Last year we launched our on-demand rides and delivery solution to help businesses improve operations as well as transform the driver and customer journey from booking to arrival or delivery. When it comes to on-demand rides and deliveries, every minute matters. When users book a ride or order food, they want a seamless experience and real-time accurate updates. Today, we’re taking a closer look into data quality improvements for location, time and distance accuracy, and motorbike routes. 

Machine learning helps drive location accuracy

Location accuracy stands at the base of our customers’ operation. The location signals that are coming from mobile devices can sometimes be off for various reasons and  a driver’s location can get stuck, or jump around. 

Recently we developed mechanisms in our fleet management product that can take in multiple location signals and determine the most reliable location to use for a given vehicle. With that, we noticed drastic improvements: 

• Eliminated long periods of location ‘stuckness’ almost completely; a vehicle is considered ‘stuck’ when we think it’s moving but the measured location is not
• Reduced the jumpiness of the location signal by 52%-86%: ‘jumpiness’ is when a vehicle shows a sudden and usually drastic change in location. A jump is determined to exist when the speed the vehicle had to go at in order to cover the distance it did is unrealistic 
• Reduced the average jump distance by 44%-86% : ‘jump distance’ is the distance between two consecutive location pings when we determined a ‘location jump’ has occurred. 

Dunzo, a local e-commerce platform in India, explains how integrating the order tracking capability within Google Maps Platform’s On Demand Rides and Delivery solution has helped reduce support calls by 90%. The out-of-the-box solution helped Dunzo’s motorbike delivery partners with updating location sync to reduce stuckness and jumpiness as well as deliver premium user experiences.

When the vehicle location is more reliable, the dispatch decision is of higher quality, meaning there is a higher chance you will be able to make the optimal decision. This can lead to less wait time for consumers, increased driver happiness and fewer cancellations. 

Improvements in ETA accuracy in motorbike routes

In many geographic areas, road space is limited and car ownership is prohibitively expensive so motorbike is a prominent transportation mode. Motorbike mapping requires unique routing, ETA models, and navigation capabilities. Improvements in motorbike routing and ETA estimation have enabled customers like Gojek to offer better overall services, even in geographies with poor wifi or missing roads.

Recently, we further improved ETA outcomes by developing new machine learning models trained specifically for motorbikes. These models help our systems account for differences in congestion and traffic flow that arise in different regions and scenarios.

Globally, we measured an ~8% improvement in ETA accuracy for riders in the general public, and a ~6% improvement in on-demand rides and deliveries ETA accuracy.  In this on-demand economy, where consumers are accustomed to real-time trip and order progress, these improvements will significantly improve their experience.

We will continue to innovate on both our car and motorbike on-demand rides and deliveries capabilities based on customer demand and requirements.  We are committed to the success of our customers by building a seamless experience for all parties—consumers, drivers, and fleet operators—in the rides and deliveries journey.
For more information on Google Maps Platform, visit our website.

The benefits of APIs are becoming more clear in an ever-evolving tech landscape, yet ITDMs still struggle to convince executives and investors to buy into an API-first strategy. Here’s a look at the importance of APIs in a changing world, and how ITDMs can make the business case in order to secure the best API strategy for their organization.

According to Google Cloud’s new “State of the API Economy 2021” report, a majority of IT decision-makers view application programming interfaces, or APIs, as essential ingredients in improved customers experiences, expanded partner engagement, accelerated innovation, and other demands of today’s business environment. This is encouraging: APIs are how software talks to other software, and since much of digital transformation involves combining disparate data and functionality into rich user experiences and process automations, APIs are an essential ingredient in modern business strategies. 

What’s less encouraging: the research surveys primarily IT professionals, not business leaders. It’s clear that IT people see the benefits of APIs in the ever-changing tech landscape, but we still hear regular concerns from these same people that they have trouble convincing executives and investors to buy into an API-first strategy. In this article, we’ll look into why they are having these difficulties and some proven ways to successfully position an API strategy not just as a technological solution, but also as a business requirement.

The importance of APIs in a changing world

The rise of APIs has been heavily influenced by the introduction of disruptive new business models and evolving customer preferences that traditional technologies are not positioned to quickly and efficiently address. 

For example, traditionally, if your business sold tickets to events, it would build physical ticket booths and maybe a website or first-party mobile app. Today, tickets in many cases aren’t so much a physical thing presented to an usher as a digital code that an usher scans. Likewise, tickets are less-often purchased in person as opposed to online, and reliance on a first-party website can be unnecessarily restrictive. It places the burden on the business to attract customers, whereas surfacing organically in social media, search engine results, and other digital experiences lets the business meet customers where they’re already assembled. 

Moreover, as COVID-19 continues to disrupt events throughout the world, many ticket sellers—and most organizations, for that matter—have pivoted to digital-first business interactions as a matter of necessity. All of these changes in the business model, and all of the interacting systems and functionality that underpin them, rely on communication among APIs. 

Similarly, today’s banks cannot grow by simply building more branches or hiring more tellers. Instead, they need to make financial information and functionality available when and where customers require it, whether that means via an ATM, a first-party app, or within some other digital experience. Many banks also need to do more than just present this functionality, as customers are increasingly interested in the analytics and insights their spending patterns can yield. Again, all of these interactions—from customers making a purchase within an app to banks applying machine learning in order to offer customers financial insights—are enabled by APIs.     

Related: The “State of API Economy 2021” report describes how digital transformation initiatives evolved throughout 2020, as well as where they’re headed in the years to come. Download for free.

When guidance meets resistance

These examples do not illustrate technology that updates the status quo, but rather technology that unlocks business opportunities that transcend the status quo—and that help businesses to thrive even as the status quo fades into irrelevance and obsolescence. APIs are thus not just an IT topic but also important business enablers that should be understood by everyone involved with the enterprise’s investments, from internal stakeholders approving business strategies to external shareholders trying to assess an organization’s trajectory. 

The challenge for investor relations is to convey these financial and operational benefits in a way that clearly communicates the need for a new business model rather than refinements to the existing models. It’s essential that IT professionals understand APIs, but it’s also essential for business leaders to understand them too.

This is even trickier given that arguments for API investments are often based on future potential, while arguments for more conservative alternatives are based on past success. 

At a high level, the API value proposition is clear: In the past, valuable functionality and data have been encased in systems and applications, making them difficult to scale or leverage for new, evolving use cases. In contrast, APIs make functionality and data infinitely reusable, infinitely scalable, and modular such that APIs can easily be combined for new uses. All of this accrues to richer user experiences and more flexibility than ever for companies to monetize their digital assets, share them with partners, or combine them with assets from third parties. 

It’s essential that IT professionals understand APIs, but it’s also essential for business leaders to understand them too.

But investors typically want as much information as possible because their decisions can affect not just productivity and output, but company stock prices and potential future growth. High-level arguments may not be persuasive. The deeper assurances investors crave would normally come from guidance.

Guidance in this context refers to insights based on growth forecasts and customer adoption, but this can be difficult early in market entry. Robust forecasting processes need to be developed to demonstrate the efficacy and value of the API economy, which can be hard to predict: whereas APIs are well understood in some sectors, and especially among digital natives, they are in the early stages of the growth rate in other verticals, making it challenging to forecast developer adoption of a given API. And since there is a shortage of information, trying to use traditional guidance comes with a risk of being wrong and thus of little value to investors.

Related: Set your 2021 API resolutions with these top 2020 posts.

How to deliver a more useful value proposition

While guidance may be premature during the early stages of market entry, investor relations teams still need to convey the full value of an enterprise to investors. To do this, they need a value proposition that emphasizes the intrinsic value of the investment while reinforcing the benefits that can best drive business and stock growth. Considering how large an investment of time, effort, and money transitioning to an API economy can be, it is vital to convey that the benefits are substantial.

A solid value proposition should demonstrate maximum returns, and while this shouldn’t include far-fetched or unobtainable claims, it can include reasonable aspirational visions alongside statistical insights. To craft these aspirational narratives, investor relations teams should look to their organization’s existing business needs and challenges, and then demonstrate how APIs can benefit the organization in these areas. Here are some options that speak to a number of common business requirements:

• Sales channel: API investments are reusable, improve speed to market, enable automated processes and partner onboarding, and can uncover unanticipated opportunities.
• Cost: Businesses can reduce operational costs by using and reusing APIs for innovation and business development, and by using the services native to your partner’s digital surface, you can further reduce innovation costs and risks.
• Earnings: API-enabled digital ecosystems unlock a variety of partner services that leverage the business’s shared data to drive new customer acquisition, new market positions, new transaction volumes, and direct API monetization.
• Risk mitigation: By investing in a credible API, businesses can mitigate downside risks that traditional enterprises can face from market disruptors, industry-wide shifts to digital tools, and inabilities to ingest and analyze growing data sources.
• Intellectual property: Unlike project-driven innovation and customized, point-to-point integration that traps enterprise knowledge in small teams and divisional silos, APIs are reusable and modular, breaking down silos and encouraging intra-organizational collaboration.
• Speed to market: The efficient, repeatable API interface informs improvements to the fulfillment process with consistent access to data from across the organization, which drives solutions that more quickly and efficiently meet customer needs.
• Ethics: APIs offer the flexibility and economical advantages that give organizations the capacity to focus on their brand’s ethical “reason for being” beyond profitability by serving economically marginal and underserved market segments.
• Customer credibility: Organizations can deliver the extended, connected digital experiences that customers expect with the tools and flexibility included with API products.
• Employee retention: Businesses can avoid losing key employees by updating their legacy technologies with APIs, giving employees the opportunity to enhance their skills with modern technologies.
• Corporate strategy: Enterprises that use APIs’ reusable, modular structure and tools are more capable of adapting to rapid structural shifts in customer demand patterns and sectoral changes in the economy.

Whichever of these business challenges a team speaks to, it is imperative that they demonstrate the benefits of APIs, and that once they’ve determined the angle they intend to use, they keep their message consistent. While we’ve seen a number of viable ways to position APIs as a winning strategy, switching among them could make the presentation—and APIs in general—seem insubstantial and unreliable. 

This is why it’s key to decide on the most relevant business concerns, and once you’ve tailored your presentation, to make sure that you have message alignment, including buy-in and support from C-level executives. With a strong pitch built around solving existing business concerns and solidarity from relevant stakeholders, you can go into your investor meeting with the confidence to secure the best API strategy for your organization.

Strengthen your pitch with additional insights. Here are five key trends in 2021 for API-first digital transformation.

About the Author: Paul Rohan is a researcher on Open Banking and a Google Cloud solutions consultant. Paul works with banking C-Suites that are examining the impact of the Platform Economy and Digital Ecosystems on financial services industry growth, market structures and governance. Paul is the author of “PSD2 in Plain English” and “Open Banking Strategy Formation”.

Time to go outside again, I guess. I’ll need a sun hat. Sunscreen. Maybe some new sandals? What else?

With the Recommendations AI service, I might be reminded to grab a reusable water bottle and a swimsuit. Or some after-sun aloe lotion. Good thing, cause I’ll need it.

Recommendations AI is a solution that uses machine learning to bring product recommendations to their shoppers across any catalog or client list. This service is part of our full suite of Retail solutions. When you integrate with the Retail API, you get the benefit of Google’s Product Discovery. Integrating once to reap the benefits over and over. Recommendations is the starting point, and you can easily extend into Retail Search and Vision Product Search in the upcoming future. 

The Recommendations solution is fully managed, global-scale and powered by deep learning, so you can focus on a great shopping experience and let someone else worry about the infrastructure.

Compared to baseline recommendation systems used by customers, Recommendations AI showed double digit uplift in conversion and clickthrough rates in A/B experiments controlled by the customers. You can optimize for click-through, conversion or session revenue, and fine tune the models to make sure you omit out-of-stock items or duplicates, for example.

So how does it work, and how do you get started? Read on, and we’ll walk you through the pipeline, starting with the data you already have to placement in your online store. 

Formula: Data -> Model -> Placement

You start with your catalog, the list of all the things (postcards, movies, pie recipes) that you want to show your customers. Then you ingest your PII-redacted user events -this is the historic event data like home page views, add to cart events and more along with real time user events. This user event is joined with the product catalog and items that allows us to construct the sequence of shoppers’ activity, thus being able to predict what the shopper has a high propensity to purchase next. The user events can come from both online activity across devices or offline store purchases

The recommendation model will return a list of products, which are the recommendations. The brains of the operation, if you will. This model is trained using all the data that you ingest, using the latest neural network models and techniques that Google has built expertise over the years in flagship products like Youtube and News, that allows us to uncover shopper intent,  so it can best predict the right recommendations to show to the right people.

Every model outputs a list of product identifiers, but where do they go? They go into placements, the spots, panels, carousels on your customer’s journey interacting with your brand that you’ve set aside to highlight recommendations. A model can send recommendations to one or more placements, but each placement only receives information from one recommendation model. Your pages will then need to render the products with the right images, text or other metadata, using the product ID that is returned by the model.

What do recommendations look like?

Let’s start by browsing our postcard-selling website, where I’ve been buying some vintage California postcards already. The recommendations algorithm has caught on to my interest, showing me other potential cards to purchase based on my history:

Put your data to work

To get started we need to bring your data into the recommendation model, so it can understand your customers, your inventory, and your sales patterns. 

The model takes in the product catalog you use, and metadata about those products to better understand nuances in assortment, pricing and variables like size and style. You might already have this data stored in BigQuery or Merchant Center, and hence we provide easy integrations that you can leverage to get started even faster.

As for the user events, don’t worry if you already have systems in place to capture web and mobile activity. We make it easy to bring in your real time event logs by providing seamless integrations with Google Tag Manager, Javascript pixel, or even historic events from Cloud Storage, BigQuery or using inline API or JSON, so you can immediately train the models on this imported  data. All this allows you to kickstart integrating with Recommendations AI in a matter of days.

The models then construct a sequence of activities that the user went through and joins with the products that the user engaged with. Once your data is ready to go, it takes a few days to train the model. Next onto making the data work for you.

Quickly customize your model

Setting up your own recommendations project in the console gives you the ability to choose what sort of model to train (based on what recommendations you want to generate) and your objective. Are you optimizing for click-through rate–more people click on the recommendation links or products–or for conversion rate–more people choose or buy what was suggested or revenue ?

Different models can be optimized for different optimization goals.; the GCP console explains what each one can do and how you can choose to optimize it.

Let’s unpack some of this terminology real quick.

We’ve got three model types:

• Recommended for you – Means we think these are items you’ll want to buy, based on your history; this is usually used on a home page to showcase items.
• Others you may like – Means if you’re browsing the page of a water bottle, we will recommend  alternative brands of water bottles that you may like as well as a backpack, based on your engagement  history.
• Frequently bought together – Means that when anyone buys sunscreen, we notice that they often also buy aloe lotion, so we will surface those items when someone adds any one of them to their cart.

And then we have three business objectives that the models optimize for:

• Click-through rate – How frequently did somebody click on a recommended item?
• Conversion rate– How frequently did somebody add a recommended item to their cart?
• Revenue per session – How much money did the recommendations generate for you?

Deliver anywhere along the journey

Now that you’re all set up in the Retail AI console, you can test out the recommendations in the console, even before you deploy to production.

You can integrate Recommendations into your frontend by calling the Predict APIt. The placements of recommendations will report data back into the dashboard and you can analyze and measure success for future iterations. 

On top of that you can use the recommendations for other parts of your customer’s journey. Email promotions, storefront kiosks, display ads or follow-up notifications can include recommendations based on past activity and cart contents. The model gives you useful product recommendations for a wide variety of touchpoints and steps in the purchasing process.

More best practices, and guides, are available inside our documentation.

How to get started

Training your own models can be tedious, time-consuming, and expensive. On top of that it requires deeper data science expertise to set up. Let us do it instead!

You can see how IKEA Retail uses Recommendations AI in this recent talk and blog from the Google Cloud Retail Summit..

To get started today you’ll need to make a Cloud project and enable the Retail API, which then allows you to access all the recommendation tools in one menu. Bring in your catalog and purchasing data, define a placement or two, and you can start putting recommendations on your site in a matter of days.

As the way people work continues to evolve, keeping security policies in place that protect organizations but give workers the ability to get things done is more important than ever. IT and security teams must aim to stay a step ahead of web-based security threats that come their organization’s way. To help, the Center for Internet Security (CIS) team has released the latest CIS Benchmark 2.1 for Google Chrome. This Benchmark offers independent recommendations on which Chrome policies to configure to help support organizations’ security and compliance needs. Thanks to Chrome being built with security at its core, in many cases, Chrome default settings are aligned with CIS recommendations.

Chrome is secure by default, but we also pride ourselves on providing customizations for enterprises to allow Chrome to better fit the needs of their business. And with hundreds of policies available through Chrome Browser Cloud Management and Group Policy Objects (Note: The CIS Benchmark is also available as a GPO), organizations can do just that. 

Throughout the CIS guide you’ll notice that there are different designations for configuration profiles. Any labeled Level 1, are considered to be a good baseline for an organization. Level 2 profiles are recommended for deployments that require the highest level of security, but note that these settings could have a trade off on user productivity. We recommend looking at each setting and determining if it’s a good fit for your business. 

The benchmark is made up of five sections:

• Enforced Defaults — Notes policies that are configured by default when you install Chrome. Enforcing these settings at an enterprise level can prevent these settings from being changed by business users to less secure options.
• Attack Surface Reduction — Details how to disable web features that may not be necessary in your enterprise environment and could reduce your overall attack surface.
• Privacy — Surfaces settings that improve user privacy.
• Data Loss Prevention — Contains settings that can help prevent data loss and protect your organization’s data. (Note: These recommendations cover additional capabilities that can be added to Chrome through BeyondCorp Enterprise). 
• Forensics (Post Incident) — Shares recommendations on policies that give insights into post incident forensics and analysis. 

Organizations can use these benchmarks to optimize the best way to secure Chrome in their environment. Download the CIS Benchmark here and check out our team’s configuration guide for additional recommendations on how to configure Chrome.

Note: This benchmark was created using a consensus review process composed of subject matter experts. Consensus participants provide perspective from a diverse set of backgrounds such as consulting, software development, audit and compliance, security  research, operations, government, and legal. While these recommendations come from a trusted source, it’s important for each organization to weigh which policies make the most sense for their business.