered unsuspected similarities that allowed it to establish 15,000 microsegments in its customer base.

It then built a next-product-to-buy model that increased the likelihood to buy 3 or over. Results like these are the good thing about analytics. But they are also the bad information. While many such projects generate eye-popping returns on investment, banks find it difficult to scale them up; the financial impact from even several great analytics initiatives is insignificant for the organization P&L often.