Housing Market Enters Into Recession Here’s What To Expect Next

The housing cycle—which began its upward climb in 2011—has officially turned over. Simply put: We’ve moved into a housing recession.

On Tuesday, we learned that homebuilders broke ground on 982,000 single-family homes in June. That’s down 19% since February, and down 16% from the same month in 2021. While it’s hardly a “blow out,” it’s clear builders are cutting back. Historically speaking, that’s exactly what happens when a housing cycle turns over: As existing home inventory—which builders compete against—begins to spike, homebuilders start to cut back.

“Peak euphoria is behind us. We are giving back some of the euphoria [home] pricing that was rolling over every housing market,” says Rick Palacios Jr., head of research at John Burns Real Estate Consulting. Existing home inventory will continue to rise, and homebuilding will continue to slow. At least that’s the view at John Burns Real Estate Consulting, which does consulting work for both builders and investors. As it does, the ongoing housing recession (i.e. a contracting housing market) could push home prices lower in bubbly regional housing markets.

Indeed, many bubbly markets, Palacios says, are barreling toward price cuts in both 2023 and 2024. That includes markets like Phoenix, Nashville, West Palm Beach, Las Vegas, and Austin. In Boise, Palacios says, home prices could go negative on a year-over-year basis as soon as December. “Builders are already [deciding] to not pour slabs in certain markets. Which is the technical trigger for a start for a home. In certain markets it will feel like [a housing bust],” Palacios says.

Soon after mortgage rates spiked this spring, the housing market slipped into a “housing correction.” It’s easy to see how those higher rates priced out many would-be buyers. If a borrower in December took out a $500,000 mortgage at a 3.1% rate, they’d owe a monthly principal and interest payment of $2,135. If a borrower took out a $500,000 mortgage at today’s average 30-year fixed mortgage rate (5.51%), they’d get a $2,839 payment.

While this housing recession has hit markets coast to coast, it’s hardly even. It’s delivering a particularly hard blow to housing markets in the Mountain West, West Coast, and Southwest. Just look at the shift in inventory levels: Over the past six months, housing inventory has spiked 247% in Denver compared to just 18% in Pittsburgh. Not too far behind Denver are Austin (220%); Colorado Springs (195%); Stockton, Calif. (175%); and Boise (161%).

What’s going on? These Western housing markets also happen to be among the most “overvalued” markets in the country. As the pandemic housing boom raged over the past two years, many of those markets—which were more constrained supply-wise—saw staggering home price appreciation. In Boise, home prices jumped 53% over the past two years. That took home prices there well beyond what economic fundamentals in the market would historically support. In fact, Moody’s Analytics deems it the most “overvalued” market in the country.

Over the coming year, Moody’s Analytics expects significantly “overvalued” housing markets like Boise and Austin to see home prices fall 5% to 10%. Nationally, Moody’s Analytics expects year-over-year home price growth to be at 0%. However, if a recession hits, Moody’s Analytics predicts significantly “overvalued” housing markets could see home prices drop by 15% to 20% while national home prices would fall by around 5%.

There’s another reason bubbly markets like Austin and Phoenix are contracting faster: investors. Investors poured into the U.S. housing market over the past two years. There were small players like Airbnb hosts and mom-and-pop landlords. Home flippers returned. There were also institutional types like Blackstone and iBuyer players like Zillow. Their favorite locales? Hot housing markets throughout the Mountain West, Southwest, and Southeast.

However, as housing markets in those boomtowns begin to contract, investors are the first ones who run for the exits.

“Investors sometimes move in a herd. If Phoenix real estate isn’t the cool investment anymore in 2022, it could have a big and quick impact on home sales. If a lot of investors decide to sell…yikes,” John Wake, an independent real estate analyst based in Phoenix, told Fortune.

Earlier this year, the Federal Reserve flipped from quantitative easing (i.e. buying bonds) to quantitative tightening (i.e. selling bonds). Immediately, financial markets pushed up both the 10-year Treasury and mortgage rates. That’s exactly what the Fed wanted: If mortgage rates rose, it would cause the pandemic housing boom—which helped fuel higher inflation—to fizzle out.

“I’d say if you are a homebuyer, somebody or a young person looking to buy a home, you need a bit of a reset. We need to get back to a place where supply and demand are back together and where inflation is down low again, and mortgage rates are low again,” Fed Chair Jerome Powell told reporters in June.

According to Palacios, that Fed housing “reset” actually means “falling home prices.” While the Fed didn’t directly say it, Palacios says many in the industry believe that’s exactly what’s coming next.

That ongoing housing contraction coupled with a determined Fed, Palacios says, is the perfect recipe for a recession. Historically speaking, Fed-induced recessions begin in rate-sensitive sectors like housing. It usually goes something like this: Spiking mortgage rates quickly translate into fewer home sales. Then inventory rises sharply and homebuilders scale back. Next, demand for both commodities and durable goods falls. Of course, a housing recession also brings with it layoffs.

“The lesson learned in reading [Paul] Volcker’s piece, that’s where [the Fed] messed up [in the 1970s]. If you have this red-light, green-light mentality around inflation, then you’re going to allow the psychology of inflation to get out in front of you…If you listen to the Fed over the last month or so, that’s what they’re so freaked out about,” Palacios says.

Source: Housing market enters into recession—here’s what to expect next | Fortune

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How Media Companies Can Use AI To Keep And Win Subscribers

Think of your favorite movie as a kid, say in the first 10 years of your life. Now think of your favorite movie from the past decade. Do you have one? Do you have 100?

In a world with basically infinite content, choice is one of our greatest joys—and frustrations. With each passing year, consumers seem to grow more fickle and demanding, regularly moving to the platforms and publications that offer not only the best catalog but also the best customer service, content experience, user interface, and bang for the buck. And even these features may not be enough, as the recent upheaval among the major streamers has shown.

Holding on to viewers, readers, and listeners has become more important than ever. Yet most consumers can only maintain so many subscription services at once. The goal for media companies needs to be to sustain their interest, and with as much share of the consumer’s wallet as possible.

As such, churn is now the most prominent enemy of the media and entertainment industry business model. Consumers can be mercurial, sensitive to price and changes in content catalogs. Just as adding a service has rarely been easier, so is dropping one, which consumers have shown themselves more than willing to do when a channel is no longer serving their needs.

With these challenges front and center, leading media and entertainment companies are increasingly turning to data analytics and personalized content recommendations to improve customer experience and retention. In the dog-eat-dog digital world, it’s no longer the loudest bark that gets the most attention. It’s about pairing the right breed to the sensibilities of a specific person, and having the best stable of information and offerings to make that match and keep it going.

As subscriptions have risen, so has churn

A good example of the challenge of churn can be seen with streaming video. Deloitte performed a series of surveys in 2020 to gauge how consumers were changing their media consumption habits amidst the pandemic.

In January 2020, the average consumer in the United States subscribed to three paid streaming services; by October 2020, the number of subscriptions had risen to five. Overall, a positive development for media, but with the increase in subscriptions came a commensurate increase in churn.

In January 2020, Deloitte found that only 20% of people who had subscribed to a paid streaming service had cut at least one of those services in the past 12 months. By October, that number more than doubled, with 46% of consumers canceling a streaming service in the preceding six months. And at that time, 34% of consumers said that they’d both added and canceled a streaming service since the pandemic started.

Why did viewers churn? Deloitte noted that 62% of people in 2020 who had signed up for a service and then canceled it had done so because they signed up to watch a specific show, then canceled the service when they’d finished watching it. Price, as always, was also a big factor. In October 2020, 31% of people who canceled a service did so because it was too expensive. Another 28% canceled because a free trial or discount period ended. About 21% cut the service because of a lack of content they found interesting.

No matter how focused on addressing churn a company may be, what can they do when the whims of the consumer are so sensitive and fluctuate wildly?

Companies need to find ways to anticipate what their audiences want at least as well as the audience does—and certainly better than their competition. Two of the best defenses against churn are having an organized data platform, then using that data to personalize content recommendations and customer experience.

Data maturity is the first step to mitigating churn

Data maturity is the ability to have accurate and reliable data that can be utilized through cloud platforms, with advanced analytics informing every decision. It is one of the most important steps for media and entertainment companies to take in the effort to mitigate churn

In our experience working with companies as varied as Spotify, The New York Times, Major League Baseball, and Hearst, the first step to achieving data maturity is building a company culture where data is prioritized within the strategic business framework, and where funding is allocated to technology and human resources to build a mature data ecosystem.

Data maturity should not be a bolt-on to existing practices, but needs to become central to the company’s strategic business goals. Companies that have achieved data maturity tend to have specific teams or centers of excellence that manage goals, strategy, and tactics of the organization’s data framework.

In a 2020 survey by EY Global Media & Entertainment, 62% of media and entertainment executives said they saw the increasing availability of data as an opportunity. About 56% prioritized first-party data, versus only 13% who prioritized third-party data. When asked about their top three data priorities, 44% said that the consolidation of customer data was a top concern. About 40% said developing proprietary data sources was a priority, while 39% prioritized improving the relevance of data.

Consolidating data out of data silos to a unified data platform is the biggest challenge that most companies will face when building a roadmap to data maturity.

A report by Deloitte in partnership with the Google News Initiative on how news and media companies can achieve digital transformation through data outlined some of the technologies that companies can adopt to achieve data maturity. Two elements are required. First, media and entertainment companies need to be able to collect and store data that they are gathering from their planet-sized audiences and users with the tools listed below.

  • Data management platform (DMP) helps to manage first-party data segments and integrate third-party data and push data to other systems.
  • Data lake or warehouse, a central repository of data from multiple sources.
  • Cloud storage for reliability, security, and scalability.
  • Customer relationship management (CRM) the backbone of customer data that records and tracks user interactions with registered subscribers.
  • Customer data platform (CDP) to record and track customer data across platforms and devices.

Second, companies need to make sense of all that data and derive actionable insights from it.

  • Data analytics and reporting tools that can collect, organize, and analyze data from multiple sources.
  • Artificial intelligence and machine learning tools. Derive even more insights through AI/ML-enabled capabilities such as computer vision, speech and object recognition, and text translation.
  • Propensity modeling helps build a better understanding of customer preferences, fulfilling the key elements of personalization to prevent churn.

Below we describe some of the unique data sources available to media and entertainment companies and how it can be applied to artificial intelligence and machine learning.

Media and entertainment have unique data sources

Media and entertainment companies can improve personalization by tapping two unique sets of data particular to the industry: media content and audience behavior.

Media content includes easily identifiable metadata such as the title, headline, genre, topic, or format of a piece of content. But media data can also include context of the actual content itself.

For instance, AI tools like object recognition and computer vision can detect items within a movie and then add the description of the object to the searchable metadata of the content. If a television show contains a border collie, the AI can recognize the good dog and surface the show in a search for “shows with dogs.” Or with speech recognition and translation, AI can build a data set of the dialogue within a movie and make certain keywords part of the search for that show.

Behavioral data of the audience can be used in a variety of ways. Data can come from many different sources including a person’s location, device, browsing and scrolling, user profile, engagement, billing preferences, purchase and support history. Companies can help personalize experiences with this data by understanding how people interact with content and how best to engage with them, such as what times of the week are best for push notifications or when a person might be most amenable to a content recommendation.

Using artificial intelligence to personalize user experience

If you’ve ever wondered how your favorite streaming service seems to so uncannily know what you want to watch—even better than you might—the answer is probably some clever AI. Personalization is the practice of combining the new, massive datasets outlined above with machine learning and artificial intelligence to create experiences tailored to the specific needs and behaviors of an individual person.

Personalization is often associated with content recommendations. For example, about 70% of what is viewed on YouTube comes from a personalized recommendation. Certain streaming services are known to have some of the best content recommendation systems in the business. The goal with the personalization of content is to surface a new show, video, movie, podcast, song, band, album, article, or blog to the person at precisely the right moment.

Personalization is also an important element in search. Consider that with the right data inputs, two users searching for the same keywords could get vastly different results attuned to their consumption preferences. In both cases, content better suited to a person’s interest will keep them from looking around at other platforms or publications, helping to reduce churn.

The same is true for more traditional outlets, as well. Take a recent example from the (digital) pages of Newsweek. The publication’s chief technology officer, Michael Lukac, recently noted that “Google Cloud Recommendations AI has not only improved our click-through rate by 50% to 75% and subscription conversion rate by 10% but also allowed us to increase total revenue per visit by 10%.”

If you’re looking for more information about why personalization matters and how to bring it to your own services and experiences, discover more in our new ebook, Personalizing Media for Global Audiences.

Lluis Canet, Solutions Lead for Media Analytics and AI, Google Cloud

Source: Churn It Down: How Media Companies Can Use AI To Keep And Win Subscribers

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How Business Intelligence Can Fuel Your Efforts

Big data holds big potential. According to IDC, businesses spent $215 billion on big data and business intelligence solutions in 2021 alone. That represents a 10% increase compared to 2020. Job growth in data analytics and business intelligence (BI) also remains strong. It’s clear that the future is doubling down on data. But all its power and glory mean very little until you can answer one question: What can data-driven intelligence do for you?

Having a theoretical understanding of how to foster an intelligent business is one thing. Putting your BI tools to work and generating results is quite another. Too many organizations fail to bridge the gap and successfully use business data to transform their operations. The effectiveness of BI data can suffer from:

  • Failure to involve the right people in the decision-making process
  • Limitations within the BI software itself
  • Poor adoption processes

So, let’s fix that. Here’s a closer look at BI, along with some steps you can take to ensure it’s broadly adopted and benefits all users.

BI Definition: What Is Business Intelligence?

Let’s start with some clarity. First, what is a good business intelligence definition? Business intelligence, or BI, refers to software that turns data into usable insights. To make sense of information, it uses:

  • Data collection tools
  • Business analytics
  • Data visualization
  • An organized data warehouse

When you wrap your data up in a neat and tidy package, you can make better business decisions based on real-world insights.

What Is Business Analytics?

So what is business analytics, then? There’s a little overlap here. Business analytics uses historical data to identify potential trends and patterns that help companies make predictions for the future.

An easy way to think about it is that business analytics is a small slice of BI. Both are important, especially for informed decision-making. Both can work together to drive better business outcomes.

Why Are BI Reporting and Business Data Important for Companies?

Imagine you are shopping for a new car. You go to the lot and see they have several of the same model in stock. On the surface, they all look the same except for the color. But after you buy one, you learn that it was actually a year older than the others on the lot and had thousands more miles than them. It had also been in a previous accident, but it still costs the same as the newer cars.

With a little more insight into what you were buying, you may have chosen differently. Since all the cars cost the same in this example, you could have gotten more value by getting a newer, less-driven model that was in better condition. BI and analytics work in a similar manner. They provide users with data they might have overlooked or might not realize is available. With those insights, users can improve business operations and data-driven decision-making.

To be clear, BI data doesn’t tell companies what to do or what will happen if they make certain decisions. Its value lies in presenting business leaders with simplified data insights related to a specific area of business. It helps to remove some of the guesswork of an endless list of what-if questions. It streamlines the process of searching for and combining various data sets to speed up the decision-making timeline.

How Do Companies Use BI Software?Typing on a laptop.

Business intelligence software sounds helpful in theory. In reality, the possible applications are nearly endless. For instance, a retail store or logistics company might use BI for predictive purposes. AI is useful for identifying supply chain risks and may help companies plan for unexpected surges in demand or delays in transport.

Sales teams will often use a BI platform to visualize their pipelines and see where all of their deals are in real-time. Take Meltwater client AxiaOrigin, for example. This consultancy specializes in best-in-class data discovery and analysis, with a particular focus on unused data that its clients struggle to unlock value from.

Much of its work is bespoke to each client, so having a flexible solution that can address a wide range of requests is critical. AxiaOrigin can explore large sets of raw data without manually mining and extracting insights. And it’s all because of our AI-powered business intelligence and analytics tools.

Another common use case is to predict future trends, which is how Fund for Peace uses Meltwater. This non-profit works to prevent conflict. It relies on easy-to-use BI to track trends and get early warnings of potential conflict. This forward-thinking approach allows the organization to respond quickly to escalating situations. Using an easy-to-understand, end-to-end solution reduces the time it takes to research and track events, which has enriched the organization’s data even more.

Specifically, BI reporting can be useful in several ways:

  • Spot trends
  • Benchmark competitors
  • Increase sales and profit
  • Optimize operations
  • Uncover problems or issues
  • Track performance
  • Predict future trends and successes
  • Understand your customers

When used to its potential, BI reporting can help to improve just about any aspect of your business.

What Kinds of Business Intelligence Tools Should You Use?

The right BI tools let you go from theoretical benefits to tangible value. To make this leap, you must first explore your options for choosing and implementing BI solutions.

Types of Intelligent Business ToolsA hand points to charts and graphs displayed on a transparent screen.

A range of tools and solutions are part of the BI market. Examples include:

  • Dashboards
  • Data visualization tools
  • Reporting features
  • Data mining
  • ETL (extract transfer load)
  • OLAP (online analytical processing)

Among the most common are dashboarding and visualization tools. Dashboards can be customized to display certain types of data at a glance. These are most often used when business leaders need to access the same information on an ongoing basis. Visualization tools turn data into visual images or models for easier information processing.

All of the above can fall into one of two buckets. There’s the “classic” BI that focuses only on in-house transactional data. And then there’s “modern” BI that takes internal and external data from a variety of sources into account. Modern BI offers additional advantages to completing and enriching data sets, which allows for faster and easier analysis.

Today’s BI solutions are largely cloud-based software-as-a-service (though some are still on-premise). They span a range of features and functionality. They’re enterprise-grade in terms of power. But even non-technical users can benefit from the approach that many BI applications take. Having your own data analysts or team of data scientists is great, but it is no longer required for deploying BI.

Going Beyond a Business Intelligence System

A person with long hair smiles while sitting at a table having a meeting with colleagues.It’s not just a matter of choosing software and tools to make BI solutions work. This is where a lot of companies go wrong. You cannot simply “solution-ize” your business. You must factor in other considerations that can make or break your BI implementation.

First, companies need to instill the right culture. Technology itself isn’t enough if the people using it can’t make heads or tails of it. Staff empowered to make decisions who know the right questions to ask are ideally suited for BI. They’re usually skilled in finding patterns in sales data or social media mentions, for example. They also view a BI solution as more than just a data tool. They see it as a valuable way to investigate how past actions triggered results and to better predict the results of future actions.

Also, companies need buy-in from the top down. BI software isn’t just a one-off activity. On the contrary, its value grows over time as it collects more data for deeper and more reliable insights. With this in mind, don’t expect BI solutions to perform miracles overnight, and don’t try to implement them quickly.

Doing so most often means that some aspects of the organization were not taken into account. Using an implementation consultant can shore up these little bumps in the road that could ultimately derail your BI program. They ask hard questions to achieve short-term goals and long-term value.

The data you feed your BI software will also contribute to your success. You need to consider structured and unstructured data sources to gauge customer sentiment and relate it to other data points. This is becoming critical as customers engage with businesses through a variety of channels. Also, data should come from verified, reliable sources to maintain its integrity and reap all the benefits of BI.

Lastly, organizations need to establish specific goals for their BI system. Goal creation ensures that companies are collecting and analyzing the right data. You’ll likely have a mix of metrics related to customer satisfaction, sales, and internal user adoption. Your BI system must be flexible and customizable to accommodate future goals and priority shifts.

By: TJ Kiely

Source: How Business Intelligence Can Fuel Your Efforts

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How To Create Attractive Hyper Personalized Landing Pages With SellitPics

If you are prospecting customers on LinkedIn, Facebook, Twitter, Instagram, Quora, Reddit, Skype or reaching out to them through emails; the only way you can convert them is by standing apart from the crowd. Do the same thing like the thousand other marketers, get the same results. It’s time to change the script.

Make your customers feel you are worth their attention by making everything seem just for them with SellItPics’ end-to-end personalization right from the first email they get, the social media message they receive, and the landing page they go to.

A user’s decision to trust, engage, or commit is made in milliseconds. A picture is worth a thousand words. Meaningful, custom images will attract more attention than a text message or stock photo. SellItPics gives you the right tools to turn outreach into results every single time.

  • Create personalized image messages for Social Media (Facebook, LinkedIn, Twitter, Reddit, Instagram & More)
  • Send emails with personalized images and boost your click-rate
  • Personalize your landing page text & images to improve conversions.
  • Web based SAAS, create your campaigns from anywhere.
  • Don’t go to Spam. With personalization, your outreach is custom made and your prospects won’t think it’s spamming.
  • Get dozens of free templates to start with. Quickly customize any template and make it yours.
  • Get access to a template library shared by other users and share your own templates too.
  • Powerful template-builder lets you create your own designs and templates easily.
  • Detailed analytics, sales reporting included.
  • Get higher click-through rates and conversions with max-level personalization.

No need to pay for Canva or a designer. Create personalized layouts & beautiful messages easily with in-build designer. Get access to public templates created by other users, Share templates with others.

Modify text color, size, boldness, font. Choose background images, icons, logos and customize anything in the design easily. Got an awesome template already? Just copy paste the HTML code and SellitPics will convert it into a personalization template.

Supports Facebook, LinkedIn, Twitter, Whatsapp, Slack, Discord & all others. Important if you are doing cold-outreach, Reduce complaint rate. Keep your account in good position and many more…

Source: SellitPics – Attract Attention With Hyper Personalized Images

Why Data Is The Key To Driving Sustainability In Retail

Both corporate values and customer expectations are driving more conscious policies and spending to benefit the planet. Here’s how data and analytics are helping retail organizations hit their sustainability targets.

We are entering the age of circular economics where “once is never enough.” Products and businesses will need to be designed for regeneration, rather than produced, delivered, and trashed.

Is your business sustainable, equitable, ethical? These days, does it have a choice not to be?In 2020, interest in “ethical brands” and online shops exploded, growing between 300% and 600% based on Google searches alone.

It can be hard to remember just how much things have changed in the months since the pandemic seized the world. Sustainability is now as much about the resiliency of your business as it is that of the planet—with both benefiting accordingly. Sustainability represents a huge opportunity to serve consumers with what they want, and the world with what it needs, in order to help keep everyone thriving—including your bottom line.

We are entering the age of circular economics where “once is never enough.” Products and businesses will need to be designed for regeneration, rather than produced, delivered, and trashed.

Sustainability is rapidly growing as a way to evaluate the non-financial performance of companies and measure the purpose and values that drive a brand.

Coupled with the ongoing concerns around the environmental impacts of carbon emissions, material waste, energy consumption, and scarce resources, retailers are using the challenges raised by the pandemic as a chance to rewire their systems to drive healthier, sustainable, and more resilient value chains that will allow them to thrive in the future. 

For example, reducing synthetic PVC plastics in products can reduce fossil fuel consumption. Sourcing raw materials ethically and sustainably helps increase supply chain longevity. Providing services that encourage consumers to repair, rather than buy new products, can reduce unnecessary waste.

Such an emphasis on sustainability may seem like a whole new way of doing business that at times runs counter to the conventional practices of the past. Yet if we don’t seriously reconsider the future of business, will there be much of a future for businesses at all?

Building this future will require an entirely new understanding of the components, inputs, and resources that go into a business. This kind of understanding is made possible on the cloud.

Sustainability sells: Consumers are driving new transformation

The turmoil of COVID-19 didn’t just bring social distancing—it marked the beginning of an eco-awakening. The increased attention on health, safety, and well-being sparked a renewed awareness around sustainability, particularly in the personal choices consumers make in their own lives and how those choices impact the environment.

In fact, Google research* shows that people now have a greater appreciation for life, are more aware of how valuable nature is for their mental and physical health, and recognize being sustainable plays a critical role in protecting it. As a result, sustainability is more top of mind than before the pandemic.

Now, shoppers are looking more closely than ever at the products they buy and the brands they support—and they’re ready to make different choices if they don’t like what they find:

  • As mentioned, Google search interest in “ethical brands” and “ethical online shopping” during 2020 grew 300% and 600% compared to the previous year.
  • 1 in 3 shoppers stopped purchasing certain brands or products due to ethical or sustainability related concerns.
  • Nearly 6 in 10 consumers say they are willing to change their shopping habits to reduce environmental impact.

Retailers were already feeling the pressure to reduce their impact on the environment long before the pandemic. After all, the fashion industry alone accounts for 20% of wastewater and up to 8% of carbon emissions globally. But this new shift in consumer behavior serves as an extra warning that it’s time to accelerate changes now—or pay the price later on. 

And it’s not just consumers looking for a commitment from retailers—suppliers, investors, employees, and policymakers are also expecting tangible, sustainable action from businesses. Sustainability is rapidly growing as a way to evaluate the non-financial performance of companies and measure the purpose and values that drive a brand.

At least 65% of world economies have made 2050 net-zero commitments and new EU regulations even require businesses to disclose ESG data about what and how they operate and manage social and environmental challenges.

These changes are already underway. So how can retail businesses stay ahead of them?

Data is key for doing good for retail and for the planet

Retailers have been pushed to illuminate the murkier aspects of their value chains to strengthen credibility and prove in concrete terms exactly how they are delivering on sustainability. But companies can only manage what they are able to measure, so data is crucial for sustainability efforts.

There is a lot of valuable data that can be generated from the first mile to the last mile of products; from direct energy consumption in stores and in warehouses; to CO2 emissions from supply chains and manufacturing; to the effects of resource procurement. Organizations can also gain insight into upstream and downstream activities, such as product distribution and delivery, consumer disposal of product packaging, and other waste.

Migrating to a sustainable cloud can reduce CO2 emissions by 59 million tons a year, which is equivalent to taking 22 million cars off the road, according to Accenture research.

Nearly every aspect of the value chain has the potential to be measured in terms of the impact on the environment as long as companies have the right technologies in place.

Given the public cloud’s inherent efficiencies, it is one of the fastest paths to hit sustainability targets and reduce energy costs. In fact, according to Accenture research, migrating to a sustainable cloud can reduce CO2 emissions by 59 million tons a year, which is equivalent to taking 22 million cars off the road.

But the cloud offers other capabilities that benefit the overall sustainability efforts of retailers, too. Namely, the cloud enables a strong data foundation that allows information to be collected, processed, managed, and analyzed in one place.

The reduction of silos and the availability of a single, centralized view of all relevant data creates the end-to-end visibility needed to understand the full environmental impact of business decisions across the value chain.

Here’s how data is helping retail organizations hit their sustainability targets:

  • Lowering carbon emissions and energy usage. Retailers need to accurately measure and understand carbon emissions and energy consumption across thousands of devices, facilities, processes, and locations. By gaining a full picture of carbon emissions, businesses will have the power to optimize and implement sustainable best practices—and track future progress—that will deliver real reductions. For example, data can be used to identify cleaner times of day or lower carbon density regions that can create big opportunities to offset and lower emissions.
  • Reducing waste and optimizing supply chains. There are numerous opportunities for retailers to apply data to supply chain sustainability problems, such as inaccurate demand and inventory planning, manufacturing inefficiencies, packaging or product surplus waste, and more. Integrating data from disparate internal systems, partners and suppliers, and external public sources can help create more sustainable and resilient supply chains. Real-time visibility and advanced analytics enable retailers to drill down into key sustainability metrics, benchmark their progress against other industry players, identify and mitigate risks, and improve overall production quality.
  • Unlocking deep insights for better decision making. Retailers are looking to answer questions about how current processes impact the environment now and how their businesses will be affected by climate change in the future. Leveraging rich datasets about the planet, new AI and machine learning models, and smarter analytics enables them to extract insights and predict outcomes around sustainability, helping them to make better decisions that keep them on track to future goals.

Retailers are already working on sustainability

Putting their vast amount of data to work, retail companies are already starting to harness, organize, and democratize data both within and outside of their organizations, identifying where environmental impact is happening and taking action.

For example, retailers are applying predictive forecasting models to chase down waste to make demand planning more accurate. Understanding what products customers are most likely to buy and where they will purchase can influence decisions about sourcing, where to place inventory, and optimize shipments and deliveries. It also provides a more personalized product selection, keeping both customers and suppliers happy.

Retailers can also reimagine last-mile delivery packaging. For instance, intelligent packing recommendation (IPR) solutions can analyze the physical dimensions of every SKU, packing materials, and other properties like fragility and temperature to make sure every box is optimally packed. Google Cloud research shows that IPR brings significant savings and an improved customer experience, reducing the total packaging cost per order by 29% and total shipping costs by 19%.

When retailers do good while doing well, everyone wins—consumers, businesses, and the planet.

To learn more about the role of technology in sustainability, check out our Sustainable IT Masterclass or watch our “Solving for Sustainability in Retail and Consumer Goods” on demand.

Maria McClay, Director, Department Stores, Fashion and Beauty, Google Cloud. Maria McClay is a director at Google Cloud, working with Fashion &

Source: Why Data Is The Key To Driving Sustainability In Retail

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