Last June, in the early months of the pandemic, we expanded Business Messages to support all kinds of businesses, from pharmacies and grocery stores to airlines and luxury brands. Then, in December, we announced the ability for small businesses to use the Google Maps app to message with their customers. We knew people would find messaging directly with businesses from Google Search and Maps helpful, but we didn’t know just how valuable it would turn out to be. 

Consumers are increasingly eager to use messaging to connect with brands, and businesses across all industries are now building innovative messaging experiences to address that growing need. The demand from consumers is reflected in recent data:

• 45% of users have spent more time on messaging services because of the pandemic
• 85% of consumers would like to message brands directly, up from 65% in 2019

Being able to message a business directly has never been more important. For me personally, there’s nothing more fulfilling than using technology to help people in the moments that matter. After the release of initial COVID-19 vaccines in late 2020, online searches for vaccines reached an all-time high. To help people searching for vaccine eligibility, availability, and appointment booking, Albertsons Companies’ banner pharmacies—like Albertsons, Safeway, and Vons—made this information easily accessible through Google Search and Maps via the “chat” button. This  gave people the information they needed, when they needed it.

To be more helpful in moments like these, over the last year, we’ve made Business Messages available globally, in more than 70 languages, and we’ve seen more than 30,000 brands and 200 partners come on board. Major brands like Carrefour, Walmart, and Woolworths have invested in Business Messages to help serve their consumers in new and meaningful ways. 

Expanding the conversation between consumers and brands

The retail sector has undergone a transformation since the start of the pandemic. As a result, we know it is critical for digital services to integrate with physical stores. We were able to do just that with Google’s Business Messages, which has been instrumental in helping better serve our customers across 1200+ stores in Europe.Thomas Rudelle
Global Digital Marketing Director, Carrefour

There were major shifts in consumer behavior during the pandemic, from the way people found and purchased products to the ways they interacted with businesses. Not only did we see higher demand for direct contact with brands, but retail searches also surged. At the end of 2020, retail searches grew at a rate that was more than three times higher than the same period in 2019 (based on Google internal data).

As Business Messages has become more widespread, brands are able to boost both responsiveness and customer satisfaction using the service. Levi’s, for example, surpassed 85% customer satisfaction scores using Business Messages and they discovered that the service drove 30x more store-related questions than web chat.

Many brands have also digitized what were traditionally in-store experiences, especially in high-consideration categories like jewelry. For example, Signet brought its virtual jewelry consultant to Business Messages, offering customers advice and answering queries in a way that’s convenient but still feels personal.We started using Google’s Business Messages pretty early on, and continue to transform and improve our experience. The ability to let customers know whether a product is in stock, and its precise location, is critical to offering a connected and frictionless shopper experience. Of course it also helps reduce the workload on our store and customer hub teams.Nick Eshkenazi
Chief Digital Technology Officer, Woolworths

As a working mother, these sorts of innovations have made a direct impact in my own life. I find it very helpful to be able to ask—and get real-time answers to—everyday questions using Business Messages, ranging from store hours and product availability to order status and how to make returns. One of my favorite features is the ability to know, in real time, what store wait times are like, and the exact location of the product I’m looking for, saving me precious time when I visit the store. 

Business Messages helps people do more with their time in other ways as well. Earlier this year I’d been meaning to change my insurance plan, but kept pushing it off because I didn’t want to get on a phone call. I can now message Geico to update my policy through the agent pretty effortlessly. Similarly, in India, Vi customers can use 24×7 real-time customer service to chat with their AI powered virtual assistant VIC, integrated with Google’s Business Messages, to check billing information, activate packs, add recharges and payments, check balances, and much more—and it all happens seamlessly.

Building a full suite of messaging products

We’re investing in building a full suite of messaging products that enable seamless conversational commerce to drive business results. As Business Messages continues to support brands in establishing their messaging presence on Google, we’re also experimenting with other ways to help them increase their reach and engagement on Search and Display. To accelerate on the digital front, we recently integrated AdLingo, a project built within Area 120, Google’s in-house incubator. This will enable brands to easily transform ads into AI powered, personalized conversations at scale.

For more information on how you can activate Business Messages, please reach us via the contact form on the Business Messages website. Merchants can also read and respond to messages via Google My Business or on the Google Maps app.

This is the fifth and final post in a multi-part series about the Kubernetes Resource Model. Check out parts 1, 2, 3, and 4 to learn more.  

In part 2 of this series, we learned how the Kubernetes Resource Model works, and how the Kubernetes control plane takes action to ensure that your desired resource state matches the running state. 

Up until now, that “running resource state” has existed inside the world of Kubernetes- Pods, for example, run on Nodes inside a cluster. The exception to this is any core Kubernetes resource that depends on your cloud provider. For instance, GKE Services of type Load Balancer depend on Google Cloud network load balancers, and GKE has a Google Cloud-specific controller that will spin up those resources on your behalf. 

But if you’re operating a Kubernetes platform, it’s likely that you have resources that live entirely outside of Kubernetes. You might have CI/CD triggers, IAM policies, firewall rules, databases. The first post of this series introduced the platform diagram below, and asserted that “Kubernetes can be the powerful declarative control plane that manages large swaths” of that platform. Let’s close that loop by exploring how to use the Kubernetes Resource Model to configure and provision resources hosted in Google Cloud.

Why use KRM for hosted resources?

Before diving into the “what” and “how” of using KRM for cloud-hosted resources, let’s first ask “why.” There is already an active ecosystem of infrastructure-as-code tools, including Terraform, that can manage cloud-hosted resources. Why use KRM to manage resources outside of the cluster boundary? 

Three big reasons. The first is consistency. The last post explored ways to ensure consistency across multiple Kubernetes clusters- but what about consistency between Kubernetes resources and cloud resources? If you have org-wide policies you’d like to enforce on Kubernetes resources, chances are that you also have policies around hosted resources. So one reason to manage cloud resources with KRM is to standardize your infrastructure toolchain, unifying your Kubernetes and cloud resource configuration into one language (YAML), one Git config repo, one policy enforcement mechanism. 

The second reason is continuous reconciliation. One major advantage of Kubernetes is its control-loop architecture. So if you use KRM to deploy a hosted firewall rule, Kubernetes will work constantly to make sure that resource is always deployed to your cloud provider- even if it gets manually deleted. 

A third reason to consider using KRM for hosted resources is the ability to integrate tools like kustomize into your hosted resource specs, allowing you to customize resource specifications without templating languages. 

These benefits have resulted in a new ecosystem of KRM tools designed to manage cloud-hosted resources, including the Crossplane project, as well as first-party tools from AWS, Azure, and Google Cloud. 

Let’s explore how to use Google Cloud Config Connector to manage GCP-hosted resources with KRM. 

Introducing Config Connector

Config Connector is a tool designed specifically for managing Google Cloud resources with the Kubernetes Resource Model. It works by installing a set of GCP-specific resource controllers onto your GKE cluster, along with a set of Kubernetes Custom Resources for Google Cloud products, from Cloud DNS to Pub/Sub.

How does it work? Let’s say that a security administrator at Cymbal Bank wants to start working more closely with the platform team to define and test Policy Controller constraints. But they don’t have access to a Linux machine, which is the operating system used by the platform team. The platform team can address this by manually setting up a Google Compute Engine (GCE) Linux instance for the security admin. But with Config Connector, the platform team can instead create a declarative KRM resource for a GCE instance, commit it to the config repo, and Config Connector will spin up the instance on their behalf.

What does this declarative resource look like? A Config Connector resource is just a regular Kubernetes-style YAML file- in this case, a custom resource called Compute Instance. In the resource spec, the platform team can define specific fields, like what GCE machine type to use. 

  apiVersion: compute.cnrm.cloud.google.com/v1beta1
kind: ComputeInstance
metadata:
  annotations:
    cnrm.cloud.google.com/allow-stopping-for-update: "true"
  name: secadmin-debian
  labels:
    created-from: "image"
    network-type: "subnetwork"
spec:
  machineType: n1-standard-1
  zone: us-west1-a
  bootDisk:
    initializeParams:
      size: 24
      type: pd-ssd
      sourceImageRef:
        external: debian-cloud/debian-9
...

Once the platform team commits this resource to the Config Sync repo, Config Sync will deploy the resource to the cymbal-admin GKE cluster, and Config Connector, running on that same cluster, will spin up the GCE resource represented in the file.

This KRM workflow for cloud resources opens the door for powerful automation, like custom UIs to automate resource requests within the Cymbal Bank org. 

Integrating Config Connector with Policy Controller 

By using Config Connector to manage Google Cloud-hosted resources as KRM, you can adopt Policy Controller to enforce guardrails across your cloud and Kubernetes resources.  

Let’s say that the data analytics team at Cymbal Bank is beginning to adopt BigQuery. While the security team is approving production usage of that product, the platform team wants to make sure no real customer data is imported. Together, Config Connector and Policy Controller can set up guardrails for BigQuery usage within Cymbal Bank. 

Config Connector supports BigQuery resources, including Jobs, Datasets, and Tables. The platform team can work with the analytics team to define a test dataset, containing mocked data, as KRM, pushing those resources to the Config Sync repo as they did with the GCE instance resource. 

  apiVersion: bigquery.cnrm.cloud.google.com/v1beta1
kind: BigQueryJob
metadata:
  name: cymbal-mock-load-job
  annotations:
    configsync.gke.io/cluster-name-selector: cymbal-admin
spec:
  location: "US"
  jobTimeoutMs: "600000"
  load:
    sourceUris:
      - "gs://cymbal-bank-datasets/cymbal-mock-transactions.csv"

From there, the platform team can create a custom Constraint Template for Policy Controller, limiting the allowed Cymbal datasets to only the pre-vetted mock dataset: 

  rego: |
        package bigquerydatasetallowname
        violation[{"msg": msg}] {
          input.review.object.kind == "BigQueryDataset"
          input.review.object.metadata.name != input.parameters.allowedName
          msg := sprintf("The BigQuery dataset name %v is not allowed", [input.review.object.metadata.name])
        }apiVersion: constraints.gatekeeper.sh/v1beta1

These guardrails, combined with IAM, can allow your organization to adopt new cloud products safely- not only defining who can set up certain resources, but within those resources, what field values are allowed. 

Manage existing GCP resources with Config Connector 

Another useful feature of Config Connector is that it supports importing existing Google Cloud resources into KRM format, allowing you to bring live-running resources into the management domain of Config Connector. 

You can use the config-connector command line tool to do this, exporting specific resource URIs into static files: 

  config-connector export "//sqladmin.googleapis.com/sql/v1beta4/projects/cymbal-bank/instances/cymbal-dev" \
    --output cloudsql/

Output:

  apiVersion: sql.cnrm.cloud.google.com/v1beta1
kind: SQLInstance
metadata:
  annotations:
    cnrm.cloud.google.com/project-id: cymbal-bank
  name: cymbal-dev
spec:
  databaseVersion: POSTGRES_12
  region: us-east1
  resourceID: cymbal-dev
...

From here, we can push these KRM resources to the config repo, and allow Config Sync and Config Controller to start lifecycling the resources on our behalf. The screenshot below shows that the cymbal-dev Cloud SQL database now has the “managed-by-cnrm” label, indicating that it’s now being managed from Config Connector (CNRM = “cloud-native resource management”).

This resource export tool is especially useful for teams looking to try out KRM for hosted resources, without having to invest in writing a new set of YAML files for their existing resources. And if you’re ready to adopt Config Connector for lots of existing resources, the tool has a bulk export option as well. 

Overall, while managing hosted resources with KRM is still a newer paradigm, it can provide lots of benefits for resource consistency and policy enforcement. Want to try out Config Connector yourself? Check out the part 5 demo.

This post concludes the Build a Platform with KRM series. Hopefully these posts and demos provided some inspiration on how to build a platform around Kubernetes, with the right abstractions and base-layer tools in mind. 

Thanks for reading, and stay tuned for new KRM products and features from Google. 

Organizations in every region and industry are developing APIs to enable easier and more standardized delivery of services and data for digital experiences. This increasing shift to digital experiences has grown API usage and traffic volumes. However, as malicious API attacks also have grown, API security has become an important battleground over business risk.

To help customers more easily address their growing API security needs, Google Cloud is announcing today the Preview of Advanced API Security, a comprehensive set of API security capabilities built on Apigee, our API management platform. Advanced API Security enables organizations to more easily detect security threats. Here’s a closer look at the two key functionality included in this launch: identifying API misconfigurations and detecting bots.

Identify API misconfigurations

Misconfigured APIs are one of the leading reasons for API security incidents. In 2017, Gartner® predicted that by 2022 API abuses will be the most frequent attack vector resulting in data breaches for enterprise web applications. Today, our customers tell us application API security is one of their top concerns, which is supported by an independent study from 2021 by Fugue and Sonatype. The report found that misconfigurations are the number one cause of data breaches, and that “too many cloud APIs and interfaces to adequately govern” are frequently the main point of attack in cyberattacks.

While identifying and resolving API misconfigurations is a top priority for many organizations, the configuration management process can be time consuming and require considerable resources.

Advanced API Security can make it easier for API teams to identify API proxies that do not conform to security standards. To help identify APIs that are misconfigured or experiencing abuse, Advanced API Security regularly assesses managed APIs and provides API teams with a recommended action when configuration issues are detected.

Advanced API Security identifies misconfigured API proxies, including the missing CORS policy.
APIs form an integral part of the digital connective tissue that make modern medicine run smoothly for patients and healthcare staff. One common healthcare API use case occurs when a healthcare organization inputs a patient’s medical coverage information into a system that works with insurance companies. Almost instantly, that system determines the patient’s coverage for a specific medication or procedure, a process which is enabled by APIs. Because of the often-sensitive personal healthcare data being transmitted, it is important that the required authentication and authorization policies are implemented so that only authorized users, such as an insurance company, can access the API.

Advanced API Security can detect if those required policies have not been applied, an alert which can help reduce the surface area of API security risks. By leveraging Advanced API Security, API teams at healthcare organizations can more easily detect misconfiguration issues and can reduce security risks to sensitive information.

Detect Bots

Because of the increasing volume of API traffic, there is also an increase in cybercrime in the form of API bot attacks—the automated software programs deployed over the Internet for malicious purposes like identity theft.

Advanced API Security uses pre-configured rules to help provide API teams an easier way to identify malicious bots within API traffic. Each rule represents a different type of unusual traffic from a single IP address. If an API traffic pattern meets any of the rules, Advanced API Security reports it as a bot.

Additionally, Advanced API Security can speed up the process of identifying data breaches by identifying bots that successfully resulted in the HTTP 200 OK success status response code.

Financial services APIs are frequently the target of malicious bot attacks due to the high-value data that is processed. A bank that has adopted open banking standards by making APIs accessible to customers and partners can use Advanced API Security to make it easier to analyze traffic patterns and identify the sources of malicious traffic. You may experience this when your bank allows you to access your data with a third-party application. While a malicious hacker could try to use a bot to access this information, Advanced API Security can help the bank’s API team to identify and stop malicious bot activity in API traffic.

API Security at Equinix

Equinix powers the world’s digital leaders, bringing together and interconnecting infrastructure to fast-track digital advantage. Operating a global network of more than 240 data centers with a 99.999% or greater uptime, Equinix simplifies global interconnections for organizations, saving customers time and effort with the Apigee API management platform.

“A key enabler of our success is Google’s Apigee, delivering digital infrastructure services securely and quickly to our customers and partners,” said Yun Freund, senior vice president of Platform at Equinix. “Security is a key pillar to our API-first strategy and Apigee has been instrumental in enabling our customers to securely bridge the connections they need for their businesses to easily identify potential security risks and mitigate threats in a timely fashion. As our API traffic has grown, so has the amount of time and effort required to secure our APIs. Having a bundled solution in one managed platform gives us a differentiated high-performing solution.”

Getting started

To learn more, check out the documentation or contact us to request access to get started with Advanced API Security.

To learn more about API security best practices, please register to attend our Cloud OnAir webcast on Thursday, July 28th, 2:00 pm PT.

Paper currency — which started gaining prominence in the 1600s — changed the face of global economics and ushered in a new era of international monetary regulation. The primary reason currency created such disruption was its ability to standardize the “medium of exchange”. APIs created a similar effect in the world of technology and digitalization by creating a standardized, reusable, and secure way to exchange information.

Modern web APIs took shape in the early 2000s and played a key role in “.com”mercializing every business. APIs started as a connective tissue primarily relegated to a technical context and quickly evolved into a gateway to new business models, revenue streams, and ecosystems. In 2017, McKinsey estimated a total of $1 Trillion in profit could be up for grabs in the API economy. And in 2022, GGV Capital created an index of API-first startups — a generation of stylistically divergent SaaS companies with leaner operating cost structures and organic usage growth. Just as currency is going through an evolution from banknotes to digital wallets, the world of API management is also on the brink of change.

With more than 15 years of experience managing APIs at Google-scale, we’ve got a unique vantage point from which to observe that change. In today’s post, we will spotlight seven API management use cases that we see growing in prominence — and how you can take advantage of these trends to future-proof your architecture.

1 “Shift left” in API security

As a gateway to a wealth of information, APIs have also quickly become the primary attack vector in security incidents. When we surveyed 500 technology leaders, we learned that more than 50% of organizations experienced an API security incident in the last 12 months. Adding to the increasing magnitude of attacks, there are an increasing number of vectors for potential API security incidents like misconfigurations, outdated APIs/data/components, and bots/spam/abuse.

These security issues aren’t just in production APIs, but at every stage in the API lifecycle. Notably, we found that 67% of the issues are discovered during testing as part of the release management process. This trend ushers in the need for forward-thinking organizations to “shift left with security” — moving controls earlier into the production workflow — by bringing security teams and API teams closer. To stay ahead of security threats, many organizations are actively looking for solutions that allow them to be proactive while minimizing the burden on their security teams. According to our research, integrating capabilities that proactively identify security threats (60%) is top of mind for most IT leaders for the next year.

2 “Knowledge graph” for your enterprise APIs

It comes as no surprise that every organization is relying on APIs to expand and even ground their digital ecosystem — a network of partners, developers, and customers facilitated by modern, cloud-first technologies. There is a growing magnitude and variety of middleware assets, contributing to the growth of IT complexity.

As the number of APIs continues to increase, there is a need to simplify consumption for internal and external developers. Even the most objectively useful APIs remain unseen by most of the organization. In turn this results in redundant code, reduced developer productivity, or worse — turned into a potential security attack vector. This complexity is shifting focus towards consolidation of all middleware assets, growing adoption, and improving education (see below) to improve developer efficiency and de-mystify the IT complexity.

This sprawl is a growing problem in the world of APIs, but it has a lot in common with an age-old phenomenon in the world of web pages and content—search. Google was born out of this problem to help organize the world’s information. Similar to Google’s knowledge graph for web pages, there is a need to index, organize, and instantly present API information for developers that need it. Although it is an emerging practice, we see an increasing number of digital leaders and security teams in larger organizations with mature API programs invest in solutions that help consolidate all APIs, organize their information, and manage their lifecycle.

3 The imminent need for “omni” control planes

APIs have taken on such a vital role in the modern application stack that they have slowly become the neural links across the entire enterprise architecture — bridging legacy and modern applications, shifting architectures towards microservices, and enabling operations across heterogeneous environments. To support all these technological decisions without sacrificing speed, organizations adopted multiple API gateways and fragmented API management solutions. However, this led to a lack of universal visibility, consistent governance, comprehensive security, and meaningful analytics across ALL the enterprise APIs (not just the ones within the confines of a given API management solution). And it increases the maintenance costs — fundamentally undercutting the value of APIs. With this evolution there is a growing need for an omni control plane — analogous to the brain in a human body — across all enterprise APIs.

4 API governance rising through the priority ranks

Despite the clear need for governance, there is still no unified understanding on a good (or right) approach to API governance. With the rapid adoption of APIs without appropriate standardization or quality standards, API governance is top of mind for IT leaders, again.

According to our research, 45% of IT leaders identified API governance as a critical component of their API program. The top three components of API security, performance analytics, and governance demonstrate the critical need for visibility, quality, and security across all APIs.

As digital consumers, we have seen this phenomenon across many industries and digital products. For example AirBnB disrupted the short-term rental market by providing standardized listings, detailed information, and high-resolution photos. In fact, the same governance phenomenon is ubiquitous in the world of e-commerce where there is a clear correlation between a high-quality website or product listing and increasing sales.

The same analogy holds true in the world of APIs, as ~90% of developers use APIs in their work there is a direct correlation between the use of APIs and developer productivity. Digital officers and CIOs need to add appropriate governance controls to standardize API design and improve reuse without adding friction to development timelines.

5 Evolution of design patterns with multiple API gateways

Adoption of new API architectural styles and microservices increased the complexity of the modern application stack. Our research found that 54% of organizations use a service mesh and API management in conjunction today to support the API gateway design pattern. In parallel, there is broad adoption of new protocols like GraphQL or AsyncAPI, outpacing the innovation in API gateways. For example, in a recent survey from DZone found that GraphQL accounted for 22.7% of application integrations.

In response to this challenge IT teams are adopting multiple API gateways — by design — which is creating the need for complex communication patterns for future scalability. But the existing design patterns were mostly sufficient when client applications used homogeneous API protocols (Ex: REST). Although patterns like Backend For FrontEnd (BFF) intended to provide specific API interactions that are relevant on a per-client basis, they still did not account for complexities from multiple gateways and protocols. In response to the adoption of new protocols, there is a need to evolve the existing BFF pattern to account for multiple API gateways and protocols.

6 Driving green value chains with digital twins

A digital twin is an effectively indistinguishable virtual representation of a physical object, system, or a process. For example the digital twin of a wind turbine (the object being studied) might be used to capture data like performance, rpm (revolutions per minute), or output captured by various sensors outfitted on the turbine. Digital twin adoption is growing and McKinsey estimates investments in digital twins will reach $58 billion by 2026 with a 58% CAGR. Every digital twin uses APIs to monitor, engage, and possibly control the physical asset. For example, Google created the Digital Buildings project — an open source, Apache-licensed effort to manage applications and analyses between a large heterogeneous portfolio of buildings.

Sustainability is one of the driving forces behind the increased use of digital twins. As the need to reach net zero emissions accelerates, many organizations are tying performance (and in some cases even executive pay) to environmental, social, and governance goals. APIs help connect the dots between digital twins and sustainability. For example, an organization operating a manufacturing process could build a digital twin with APIs to collect behavioral data from sensors, monitoring systems, or other sources — which can eventually be integrated into the organization’s digital platform or applications. These digital twins could be used to analyze and optimize the use of materials and energy, to minimize waste and emissions. Additionally, digital twins could be used to monitor and analyze the performance of systems over time, to identify opportunities for continuous improvement.

Overall, APIs play a valuable role in supporting sustainability efforts by enabling digital twins, effectively driving more efficient operation of systems, and providing insights to improve environmental impact. For further examples, check out this video about driving a green value chain with APIs.

7 Commercializing access to data products

The growing use of data-rich services (like IoT, ML models, remote access services, and web scraping, etc.) coupled with massive ingestion of data everyday is creating massive growth in data delivery paradigms like data lakehouses, data marketplaces, and data streaming systems (global data marketplaces alone are poised to reach $3.5 billion by 2028). Unfortunately, most of these systems are fragmented with almost no relationship or interoperability.

APIs are filling this critical gap for organizations in two critical ways. First, APIs are providing standard and easy access to systems like data lakehouses or analytics hubs. Second, APIs are a key enabler of data products (digital products or services built using data as a core value proposition), a core component of any data sharing system. APIs provide a standardized way for different applications to interact with the data product. For example, an API could be used to allow a mobile app to access data from a weather forecast or a recommendation engine data product. Beyond data products, APIs also provide easy and standardized access various data management platform 

APIs continue to play a critical role in every application, experience, and ecosystem. Robust API strategies help organizations adapt to any architecture, business model, or environment in the face of changing technology landscape. Learn more about how Apigee is driving innovation and helping companies future proof their architectures to stay ahead of the top API trends.

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.}