IKEA’s AI-driven Personalized and Real-time Recommendations Up its Conversion Rates and Average Order Value
4872
Of your peers have already read this article.
3:00 Minutes
The most insightful time you'll spend today!
Background
At IKEA we have multiple places in our customer journey in various channels where different kinds of personalization can deliver a superior customer experience. Product recommendations in the shopping basket, content recommendations in editorial sections, inspirational recommendations on product pages and more. After a while in the broader “recommendations” team there was a decision to split the team to have one sub-team focused on product recommendations. The pandemic altered customer behavior and needs as well. At that inflection point we decided to change our way of working and dive head-first into a more scientific approach to handle the operational complexities of delivering high quality product recommendations at scale. We deemed this necessary to improve our level of personalization and to have a holistic understanding of our customers.
Data Driven Decisions
The first step was to radically improve our ability to get high-quality quantitative information to understand how our ‘recommendation’ solutions affected personalization. We did this through high volume A/B testing on customer behaviour and after initial experimentation, we had a few key learnings:
- The mix of both UX and algorithms are really important for a cohesive customer experience.
- The quality of personalization can’t be measured in silos. Statistical significance can be attained by testing several groups of recommendations at once.
Once we came up with a solid framework for gathering data and acknowledged how little we knew about our customers, we were able to explore an incredible number of creative options – nothing was off the table. This was a very humbling experience, in that it opened up new perspectives for personalization, a more curious and less confined way of thinking. We learned to trust the data because it might show you things you don’t expect.
Experimentation and Learning Framework
Our teams created ways to quickly deploy experimental modifications to our existing solution. This enabled experimentation in the front-end with the user experience, including details in headings and images. This also covered tweaks in the backend with anything from detailed manual additions or removals of recommendations to mixing and matching of various algorithms both home grown and from Recommendations AI.
This flexibility came with an overhead–more complexity and cost relative to directly retrieving recommendations from Recommendations AI. However, the benefit was that we were no longer dependent on manual evaluation of what made for a good recommendation system. We aligned on a data-driven and qualitative approach to provisioning recommendations and significantly accelerated our experimentation timeline. Together with optimization of the CI/CD pipeline this enabled the team to take an idea or hypothesis from inception to A/B testing with customers in less than half an hour.
Recommendations AI Experiments
Our team’s infrastructure was already running on GCP and when we received early access to Recommendations AI, the requirements to get started were minimal and that allowed us to start with initial tests requiring minimal effort and investment.
We started with a few use-cases and identified places where our existing recommendation algorithms needed improvement or complementary recommendations. We also explored additional ways where more useful information could be presented to the customers through personalized recommendations.
Recommendations AI Model Combinations
While Recommendations AI might be considered a simple API to get a set of product recommendations, as we dove deeper into the solution it became apparent that it could be tweaked in several different ways to offer many fine tuning configurations to meet business goals. While too much fine tuning and customization could lead to subpar performance, in general we found that it was a great strategy to give us several versions of ML powered recommendations to work with. The further you personalize the experience, the more options you have to likely pick the best one for the customer.
Recommendations AI models like ‘Recommended for you’, ‘Frequently Bought Together’ and ‘Others you may like’; are coupled with business goals like optimizing for conversion rate, click through rate and revenue. We experimented with many different model combinations and custom rules. All this was easily configurable right in the GCP console. One of the simplest custom configurations we used was to only recommend items that were in stock, and when items were out of stock we looked at similar items that were available to augment the experience.
Collaboration with Google
Our collaboration with Google Cloud accelerated our learning process during experimentation. We worked closely together early in the product development. Additionally, their model provided flexibility to change direction and allow for more options than we had previously. Ultimately, this provided us a way to drastically improve our time to market with a product that produced tremendous results that we could not have accomplished on our own.
Results and Takeaways
With more personalized and real-time recommendations available we saw great success. We were able to increase the number of relevant recommendations displayed on a page by +400%. To accommodate the wider repertoire of recommendations we had to change the user experience. For example, in some places we had horizontally scrolling displays of product recommendations which were much easier for customers to use.
Another consequence of displaying more personalized recommendations was tangible improvement to conversion rate and average order value. Recommendations AI algorithms helped customers in two ways:
- Customers were able to find products that they liked quickly and establish their preferred choice among other options more quickly as well, giving them confidence to make a purchase through much fewer clicks. Even though we previously already had well tuned recommendations of several types, with Recommendations AI we measured +30% improvement in click through rates.
- Average order value saw a +2% surge with numerous examples of how Recommendations AI could help customers find both attractive and directly complementary products, expanding the customer purchase from a single product to an entire home furnishing solution.
As a direct effect of having stronger business results, the team started exploring more places in the customer journey where our growing buffet of recommendations could be used. We’d start with an initial experiment to answer if displaying recommendations in the specific context made sense at all. Frequently the data that emerged from these experiments prodded us to iterate further on what additional types of recommendations would be most appropriate to show to the customer as the customer’s behaviour evolved. Today, most of IKEA’s site recommendations are powered by Recommendations AI.
One key takeaway is that for some types of personalized recommendations there are benefits to using advanced algorithms that require a lot of high level data science and engineering competence to build since they outperform simplistic approaches. In some places, simplistic approaches work very well and in others the right decision is to not have product recommendations at all. For an effective use of product recommendations you need to have all the above options and the ability to tell when to use which one.
Next steps
When working with something so tightly related to customer experience, there is a constant change in user behaviour and new learnings to observe and adapt to. Product recommendations are rarely the main stand alone experience and frequently something that is used to help and enhance an experience. We see a lot of value in having a large toolbox of possible options and a team with a relentless focus on collaboration to improve the customer experience. We’re working directly with the Recommendations AI team and experimenting with several new features that we’re excited about.
In the future we see opportunities of improving the customer journey through a more visual experience that inspires the customer rather than relying on customers to use their imagination to visualize groups of products together. Vision Product Search provides that and is something we’re looking into deploying next. We’ll be sharing more about our journey with Recommendations AI at the Google Cloud Retail Summit session ‘IKEA’s Approach to Building a Powerful Recommendations Engine’ on July 27th 2021.
Best wishes to all developers from the IKEA product recommendations team & the Google Recommendations AI team!
Held Back by Database Scalability, This Financial Services Company Switches to Google Cloud and Cloud Spanner
11582
Of your peers have already read this article.
4:30 Minutes
The most insightful time you'll spend today!
Azimut Group operates an international network of companies handling investment and asset management, mutual funds, hedge funds, and insurance. Founded in Milan, Italy in 1988, Azimut Group today has branches in fifteen countries, including Brazil, China, and the USA.
“We have subsidiaries and manage funds all over the world,” explains Simone Bertolotti, IT Manager at Azimut Holding S.p.a. “That means that any technology that we put in place has to cover needs from many different countries.”
“When complicated analysis has to be executed, we have to increase our table space in a couple of minutes so that the AI can drill down into the data and deliver the information we need.”
—Simone Bertolotti, IT Manager, Azimut Holding S.p.a.
Azimut manages its funds with investment advisors who use information sourced from Bloomberg, Reuters and others. “They use a huge amount of data,” says Simone. “They work with spreadsheets, algorithms, formulae and they analyse data in minutes.” In finance, every second is crucial, which is why Azimut decided to develop a risk management dashboard that can process information even more quickly, then distribute it worldwide.
“When an advisor manages data, that data is used to make immediate decisions on funds, capital movements or whether to sell stock,” says Simone. “They have to be ready to make recommendations for any amount of data that comes to them. For our dashboard, that means that when additional information arrives or complicated analysis has to be executed, we have to increase our table space in a couple of minutes so that the AI can drill down into the data and deliver the information we need.”
Generating insights at speed
Investors and investment managers make decisions based on the most accurate, up-to-date information possible. For Azimut Group, information sourced through financial data vendors such as Bloomberg and Reuters provided only part of the data that the group required.
“We looked to collect information from a range of different providers,” explains Simone, “then analyse it to develop a predictive algorithm that could work faster than an advisor stationed at the terminal. We set ourselves the challenge to try to manipulate that data to add new insights into our matrix, so that every one of our branches across the world can see risk information about the funds in real-time.”
“We compared Google Cloud Platform’s performance with our previous cloud provider, and saw huge benefits of switching to Google. For me, the key performance issue is scaling. With Google Cloud Platform I know that I can increase and decrease my infrastructure quickly, when I need it.
—Simone Bertolotti, IT Manager, Azimut Holding S.p.a.
The first cloud provider Azimut used to build its system struggled to scale quickly to meet different kinds of data challenges. “If we wanted to add more cores, that was fine,” says Simone. “But the previous cloud provider made it complicated to raise the amount of space in a database infrastructure and scale up to demand. Scaling up for more in-depth analysis would take a day, and our need was immediate.”
That’s why Azimut switched one year ago to Google Cloud Platform to run the 150 VMs on its risk analysis platform. “We compared Google Cloud Platform’s performance with our previous cloud provider, and saw huge benefits of switching to Google. For me, the key performance issue is scaling,” says Simone. “With Google Cloud Platform I know that I can increase and decrease my infrastructure quickly, when I need it. Instead of waiting a day to scale up infrastructure, we can request and add space to our database in a couple of minutes.”
The infrastructure of Azimut’s solution handles around 800TB of data per month, and Google’s global network of servers and high-speed connections ensure that it gets to where it’s most needed by the most direct route. Impressed by the speed, security and availability of Google Cloud Platform, Azimut has moved its intranet on to Google Cloud Platform, too, eliminating the need for staff to login with VPNs.
“Instead of waiting a day to scale up infrastructure, we can request and add space to our database in a couple of minutes.”
—Simone Bertolotti, IT Manager, Azimut Holding S.p.a.
Driving ahead with Noovle
For Azimut, migrating the risk management dashboard is the latest of many Google product collaborations with cloud consultancy Noovle. “Everything started five years ago,” says Simone, “when Noovle assisted us in migrating to Gmail from our on-premise email solution. From G Suite to Google Cloud Platform, we’ve had a great relationship. Noovle provides consultancy services, support for mobility, and external advisors who work on our premises, such as when they trained us how to broadcast our meetings on Google Hangouts. As an independent company, we know we can trust them for transparent advice. All they care about is the best way to get a job done and to help us reach our goals.”
New app, new customers
In a business case comparison, Google Cloud Platform cost Azimut 35% less to run than the previous cloud provider. Now the group is building a major new mobile application on Google App Engine to be released in 2018.
“The new mobile application will allow customers to trade directly, without human advisors, by proposing different investment solutions depending on targets the customers set,” says Simone. “So if a customer aims to make money with investments, they enter their relevant personal information and we carry out the necessary regulatory checks and suggest what they could buy. The entire project will be based on Google Cloud Platform, so customers can control their investments through the app while we manage the fund, using Google Cloud Spanner on the backend.”
Tackling Real-Time Bidding Challenges: Arpeely’s Fresh Approach with Google Cloud
1128
Of your peers have already read this article.
4:30 Minutes
The most insightful time you'll spend today!
At Arpeely, we’ve developed some of the world’s most advanced advertising technology. Our machine learning (ML) media acquisition platform and “win-win” business model enables customers to bring highly intentful users to their offerings with precision, peace of mind and minimal overhead.
Real-time bidding is a dynamic and intricate process that involves buying and selling ad impressions in milliseconds. Each time a user opens an app or website, advertisers or ad-tech companies acting on behalf of advertisers have milliseconds to bid on ad spaces in real-time auctions, and the highest bidder wins the opportunity to display their ad. This is where Arpeely comes in, leveraging advanced algorithms, innovative UX and funnels to optimize the bidding process and maximize performance for advertisers.
At Arpeely, we use various signals often not used by traditional competitors to outperform the market and zero-down on high-intent and soon-to-be loyal users. For example, in advertising a mobile app, we predict, based on real-time conditions and user context, the user’s likelihood to make an in-app purchase many weeks into the future.
As for the ads themselves, gone are the days of simple banners; today’s ads are “mini products” that captivate and engage users. We employ a wide range of ad formats that go beyond industry standards. Our ads can be immersive videos, compelling messages, interactive experiences like mini-games or mini-apps, or even a multi-step mixture of all of the above. By integrating logic and interactivity, Arpeely enables users to engage with the ad content seamlessly and gauge user intent without leaving their main activity.
Our business model dictates that we don’t get paid if our advertiser doesn’t get paid. We like to say that our algorithms, like water, can trickle into hidden market opportunities missed by the rest of the industry that uses less granular tools. These and other capabilities make Arpeely a strategic partner in the challenging space of media and user acquisition.
Innovation at the edges of data science and engineering
Today, we handle millions of impressions per second and over a billion ML predictions daily. We are directly connected to seven of the world’s largest real-time bidding exchanges, including Google AdX. We also work very closely with our clients, ranging from prominent startups to companies in the S&P Top 20.
Daily, we tackle complex engineering and data challenges on multiple fronts. On the engineering side, we ensure that every real-time auction receives a lightning-fast response within a strict 150ms timeframe. On the data side, we fire multiple ML predictions per auction and ingest TBs of data daily. On the user-serving front, we serve A/B-tested assets across a long tail of geos and devices, with even the slightest fluctuations in load speeds affecting business dramatically.
Right from the start, we knew we couldn’t do it alone when it came to building our technology stack. When you look at available platforms, it’s clear Google Cloud has a robust architecture that is easy to manage, use and scale, especially for our use cases. They also have reliability and feature completeness which are critical in our line of business.
Under the hood
Building upon Google Kubernetes Engine (GKE), we run multiple services that handle our main bidding flows. We utilize Golang for services that run at a large scale and Python for when we prioritize development speed, community and readability. All of these can reach an immensely high scale, which is managed and monitored automatically in GKE. Communication between these services and our Redis (our Google Cloud partner) cluster happens in sub-millisecond latency over Google Cloud’s strong network infrastructure and enables us to run complex real-time logic for every impression.
Once we have tackled the actual bidding, we are left with the challenge of streaming our data into BigQuery for analytics and model training. We utilize a mix of Cloud Pub/Sub, Memorystore and Cloud Storage to create a mechanism capable of ingesting many TBs per day in near real-time without compromising on cost. Real-time data is critical for a company like Arpeely to test and reiterate at a fast pace.
BigQuery is our data warehouse for operational analysis and is an essential part of our business. We use it both as a warehouse, for large-scale computations and in an operational capacity that closes the loop between production, data, and ML retraining. A team of two or three people can manage petabytes at scale with minimal maintenance.
On top of these, we’ve built a state-of-the-art in-house model pipeline suited specifically for ad-tech industry purposes. It allows us to effectively deploy complex solutions — on-the-fly calibration, flexible conversion steps, sampling of heavily imbalanced data sets, adjusting weights, and A/B-tested model deployment and more.
Google Cloud also offers us an entry point for several very useful built-in products that have become deeply embedded in our daily stack and routine. Among them are Operations Suite (formerly Stackdriver), Cloud Profiler, Cloud Storage, Cloud CDN, Cloud SQL, Memorystore, Cloud Scheduler, Error Reporting, and more.
In addition to all the technology, there’s also a human touch. Google’s skilled Customer Success team possesses a unique blend of technical expertise and business acumen, acting as strategic advertisers and opening doors we did not know existed.
Opening the door to the future of advertising
Standardizing on Google Cloud enables us to focus our resources on innovation and growth. Instead of having to research business solutions and invest time integrating disparate technologies, we can tap into a wide range of Google Cloud tools as needed. Thus, it is crucial that we set up a good technological foundation and prepare for future growth of the business.
Google Cloud enables us to focus our resources on innovation rather than our infrastructure. This means we can put more effort into finding ways to match clients with high-value customers and grow their revenues. Even though online advertising has been around for over 20 years and pioneered by Google itself, Google Cloud gives us the impetus to disrupt the market and deliver greater levels of value to our customers today and in the future.
If you want to learn more about how Google Cloud can help your startup, visit our page here to get more information about our program, and sign up for our communications to get a look at our community activities, digital events, special offers, and more.
Check out the Redis listing on Google Cloud Marketplace. Please give it a try and let us know what you think!
Largest Beauty Retailer in the US Powers Digital Transformation with Google Cloud Smart Analytics
9256
Of your peers have already read this article.
3:15 Minutes
The most insightful time you'll spend today!
Digital technology offers increasing flexibility and choice to consumers. As a result, the retail industry is dramatically shifting toward more tailored and personalized experiences for shoppers, and businesses are rethinking how they deliver value to customers.
This couldn’t be more true for the beauty retailing industry where leading companies are turning to digital technology to create customized shopping experiences.
At Google Cloud, we’re particularly excited about our work with Ulta Beauty, the largest beauty retailer in the United States with more than 1196 stores in all 50 states, and how the company is using Google Cloud technology solutions to power personalization and redefine beauty retailing.
Established in 1990, Ulta Beauty has had incredible success as a company, and as customers become more discerning and curious about their purchases, the company is finding new ways to meet their changing needs.
Recently, leaders at Ulta Beauty recognized a huge opportunity to complement and enhance the shopping experience by helping beauty enthusiasts navigate through more than 500 brands and 25,000 products carried in their stores and online channel.
They decided to leverage the data from Ulta Beauty’s successful Ultamate Rewards loyalty program to create and offer more unique and personalized user experiences.
With more than 30 million members generating data through sales, transactions, product reviews, and social media engagement, Ulta Beauty’s Loyalty Program creates a comprehensive data set, and the company sought the right technology partner to help organize, analyze and transform that data into valuable insights for its customers.
Ulta Beauty’s leaders knew they had an opportunity to leverage data analytics and machine learning to reach customers in new ways, enhance the guest experience, and continue to grow their active loyalty member base. After considering a number of cloud providers, they chose to expand their existing partnership with Google Cloud.
“Google Cloud listened to our needs and worked in tandem with our engineering team to address our challenges,” said Michelle Pacynski, vice president of digital innovation at Ulta Beauty. “The ease of working with the Google Cloud team and their breadth of experience made the decision a no-brainer, laying the foundation for a great partnership.”
In 2019, Ulta Beauty announced it was working with Google Cloud Platform to unify and organize its data, using:
- BigQuery to perform data analysis and generate dynamic content, personalized product recommendations, and event-based messages for customers.
- Cloud Storage to provide highly available, secure, resilient and cost-effective access to data across the entire enterprise.
- Compute Engine for the high-performance scalability needed to grow with customer demand while painlessly migrating existing applications to the cloud.
- Anthos to build a hybrid cloud foundation that allows their applications to take advantage of all this data, combining the power and flexibility of GKE with the ability to leverage their existing investment in secure infrastructure on-premises.
Our partnership with Ulta Beauty has enabled increased engagement with customers in store and online, and the creation of new tools and capabilities, including a new Virtual Beauty Advisor tool to deliver tailored recommendations and help shoppers choose the right products, and a Customer Conversation Platform that’s enabling deeper connections with guests, ultimately driving customer loyalty.
“It’s been a really efficient process so far due in part to the ease of working with the Google team,” said Michelle Pacynski, vice president of digital innovation at Ulta Beauty. “They’re experienced, approachable, and their can-do style makes for a great partnership. They listened to our needs and worked in tandem with our engineering team, figuring things out, and getting it done.”
10521
Of your peers have already watched this video.
2:15 Minutes
The most insightful time you'll spend today!
The True Story of How HotStar Broke a World-Record–Thanks to Firebase and Google BigQuery
Hotstar, India’s largest video streaming platform with 150 million monthly active users around the world, provides live-streaming of TV shows, movies, sports, and news on the go.
By using a combination of Firebase products together, Hotstar safely rolled out new features to its watch screen during a major live-streaming event without disrupting users, sacrificing stability, or releasing a new build. They also used Firebase with BigQuery to analyze their event data and reduce app startup time.
“We have an ambitious mission, but our engineering team is only a fraction of the size of most of our competitors. But we are still keeping up, and we are doing it with the help of Firebase,” says Ayushi Gupta, Android Engineer, Hotstar.
AI Solutions for Government Organizations: How to Get Started
4385
Of your peers have already read this article.
3:00 Minutes
The most insightful time you'll spend today!
Cloud-native features are helping public sector teams innovate faster than ever. Ideas discussed in a morning meeting can be a working proof of concept later that day. Managed services can remove administrative burden and reduce the steps needed to design and provision cloud infrastructure. Security can be built-in from the beginning with Identity and Access Management (IAM), Virtual Private Cloud Service Controls (VPC-SC), and Data Loss Prevention (DLP). Short-lived services and infrastructure-as-code allow rapid and cost-effective prototyping. These technologies can be used to architect a solution that follows the principle of least privilege and helps you secure your data.
So the question becomes: given the complex problems agencies face, where do you start? Google Public Sector now offers “Getting Started” and “Scaling” service offerings for CCAI, DocAI, and BigQuery to help you jumpstart your AI journey, based on where you are.
Complex problems, simpler AI-based solutions
Solving more challenging problems with cloud-native technology doesn’t have to be overwhelming. You can approach them the same way you might solve a puzzle: start with one piece that follows another until the larger picture takes shape. Though you can simplify the steps, solving these problems still requires powerful tools. Google Cloud’s AI/ML capabilities may be the answer for your team.
Google Public Sector is making it easier to get started with advanced technologies, beginning with artificial intelligence and machine learning (AI/ML) workloads for government organizations, and it’s something you can do now, one piece at a time.
Automating your FAQs with CCAI
Does your agency require a team to answer commonly asked questions? What if you could train an agent to answer questions immediately and operate 24/7? Contact Center AI (CCAI) can do this and more. Already using CCAI and need the agent to level up to address complex interactive dialogs? Getting Started with CCAI and Scaling with CCAI are new Google service offerings specifically designed to help public sector organizations tackle situations like these.
Automate data entry with DocAI
How many hours does your team spend manually reviewing or entering data from standardized forms? What if you could automatically pull data right from the page? Google Document AI (DocAI) specializes in exactly this—even if the form has handwritten text. Getting Started with DocAI and Scaling with DocAI are new service offerings that help you remove this burden from your team. DocAI automates data entry and makes that data available to other teams while prioritizing both security and ease of use.
Making data and insights accessible with BigQuery
Then there’s all your existing data. You may have years of it stored in many places, and you may not have a way to make use of it when you need it. BigQuery is Google’s enterprise data warehouse. It was designed for data analytics—looking back at historical data to make conclusions about it. But BigQuery’s analytics don’t stop there. It can also look forward in time to make predictions, often using the same datasets. Getting Started with BigQuery and Scaling with BigQuery are new service offerings that help you take your first steps toward AI/ML capabilities by starting with a single table or pipeline that can help make sense of all your data.
The best help is the kind that meets you where you are and gets you where you want to be. Google Public Sector’s new service offerings do just that: help you work through complex problems by meeting you wherever your starting line is, whether you’re ready to start or ready to scale. Let us know if you would like us to contact you about the services mentioned in this article. Let’s solve your highest impact problems together, one puzzle piece at a time.
More Relevant Stories for Your Company
Responsible AI: From Theory to Practice
In less than 10 years, AI will be the number one driver of global GDP growth. And organizations that achieve AI absorption will be the leaders of the global economy. But as fast as AI is progressing, it also requires more care and attention from a responsibility standpoint. A more
Upgrade Your Contact Center with Knowlarity’s AI-powered Speech Analytics for Higher CX
Did you know, everyday about 56 million hours worth of phone conversations, equalling to 420 billion spoken words are handled by contact centers? Knowlarity, a renowned cloud business communication service provider with nearly 6,000 customers and over a million virtual users, leverages AI-powered speech analytics that offer insights to gauge
Google Introduces ML-based Predictive Autoscaling to Forecast Capacity and Match Scaling Demands
At Google Cloud, we believe you get most benefits from the cloud when you scale infrastructure based on changing demand. Compute Engine allows you to configure autoscaling to save costs during periods of low demand, and add capacity to support peak loads. When you use a managed instance group (MIG), you can have an autoscaler
ESG Report: Economic Advantages of Google BigQuery OnDemand Serverless Analytics
Traditional big data solutions require a significant upfront investment, ongoing maintenance of hardware and software, and manual provisioning to match compute and storage resources with demand. Google’s serverless analytics warehouse does away with all that extra work, helping businesses focus on what matters most: getting value from their data. Enterprise
