For more than 10 years, NMK Textile Mills has manufactured bed linens for major retailers in the United States and Canada. As e-commerce exploded, the company’s co-founder saw an opportunity to grow the business. So, in addition to manufacturing bed linens wholesale for retail customers, NMK Textile Mills reworked its complete supply chain to manufacture products for California Design Den, which became an e-commerce retailer selling its own fashion-forward products directly to consumers online.

With California Design Den’s push into e-commerce, it became apparent the SMB company (with about 250 global employees) faced the same tough supply chain questions as large retail customers, including maintaining enough inventory to meet customer demand in a timely, efficient way.

California Design Den depended upon a myriad of systems to track its complex forecasting and reordering processes. Team members typically planned inventory manually using desktop spreadsheet software, which could lead to excess inventory. Accurately forecasting demand and supply was essential to the company’s financial success—but it was also a challenge.

A couple years ago, California Design Den partnered with Pluto7, a technology solutions provider that offers a software as a service (SaaS) called Planning In A Box. Leveraging Google Cloud Platform machine learning and artificial intelligence, Planning In A Box intelligently helps predict demand and balances it with supply.

But that was just the beginning. “Along the way, we realized that to compete with larger retailers, make quicker decisions, and move faster, we needed to go further,” says Deepak Mehrotra, Co-founder and Chief Adventurer at California Design Den.

With guidance from Pluto7, California Design Den began migrating its database to Google Cloud Platform. Using Google BigQuery, Google Compute Engine, Google Cloud SQL, and Google Cloud Storage, and experimenting with Google Cloud Vision and Google Cloud AutoML, the company is reducing inventory carryovers by more than 50%, improving the accuracy of demand planning quarter over quarter, and gaining granular insights into how individual SKUs are performing.

“We would need an army of data scientists to make faster decisions on pricing and inventory levels. With Google Cloud Platform machine learning and artificial intelligence, we don’t need that. We can make much faster pricing decisions to optimize profitability and move inventory.”

—Deepak Mehrotra, Co-founder and Chief Adventurer, California Design Den

No need for army of data scientists

“Using Google Cloud Platform machine learning and AI was essential for California Design Den if it was to compete successfully with larger retailers,” Deepak says.

For example, tastes and fashions in bed linens can change quickly and consumer prices fluctuate. With more than 2,500 SKUs, it wasn’t possible for California Design Den’s team to continuously monitor product demand and experiment with competitive pricing in real time.

“We would need an army of data scientists to make faster decisions on pricing and inventory levels,” says Deepak. “With Google Cloud Platform machine learning and artificial intelligence, we don’t need that. We can make much faster pricing decisions to optimize profitability and move inventory.”

By integrating all its data onto Google Cloud Platform, California Design Den’s team gains deeper insights into product sales over time, which in turn helps the company improve demand planning by better determining which styles to manufacture and sell in the future.

“When experienced employees leave, their knowledge leaves with them. By having all data in one place, and with machine learning and AI, California Design Den can go back in its history, look at products made or sold years ago, and analyze product performance.”

—Deepak Mehrotra, Co-founder and Chief Adventurer, California Design Den

Merging visuals with data

Before Google Cloud Platform, team members had to dig through spreadsheets and run scenarios to get a sense of how particular products had sold. The next step was to perform keyword searches across the company’s photo library in the cloud to find each product’s image. From there, a team member would insert the product images into a presentation, along with relevant data points, to provide a report for stakeholders on how particular styles performed.

Today, California Design Den, with the help of Pluto7, is integrating its entire product image library with its database on Google Cloud Platform. Experimenting with Google Cloud Vision and Google Cloud AutoML, California Design Den is moving towards a day when team members can run sales scenarios and get deep background data on individual product performance while viewing images of the relevant products.

Merging product visuals with data will help designers and team members better understand sales patterns over time and in context. In the past, making correlations between things like which sheet colors sold well in California, compared to how the same sheet colors performed on the East Coast, was something that California Design Den employees primarily did in their heads.

“When experienced employees leave, their knowledge goes with them,” says Manjunath Devadas, Founder and CEO at Pluto7. “By having all data in one place, and with machine learning and AI, California Design Den can go back in its history, look at products made or sold years ago, and analyze product performance.”

“We are literally growing the complexity of our business on all levels, including designing, manufacturing, selling, reordering, inventory holding—everything.”

—Deepak Mehrotra, Co-founder and Chief Adventurer, California Design Den

Reimagining supply-demand balancing

Pluto7’s mission statement is to democratize supply demand balancing with machine learning and AI. California Design Den is a case in point, as the combination of Planning In A Box and Google Cloud Platform gives the company greater control over its destiny.

“Big retailers used to tell us what to manufacture and how much they would pay for it,” says Deepak. “That was our primary business, and if we didn’t accept the terms, a competitor would.” Today, in addition to continuing to make products for retailers, California Design Den can design, make, and sell a variety of designs for itself, including custom and limited-edition products, thanks to Google Cloud Platform and Pluto7 software offerings. The payoff is not only in having a more diversified business. California Design Den also receives more favorable profit margins by selling its own products.

“We are literally growing the complexity of our business on all levels, including designing, manufacturing, selling, reordering, inventory holding—everything,” Deepak says. “We can make smaller batches. We can connect directly with consumers. We can identify the missing pieces—what should we produce next, when, and how much? We otherwise couldn’t afford the level of talent it would take to do this.”

Cutting through the noise

Google and Pluto7 software helped California Design Den reduce inventory carryovers by more than 50%. Inventory tracking and distribution, along with insights and visibility into product sales, are faster, more efficient, and accurate. Google Cloud Platform flexible pricing, speed, reliability, security, and scalability enable California Design Den to stay relevant and be more competitive.

In addition to benefiting from Google Cloud Platform, California Design Den relies on G Suite—also part of Google Cloud—to enhance collaboration among its global teams. Previously, the company’s email server would sometimes crash, due to the heavy load of sharing product photos and other data. “Gmail and Google Drive handle the everyday demands on the business effortlessly and reliably,” Deepak says.

“Google machine learning and AI enable us to cut through all the noise from raw data, so we can see what’s important. We can focus on analytics to guide us to success today and in the future.”

—Deepak Mehrotra, Co-founder and Chief Adventurer, California Design Den

The company is exploring additional ways to leverage Google Cloud Platform in the near future. For example, one possibility is to import customer reviews from sites where products are sold into Google BigQuery, and to use that data to perform sentiment analysis via Google Cloud Natural Language. It could provide another valuable data source to help California Design Den’s team decide where to focus future designs.

“Google machine learning and AI enable us to cut through all the noise from raw data, so we can see what’s important,” Deepak says. “We can focus on analytics to guide us to success today and in the future.”

The Securities Technology Analysis Center (STAC®), an organization that improves technology discovery and assessment in the finance industry through dialog and research, recently audited the STAC-M3™ benchmark suite on Google Cloud (SUT ID KDB211210). These enterprise tick-analytics benchmarks assess the ability of a solution stack such as database software, servers, and storage, to perform a variety of I/O-intensive and compute-intensive operations on historical market data.

Following up on our previous STAC-M3 benchmark audit (SUT ID KDB181001), a redesigned Google Cloud architecture leveraged the most recent version of kdb+ 4.0, the time-series database from KX, and achieved significant improvements: 35 out of 41 benchmarks ran faster in the new cluster – by up to 18x faster than Google Cloud’s prior results. Key highlights include the following:

Compared to the previous STAC-M3 Antuco suite results on Google Cloud:

• Was faster in 13 of 17 mean response-time benchmarks
• Was 18x faster – a 94% reduction in run time – in the version of Year-High Bid that allows caching (STAC-M3.ß1.1T.YRHIBID-2.TIME), which also set an overall record for all published results
• Had 9x higher throughput in Year-High Bid (STAC-M3.ß1.1T.YRHIBID.MBPS)

Compared to the previous STAC-M3 Kanaga suite results on Google Cloud:

• Was faster in 22 of 24 mean response-time benchmarks
• Was over 10x faster in all four Market Snapshot workloads (STAC-M3.ß1.10T.YR[2,3,4,5]-MKTSNAP.TIME)
• Had 5x the throughput in Year-High Bid involving 2 years of data (STAC-M3.ß1.1T.2YRHIBID.MBPS)

“The STAC-M3 standard was designed by financial firms to reveal the performance of tick analytics stacks. Generational improvements like those exhibited by Google Cloud’s most recent STAC-M3 audit, are important data points for firms evaluating new architectures for performance and scale,” said Peter Nabicht, President of STAC.

These performance results may translate to real-world advantages that may be difficult for investment firms to achieve in static and costly on-premises environments: immediate answers in high data velocity markets, more thoroughly explored research theories by adding data or new quantitative approaches, and reduced costs by releasing cloud resources more quickly.

STAC-M3: High-speed tick analytics

Designing for record-breaking results

In our STAC-M3 audit, the stack under test (SUT) was designed to take advantage of horizontal scalability in the cloud by sharding data across independent compute nodes. The cluster of 12 Google Compute Engine N2 instances was powered by Intel Cascade Lake, with each node using 32 vCPUs, 160GiB of memory, and 9TiB of local NVMe SSDs. The full STAC-M3 Antuco and Kanaga data set was split across the cluster and kdb+ scripts distributed queries between nodes.

This configuration was the sweet spot for this particular workload, but this architecture does not need to be limited to 12 nodes for other workloads – the data sharding algorithm could scale to any number of nodes as required by workload demands. Since scaling out the cluster in this manner increases the total pool of available storage, this architecture can continue scaling out to petabytes of storage across hundreds of nodes.

The ability to spawn large clusters with hundreds of thousands of processors on demand at low cost, and to delete the resources when jobs complete, not only changes the economics of running computations on large financial data sets, it also opens up opportunities to explore solutions to new types of problems that were previously overlooked due to the constraints of fixed hardware on-premises. You can check the pricing of this VM configuration using the Google Cloud Pricing Calculator. The costs can be reduced even further by using preemptible VMs.

While the new cluster used a similar number of nodes, cores, and total memory as the previously-audited cluster, the redesigned architecture allowed us to harness the low latency and high throughput of Local NVMe SSDs.

Resources on demand

The cluster was created on demand using Terraform and Ansible during testing and auditing. The use of infrastructure as code (IaC) techniques ensured that the cluster, fully loaded with the STAC-M3 data set, could be created when needed and then removed when benchmarking was complete. It also meant that the cluster configuration was enforced by code on each deployment, eliminating configuration variance and drift. The full IaC definition to create the cluster can be retrieved from the report in the STAC Vault.

Each time the cluster was created, data was streamed to Local SSDs from Google Cloud Storage, our reliable and secure object storage, at up to the line rate of 32Gbps per node. The entire 57TiB STAC-M3 Antuco and Kanaga data was replicated from Cloud Storage to local storage in approximately 20 minutes.

Since each node was independent and responsible for its own shard of data, doubling the cluster size would cut the synchronization time in half, or copy twice as much data in the same amount of time. Using higher bandwidth options of up to 100Gbps would triple the possible throughput for a relatively small incremental cost, trading an approximately 11%-23% price increase at current list prices for a 200% data synchronization performance increase. Taking advantage of fast networking to cache sharded data in parallel to a large cluster makes storing bulk data in Cloud Storage viable for even the largest workloads.

For quants working on vast data sets in sprawling compute clusters, the ability to fully describe infrastructure as declarative code, create elastic resources on demand, cache data quickly from cheap bulk storage, and turn resources off when computations complete is a dramatic change compared to waiting months to grow on-premises clusters – and a compelling reason to use cloud infrastructure.

To see how we designed and optimized the cluster for API-driven cloud resources, read our new whitepaper.

STAC-A2™: Calculating derivatives risk

In 2018, we showed that cloud instances can outperform bare metal when analyzing large tick history data sets in the demanding suite of STAC-M3 benchmarks. Last year, Google Cloud’s partner Appsbroker showed that the same was true for calculating derivatives risk in STAC-A2 on Google Cloud. You can read about how Appsbroker built its record-breaking STAC-A2 compute cluster on Google Cloud in its blog post, or access the STAC Report directly. Here are the highlights:

Compared to all other publicly reported solutions, this solution, based on a cluster of 10 virtual machines, had:

• The highest throughput (STAC-A2.β2.HPORTFOLIO.SPEED)
• The fastest cold time in the large problem size (STAC-A2.β2.GREEKS.10-100k-1260.TIME.COLD)

Compared to a solution involving an 8-node, on-premises cluster (SUT ID INTC181012), this 10-node, cloud-based solution:

• Had 5 times the maximum paths (STAC-A2.β2.GREEKS.MAX_PATHS)
• Had 10% greater throughput (STAC-A2.β2.HPORTFOLIO.SPEED)
• Was 18% faster in cold runs of the large problem size (STAC-A2.β2.GREEKS.10-100k-1260.TIME)
• Was 9% faster in cold runs of the baseline problem size (STAC-A2.β2.GREEKS.TIME.COLD)

Finding market advantages with Google Cloud

Across the investment management industry, every firm is seeking many of the same competitive advantages. However, finding unique opportunities and managing larger and larger data sets is becoming a major strain. Cloud is fundamentally changing how quants tackle the problem while empowering them to manage risk and generate higher returns.

Building on-premises computing clusters with tens or hundreds of thousands of cores and petabytes of storage requires huge up-front investments and lead time measured in months or years. Google Cloud makes the same scale available to its customers, provisioned on demand and paid per use. More importantly, the elasticity of cloud resources enables agility that is simply not available in a fixed data center cluster – the agility to explore, experiment, iterate, and respond to markets faster than before.

Scaling out to tens of thousands of cores in minutes and then removing the resources immediately not only changes the speed at which questions can be answered; it encourages different and more frequent questions, asked simultaneously on many independent clusters, free from the constraints of fixed on-premises hardware.

It is this flexibility and power that enables financial services firms to leverage larger data sets and get results, backtest, research, and analyze large amounts of data, faster and whenever they need it.

Download our whitepaper to learn more about our latest STAC-M3 tick history analytics benchmark results and how to optimize cloud infrastructure for high-speed market data analysis.

Japan is home to millions of vending machines installed on streets and in buildings, sports stadiums and other facilities. Vending machine owners and operators, including beverage manufacturers, stock these machines with different product combinations depending on location and demand. For example, they primarily display coffee and energy drinks in machines placed in offices and sports drinks and mineral water in machines at sports facilities. The combinations also vary by season: for example, owners and operators may display cold beverages in summer and hot beverages in winter. 

Traditionally, vending machine operators have relied on the intuition and experience of sales managers to determine the optimum product mix for each vending machine. However, in recent years, manufacturers such as Coca-Cola Bottlers Japan (CCBJ) have turned to data to analyze and make strategic decisions about when and where to locate products in machines.

CCBJ is the number one Coca-Cola bottler in Asia and vending machines comprise the bulk of its business. The organization operates about 700,000 machines across Tokyo, Osaka, Kyoto, and 35 prefectures. Minori Matsuda, Google Developer Expert and also Data Science Manager at CCBJ, says “The billions of data records collected from 700,000 physical devices are a great asset and a treasure trove we can take advantage of.”

Minori points out that when considering the mix of products in vending machines in sporting facilities, the managers naturally assume sports drinks would generally sell well. However, analysis of purchase data – including hot drinks and hot drinks plus sports drinks – found many parents purchased sweet drinks such as milk tea when they attended games or sessions involving their children.  “Analyzing data gives us new discoveries and, by using catchy storytelling techniques from exploratory data analysis, we are instilling a data culture within our company,” he says. “It’s worth creating by looking at facts rather than making assumptions!”

Minori believes that to analyze the vast amount of data collected from more than 700,000 vending machines, the business needs a powerful analytical platform. However, until recently, CCBJ had to extract data for analysis from its core systems, load this data into a warehouse it created and perform the required analyses.  The billions of records of data generated across the fleet – including transaction data – exposed some challenges for traditional analysis platforms. They could not efficiently process data at a considerable scale: it could take a day to return results and required extensive maintenance due to the size.

CCBJ considered building a machine learning (ML) platform as a layer on top of existing systems in August 2020 and opted for Google Cloud the following month.  “I feel that Google Cloud has an edge in all products and is very well thought out,“ says Minori, noting the scalability and cost of the platform allow the business to take a ‘trial and error’ approach to achieve the best outcomes from ML. Google Cloud also delivered the required visibility and flexibility to help the business deliver change every day against key performance indicators. 

MLOps platform streamlines ML pipeline development

CCBJ built its analysis platform using Vertex AI (formerly AI Platform) centered on a BigQuery analytics data warehouse, and partly using AutoML for tabular data. “We have created a prediction model of where to place vending machines, what products are lined up in the machines and at what price, how much they will sell, and implemented a mechanism that can be analyzed on a map,” says Minori, adding that building the platform with Google Cloud was not difficult. “We were able to realize it in a short period of time with a sense of speed, from platform examination to introduction, prediction model training, on-site proof of concept to rollout.”

The new data analytics platform of CCBJ consists of the following parts:

Data Sources

• The data collected from the vending machines are all stored on BigQuery.

Data Discovery and Feature Engineering

• Minori and other data scientists at CCBJ are using Vertex Notebooks, where they access the data on BigQuery by executing SQL queries directly from the Notebooks. This environment is used for the data discovery process and feature engineering. 

ML Training

• For ML training, CCBJ uses AutoML for Tabular data, Custom model training on Vertex AI, and BigQuery ML. AutoML gives model performance with AUC curves and also feature importance graphs.

ML Prediction and Serving

• For ML prediction, CCBJ uses Online Prediction for AutoML models and Online Prediction for custom models for real-time prediction when the salesperson finds the interesting point
• Batch Prediction is used for generating a large prediction map that covers the whole country
• The prediction results are distributed to sales managers’ tablets

CCBJ started constructing the platform in September 2020, and completed it within a month. The business has conducted proofs of concept at its base in Kyoto since February 2021, and since April, has rolled out the platform to sales managers in 35 prefectures in one metropolitan area. “Data analysis is built into the day-to-day routines of sales managers with 100% utilization,” says Minori. “They can utilize the prediction results on tablets that were able to achieve pretty high accuracy from the start.”

The hardest part was the education of sales managers in the field; having them understand the reasoning behind the ML prediction results for particular outcomes, so they could be convinced to make use of the results. “For example, regarding a new installation location predicted by the model, it seemed that there was no effective information for installation from the map information, but when I actually went there, there was a motorcycle shop and it was a place where young people who like motorcycles gathered,” says Minori. “Or there is a small meeting place where the elderly in the neighborhood are active. 

“In many cases, new discoveries that cannot be understood from map information alone can be derived from the data.”

Minori also points to a phenomenon whereby humans pursued and confirmed factors inferred by the model – meaning that once they experienced analysis and it worked effectively, they asked why the same type of analysis or prediction could not be undertaken next time. The resulting cycle of more inquiries generated, more information gathered and more data captured for analysis meant the accuracy of results was improved.

Minori describes Vertex AI as having a number of strengths in helping CCBJ build a ML data analysis platform. “One of the major merits of Vertex AI was that we were able to realize MLOps that streamlines the entire development life cycle from construction of the ML pipeline to its execution,” he says.

With near real-time data analysis through Google Cloud, CCBJ teams can spend time developing strategies rather than waiting for data requested from the IT systems department. Exploratory data analysis is also considerably easier as repeated trial and error has greatly improved the accuracy of analyses. Before we used Machine Learning, most machine placement processes were done by human senses, by looking at a map to find the suggestion points. By using Machine Learning to generate a massive number of placement point suggestions, the efficiency of routing of salespeople has been dramatically improved. 

In the future, CCBJ aims to automate the continuous training pipeline with Vertex AI. “CCBJ is a tech company that operates in the food industry,” says Minori. With the organization operating a vending machine network of 700,000 units, it would like to create new businesses based on utilization and analyzing data. Some of these businesses may be based on Sustainable Development Goals (SDGs) initiatives such as the utilization of recycled PET bottles, measures to prevent food loss and ways of using vending machines to contribute to local communities, which we have been working on for some time. It would be interesting if we could collaborate with Google Cloud on these in the future.”

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.

Editor’s note: We invited partners from across our retail ecosystem to share stories, best practices, and tips and tricks on how they are helping retailers transform during a time that continues to see tremendous change. This original blog post was published by OnlineSales.ai. Please enjoy this updated entry from our partner.

Tata’s online healthcare entity, Tata 1mg, aims to revolutionize how a consumer finds the right healthcare professional for their needs. The platform offers e-pharmacy services, diagnostics, and health content and has more than 200,000 active brands in its rapidly expanding list of suppliers, sellers and manufacturers. Several of these brands would need regular involvement from the marketplace team in order to set up and run ad campaigns. As one of the largest platforms, Tata 1mg approached OnlineSales.ai to address their growing needs for an efficient Retail Media Monetisation suite to help scale up their existing monetisation efforts. 

Tata 1mg needed more data-driven insights and customizability for their monetisation efforts

Tata 1mg saw several challenges that they needed to address.

1. Manual effort: Tata 1mg marketplace team’s monetization process was largely manual and required heavy time and resource investment to activate advertisers, which subsequently ate into their overall ad revenues.
2. Non-scalable framework: Given the fast-growing number of advertisers on their marketplace – well into the hundreds of thousands – it became increasingly clear that their existing monetization framework was not scalable.
3. Inadequate reporting & analytics: The existing framework facilitated only simple reporting, and brands were not able to draw the deep insights they needed to make data-led decisions and refine their advertising ROIs.
4. Steep learning curve: Several brands lacked dedicated advertising experts who could bring the experience and skills needed to plan successful ad campaigns and optimize budgets. The involved learning curve led to increased advertiser-churn.
5. Lack of personalized offerings: The manual nature of Tata 1mg’s monetization framework left little room for customizability, and account managers could offer very little in terms of tailored ad-buying experiences to different types of advertisers.

All of these issues led to regular revenue leakage. Tata 1mg realized the need for a central platform that addressed the above-mentioned issues and would also be able to cater to new ad channels and the requirements thereof. Developing such a solution in house was evaluated, but was found to be expensive and would require a lot of time to build.

OnlineSales.ai’s AI/ML-powered solution on Google Cloud offered the automation and flexibility to support modern omnichannel advertising needs

Therefore, Tata 1mg looked to OnlineSales.ai Monetize, an AI/ML-powered retail media monetisation suite on Google Cloud, to help address their needs for a cloud-based monetisation platform that offered the flexibility to support omnichannel advertising models, had automation built in to reduce manual tasks, provided detailed intelligence and analytics, and delivered quick, reliable scalability.

1. White labeled self-serve platform: OnlineSales.ai’s retail media platform was implemented within a short 4 weeks. After thorough testing and implementation, the platform was successfully launched, and enabled sellers to run ads on a fully self-serve platform. This required zero involvement from the Tata team.
2. Unified omni channel buying: The team at OnlineSales.ai worked closely with the platform’s core teams to activate and mobilize various advertising products through a self-serve platform, while simultaneously addressing any unique requirements they had.
3. Omnichannel analytics with AI-led suggestions: Sellers are now also able to generate detailed reports that provide them critical insights into campaigns, and help them make better use of their budgets – enhancing their experience and interest in advertising on the platform.
4. Personalized buying experiences: The tailored UX offered by OnlineSales.ai’s Monetize helped Tata engage brands of all levels in technical aptitude to use the platform easily. This also allowed admins to configure what types of inventories were available to different brands or advertiser segments.

OnlineSales.ai makes use of Google Cloud Dataflow to facilitate computations on real-time streaming. Microservices are deployed on the Google Kubernetes Engine (GKE) platform due to the solution’s well-known ability to handle scale.

In addition, Dataproc is used by the company to run batch processing apps while Cloud Load Balancing helps manage traffic across different regions; all at scale. Onlinesales.ai also uses Memorystore as a database for their ad servers in order to have very low latency, and deploys BigQuery to run analytical applications and facilitate powerful reporting. The bulk of their office applications are deployed on different VMs on Compute Engine, and the company also makes use of Cloud Storage for data storage and transfers between applications.

Learn more about OnlineSales.ai available on the Google Cloud Marketplace.

^{About OnlineSales.ai}

^{OnlineSales.ai, offers a fully white labeled retail media platform which helps retailers unlock additional revenue by activating advertising real estate on their platform. With its AI/ML-powered solution, OnlineSales.ai allows retailers to turn brands of all sizes into advertisers – at scale. Its self-serve platform simplifies the ad buying process, and enhances outcomes with smart automation, boosting ad retailers’ ad revenues by multiples.}