How to Use Empathy Mapping in Marketing

How to Use Empathy Mapping in Marketing

Have you noticed that the best books and movies are so totally immersive you feel as if you’ve actually entered the world they depict? Which is why I’d argue that the people most skilled in content aren’t necessarily the best writers; rather, they’re the ones who are able to empathize with characters so well that their creations seem real.

When writing for marketing, you need to connect with your audience. But first you have to understand who they are and what they care about. That’s where empathy mapping comes in.

What Is an Empathy Map?

An empathy map is a tool that brings to light to your most likely customers. It teaches you how to connect with the people who will consume your content, products, or services.


Typically divided into quadrants of say, think, do, and feel, the empathy map is intended to get into the head—and heart—of the customer. It helps you to visualize that what someone says doesn’t always align with what they do. That’s why it’s also important to understand what they think and feel.

For example, someone might say they love the service you provide, yet still shun your products. Could it be that the cost is too high, and in their social circles that may not be something they’re willing to say? Or maybe they feel passionately about your products but think the purchase isn’t practical.

Unfortunately, those conflicting “boxes” in the empathy map are the norm: Human life is rife with examples of cognitive dissonance that make little sense if you think purchase decisions follow the 19th century buyer funnel of attention-interest-desire-action. They don’t.

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Exclusive: Advent readies new $2.2 billion Latam private equity fund – sources

BlueMountain names slate for PG&E board

SAO PAULO (Reuters) – U.S. private equity firm Advent International Corp is preparing to launch a $2.2 billion fund to invest in Latin American companies, two people with knowledge of the matter told Reuters.

The fund launched in coming days will be Advent’s seventh for Latin America and its largest for the region. Advent’s last Latin American fund reached $2.1 billion in commitments in 2014.

Advent declined to comment on the matter.

Advent has invested in companies in Brazil, Colombia, Argentina, Chile, Mexico and Peru. The firm intends to close the fund-raising by September, according to one of the sources, who asked for anonymity because discussions are still private.

Advent’s move underscores how long-term investors have not soured on Latin America despite weaker growth and political risks, betting on rebounding growth in coming years as the International Monetary Fund forecasts.

One of Advent’s largest bets in Latin America was the acquisition of an 80 percent stake in the Brazilian operations of Walmart Inc in June.

The world’s biggest retailer did not receive payment for the unit and took a non-cash charge of roughly $4.5 billion. Advent agreed to invest 1.9 billion reais ($483 million) to turn around the geographically sprawling and poorly integrated operations.

Advent, which had $36 billion in assets under management in December, has raised more than $6 billion in private equity funds for Latin America since 1996.

Its portfolio in Latin America also includes Argentine card processor Prisma Medios de Pagos SA, Brazilian education company Estácio Participações SA and Peruvian IT outsourcer Canvia.

Reporting by Carolina Mandl; editing by Brad Haynes and Leslie Adler

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Here’s how you build creative video experiences for a multi-screen world

Content alone is not going to win the streaming war. Here's why

Creative has changed. Gone are the days when a brand could sink all of its heart, soul and budget into a single creative experience – say, a television ad. It’s 2019; you can’t be so singularly focused. Your audience sure isn’t.

You still need big ideas, like Dos Equis’ “Most Interesting Man in the World,” P&G’s “Thank you, Mom” campaign or Apple’s “Mac versus PC” concept. But today, brands need to consider how to translate those big ideas across channels and screens, especially when it comes to video so that they can tell a cohesive and engaging story.

Companies scrambled to secure the platforms and support they needed to transact advertising programmatically, but without effective creative, those tools don’t mean much. Technology and creative must go hand in hand. It is time for brands to consider the nuances of each format at every stage of campaign production, allowing them to create an effective video for today’s multi-screen world.

Pre-production

Begin by understanding your audience. Be willing to dig deeper and uncover things that challenge what you thought you knew. To do so, you will need to access and analyze data on an ongoing basis. Brands that use data smartly often discover surprising insights. Perhaps their audience has evolved over time, or they have a secondary customer base they had never thought of before.

In addition to the “who,” you need to understand the “where” and the “how” before you start shooting your creative. Is your mobile audience watching video while they commute to work, or are they “second-screening” while they watch TV? You will want your creative to reflect these different scenarios.

Quick Tip: With these insights in mind, plan to create customized creative experiences for each audience segment, screen and use case.

Production

Creative is too often an afterthought in the digital world. Let’s use an automotive brand as an example. They put all this work into shooting a beautiful video ad that looks great on TV screens. Many months later, there is a digital media plan, and that beautiful TV spot needs to be repurposed for various digital screens, where user behavior can be quite different. The creative partner ends up cutting the original footage into shorter spots or letting the TV spot run as-is, which can lead to pretty bad user experiences. For example, a car driving in the distance against a beautiful background looks terrific on TV, but on mobile, that car looks like an ant. That brand should have shot with digital in mind from the beginning.

For digital, you need more close-ups and quick cuts. You also need to frontload the most important part of your ad, whether that is a brand tagline or an aspect of your product. If you can, always overshoot. You may not know all your strategies at the time you are shooting, so extra footage will come in handy as your media plan evolves.

Quick Tip: Consider how to tell your story without relying on audio. This may mean using subtitles, stronger visuals or more logos and product shots.

Post-production

As you execute your campaign, stay agile and open to trying new tactics. Just be sure your creative reflects each strategy. Repurposing assets are possible but do so with care. For example, if you are repurposing footage for mobile, you may need to add overlays or interactive features. If your video was shot for landscape, you would need a creative partner who can edit it for vertical screen environments that many mobile viewers consume content in.

Quick Tip: When evaluating your campaign, think more broadly about what different metrics tell you about how successful your campaign has been at driving consumer behavior. Think beyond the standard KPIs like CTR and completion rates, and look at things like engaged-time spent and what happens post-click. What about the creative specifically drove these metrics, and how can you apply those learnings to current and future campaigns?

Brands have the data and tools they need to reach their audiences with unprecedented precision, but if the creative isn’t effectively speaking to them, that is all for naught. It is time to put the pieces together.


Opinions expressed in this article are those of the guest author and not necessarily Marketing Land. Staff authors are listed here.


About The Author

As Senior Creative Director, Les Seifer leads Tremor Video DSP’s in-house Creative Studio. His team brings creative video intelligence to media campaigns, combining leading-edge advanced creative with unique data and insights. His creative roots run deep outside the office. As a painter, his artwork has been displayed in galleries and museums across the country.

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Kraft Heinz banks on Anheuser-Busch executive in strategy shift

Kraft Heinz banks on Anheuser-Busch executive in strategy shift

(Reuters) – Kraft Heinz Co said on Monday it would replace Chief Executive Officer Bernardo Hees with Anheuser-Busch InBev marketing chief Miguel Patricio, as one of the world’s largest packaged food companies looks to reinvigorate its brands after years of cutting costs dented their value.

FILE PHOTO: A Heinz Ketchup bottle sits between a box of Kraft macaroni and cheese and a bottle of Kraft Original Barbecue Sauce on a grocery store shelf in New York March 25, 2015. REUTERS/Brendan McDermid/File Photo

In February, the Heinz ketchup maker cut its dividend payouts, wrote down the value its marquee Kraft and Oscar Mayer brands and other assets by more than $15 billion and disclosed a regulatory probe into its accounting practices.

The broad sector has struggled with rising transportation and commodity costs along with a shift in consumer preferences to more niche health-focused brands.

The Velveeta cheese maker’s second biggest shareholder 3G Capital has pushed the company to rein in expenses to tackle higher costs and sluggish growth, a strategy it has used effectively at Heinz and Anheuser-Busch, another company in which it has a stake.

3G and Warren Buffett’s Berkshire Hathaway Inc together own more than 50 percent of Kraft Heinz.

“The change at the top of Kraft Heinz is a positive development,” said Roosevelt Investment Group fund manager Jason Benowitz, which previously held a stake in Kraft Heinz.

“It shows that management and the board understand the serious nature of the challenges facing the company. Kraft Heinz … cannot further cost cut its way to prosperity.”

The company’s shares rose about 1 percent in early trading, after more than more than halving in value since H.J. Heinz and Kraft Foods, two of the United States’ biggest food and beverage producers, merged in 2015.

Kraft Heinz has been the worst performing stock on the S&P 500 Packaged Foods and Meats index over the last year, falling some 43 percent.

Patricio takes over the top job in July after spending two decades at Anheuser-Busch, most recently as the Budweiser brewer’s global chief marketing officer.

Prior to AB InBev, Patricio worked at a range of major consumer goods producers including Philip Morris, Coca-Cola Co and Johnson & Johnson.

“By appointing Mr. Patricio as the new CEO, it appears that Kraft Heinz is doubling down on its efforts to reinvigorate the top line,” Bernstein analyst Alexia Howard wrote in a note.

His prior experience as the president of Asia Pacific of Anheuser-Busch InBev might enable him to explore more growth opportunities in emerging markets at Kraft Heinz, she said.

Reporting by Uday Sampath in Bengaluru; Editing by Shailesh Kuber and Saumyadeb Chakrabarty

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Searching for facts, directions, local businesses are top digital assistant use cases, says survey

Searching for facts, directions, local businesses are top digital assistant use cases, says survey

Nearly three-fourths (72 percent) of smartphone owners are using digital assistants, according to a new report from Microsoft. The findings are based on two surveys – one from mid-2018 that includes an international sample, and a 2019 follow-up involving 5,000 U.S. consumers. The study also found that 35 percent of the survey population had used “voice search” through a smart speaker.

Google and Apple tied for usage lead. In terms of usage market share, the report found Siri and Google Assistant tied at 36 percent, followed by Alexa (25 percent) and then Cortana (19 percent). The overwhelming majority of Cortana’s usage is on the desktop. These figures are not the same as device share. Google Assistant is available on more than a billion devices and Amazon dominates the smart speaker hardware market.

Top assistant use cases. Like many reports covering digital assistants, this one sometimes fails to make clear distinctions between smart speakers and smartphone usage. However, the report spends considerable time discussing smart speaker adoption and use cases.

In the context of that smart speaker discussion, Microsoft presents the following hierarchy of digital assistant usage:

  1. Searching for a quick fact — 68 percent
  2. Asking for directions — 65 percent
  3. Searching for a business — 47 percent
  4. Researching a product or service — 44 percent
  5. Making a shopping list — 39 percent
  6. Comparing products or services — 31 percent
  7. Adding items to a shopping cart — 26 percent
  8. Making a purchase — 25 percent
  9. Contacting customer service or support — 21 percent
  10. Providing feedback for a product/service — 19 percent

Some of the answers on this list (e.g., comparing products or services) suggest that respondents were commenting broadly about assistant usage – not just smart speakers. Indeed, the absence of responses such as “checking the weather” or “playing music” (answers common in other smart speaker surveys) suggests this as well.

The study found that 80 percent were “satisfied” with their digital assistant experiences (most likely on smart speakers this time), while 14 percent were “neutral” and only 6 percent were dissatisfied.

22 percent jump in ownership. In terms of smart speaker ownership, the 2018 survey discovered 23 percent of respondents had one. That number has jumped to 45 percent this year. Under the assumption that this is a U.S.-based population, that would mean roughly 112 million Americans today own at least one smart speaker, with an additional 26 percent saying they’re going to purchase one this year.

A very interesting finding surrounds brand-purchase intent. Amazon Echo has gained compared with 2018 and Google Home has lost share of intent to purchase. The number of people who said they want to buy a Google Home speaker declined from 58 percent in 2018 to 17 percent this year. It’s possible that the 58 percent bought Google Home devices, hence the drop. But the decline is noteworthy.

The Google Home Mini didn’t suffer the same decline in purchase intent. Finally, 26 percent of the audience said that they were interested in buying an alternative brand, which may include Sonos and the Apple HomePod, although that’s not clear from the report.

Digital assistant privacy concerns. A substantial minority (41 percent) of respondents said they had “concerns” about digital assistants — again, probably smart speakers here. Asked to elaborate, the top response was “that my personal information is not secure” (52 percent), followed by “that it is actively listening and/or recording me” (41 percent) and then “I don’t want my personal information or data used” (36 percent). These fears are not entirely unfounded, given recent revelations about Amazon employees listening to Alexa recordings — justified to improve voice recognition and understanding.

The surveys also asked about shopping using a digital assistant or smart speaker. Just over 41 percent said they had made a purchase through one or both channels (with 6.5 percent saying they didn’t enjoy it). The other roughly 59 percent had not made a purchase, with 27 percent in that group saying they that they were interesting making future purchases using assistants. More than half (54 percent) of respondents said they believed that digital assistants will help them make retail purchases within 5 years.

Why we should care. Both consumers and retailers expect smart speakers (and smartphone assistants) to become an important purchase channel in the next few years. The activities detailed in the list above argue that some search behaviors will transfer to voice channels over time.

There are clear implications for marketers, tied to voice optimization and other tactics. For example, if you’re a local service business there are specific things that must be done to appear in Google Home local listings. It’s also incumbent upon marketers to experiment with smart speakers to determine the most effective use cases for their brands and content.

Finally, certain shopping and commerce experiences may become common through smart speakers. Walmart’s updated voice grocery shopping experience represents a potentially successful voice-commerce model, involving list creation and reordering. 


About The Author

Greg Sterling is a Contributing Editor at Search Engine Land. He researches and writes about the connections between digital and offline commerce. He is also VP of Strategy and Insights for the Local Search Association. Follow him on Twitter or find him at Google+.

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Collapsed hedge fund Columna sues Permira-backed former managers

BlueMountain names slate for PG&E board

LONDON (Reuters) – The new managers of Columna Commodities Fund, a Luxembourg hedge fund which went into liquidation in early 2017, have said they are suing its former managers Alter Domus for $56 million in lost assets and fees.

Columna, launched in 2013, was a top-performing fund in a stable known as LFP I SICAV, managed by Luxembourg Fund Partners.

Alter Domus, a Luxembourg fund platform and administrator that has financial backing from private equity giant Permira, bought Luxembourg Fund Partners in December 2017, after Columna’s collapse, when LFP I SICAV’s assets under management totaled nearly 400 million euros ($450 million).

In a statement released earlier this week, the new directors said they had launched a claim to recover investment losses, management and performance fees from Alter Domus Management Company. LFP I SICAV’s assets under management now total around 80 million euros.

Columna made double-digit gains in 2014, 2015 and 2016 investing in a range of commodity products, according to information it sent its investors. But it then closed abruptly in December 2016 without returning any of its assets.

An Alter Domus spokesman said the firm was only aware of “significant issues” with Columna between 2013 and 2016 after buying Luxembourg Fund Partners.

The spokesman declined to comment on the legal claim.

In a previous email, he said Alter Domus was looking into the issues with Columna and had “engaged various external firms to assist with our investigation, the findings of which has led to the commencement of legal actions”. He declined to comment further on the legal actions, saying they were ongoing.

Permira declined to comment.

After being asked by Columna investors to help, asset recovery specialist David Mapley was one of three directors appointed to a new board of LFP I SICAV late last year and authorized by the Luxembourg regulator in February 2019 to take over management of the fund stable from Alter Domus.

Luxembourg’s financial regulator declined to comment on individual firms or court cases.

Reporting by Carolyn Cohn, Simon Jessop and Maiya Keidan; editing by David Evans

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Using Python to recover SEO site traffic (Part three) Search Engine Watch

Using Python to recover SEO site traffic (Part three)

Using Python to recover SEO site traffic (Part three)

When you incorporate machine learning techniques to speed up SEO recovery, the results can be amazing.

This is the third and last installment from our series on using Python to speed SEO traffic recovery. In part one, I explained how our unique approach, that we call “winners vs losers” helps us quickly narrow down the pages losing traffic to find the main reason for the drop. In part two, we improved on our initial approach to manually group pages using regular expressions, which is very useful when you have sites with thousands or millions of pages, which is typically the case with ecommerce sites. In part three, we will learn something really exciting. We will learn to automatically group pages using machine learning.

As mentioned before, you can find the code used in part one, two and three in this Google Colab notebook.

Let’s get started.

URL matching vs content matching

When we grouped pages manually in part two, we benefited from the fact the URLs groups had clear patterns (collections, products, and the others) but it is often the case where there are no patterns in the URL. For example, Yahoo Stores’ sites use a flat URL structure with no directory paths. Our manual approach wouldn’t work in this case.

Fortunately, it is possible to group pages by their contents because most page templates have different content structures. They serve different user needs, so that needs to be the case.

How can we organize pages by their content? We can use DOM element selectors for this. We will specifically use XPaths.

Example of using DOM elements to organize pages by their content

For example, I can use the presence of a big product image to know the page is a product detail page. I can grab the product image address in the document (its XPath) by right-clicking on it in Chrome and choosing “Inspect,” then right-clicking to copy the XPath.

We can identify other page groups by finding page elements that are unique to them. However, note that while this would allow us to group Yahoo Store-type sites, it would still be a manual process to create the groups.

A scientist’s bottom-up approach

In order to group pages automatically, we need to use a statistical approach. In other words, we need to find patterns in the data that we can use to cluster similar pages together because they share similar statistics. This is a perfect problem for machine learning algorithms.

BloomReach, a digital experience platform vendor, shared their machine learning solution to this problem. To summarize it, they first manually selected cleaned features from the HTML tags like class IDs, CSS style sheet names, and the others. Then, they automatically grouped pages based on the presence and variability of these features. In their tests, they achieved around 90% accuracy, which is pretty good.

When you give problems like this to scientists and engineers with no domain expertise, they will generally come up with complicated, bottom-up solutions. The scientist will say, “Here is the data I have, let me try different computer science ideas I know until I find a good solution.”

One of the reasons I advocate practitioners learn programming is that you can start solving problems using your domain expertise and find shortcuts like the one I will share next.

Hamlet’s observation and a simpler solution

For most ecommerce sites, most page templates include images (and input elements), and those generally change in quantity and size.

Hamlet's observation for a simpler approach based on domain-level observationsHamlet's observation for a simpler approach by testing the quantity and size of images

I decided to test the quantity and size of images, and the number of input elements as my features set. We were able to achieve 97.5% accuracy in our tests. This is a much simpler and effective approach for this specific problem. All of this is possible because I didn’t start with the data I could access, but with a simpler domain-level observation.

I am not trying to say my approach is superior, as they have tested theirs in millions of pages and I’ve only tested this on a few thousand. My point is that as a practitioner you should learn this stuff so you can contribute your own expertise and creativity.

Now let’s get to the fun part and get to code some machine learning code in Python!

Collecting training data

We need training data to build a model. This training data needs to come pre-labeled with “correct” answers so that the model can learn from the correct answers and make its own predictions on unseen data.

In our case, as discussed above, we’ll use our intuition that most product pages have one or more large images on the page, and most category type pages have many smaller images on the page.

What’s more, product pages typically have more form elements than category pages (for filling in quantity, color, and more).

Unfortunately, crawling a web page for this data requires knowledge of web browser automation, and image manipulation, which are outside the scope of this post. Feel free to study this GitHub gist we put together to learn more.

Here we load the raw data already collected.

Feature engineering

Each row of the form_counts data frame above corresponds to a single URL and provides a count of both form elements, and input elements contained on that page.

Meanwhile, in the img_counts data frame, each row corresponds to a single image from a particular page. Each image has an associated file size, height, and width. Pages are more than likely to have multiple images on each page, and so there are many rows corresponding to each URL.

It is often the case that HTML documents don’t include explicit image dimensions. We are using a little trick to compensate for this. We are capturing the size of the image files, which would be proportional to the multiplication of the width and the length of the images.

We want our image counts and image file sizes to be treated as categorical features, not numerical ones. When a numerical feature, say new visitors, increases it generally implies improvement, but we don’t want bigger images to imply improvement. A common technique to do this is called one-hot encoding.

Most site pages can have an arbitrary number of images. We are going to further process our dataset by bucketing images into 50 groups. This technique is called “binning”.

Here is what our processed data set looks like.

Example view of processed data for "binning"

Adding ground truth labels

As we already have correct labels from our manual regex approach, we can use them to create the correct labels to feed the model.

We also need to split our dataset randomly into a training set and a test set. This allows us to train the machine learning model on one set of data, and test it on another set that it’s never seen before. We do this to prevent our model from simply “memorizing” the training data and doing terribly on new, unseen data. You can check it out at the link given below:

Model training and grid search

Finally, the good stuff!

All the steps above, the data collection and preparation, are generally the hardest part to code. The machine learning code is generally quite simple.

We’re using the well-known Scikitlearn python library to train a number of popular models using a bunch of standard hyperparameters (settings for fine-tuning a model). Scikitlearn will run through all of them to find the best one, we simply need to feed in the X variables (our feature engineering parameters above) and the Y variables (the correct labels) to each model, and perform the .fit() function and voila!

Evaluating performance

Graph for evaluating image performances through a linear pattern

After running the grid search, we find our winning model to be the Linear SVM (0.974) and Logistic regression (0.968) coming at a close second. Even with such high accuracy, a machine learning model will make mistakes. If it doesn’t make any mistakes, then there is definitely something wrong with the code.

In order to understand where the model performs best and worst, we will use another useful machine learning tool, the confusion matrix.

Graph of the confusion matrix to evaluate image performance

When looking at a confusion matrix, focus on the diagonal squares. The counts there are correct predictions and the counts outside are failures. In the confusion matrix above we can quickly see that the model does really well-labeling products, but terribly labeling pages that are not product or categories. Intuitively, we can assume that such pages would not have consistent image usage.

Here is the code to put together the confusion matrix:

Finally, here is the code to plot the model evaluation:

Resources to learn more

You might be thinking that this is a lot of work to just tell page groups, and you are right!

Screenshot of a query on custom PageTypes and DataLayer

Mirko Obkircher commented in my article for part two that there is a much simpler approach, which is to have your client set up a Google Analytics data layer with the page group type. Very smart recommendation, Mirko!

I am using this example for illustration purposes. What if the issue requires a deeper exploratory investigation? If you already started the analysis using Python, your creativity and knowledge are the only limits.

If you want to jump onto the machine learning bandwagon, here are some resources I recommend to learn more:

Got any tips or queries? Share it in the comments.

Hamlet Batista is the CEO and founder of RankSense, an agile SEO platform for online retailers and manufacturers. He can be found on Twitter .

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Union Pacific operational overhaul gains traction, shares rise

Union Pacific operational overhaul gains traction, shares rise

(This April 18 story corrects to show operating ratio declined, error also occurred in previous updates)

FILE PHOTO: A Union Pacific rail car is parked at a Burlington National Santa Fe (BNSF) train yard in Seattle, Washington, U.S., February 10, 2017. REUTERS/Chris Helgren

By Lisa Baertlein and Rachit Vats

(Reuters) – U.S. railroad operator Union Pacific Corp on Thursday reported a better-than-expected quarterly profit as price increases and cost controls offset the impact of severe winter weather and record flooding that damaged rails in the Midwest.

The quarter was a test for the second-largest U.S. railroad’s sweeping operational overhaul, and the results sent shares up 4.9 percent to $177.63.

Efforts to streamline operations and create surge capacity helped railway crews reroute the 50 to 60 daily trains that use the east-west main line that floodwaters severed for almost two weeks, Chief Executive Lance Fritz told Reuters.

“We’re gaining traction. … I see us coming back quickly and strongly,” Fritz said.

Net income at Union Pacific, which serves the Western two-thirds of the country, rose 6.2 percent to $1.4 billion, or $1.93 per share, in the first quarter. That topped analysts’ average forecast of $1.89, according to IBES data from Refinitiv.

Total operating revenue fell 1.7 percent to $5.4 billion. Weather and the U.S. trade war with China reduced export grain carloads, but pricing rose nearly 2.8 percent.

Expenses dropped 3.2 percent, assisted by workforce reductions and a switch to longer trains, which reduces fuel, maintenance and labor costs.

The Omaha, Nebraska-based company early this year hired former Canadian National Railway Co executive and turnaround expert Jim Vena as its chief operating officer and tasked him with overseeing its plan to lower costs and improve service and reliability.

Union Pacific’s first-quarter operating ratio – a measure of operating expenses as a percentage of revenue – declined 1 point to 63.6 percent, despite the weather disruptions. A lower ratio means more efficiency and higher profitability.

The railroad, which is working to get that key performance metric below 60 percent by 2020, said it was increasing network flexibility by reallocating investments.

For example, it paused work on its $550 million rail yard facility in Brazos, Texas, and earmarked unused 2019 capital for projects along its southwestern “Sunset” corridor. Seven rail lines converge at Brazos and the project, started in January 2018, was the largest facility investment in its 155-year history.

Transportation companies are a bellwether for business activity and investors are watching them closely as the global economy cools.

The U.S. economy is flashing warning signs as manufacturing softens and stimulus from the $1.5 billion tax-cut package ebbs.

Trade “is the thing that could tip us into a worse economy,” Fritz said.

Reporting by Rachit Vats in Bengaluru and Lisa Baertlein in Los Angeles; Editing by Dan Grebler and Peter Cooney

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UBS Wealth closes U.S. equity overweight, favors emerging stocks

UBS Wealth closes U.S. equity overweight, favors emerging stocks

FILE PHOTO: Mark Haefele, Global Chief Investment Officer of UBS Wealth Management attends the Reuters Investment Summit, London, Britain, November 16, 2017. REUTERS/Paul Hackett

(Reuters) – UBS Global Wealth Management has closed its overweight position in U.S. equities and shifted to an overweight in emerging market and Japanese stocks, it said on Thursday.

UBS Global Wealth Management chief investment officer Mark Haefele said also that the firm preferred the euro over the Swiss franc and Norwegian crown versus the Canadian dollar.

Reporting by Sujata Rao and Tom Arnold

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