In the past year, Google has worked to surface timely and reliable health information, amplify public health campaigns, and help nonprofits get urgent support to Indians in need. Now, we are continuing to focus on helping India’s businesses accelerate their digital transformation, deepening our commitment to India’s digitization and economic recovery. To support customers and the public sector in India and across Asia Pacific, we’re excited to announce that our new Google Cloud region in Delhi National Capital Region (NCR) is now open. 

Designed to help both Indian and global companies alike build highly available applications for their customers, the Delhi NCR region is our second Google Cloud region in India and 10th to open in Asia Pacific. 

What customers and partners are saying

Navigating this past year has been a challenge for companies as they grapple with changing customers demands and economic uncertainty. Technology has played a critical role, and we’ve been fortunate to partner with and serve people, companies, and government institutions around the world to help them adapt. The Google Cloud region in Delhi NCR will help our customers adapt to new requirements, new opportunities and new ways of working, like we’ve helped so many companies do in the region: 

• InMobi scaled a personalized AI platform to support 120+ million active users. “With the arrival of the Google Cloud Delhi NCR, InMobi Group sees the opportunity to continue closing the gap between our users and products,” says Mohit Saxena, Co-founder and Group CTO of Inmobi. “Glance, especially, has been serving AI-powered personalised content to over 120 million active users. We can’t wait to continue giving them truly meaningful experiences that are speedy, scale well, and are relevant to them, by expanding the use of our current tools working on Google Cloud with the opening of a new region.”
• Groww now supports a sizable user base. “Google Cloud provides great technology that enables us to build and scale infrastructure to millions of users, and the new Google Cloud region in Delhi NCR will continue to help more businesses and startups in India access powerful cloud-based infrastructure, products and services,” says Neeraj Singh, Co-founder and Chief Technology Officer, Groww.
• HDFC Bank is positioned for the future. “At HDFC Bank, we are harnessing technology platforms to both run and build the bank. As we progress to be future ready, the objective is to invest in future technologies that give us scale, efficiency and resiliency. Towards this the Google Cloud region in Delhi NCR will enable us to enhance our resiliency and help us in building an active-active design framework for our new generation applications on cloud,” says Ramesh Lakshminarayanan, CIO, HDFC Bank.  
• Dr. Reddy’s Lab built a modern data platform with Google Cloud. “At Dr Reddy’s, we pride ourselves in helping patients regain good health, acting quickly to provide innovative solutions to address patients’ unmet needs and in accelerating access to medicines to people worldwide. Our Google Cloud-powered data platform is helping us realize these objectives and we welcome Google’s investment in the new Delhi NCR region as helping us and other businesses in India make further contributions to our social and economic future,” says Mukesh Rathi, Senior Vice President & CIO, Dr. Reddy’s Laboratories.
• “To survive the disruption caused by the pandemic and to succeed in the long term, organizations need to become digital natives, so they can be more agile, explore new business models and build new capabilities that boost resilience. A cloud-first strategy plays a key role in enabling businesses to do this,” said Piyush N. Singh, Lead – India market unit & lead – Growth and Strategic Client Relationships, Asia Pacific and Latin America, Accenture. “Harnessing the potential of cloud requires the right data infrastructure and this expansion by Google Cloud will undoubtedly help Indian enterprises in their digital transformation journeys.”

A global network of regions

Delhi NCR joins 25 existing Google Cloud regions connected via our high-performance network, helping customers better serve their users and customers throughout the globe. As the second region in India, customers benefit from improved business continuity planning with distributed, secure infrastructure needed to meet IT and business requirements for disaster recovery, while maintaining data sovereignty.

With this new region, Google Cloud customers operating in India also benefit from low latency and high performance of their cloud-based workloads and data. Designed for high availability, the region opens with three availability zones to protect against service disruptions, and offers a portfolio of key products, including Compute Engine, App Engine, Google Kubernetes Engine, Cloud Bigtable, Cloud Spanner, and BigQuery. 

Supporting India’s recovery with training and education

Google and Google Cloud will also continue to support our customers with people and education programs. We’re investing in local talent and the local developer community to help enterprises digitally transform and support economic recovery. 

Through the India Digitization Fund, we expanded our efforts to support India’s recovery from COVID-19—in particular, through programs to support education and small businesses. In addition to expanding internet access, and investments to help start-ups accelerate India’s digital transformation, we’ve grown our Grow with Google efforts. Businesses can access digital tools to maintain business continuity, find resources like quick help videos, and learn digital skills—in both English and in Hindi.

Helping customers build their transformation clouds

Google Cloud is here to support businesses, helping them get smarter with data, deploy faster, connect more easily with people and customers throughout the globe, and protect everything that matters to their businesses. The cloud region in Delhi NCR offers new technology and tools that can be a catalyst for this change. To learn more, visit the Google Cloud locations page, and be sure to watch the region launch event here.

The Home Depot (THD) is the world’s largest home-improvement chain, growing to more than 2,200 stores and 700,000 products in four decades. Much of that success was driven through the analysis of data. This included developing sales forecasts, replenishing inventory through the supply chain network, and providing timely performance scorecards.

However, to compete in today’s business world, THD has taken this data-driven approach to an entirely new level of success on Google Cloud, providing capabilities not practical on legacy technologies.

The pressures of contemporary growth that drove much of the work are familiar to many businesses. In addition to everything it was doing, THD needed to better integrate the complexities in its related businesses, like tool rental and home services. It needed to better empower teams, including a fast-growing data analysis staff and store associates with mobile computing devices. It wanted to better use online commerce and artificial intelligence to meet customer needs, while maintaining better security.

Even before addressing these new challenges, THD’s existing on-premises data warehouse was under stress as more data was required for analytics and data analysts were utilizing the data with increasingly complex use cases. This drove rapid growth of the data warehouse, but also created constant challenges for the team in managing priorities, performance, and cost.

In order to add capacity to the environment, it was a major planning, architecture, and testing effort. In one case, adding on-premises capacity took six months of planning and a three-day service outage. Within a year, capacity was again scarce, impacting performance and ability to execute all the reporting and analytics workloads required. The capacity refresh cycles were shrinking, and the expecations for data were growing. There had to be a better way.

Still, THD did not take its move to the cloud lightly. A large-scale enterprise data warehouse migration involves tremendous effort among people, process, and technology. After careful consideration, THD chose Google Cloud’s BigQuery for its cloud enterprise data warehouse.

BigQuery, a scalable serverless data warehouse, was better on cost, infrastructure agility, and analytics capability, driving better insights with improved performance. There are no service interruptions when capacity is added, and that capacity can be added within a week (and soon same day). It doesn’t require complex system administration, and its standard SQL support means people can easily ramp up quickly. Valuable BigQuery products like Identity and Access Management meant THD could create many separate Google Cloud projects, while ensuring that different teams weren’t interfering with each other or accessing protected data.

THD also utilizes BigQuery’s flat-rate monthly pricing model that allows teams to budget their capacity based on need and provides billing predictability. The capacity not being used by a given project is available for enterprise use. This ensures no surprises when the monthly bill arrives and provides all analytical users access to significant computing power.

While THD’s legacy data warehouse contained 450 terabytes of data, the BigQuery enterprise data warehouse has over 15 petabytes. That means better decision-making by utilizing new datasets like website clickstream data and by analyzing additional years of data.

As for performance, look at this chart:

With the cloud EDW migration complete, and the legacy on-premises data warehouse retired, analysts now execute more complex and demanding workloads that they would not have been able to complete before, such as utilizing Datalab for orchestrating analytics through Python Notebooks, utilizing BigQuery ML for machine learning directly against the BigQuery data (no movement of large datasets), and AutoML to help determine the best model for predictions.

Additionally, engineers at THD have adapted BigQuery to monitor, analyze, and act on application performance data across all its stores and warehouses in real time, something that was not practical in the on-premises system.

With over 600 projects that THD now has on Google Cloud, the BigQuery story is just one of the many ways that Google Cloud is working with THD to deliver meaningful business results, every day.

In recent years, the grocery industry has had to shift to facilitate a wider variety of checkout journeys for customers. This has meant ensuring a richer transaction mix, including mobile shopping, online shopping, in-store checkout, cashierless checkout or any combination thereof like buy online, pickup in store (BOPIS).  

What’s more, in the past year and a half alone grocers have had to enable consumers new ways to shop for essentials. This has included needing to rapidly integrate or build on-demand delivery apps, offer curbside pickup with near-instant fulfillment as well as support touchless and cashless checkout experiences. Searches on Google Maps for retailers in the US with curbside pickup options have increased by 9000% since March 2020, and we believe these trends from 2020 will continue to define the future of grocery shopping.

The future of grocery will require agility and openness

Firstly, the need to rapidly adapt to changing consumer habits will be the new normal. Grocers will increasingly look to digitally transform legacy retail systems and modernize point of sale (POS) platforms to deliver and scale omnichannel experiences as quickly as possible. This necessitates a more agile and open architectural approach to technology – one built on microservices and leverages APIs so that new applications and experiences can be built, integrated and delivered faster.

Automation and data-driven retailing will be table stakes

In order for retailers to blend what they’re offering in the store with digital experiences more efficiently, they will also need to automate more. For example, with automation and business intelligence, grocers can take labor that might have been tied up with tender operations and checkout and redistribute those resources to restocking shelves, curbside pick-up or improving customer experiences. 

Automation and access to real-time in-store inventory & supply chain data can also help grocers avoid the supply chain challenges seen in the early days of COVID-19. Grocers will need to find ways to leverage automation to ingest, organize, and analyze data from physical store networks, digital channels, distribution centers to better forecast demand and manage future fluctuations.

How NCR and Google Cloud are helping grocers adapt to disruption with operational agility

Helping grocers improve operational agility to address changing consumer shopping habits and to thrive during times of disruption is something that NCR and Google Cloud have teamed up to do. NCR has over 135 years of experience in retail, having invented the cash register and are continuing to help grocers innovate. NCR Emerald builds upon the company’s leadership in POS software and has turned it into a unified platform that helps grocers operate the entire store from front to back. The solution supports cashier-led checkout, self-checkout, integrated payments, merchandising, and enables regional managers and corporate employees access to the analytics and tools needed to optimize loyalty programs and promotions.

NCR has invested in a comprehensive, agile, and API-led retail architecture that lets grocers continually innovate and design new experiences as customers and the industry evolve. By running Emerald on Google Cloud, NCR can offer the solution on a subscription basis, helping grocers lower upfront capital expenditures and ensuring scalability. What’s more, NCR can tap into Google Cloud’s strength in data, analytics, and openness to deliver three key imperatives. Let’s take a look at each of these below.

Run the way grocers need to while leveraging Google Cloud as a single source of logic

Traditionally the POS system lived in the store. If disaster strikes, people still need access to food and essentials so the grocery store still needs to operate. It hardly gets more mission-critical than that. NCR Emerald is built on microservices, leveraging Kubernetes for front-of-house compute, and VMs (See graphic 1 below). This makes it easy to support lightweight clients accessible by store employees via any range of mobile devices, computer terminals, self-service kiosks, peripheral devices like receipt printers as well as legacy applications.

What’s unique is that because Emerald runs on Google Cloud, it supports all those in-store and digital touchpoints mentioned above, but also allows grocers to run lean. Emerald leverages Google Cloud as a single source of truth and operates a lot of what it does out of logic. Every sales transaction coming from every channel, including e-commerce, can be logged via NCR’s Hosted Service and centralized in BigQuery and Bigtable as a transaction data master. This enables the grocer to manage any transactional use case very consistently, whether it e supporting customers who want to purchase in one store and return in another, offering digital receipts or the ability to exchange online purchases in store. Emerald on Google Cloud can help retailers extend capabilities through the power of the cloud but not need to live exclusively in the cloud. In other words, the solution allows grocers the ability to run the way they need to.

Enable data-driven and real-time decision making for grocers

Store managers, regional managers, category managers, and others all require different cuts of the data to do their jobs effectively. However, data silos persist and how data is formatted and arranged can still remain pretty static. Therefore allowing users with different roles the ability to view and analyze that data quickly and in different ways continues to be a challenge. 

As mentioned above, Emerald leverages Google Cloud data management solutions as the central repository for transactional, behavioral, and merchandising data. Every transaction from every store and every channel can be stored via NCR Hosted Service on BigQuery and Bigtable. NCR Analytics then harnesses the advanced analytical and data visualization capabilities of Looker to help grocers get a consolidated view of their business across all channels and then allow employees to slice and dice the data they way they need to. NCR Analytics also leverages the power of Google Cloud AI and machine learning to add another level of intelligence to the retailer’s data. For example, store managers can visualize how well they’re using their real estate and see how productive lanes 1-3 are compared with 7-10 or compare self-service versus manned lanes. By mapping to the retailer’s own catalog, they can also break down category-level performance and trends.

NCR Analytics takes advantage of Google Cloud’s data pipeline to reduce processing time, with scaling and resource management provided out of the box. By letting the cloud store and process the data, NCR is providing the ability for retailers to analyze their data in near real-time across all platforms – a real game changer in the grocery business.

Open APIs let grocers continually enrich the retail experience

Finally, Emerald is built on an API-first architecture managed through Apigee. It uses the power of Apigee as an open API platform to expose how Emerald can work with other NCR applications like loyalty and promotions, and third party applications like mobile ordering and order delivery to enrich the grocery experience for employees and customers. Every API that Emerald uses is available on Apigee, allowing them to share code samples and giving developers the ability to run scripts. This approach can allow retailers the ability to innovate in a fraction of the time and cost, speeding up 3rd party integrations up front and as businesses grow. 

Take, for example, Northgate Market, a chain of 40 stores in California, that were able to transform its digital operations and enable experiences that set it apart from competitors – quickly and simply with Emerald. It took less than 6 months to go from contract to live deployment in the first store. Since then, Northgate Market has been able to extend their intelligence by leveraging the power of Looker and NCR Analytics.

Learn more about how NCR has been able to leverage an open, cloud-enabled architecture to help customers innovate across the retail, hospitality, and banking industries on the webinar “Role of APIs in Digital Transformation”. You can also learn more about how Northgate uses e-commerce to transform customer experience and gain consumer insights.

If you’re here you’re probably wondering: what on Earth is the connection between muffins and machine learning, and what is National Muffin Day? To understand this, let’s start with National Muffin Day: an annual holiday co-founded by Jacob and his friend Julia Levy in 2015 to bake muffins and raise money for homelessness. National Muffin Day will occur on Sunday, February 20 this year. For more information on how to participate in National Muffin Day (which involves delicious baked goods and donations to people in need), please see the information at the bottom of this post. Last year, a colleague connected Jacob with Sara, who had done several baking projects that used machine learning to generate new recipes. They decided to collaborate for this year’s National Muffin Day, adding a new muffin recipe created with the help of machine learning.

In this post, we’ll explain how Sara used Google Cloud to develop a new muffin recipe, show you how you can participate virtually in National Muffin Day, and of course—share the recipe.

Machine learning for muffins

At its core, machine learning is the process of finding patterns in data and using those patterns to make predictions on new data. After a lot of baking over the past few years, Sara learned that baking is also based on patterns. For example, the ratio of flour, fat, liquid, and sugar that make up a cookie is very different from the ratio of those ingredients for a bread, a pie crust, or a muffin. She used that discovery to create a recipe for a hybrid cake + cookie, and a cake filled with Maltesers. Next up: muffins! 

The first step was figuring out how to translate the task of generating a new muffin recipe into a machine learning task. To solve this, she planned to use numerical data on the amounts of different ingredients in a muffin recipe to train the model. Sara considered two types of models for this task: classification and regression. A classification model would categorize muffin recipes into different muffin types based on their ingredient amounts, and a regression model would do the reverse: take a type of muffin and return the amount of each ingredient needed to make it. She decided to build a regression model, since it would be more fun for the model to return ingredient amounts, rather than tell you which type of muffin recipe you’re already making. 

Implementing this first required identifying a few muffin categories and collecting recipe data. This presented a new challenge, since her previous baking models used categories for distinct baked goods (i.e. cakes, cookies, breads). After scouring through quite a few recipes, Sara discovered that many muffins fall into two types: those that use only traditional ingredients as their base (flour, sugar, butter, milk, etc.), and those that include an alternative ingredient, most commonly a pureed fruit, to make the base (like bananas, applesauce, or pumpkin). Using those two categories, the model would take the type of muffin as input and return the amounts of base ingredients required to make that recipe. Here, the inputs can be any values adding up to 100%:

The next step in the ML process was collecting recipe data to use for model training and narrowing down the ingredients used to train the model. Sara wanted the model to learn the combination of core ingredients that make up a muffin batter, rather than flavorings and additions like blueberries, vanilla extract, or chocolate chips. These tasty additions could be added after the model helped create the muffin batter. Once she gathered enough recipes, she removed extra ingredients for training purposes and converted ingredients from different recipes into the same unit (grams, milliliters, and teaspoons).

Building a muffin model with Vertex AI

Sara uploaded the muffin ingredient data into BigQuery, and then created a notebook instance in Vertex AI Workbench to analyze the data. With the new Workbench managed instances, you can interactively query BigQuery tables directly from your instance and copy the code to download your data to a notebook as a Pandas DataFrame:

From her notebook instance, Sara experimented with different ML frameworks and model types. She landed on a Scikit-learn regression model to solve this task, and to mimic a real-world production environment, decided to convert this workflow into a ML pipeline. Using the Kubeflow Pipelines SDK, she ran the following on Vertex Pipelines:

The first component reads the ingredient data from BigQuery and converts it into a Pandas DataFrame which is passed to the next pipeline step. In this step, we train a custom Scikit-learn model on the recipe data. Finally, this model is deployed to an endpoint in Vertex AI. To put it all together, Sara built a web app that allowed her to easily generate ingredient amounts for different muffin types. The web app uses the Vertex AI SDK to call the deployed model endpoint and return ingredient amounts.

The recipe

With a deployed recipe generation model, the only thing left to do was test recipes in the kitchen! Because the model only returns ingredient amounts, there were still many key human elements to complete the baking process: adding yummy additions to the core muffin batter, making adjustments to optimize taste, determining the method for adding ingredients, baking time, and more. After testing a few recipes generated by the model, we landed on a favorite which we’re very excited to share with you here.

Berry ML Muffins

Makes 12 muffins

Flour 285 grams (2 cups)

Granulated sugar 250 grams (1 cup)

Baking powder 2 teaspoons

Baking soda ¼ teaspoon

Salt ½ teaspoon

Cinnamon ½ teaspoon

Milk 170 ml (⅔ cup), room temperature

Butter 55 grams (¼ cup), melted and slightly cooled

Eggs 1 egg plus 1 egg white, room temperature

Canola or vegetable oil 50 grams (¼ cup)

Sour cream 50 grams (3 tablespoons + ¾ teaspoon), room temperature

Vanilla extract 1 ½ teaspoons

Blueberries or raspberries 240 grams (1 ½ cups)

Coarse sugar, like demerara or turbinado (optional for topping) 1 tablespoon 

1. Measure your three cold ingredients and allow them to come to room temperature: 1 egg + 1 egg white, sour cream, and milk. 
2. Preheat the oven to 375 F / 190 C. Line a 12-muffin tin with cupcake liners or lightly grease with baking spray.
3. In a large bowl, whisk together flour, baking powder, baking soda, salt, and cinnamon. Set aside.
4. Melt your butter in a medium heat proof bowl, and allow it to cool slightly for a few minutes. Whisk in sugar until combined. Then add egg, oil, and vanilla, milk, and sour cream and whisk until fully incorporated.
5. Pour the wet ingredients into the dry ingredients, mixing with a spatula until just combined. Be careful not to overmix, it’s ok if there are a few lumps in your batter.
6. Prepare your fruit. If you can’t decide whether to use blueberries or raspberries, divide your batter into two bowls and do both! Crush half of your fruit and fold it into the batter. Then mix in the remaining whole berries.
7. Divide the mixture evenly into the muffin tin. Optionally (but extra tasty), sprinkle the tops of each muffin with about ⅛ teaspoon of coarse sugar. Turbinado or demerara sugar work well for this. This will caramelize and add a nice texture to the tops of your muffins.
8. Bake at 375 for 22 – 24 minutes, or until a toothpick inserted in the center comes out clean. For best results, do a toothpick test in a few muffins since not all ovens have an even temperature throughout. Let the muffins cool in the muffin tin for a few minutes, then transfer to a wire rack to cool completely.
9. Enjoy!

How can you participate in National Muffin Day?

Participation in National Muffin Day is as easy as 1-2-3!

1. On February 20, Bake Muffins. It’s time to dust those muffin tins, grab your blueberries, chocolate chips, rhubarb, and favorite ingredients, and create some magical scrumdiddlyumptiousness! If you want to join Jacob in a virtual baking party, you can register here.
2. Then, Give. In non-pandemic years, we asked our bakers to personally hand muffins to hungry folks in their cities. While this is a valuable and rewarding experience, the current state of Covid means this practice is still unsafe, so we request that you refrain from doing this. Instead, if it feels safe, we encourage you to take your delicious baked goods and donate them to local homeless shelters, which can distribute them to those in need. Alternatively, you can share your muffins with friends and families and then make a donation to an organization that benefits people experiencing homelessness, like the ones listed below in step 3.
3. Share Your Muffin Pics on Social Media. We’d love to see your muffins!  Share your pictures on Twitter or Instagram with the hashtags #givemuffins, or share them to our official Facebook Event page. For each individual baker who participates, we will make donations to Project Homeless Connect, which provides much needed resources to people experiencing homelessness in San Francisco, Family promise, which supports unhoused families nationwide, and Pine Street Inn which provides resources for people experiencing homelessness in Boston. Donations will be on a per-baker basis (with up to $80 donated per baker!), so please feel free to loop in your significant others, kids, nieces and nephews, roommates, friends, and anybody else with a giving spirit who loves deliciousness!

The increasing adoption of technologies like connected devices, augmented reality, and machine learning has changed the way we shop, and retailers are evolving how they do business to meet the needs of their customers.

Retailers say it’s no longer enough to keep pace with shoppers’ growing expectations—they must get ahead of them. That’s why more and more are turning to the cloud. They’re using it to eliminate data silos and take advantage of cloud-based analytics. They’re tapping into machine learning to improve all aspects of the value chain. And they’re making use of reliable and secure cloud infrastructure to scale their businesses.

Although every retail customer is different, many of them share similar objectives. Here are three major ways retailers take advantage of the cloud.

Storing and Analyzing Data in the Cloud

Data presents both a challenge and an opportunity for retailers. Which is why Ulta Beauty, the largest beauty retailer in the US, is moving to Google Cloud Platform (GCP). Now, with the help of BigQuery, Ulta Beauty will be able to more efficiently predict and analyze outcomes and develop more meaningful data insights that can be leveraged to deliver a more personalized, relevant guest journey.

They are not alone. DSW has also chosen to use GCP to help relaunch their DSW VIP loyalty program for the first time in over 10 years. With more than 90% of transactions running through their loyalty program, DSW needed a flexible and scalable solution to deliver a real-time loyalty program for 26 million active members. They’ve already seen a 9% uptick in new customers and have improved their already strong retention rate.

Improving Customer Experiences with AI and Machine Learning

Once retailers are able to access these insights, they are turning to AI to help personalize the overall shopping experience. At first, retail companies leveraged AI tools such as machine learning for product recommendations.

Now, more retailers use AI to forecast trends, predict inventory needs and prevent stock outs, and provide personalized recommendations to customers to intelligently and efficiently serve them.

Just look at METRO AG, one of the largest B2B wholesalers globally. They’re using AI and machine learning to better serve their customers. For example, many of their customers are restaurant owners. With Google Cloud AI capabilities, they can create tools that identify when a restaurant is out of a particular ingredient and automatically order more.

Ocado is another great example. The world’s largest online-only grocery retailer drove a 3.5% increase in contact center efficiency by using Google Cloud machine learning technology to respond to customer emails four times faster.

To help businesses further accelerate their AI solutions, Google has developed the Advanced Solutions Lab (ASL), which gives businesses the opportunity to work side-by-side with Google’s AI and ML experts to solve high impact challenges.

Fast Retailing, the Japanese retailer behind Uniqlo, is working with Google Cloud and ASL to help them better analyze customer data to forecast demand and deeply understand what their customers want.

Carrefour, one of the world’s leading retailers, also announced last year that its engineers will be working side-by-side with our AI experts to co-create new consumer experiences. This is in addition to deploying G Suite to their employees to support the company’s digital transformation.

Scaling Infrastructure to Meet Demand

Of course, none of this innovation is possible without a reliable infrastructure that can scale instantly to meet surges in traffic.

And many have found the reliability and security they need with the cloud. That’s why global cosmetics brand Lush chose Google Cloud. They migrated their e-commerce platform to GCP to handle increased traffic without compromising stability.

This move that ultimately reduced infrastructure hosting costs by 40 percent.

L.L.Bean also modernized its IT infrastructure by moving capabilities from its on-premises systems to GCP, improving customer satisfaction and IT efficiency across multiple sales channels.