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:  

1. The mix of both UX and algorithms are really important for a cohesive customer experience. 
2. 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: 

1. 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. 
2. 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!

Over 2.14 billion people worldwide are expected to buy online this year, according to Statista. Online retail sales will account for 22% of all purchases by 2023. But in a competitive retail landscape, positive interactions can mean the difference between a sale and an abandoned shopping cart.

One of the leading global marketplaces – Kinguin.net is a haven for gamers. Their bustling ecommerce business conducts over 500,000 new transactions monthly. Users will encounter over 50,000 unique digital products, from video games, gift cards, in-game items to computer software and services. With over 10 million registered users, Kinguin improved their experience by helping users find items quickly and deliver service at scale.

Helping customers find what they want, fast

Because of Kinguin’s high volume of users—both buyers and sellers—and breadth of digital products, browsing and shopping can be challenging. “Customers shop online for choice and convenience, but it can sometimes be overwhelming. We want anyone who shops at Kinguin to find what they are looking for quickly and easily,” says Viktor Romaniuk Wanli, Kinguin CEO and Founder.

Today’s retailers know that creating personalized shopping experiences is crucial for establishing and maintaining customer loyalty. Kinguin discovered their users were getting a rather standard retail experience. They wondered how they could offer them a more tailored, personalized experience.

They knew product recommendations were a great way to personalize experiences because they help customers discover products that match their tastes and preferences. But it’s not that easy to recommend products. Various shifting factors make recommendations much more complex:

• Customer behavior. Understanding customers is tough. How do you recommend something to a cold start user who’s never been to your site before? What happens when their behavior changes?
• Omnichannel context. According to Harvard Business Review, 73% of all customers use many channels when they buy. What happens when they go from desktop to mobile or from social media shopping to a proprietary app?
• Product data challenges. How do you recommend new products within a large catalog of items? What if your product data has sparse labeling or unstructured metadata?

Data wasn’t a problem for Kinguin. They had data orders, history, wishlists, and could collect events based on their platform interactions. It was the machine learning model expertise they lacked. So rather than building their own solution, they determined it was more cost effective for them to find a reliable partner. It was also essential that the solution integrated easily with Kubernetes, which enabled their global network.

With these considerations in mind, they applied for the Google Recommendations AI beta program. Kinguin became the first gaming e-commerce platform in Europe to use Recommendations AI when it launched in 2020.

Pro gamer move: using a fully managed AI service 

Google Recommendations AI uses algorithms to deliver highly personalized suggestions tailored to a customer’s preferences. Google Cloud based these algorithms on the same research that powers models by YouTube search and Google Shopping. Algorithms are always being tuned and adjusted to focus on individuals themselves—not just items.

Many shopping AIs rely on manually provisioning infrastructure and training machine learning models. Instead, Recommendations AI’s deep learning models use item and user metadata to gain insights. It processes Kinguin’s thousands of products at scale, iterating in real time. First, Kinguin pieces together a customer’s history and shopping journey. Then, using Recommendations AI, they can serve up personalized products—even for long-tail products and cold-start users. 

By leveraging internal tools, Kinguin didn’t need to start implementation from scratch. After a few trial sessions with Google Cloud engineers, they got started right away. Due to the fast-paced nature of a marketplace—i.e., price changes, out-of-stock items—Kinguin needed their recommendations to be as close to real time as possible. They used internal event buses to stream events and their product catalog directly to the recommendations API.

Kinguin rolled out in high-traffic areas, including their home page, product page, and category pages. They analyzed heat maps and scroll maps to figure out where to test placements. They also experimented with different recommendation models such as “recently bought together” and “you may like.” Engineers also factored in where they were implementing the models. For example, the “others you might like” model would fit best on the homepage, while “frequently bought together” made sense at checkout.

Understanding how product recommendations influence financials is critical for demonstrating the impact of personalization. Using BigQuery, Kinguin could analyze different cost projection models. BigQuery helped them dig into specific financial data to understand their margins and revenue gains.

Playing to win: enhanced customer experience

Since adopting Recommendations AI, Kinguin has improved both customer experience and satisfaction. Search times have shortened by 20 seconds. Additionally, their average cart value has increased by 5 EUR. Conversion rates have quadrupled since the outset. Click-thru rates have doubled, increasing by 2.16 on product pages and 2.8 times on recommendations pages.

“Google Recommendations AI has helped us evolve our service, increase customer loyalty and satisfaction. It has also contributed to a significant rise in sales,” says Wanli. Kinguin is already thinking about other ways of enhancing user experiences with recommendations. Ideas include their checkout process, other landing pages, and email marketing.

Kinguin’s journey with Google Cloud shows how companies can leverage AI to optimize sales and deliver high-performing, low-latency recommendations to any customer touchpoint. 

Learn more about Recommendations AI and Google Cloud AI and machine learning solutions.

As more and more businesses turn to advanced data analytics to help them make smarter decisions, run real-time analytics, and improve business operations, an increasing number are modernizing their data warehouses to make it all possible.

For many businesses, knowing how to modernize means understanding where their data warehouse sits on the spectrum between traditional and cutting edge.

To help, we collaborated with TDWI to offer the data warehouse maturity assessment. By answering a few questions on their current warehouse environment, businesses can get a customized report with helpful tips and recommendations.

A report card for your data warehouse

The data warehouse maturity assessment evaluates five different categories, assigning your business a rating for each that spans from “traditional warehouse” to “advanced/visionary.”

1. Data diversity: How well does your warehouse support disparate data types, and does it provide users with a current and holistic view of data from a variety of internal and external sources?
2. Infrastructure agility: How well does your warehouse support the various needs of your organization by allowing access from anywhere, ingesting data in real time, and separating storage from compute to enable independent scaling?
3. Analytics support: How easy is it to adopt sophisticated analytical techniques at scale, such as machine learning, predictive analytics, deep learning, and IoT analytics?
4. Collaboration and sharing: Can teams within your organization and beyond share information and work together quickly and easily using data from your warehouse, and is your data strategy aligned with your business goals?
5. Security and governance: Are there tools and techniques in place to control data access, support data governance, and ensure compliance with data security standards?

In addition to the assessment you’ll also receive a detailed report with guidance and best practices that can help you determine your next steps in your modernization journey.

Getting started

Fill out a brief form to launch the survey, then take 15 minutes to answer a series of questions about the platforms, systems, and hardware you use to integrate, manage, and store business data for reporting and analytics. You’ll see your results right away.