Embedded Intelligence Helps Businesses Prepare for the Unknown
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The disruptions of 2020 elevated the importance of having the right data and insights to pivot quickly when necessary. Here’s a look at how businesses can use embedded intelligence to prepare for uncertainty and meet ever-changing customer expectations.
At work and in life, some unpredictability is always a part of the package. Ten years ago, your business might have experienced sudden product demand or a system outage that slowed down deliveries. Servers or data platforms might have run out of capacity earlier than expected.
In 2020, though, the concept of unpredictability in business reached new heights. These disruptions have elevated the importance of embedded intelligence—that is, having machine learning built into the tools people use every day, so that when a pivot is necessary, everyone has the data and insights they need at their fingertips. In fact, 2020 was so disruptive, with so many changes in customer behavior and so many ripple effects, a lot of historical data and forecasting assumptions may not be helpful in 2021. This only increases the onus on businesses to make the freshest data actionable for more of the workforce.
A decade or so ago, the idea of embedded intelligence might have seemed like science fiction. You might remember hearing that analytics would be able to make predictions, and that technology would be able to take on the complex work of predicting retail demand or helping to create a responsive supply chain. But insufficient hardware, older architecture models, slow queries, and untrustworthy data often got in the way.
Now, that concept has become reality as enterprise decision-making has moved from legacy tools to cloud-powered data intelligence services. Today, it’s possible to perform complex analytics tasks and obtain valuable, trusted outputs much faster than ever before. That speed and scale has allowed businesses to tackle entirely new projects and release new features and products very quickly. In addition, APIs have become a lot more intelligent, making it easy to connect siloed solutions. No matter the industry, businesses can access the technology to get to the bottom of what customers need.
Related: Top 5 trends for API-powered digital transformation in 2021
Meeting ever-changing customer expectations with embedded ML
Bringing embedded analytics to real-world uses continues to evolve, with a number of inspiring examples surfacing in the past year. As a result of the pandemic and shifting public health guidelines, many businesses didn’t know month by month if they’d be interacting with customers primarily through in-person or digital channels. And even if both channels were available, it wasn’t obvious how changing customer behaviors would net out.
At patient engagement platform Force Therapeutics, for example, daily activity on their virtual care platform went up by over 140% during the pandemic. With such a large influx of incoming data, it would have been difficult—if not impossible—for a team of humans to gather, organize, and draw insights from all of that information, especially in a timely enough manner to be of use to healthcare providers.
To deliver the necessary care when and how it was needed, Force Therapeutics required a machine learning solution that could identify patient needs based on a wide range of data. Using an embedded analytics platform, they created an application that allowed them to monitor the progress of post-op patients, answer questions, or triage concerns remotely. The platform also enabled providers to check for spikes and anomalies, in order to identify patients who needed to come in due to a critical issue.
Amidst all of the disruption, it became clear that teamwork is essential, and that effective teamwork relies on having the right data-driven tools to get the job done.
Likewise, home delivery became a bigger part of consumers’ routines. This increased pressure on companies to adapt quickly to changes that might prevent packages from arriving on time, such as worsening weather conditions or upstream supply chain disruptions. Amidst all of the disruption, it became clear that teamwork is essential, and that effective teamwork relies on having the right data-driven tools to get the job done.
One example of this can be seen in Google Cloud customers who are using public data to accelerate their journey from data to actionable insights. Some retailers are utilizing the Google Cloud Public Datasets Program to leverage NOAA’s Global Surface Summary of the Day (GSOD) and Severe Weather Data Inventory datasets in order to better understand disruptive weather events, reroute their supply chains to prevent disruptions, and predict their in-store inventory needs to support communities as they recover from natural disasters.
Implementing ML without the complexity
The idea of embedded ML has been hyped for years, but for many use cases, the status quo tools have not caught up to the enthusiasm. Many business intelligence tools rooted in older database architectures require intense engineering work to deliver insights, queries are often slow, and the output is not always consistent or accurate. Part of the challenge is that building ML pipelines is difficult. Data in a database or data warehouse typically needs to move to an intelligence platform so models can be trained, and the models then need to be deployed and integrated into business workflows.
But modern data warehouses such as BigQuery let users train models in the warehouse itself, without having to move the data—and once the models are created, they can be applied and integrated into business processes using simple SQL. When it comes to embedding ML into enterprise processes, these modern approaches significantly lower the barrier for entry. Plus, tools like Looker, Google Cloud’s platform for modern BI and data applications, were created specifically for modern data needs, with the assumption that data needs would constantly evolve and that iterations should be made quickly without eating up inordinate engineering resources.
For Commonwealth Care Alliance (CCA), Looker was originally implemented to alleviate their pain points around data bottlenecks and data chaos. But when the pandemic hit, the nonprofit, community-based healthcare organization pivoted to make use of Looker’s tools to better serve patients. CCA used BigQuery and Looker to combine numerous datasources, create a predictive model that assesses risk, and distribute that model to its clinicians. This has given response teams the insights to determine who is too high risk to come in for care so they can reach out with home care solutions.
This kind of agility is not a one-time antidote to a one-time disruption, but rather the norm to which organizations must aspire if they want to remain competitive and protect themselves against future disruptions.
This same functionality is also helping businesses like SoundCommerce. Retailers like Constellation Brands, Eddie Bauer, and FTD/ProFlowers use SoundCommerce’s out-of-the-box data platform, which is powered by BigQuery and Looker, to collect retail data from any source and build a model around the metrics and relationships that are most crucial to retail. This has saved brands hundreds of manual reporting hours each month, and reduced platform licensing costs by almost 75%. Just as importantly, during the uncertain times of 2020, brands that used SoundCommerce were able to align real-time and predictive business decisions across marketing and operations with critical retail KPIs like contribution margin and customer lifetime value (CLV).
As 2020 showed us, we can never predict the future—but we can prepare for unpredictability by having the agility to always improve, and by positioning ourselves to make quick, intelligent pivots when the time comes. Last year was in many ways a rubicon: This kind of agility is not a one-time antidote to a one-time disruption, but rather the norm to which organizations must aspire if they want to remain competitive and protect themselves against future disruptions.
Looking for an ‘easy button’ to speed up your BI workloads running on BigQuery? Check out our latest announcement about BI Engine on the Google Cloud Blog.
Debanjan Saha is GM of Data Analytics at Google Cloud, where he leads the strategy and execution of analytics services in GCP. Prior to joining Google, Debanjan was VP of Amazon Aurora and RDS at Amazon Web Services. Earlier in his career Debanjan held multiple executive and technical leadership positions at IBM and Tellium, an optical networking pioneer that he helped grow from an early stage start-up to a public company.
Debanjan is a Fellow of the IEEE and a Distinguished Scientist of the ACM. He has co-authored a book, 50 patent applications, and numerous technical articles including award winning papers and Internet standards. He received MS and PhD degrees from the University of Maryland, and a B.Tech from IIT, all in Computer Science. In 2019, Business Insider named him as one of the top 10 technology executives transforming business.
What Swiggy and You Can Learn From This Company’s Use of ML to Engage Customers
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The app economy has enabled a huge range of unique business models to flourish. One such model is online food ordering and delivery services, in which apps leverage geo-location data to aggregate local food choices and offer personalized options to consumers.
A leading company in this space is Just Eat. Launched in the UK in 2001 with a vision of ‘serving the world’s greatest menu. Brilliantly.’ The company has capitalized on the popularity of online food delivery and grown its presence across 12 markets.
Just Eat acts as an intermediary between take-out food outlets and hungry customers, giving local restaurants access to a broader base of potential diners, while providing consumers with an easy and secure way to order and pay for food from their favourite restaurants.
Today the company helps 27 million customers find food from more than 112,000 restaurants—everything from homemade Italian pasta, to Chinese noodle bowls, to fish-and-chips.
Data is the fuel of Just Eat’s rapid growth, but it wasn’t always looked at that way. In its early days, Just Eat struggled with the deluge of information and faced fragmentation across its systems. In fact, the company realized its legacy data vendor wasn’t capable of ingesting 90 percent of the data produced by its food platform. This was incredibly frustrating for Just Eat’s analysts and data scientists, who had to waste time cleaning up sources instead of leveraging the data to create a better user experience.
Just Eat turned to Google Cloud, and now uses machine learning (ML) to power sophisticated consumer recommendations on both its app and website. It also makes heavy use of features offered by Google Cloud Platform, including BigQuery for running analytics on its customer data set and Cloud Pub/Sub for messaging app users with relevant offers in real-time.
Having all of Just Eat’s data in one platform has translated into real value for its customers. With Google Cloud tools, Just Eat has created its own proprietary Customer Ontology framework, which today contains 5.5 billion features that better understand consumers’ behavior and food habits, and provides insights into previous visits.
Just Eat recently created an “Adventurous Index” to map its customers according to their ordering habits, enabling them to tailor their marketing and user experiences. For example, mid-adventurous customers are shown a choice of restaurants that serve their most ordered cuisine, while adventurous customers can choose from restaurants that serve a wider variety. This not only has prompted consumers to be more adventurous with their choices, but also has led to more business at a more diverse set of restaurants.
Matt Cresswell, Director of Customer Platforms at Just Eat said that Google Cloud has become integral to its product delivery: “Consumer food choice is a hugely nuanced topic. We know that individuals have their own unique journeys when they use Just Eat. We’ve sought to create a truly one-to-one relationship with every customer. The changes we’ve made to the platform mean they can access the dishes they enjoy at the touch of a fingertip, and find inspiration to discover new dishes they’ll love. We’re grateful to Google Cloud for helping us support our customers on their culinary explorations.”
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Since 1994, IDOM, Japan’s leading buyer and retailer of used cars, has enjoyed success in the auto industry with a simple yet traditional business model: buy pre-owned vehicles directly from car owners and auction them to third-party dealers, or sell them to other consumers at retail stores.
In an increasingly frugal economy, Japanese consumers are buying fewer new cars. Most young urban workers take public transport, a cheap alternative for getting from point A to point B. Additionally, people who do own cars are keeping them longer: the average period of ownership is 7.5 to 10 years.
Although Japanese consumers are buying fewer new cars, used car sales are steadily on the uptick. Pre-owned car sales in Japan rose by 1.7% in 2015—the first big spike in three years. IDOM dominates this industry with about 40% market share, and it wanted to continue to take advantage of this growing market trend.
To do so, IDOM reinvented its marketing strategy, using Google’s machine-learning technology to make full use of its available customer data. The brand’s main goal was to attract more prospective car sellers to its physical stores because (1) that’s where they could close trade-in deals and (2) sourcing used cars efficiently is integral to the success of its business model.
Secondly, rather than measure marketing success solely on clicks, views, brand awareness, or favorability, IDOM relied on data to determine which advertising techniques—including phone calls and customized ads to prospective sellers—turned a real profit.
After successfully identifying and targeting existing car owners with a high chance of selling their car, it was only natural for IDOM to leverage this approach to identify and target potential customers with a higher chance of buying a car—key for the other side of its business as well. Thus, IDOM also showed customized ads to potential car buyers and prioritized follow-up phone calls to high-value potential car buyers.
Find out how IDOM increased the number of sellers and buyers visiting its stores by a whopping 25% and grew gross profits by 300% in a key market segment. Download now!
Woolaroo App and Vision AI are Helping Users Explore Native Languages
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One of the most vibrant elements of culture is the use of native languages and the time-honored tradition of storytelling. Anthropologists and linguists have been vocal on the role that language plays in the preservation of culture and how it contributes to the appreciation of heritage.
Unfortunately, of the more than 7,000 languages that are spoken around the globe, nearly 3,000 are at risk of disappearing. In fact, it’s estimated that on average a language becomes extinct every fourteen days. Google Arts & Culture realized that with some creative technology and partnering with language organisations, we could help create an interactive and educational tool to help promote them.
Enter Woolaroo, an open-source photo-translation platform powered by machine learning and image recognition. The application was built on Google Cloud to encourage users to explore endangered languages around the world. Users are able to take a picture of an object in real-time, and the application returns the word in its native language, along with its pronunciation.
Woolaroo was created with the philosophy that learning languages is greatly enhanced through engagement and context. By seeing an object in its environment, it’s easier to retain the information and then use it more naturally in conversation.
With the help of Googlers, Woolaroo was launched in 10 languages, including Calabrian Greek, Louisiana Creole, Maori and Yiddish. During the conception stage of the app, teams from Partner Innovation and Google Arts & Culture put out an open call to the rest of Google to see what lesser-known languages our employees spoke. They then worked with the individuals that responded to develop dictionaries that were reviewed by partner institutions to ensure translations were correct and consistent.
Woolaroo uses Google Cloud Vision API, which derives insights from images using AutoML or pre-trained models to quickly classify images into millions of predefined categories. This makes AI accessible and useful to more people as AutoML automates the training of these machine learning models.
Our team at Google Arts & Culture creates immersive experiences for people to learn about art, history, culture and more. We are committed to supporting the preservation of heritage and cultural landmarks – including spoken language – through the use of modern technology. The magic of Woolaroo is that it is open source, which means any person or organisation can use it to build something for their own endangered language. To learn about the efforts Google Arts & Culture is involved in, download the Google Arts & Culture app or visit our blog.
How Digital Simulation of Physical Stores Helped e-Commerce Companies Replenish Stocks and Fulfil Online Orders at Scale in 2020
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Editor’s note: We’re inviting 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 has seen tremendous change. The original version of this blog was published by Trax Retail in October 2021. Please enjoy this updated entry from our partner.
In 2020, we saw a massive leap in online grocery. Within a few weeks, the industry reached numbers that were unexpected for at least four more years. In a snap, what seemed like a relatively insignificant channel became one that grocery retailers simply couldn’t ignore and, what’s more, is likely to remain a norm.
Retailers trying to stay ahead of the curve have already started experimenting with methods like building warehouses and installing robots to fulfill the growing demand, but in-store picking has been and will continue to remain one of the primary methods of fulfilling online orders as it offers several advantages. First, it allows the use of existing infrastructure – large retailers already have geographically well-located outlets in major cities, which enables shorter lead time to delivery. Secondly, it allows retailers to stay flexible and move swiftly between in-store and online order fulfillment. And finally, it fits perfectly with the omnichannel approach to allow customers to shop wherever and however they want – click and collect, curbside pick-up, or inside brick-and-mortar stores.
How improving on-shelf availability helps ecommerce profitability
Despite its many advantages, in-store picking is expensive and affects profitability in an already competitive space since the cost of picking is directly tied with hourly labor fees. Moreover, with labor being a major challenge to obtain and retain, every minute spent picking counts. However, there is an easy way to keep costs down – optimizing the picking process by ensuring product availability and reducing the need for substitution.
An average of 8% of products are not available on-shelf in stores at any given time. As a result, pickers waste much of their time hunting for products that simply aren’t on the shelf or are misplaced, or spend time looking for substitutes, further increasing the time and cost for picking. In fact, grocery pickers often claim that out-of-stock (OOS) occurrences are the main reason that slows down the picking process. Without these factors, pick rates could be two times faster. Since picking accounts for 50% of the fulfillment cost, it has a substantial impact on the order’s bottom line and often makes the difference between a loss-making and a profitable basket.
Maintaining on-shelf availability for the thousands of products in every store has always been a significant problem to solve. The shift to online ordering has made the availability issue even more significant as the shelf is also now the fulfillment center for the majority of online orders. For e-commerce, out of stocks present two major issues for retailers. First, the impact of out-of-stock items is even more noticeable for online shoppers as they do not select their own substitutions. Additionally, out-of-stocks cause inefficiencies in the item fulfillment process, which is already a costly and margin-diluting practice for retailers. The good news is that through a partnership between Trax and Google Cloud, and the power of Google Cloud’s AI/ML capabilities, these challenges are addressed. The granular, real-time data at scale to enable picking efficiency and the data transparency to unlock a better shopping experience are now a reality which benefits both shoppers and store associates.
Trax and Google Cloud technology helps the bottom line
Trax and Google Cloud offer solutions that can help retailers improve on-shelf availability (OSA). Trax Retail Watch powered by Google Cloud uses computer vision (CV) and AI to create a digital version of the physical store in real-time and informs out-of-stocks so that retailers always have full visibility of items that are running low and can replenish them quickly. In turn, pickers are more likely to find everything they need quickly without facing the problem of missing products or having to look for substitutes. Trax Retail Watch also allows store associates to quickly spot any issues arising on shelves without having to physically walk down each aisle. From misplaced products to low stock, having full visibility means that shelf managers make more informed decisions, so that pickers face fewer obstacles, are aware of the true inventory, and significantly improve their pick rates. Faster picking means lower costs and ultimately, higher margins for online grocery orders.
With more e-commerce retailers crowding the online space, competition is tough. Want to know how frustrated e-commerce shoppers are about on-shelf availability? Trax uncovered this problem through an in-depth consumer study. Learn how to address shopper happiness while driving more profitable fulfillment and stay on the leaderboard with this Trax and Google Cloud whitepaper: Winning the online grocery race.
New Technology: The Projected Total Economic Impact™ Of Google Cloud Contact Center AI
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According to Forrester Research, customers expect easy and effective customer service that builds positive emotional connections every time they interact with a brand or organization. Additionally, 40% of surveyed business leaders say that improving their organization’s customer experience (CX) is a high priority, ahead of initiatives like improving products and differentiation and reducing costs.
While this is a huge opportunity, improving CX in contact centers presents a significant challenge to organizations because most legacy interactive voice response (IVR) systems were never designed with CX in mind, and they are often left unchanged for years at a time except for the addition of more options when a new product or service is launched.
Providing great CX is a top priority for most organizations, but because contact centers typically operate 24/7, decision makers are hesitant to make significant changes or upgrades out of fear of breaking their already overtaxed systems. This paradox has left many organizations to rely on outdated or bloated IVR systems far too long. And with constantly rising customer expectations around service and support, these organizations are falling further and further behind competitors that are investing in next-generation solutions.
Google Cloud Contact Center Artificial Intelligence (CCAI) provides a cloud-based platform that leverages Google Cloud’s artificial intelligence (AI) and machine learning (ML) capabilities, including natural language processing and speech capabilities to augment, support, and assist contact center agents, and to deploy voice bots and chatbots that can naturally converse with customers to understand their intent and help resolve their calls with minimal intervention from an agent.
CCAI also has the ability to tie into an organization’s back-end data to enable bots to perform higher-value tasks, identify and authenticate customers, and augment agent desktops to provide relevant information and turn-by-turn guidance through different scenarios.
Google commissioned Forrester Consulting to conduct a New Technology: Projected Total Economic Impact™ (New Tech TEI) study and examine the projected return on investment (PROI) enterprises may realize by deploying CCAI.
Read the report and find out:
- Why businesses say their traditional contact center tools introduced challenges
- How Google’s Contact Center AI overcomes these challenges
- What effect the switch had to their financial and productivity investments
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