As Googlers transitioned to working from home during the pandemic, more and more turned to chat-based support to help them fix technical problems. Google’s IT support team looked at many options to help us meet the increased demand for tech support quickly and efficiently. 

More staff? Not easy during a pandemic. 

Let service levels drop? Definitely not. 

Outsource? Not possible with our IT requirements. 

Automation? Maybe, just maybe…

How could we use AI to scale up our support operations, making our team more efficient?

The answer: Smart Reply, a technology developed by a Google Research team with expertise in machine learning, natural language understanding, and conversation modeling. This product provided us with an opportunity to improve our agents’ ability to respond to queries from Googlers by using our corpus of chat data.  Smart Reply trains a model that provides suggestions to techs in real time. This reduces the cognitive load when multi-chatting and helps a tech drive sessions towards resolution.

In the solution detailed below, our hope is that IT teams in a similar situation can find best practices and a few shortcuts to implementing the same kind of time saving solutions. Let’s get into it!

Challenges in preparing our data

Our tech support service for Google employees—Techstop—provides a complex service, offering support for a range of products and technology stacks through chat, email, and other channels. 

Techstop has a lot of data. We receive hundreds of thousands of requests for help per year. As Google has evolved we’ve used a single database for all internal support data, storing it as text, rather than as protocol buffers. Not so good for model training. To protect user privacy, we want to ensure no PII (personal identifiable information – e.g. usernames, real names, addresses, or phone numbers) makes it into the model.

To address these challenges we built a FlumeJava pipeline that takes our text and splits each message sent by agent and requester into individual lines, stored as repeated fields in a protocol buffer. As our pipe is executing this task, it also sends text to the Google Cloud DLP API, removing personal information from the session text, replacing it with a redaction that we can later use on our frontend. 

With the data prepared in the correct format, we are able to begin our model training. The model provides next message suggestions for techs based on the overall context of the conversation. To train the model we implemented tokenization, encoding, and dialogue attributes.

Splitting it up

The messages between the agent and customer are tokenized: broken up into discrete chunks for easier use. This splitting of text into tokens must be carefully considered for several reasons:

• Tokenization determines the size of the vocabulary needed to cover the text.
• Tokens should attempt to split along logical boundaries, aiming to extract the meaning of the text.
• Tradeoffs can be made between the size of each token, with smaller tokens increasing processing requirements but enabling easier correlation between different spans of text.

There are many ways to tokenize text (SAFT, splitting on white spaces, etc.), here we chose sentence piece tokenization, with each token referring to a word segment. 

Prediction with encoders

Training the neural network with tokenized values has gone through several iterations. The team used an Encoder-Decoder architecture that took a given vector along with a token and used a softmax function to predict the probability that the token was likely to be the next token in the sentence/conversation. Below, a diagram represents this method using LSTM-based recurrent networks. The power of this type of encoding comes from the ability of the encoder to effectively predict not just the next token, but the next series of tokens.

This has proven very useful for Smart Reply. In order to find the optimal sequence, an exponential search over each tree of possible future tokens is required. For this we opted to use beam search over a fixed-size list of best candidates, aiming to avoid increasing the overall memory use and run time for returning a list of suggestions. To do this we arranged tokens in a trie, and used a number of post processing techniques, as well as calculating a heuristic max score for a given candidate, to reduce the time it takes to iterate through the entire token list. While this improves the run time, the model tends to prefer shorter sequences. 

In order to help reduce latency and improve control we decided to move to an Encoder-Encoder architecture. Instead of predicting a single next token and decoding a sequence of following predictions with multiple calls to the model, it instead encodes a candidate sequence with the neural network.

In practice, the two vectors – the context encoding and the encoding of a single candidate output – are combined with dot product to arrive at a score for the given candidate. The goal of this network is to maximize the score for true candidates – e.g. candidates that did appear in the training set – and minimize false candidates.

Choosing how to sample negatives affects the model training greatly. Below are some strategies that can be employed:

• Using positive labels from other training examples in the batch.
• Drawing randomly from a set of common messages. This assumes that the empirical probability of each message is sampled correctly. 
• Using messages from context.
• Generating negatives from another model.

As this encoding generates a fixed list of candidates that can be precomputed and stored, each time a prediction is needed, only the context encoding needs to be computed, then multiplied by the matrix of candidate embeddings. This reduces both the time from the beam search method and the inherent bias towards shorter responses.

Dialogue Attributes

Conversations are more than simple text modeling. The overall flow of the conversation between participants provides important information, changing the attributes of each message. The context, such as who said what to whom and when, offers useful bits of input for the model when making a prediction. To that end the model uses the following attributes during its prediction:

• Local User ID’s – we set a finite number of participants for a given conversation to represent the turn taking between messages, assigning values to those participants. In most cases for support sessions there are 2 participants, requiring ID 0, and 1.
• Replies vs continuations – initially modeling focused only on replies. However, in practice conversations also include instances where participants are following up on the previously sent message. Given this, the model is trained for both same-user suggestions and “other” user suggestions.
• Timestamps  – gaps in conversation can indicate a number of different things. From a support perspective, gaps may indicate that the user has disconnected. The model takes this information and focuses on the time elapsed between messages, providing different predictions based on the values. 

Post processing

Suggestions can then be manipulated to get a more desirable final ranking. Such post-processing includes:

1. Preferring longer suggestions by adding a token factor, generated by multiplying the number of tokens in the current candidate.
2. Demoting suggestions with a high level of overlap with previously sent messages.
3. Promoting more diverse suggestions based on embedding distance similarities.

To help us tune and focus on the best responses the team created a priority list. This gives us the opportunity to influence the model’s output, ensuring that responses that are incorrect can be de-prioritized. Abstractly it can be thought of as a filter that can be calibrated to best suit the client’s needs. 

Getting suggestions to agents

With our model ready we now needed to get it in the hands of our techs. We wanted our solution to be as agnostic to our chat platform as possible, allowing us to be agile when facing tooling changes and speeding up our ability to deploy other efficiency features. To this end we wanted an API that we could query either via gRPC or via HTTPs. We designed a Google Cloud API, responsible for logging usage as well as acting as a bridge between our model and a Chrome Extension we would be using as a frontend.

The hidden step, measurement

Once we had our model, infrastructure, and extension in place we were left with the big question for any IT project. What was our impact? One of the great things about working in IT at Google is that it’s never dull. We have constant changes, be it planned or unplanned. However, this does complicate measuring the success of a deployment like this. Did we improve our service or was it just a quiet month?

In order to be satisfied with our results we conducted an A/B experiment, with some of our techs using our extension, and the others not. The groups were chosen at random with a distribution of techs across our global team, including a mix of techs with varying levels of experience ranging from 3 to 26 months. 

Our primary goal was to measure tech support efficiency when using the tool. We looked at two key metrics as proxies for tech efficiency: 

1. The overall length of the chat. 
2. The number of messages sent by the tech.

Evaluating our experiment

To evaluate our data we used a two-sample permutation test. We had a null hypothesis that techs using the extension would not have a lower time-to-resolution, or be able to send more messages, than those without the extension. The alternative hypothesis was that techs using the extension would be able to resolve sessions quicker or send more messages in approximately the same time.

We took the mid mean of our data, using pandas to trim outliers greater than 3 standard deviations away. As the distribution of our chat lengths is not normal, with significant right skew caused by a long tail of longer issues, we opted to measure the difference in means, relying on central limit theorem (CLT) to provide us with our significance values. Any result with a p-value between 1.0 and 9.0 would be rejected. 

Across the entire pool we saw a decrease in chat lengths of 36 seconds.

In reference to the number of chat messages we saw techs on average being able to send 5-6 messages more in less time. 

In short, we saw techs were able to send more messages in a shorter period of time. Our results also showed that these improvements increased with support agent tenure, and our more senior techs were able to save an average of ~4 minutes per support interaction.

Overall we were pleased with the results. While things weren’t perfect, it looked like we were onto a good thing.

So what’s next for us?

Like any ML project, the better the data the better the result. We’ll be spending time looking into how to provide canonical suggestions to our support agents by clustering results coming from our allow list. We also want to investigate ways of making improvements to the support articles provided by the model, as anything that helps our techs, particularly the junior ones, with discoverability will be a huge win for us.

How can you do this?

A successful applied AI project always starts with data. Begin by gathering the information you have, segmenting it up, and then starting to process it. The interaction data you feed in will determine the quality of the suggestions you get, so make sure you select for the patterns you want to reinforce.

Our Contact Center AI allows tokenization, encoding and reporting, without you needing to design or train your own model, or create your own measurements. It handles all the training for you, once your data is formatted properly. 

You’ll still need to determine how best to integrate its suggestions to your support system’s front-end. We also recommend doing statistical modeling to find out if the suggestions are making your support experience better. 

As we gave our technicians ready-made replies to chat interactions, we saved time for our support team. We hope you’ll try using these methods to help your support team scale.

Data scientists choose models based on various tradeoffs when solving machine learning (ML) problems that involve tabular (i.e., structured) data, the most common data type within enterprises. Among such models, decision trees are popular because they are easy to interpret, fast to train, and can obtain high accuracy quickly from small-scale datasets. On the other hand, deep neural networks offer superior accuracy on larger datasets, as well as the benefits of end-to-end learning, but are black-box and difficult to interpret. TabNet, an interpretable deep learning architecture developed by Google AI, combines the best of both worlds: it is explainable, like simpler tree-based models, and can achieve the high accuracy of complex black-box models and ensembles.

We’re excited to announce that TabNet is now available in Vertex AI Tabular Workflows! Tabular Workflows provides fully managed, optimized, and scalable pipelines, making it easier to use TabNet without worrying about implementation details, and to deploy TabNet with the MLOps capabilities of Vertex. TabNet on Vertex AI Tabular Workflows is optimized for efficient scaling to massive tabular datasets. Moreover, TabNet on Vertex AI Tabular Workflows come with machine learning improvements on top of the original TabNet, yielding better accuracy for real-world data challenges.

TabNet on Vertex AI is well-suited for a wide range of tabular data tasks where model explainability is just as important as accuracy, such as financial asset price prediction, fraud/cyberattack/crime detection, retail demand forecasting, user modeling, credit/risk scoring, diagnosis from healthcare records, and product recommendations.

Overview of Tabnet

TabNet has a specially-designed architecture (overviewed in Fig. 1), based on sequential attention, that selects which model features to reason from at each step. This mechanism makes it possible to explain how the model arrives at its predictions and the thoughtful design helps with superior accuracy. TabNet not only outperforms alternative models (including neural networks and decision trees) but also provides interpretable feature attributions. More details, including results on academic benchmarks, can be found in our AAAI 2021 paper.

Since its publication, TabNet has received significant traction from various enterprises across different industries and a variety of high-value tabular data applications (most of which include the ones for which deep learning was not even used a priori). It has been used by numerous enterprises like Microsoft, Ludwig, Ravelin, and Determined. Given high customer interest on TabNet, we’ve worked on making it available on Vertex given the real-world deep learning development and productionization needs, as well as improving its performance and efficiency.

Highlights of TabNet on Vertex AI Tabular Workflows

Scaling to Very Large Datasets
Fueled by the advances in cloud technologies like BigQuery, enterprises are increasingly collecting more tabular data, and datasets with billions of samples and hundreds/thousands of features are becoming the norm. In general, deep learning models get better learning from more data samples, and more features, with the optimal methods as they can better learn the complex patterns that drive the predictions. The computational challenges become significant though when model development on massive datasets is considered. This results in high cost or very long model development times, constituting a bottleneck for most customers to fully take advantage of their large datasets. With TabNet on Tabular Workflows, we’re making it more efficient to scale to very large tabular datasets.

Key Implementation Aspects: The TabNet architecture has unique advantages for scaling: it is composed mainly of tensor algebra operations, it utilizes very large batch sizes, and it has high compute intensity (i.e., the architecture employs a high number of operations for each data byte transmitted). These open a path to efficient distributed training on many GPUs, utilized to scale TabNet training in our improved implementation.

In TabNet on Vertex AI Tabular Workflows, we have carefully engineered the data and training pipelines to maximize hardware utilization so that users can get the best return for their Vertex AI spending. The following features enable scale with TabNet on Tabular workflows:

• Parallel data reading with multiple CPUs in a pipeline optimized to maximize GPU utilization for distributed training, reflecting best practices from Tensorflow.
• Training on multiple GPUs that can provide significant speedups on large datasets with high compute requirements. Users can specify any available machine on GCP with multiple GPUs, and the model will automatically run on them with distributed training.
• For efficient data parallelism with distributed learning, we use Tensorflow mirrored distribution strategy to support data parallelism across many GPUs. Our results demonstrate >80% utilization with several GPUs on billion-scale datasets with 100s-1000s of features.

Standard implementations of deep learning models could yield a low GPU utilization, and thus inefficient use of resources. With our implementation, TabNet on Vertex, users can get the maximal return on their compute spend on large-scale datasets.

Examples on real-world customer data: We have benchmarked the training time specifically for enterprise use cases where large datasets are being used and fast training is crucial. In one representative example, we used 1 NVIDIA_TESLA_V100 GPU to achieve state-of-the-art performance in ~1 hour on a dataset with ~5 million samples. In another example, we used 4 NVIDIA_TESLA_V100 GPUs to achieve state-of-the-art performance in ~14 hours on a dataset with ~1.4 billion samples.

Improving Accuracy given Real-World Data Challenges

Compared to its original version, TabNet on Vertex AI Tabular Workflows has improved machine learning capabilities. We have specifically focused on the common real-world tabular data challenges. One common challenge for real-world tabular data is numerical columns having skewed distributions, for which we productionized learnable preprocessing layers (e.g. including parametrized power transform families and quantile transformations) that improve the TabNet learning. Another common challenge is the high number of categories for categorical data, for which we adopted tunable high-dimensional embeddings. Another one is imbalance of label distribution, for which we added various loss function families (e.g. focal loss and differentiable AUC variants). We have observed that such additions can provide a noticeable performance boost in some cases.

Case studies with real-world customer data: We have worked with large customers to replace legacy algorithms with TabNet for a wide range of use cases, including recommendation, rankings, fraud detection, and estimated arrival time predictions. In one representative example, TabNet was stacked against a sophisticated model ensemble for a large customer. It outperformed the ensemble in most cases, leading to a nearly 10% error reduction on some of the key tasks. This is an impressive result, given that each percentage improvement on this model resulted in multi-million savings for the customer!

Out-of-the-box Explainability

In addition to high accuracy, another core benefit of TabNet is that, unlike conventional deep neural network (DNN) models such as multi-layer perceptrons, its architecture includes explainability out of the box. This new launch on Vertex Tabular Workflows makes it very convenient to visualize explanations of the trained TabNet models, so that the users can quickly gain insights on how the TabNet models arrive at its decisions. TabNet provides feature importance output via its learned masks, which indicate whether a feature is selected at a given decision step in the model. Below is the visualization of the local and global feature importance based on the mask values. The higher the value of the mask for a particular sample, the more important the corresponding feature is for that sample. Explainability of TabNet has fundamental benefits over post-hoc methods like Shapley values that are computationally-expensive to estimate, while TabNet’s explanations are readily available from the model’s intermediate layers. Furthermore, post-hoc explanations are based on approximations to nonlinear black-box functions while TabNet’s explanations are based on what the actual decision making is based on.

Explainability example: To illustrate what is achievable with this kind of explainability, Figure 2 below shows the feature importance for the Census dataset. The figure indicates that education, occupation, and number of hours per week are the most important features to predict whether a person can earn more than $50K/year (the color of corresponding columns are lighter). The explainability capability is sample-wise, which means that we can get the feature importance for each sample separately.

Benefits as a Fully-Managed Vertex Pipeline

TabNet on Vertex Tabular Workflows makes the model development and deployments tasks much simpler – without writing any code, one can obtain the trained TabNet model, deploy it in their application, and use the MLOps capabilities enabled by Vertex Managed Pipelines! Some of these benefits are highlighted as:

• Compatibility with Vertex AI ML Ops for implementing automated ML at scale including products like Vertex AI Pipelines and Vertex AI Experiments.
• Deployment convenience: Vertex AI prediction services, both in batch and online mode, are supported out-of-the-box.
• Customizable feature engineering to enable the best utilization of the domain knowledge of users.
• Using Google’s state-of-the-art search algorithms, automatic tuning to identify the best-performing hyperparameters, with automatic selection of the appropriate hyper-parameter search space based on dataset size, prediction type, and training budget.
• Tracking the deployed model and convenient evaluation tools.
• Easiness in comparative benchmarking with other models (such as AutoML and Wide & Deep Networks) as the user journey would be unified.
• Multi-region availability to better address international workloads.

More details

If you’re interested in trying TabNet on Vertex AI on your tabular datasets, please check out Tabular Workflow on Vertex AI and fill out this form.

Acknowledgements: We’d like to thank Nate Yoder, Yihe Dong, Dawei Jia, Alex Martin, Helin Wang, Henry Tappen and Tomas Pfister for their contributions to this blog.

The combination of a vital need for IT experts among businesses and a digital skills gap is making lifelong learning increasingly critical. Beyond professional development, learning new skills offers additional rewards from building peer connections to boosting your creativity. That’s why in 2020 Krishna Deepak Nallamilli and I launched KIMO.ai to reimagine how people approach learning, especially in developing markets. Our team is building the artificial intelligence needed to generate individual learning paths through a wide range of quality digital learning content.

Google Cloud and its Startup Program have been instrumental in connecting our team with the tools, people, processes, and best practices to grow our business. 

Existing learning platforms lack engagement

Outside of traditional education settings, massive open online learning courses (MOOCs)—often modeled after university courses—can provide a flexible and affordable way to upskill or reskill. But the vast majority of people who participate in MOOC programs fail to complete courses. Based on our research, the challenge with existing learning platforms is a lack of engagement, primarily caused by limited direction on which skills to learn, whether AI, fintech, blockchain, or other in-demand disciplines.   

We’ve also received feedback that many corporate learning management systems–developed as online training systems to upskill employees–tend to be poorly designed and time-consuming to use. 

Overall, a significant challenge with most existing learning platforms is that they’re generic. For example, suppose you’re interested in learning about AI. In that case, you need AI-related coursework that applies to your industry and the job you want because AI in medicine is vastly different from AI in financial services. Today’s online learning options typically take a one-size-fits-all approach and fail to capture the nuances of what learners really need to get ahead. 

Building a future-proof learning platform 

The commitment to highly personalized, accessible learning inspired KIMO.ai, a platform that we believe is the future of education. Depending on your goals, current skills, location, and other factors, our AI-based platform will identify which coursework (and where to find those classes) to build the skills you need. The more personalized, relevant learning recommendations even take into account people’s preferences for podcasts, MOOCs, books, articles, videos, courses, publications, and more.  

In a mix of cooperation and competition we call “coopetition,” KIMO.ai will regularly recommend courses from other established online learning systems if, based on our automated assessment, it’s the best option for a learner. There’s also the option to access free content only. 

Google cultural alignment fosters trust

Our platform started with one developer exploring NLP models and Google APIs. As we’ve grown our team and launched our beta to 110,000 users in developing markets, we discovered there is a lot of interest in our platform, and we believe we can make a significant impact. In feedback forums, we also learned that we need to focus our efforts on the mobile experience to improve engagement since 99% of the beta testers use mobile devices. 

Beyond our team’s high level of trust in Google Cloud solutions, our team also appreciates the cultural alignment with Google. We value Google’s developer-centric approach and rely on tools like Dataflow for batch data processing and Cloud TPU to reliably run machine learning models with AI services on Google Cloud. We also build all of our deployments on Google Kubernetes Engine (GKE), which makes it easy to manage all our containerized workloads 

On the front end, Google App Engine makes it easy to deploy apps and experiment, and it integrates seamlessly with Firebase for authentication and more. BigQuery is our serverless data warehouse that efficiently scales to support the millions of articles, videos, and other learning resources we need to analyze to provide the targeted coursework recommendations our learners require.

As we grow our business having a network of trusted advisors is also extremely valuable. By working closely with DoIT International, the 2020 Google Cloud Global Reseller Partner of the Year, our team has access to their cloud, Kubernetes, and machine learning expertise. DoIT has already helped us quickly resolve IT issues and create analytics dashboards that give us insights to continually enhance our services. 

Building for a growing industry

The dynamic edtech market is growing rapidly and estimated to become an $11B industry by 2025. We’re proud to be part of the next wave of personalized education that has the potential to empower people in developing markets and beyond to grow their skills with coursework tailored to their exact needs and how they like to learn. This year, we will deliver our platform to at least 400,000 more people. We’re excited to see how they use it and where it takes them. 

If you want to learn more about how Google Cloud can help your startup, visit our Startup Program application 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.

Today we’re hosting our Retail & Consumer Goods Summit, a digital event dedicated to helping leading retailers and brands digitally transform their business. For me, this is a personally exciting moment, as I see tremendous opportunities for those companies that choose to focus on their customers and leverage technology to elevate experiences.

Our event includes breakout sessions to help retailers and brands become customer centric, embrace the digital moment and transform their operations. Some of my favorite sessions include: 

• Why Search Abandonment is the metric that matters, highlighting Retail Search, and featuring a conversation with Macy’s
• Driving Consumer Closeness in a Privacy-Centric World, discussing how retailers and brands can create a successful first party data strategy, featuring a conversation with P&G
• The Modern Store: 7 Innovation Hotspots, sharing how retailers and brands can approach store transformation to unlock the most value from technology, featuring a conversation with The Home Depot.   

I’ll be speaking in our Retail Spotlight session, discussing the current retail landscape and our industry approach, followed by conversations with Albert Bertilsson, Head of Engineering – Edge at IKEA Retail (Indga Group) and Neelima Sharma, Senior Vice President, Technology Ecommerce, Marketing and Merchandising at Lowe’s. 

Let me share a bit more about the topics we’ll discuss in that session.

In retail specifically, digital-first shopping journeys are blurring the lines between the physical and digital brand experience. Shoppers want to know what’s available before they visit your stores, and they expect fulfillment options like curbside pickup. We see this when tracking trends for interest in curbside pickup or in-stock items.

This has left many retailers asking how they can get smarter with their data, tackle the $300 billion dollar problem of “search abandonment,” move faster to create new customer experiences, and do a better job of connecting their employees and customers – with confidence.

Our team has been spending time thinking about how we can rise and succeed in this new era together. We continue to focus on areas where we can bring the best of our capabilities to our retail customers around the world. And we’re focused on ways we can bring the best of what Google has to offer through cloud integrations.

Our goal is to help retailers become customer-centric and data-driven, capture digital and omni-channel revenue growth, create the modern store and drive operational improvement.

Let’s dig into each of these strategic pillars in a bit more detail. 

Become customer centric and data driven

Customers today expect experiences that are timely, targeted, and tailored for them and their needs, and reject experiences that can’t deliver these features. Data modeling, legacy technology, and siloed systems often prevent retailers from providing that level of personalized experience. 

At Google Cloud, we work with global retailers and our ecosystem partners to activate and bring value from first-party data, particularly in the field of customer data platforms (CDPs). This includes integrations from Google Cloud, such as our business intelligence platform Looker and other popular platforms to power one source of customer data through the organization. We also help retailers modernize their data warehouse with Looker for gathering business intelligence across their organization. This is important not just for consumer data, but inventory, supply chain, and store operations as well. 

Capture Digital and Omnichannel Growth 

We power some of the largest e-commerce sites in the world, helping them scale for Black Friday, Cyber Monday, and other holiday events. While scale is critically important, it’s also important to consider the quality of the online experience. How do your customers find products? How can you help deliver seamless online and omnichannel experiences? 

To help, we’re building product discovery solutions that bring together the best of our technologies that help retailers drive engagement with their consumers. Retail Search, for example, gives retailers the ability to provide Google-quality search on their own digital properties – search that is customizable for their unique business needs and built upon Google’s advanced understanding of user intent & context. 

The imperative is clear. Recent research found that retailers lose more than $300 billion to search abandonment — when purchase intent is not converted into a sale due to bad search results — every year in the US alone. 

Today, we announced that Retail Search is available to a larger set of retailers. If you are interested in learning more about Retail Search you can contact your sales representative for additional details.

Create the modern store  

With the rise of buying trends like curbside pickup and proximity-based search, our Google Maps Platform team is working on new products and features to help raise inventory awareness for your shoppers. We want to help you make it easier for them to understand what’s available to purchase in their channel of choice.

With Product Locator, each product page connects customers with information they need for local pickup and delivery options. This ensures customers are aware of pickup and delivery options throughout the buying journey—not just checkout. 

Awareness of local inventory can boost a wide range of key metrics for your business. Shopify recently shared that shoppers who opt for local pickup over delivery had a +13% higher conversion rate and that 45% of local pickup customers make an additional purchase upon arrival.

This is just one quick example of how our Google Maps Platform team can improve experiences for your shoppers.

Operational improvement

It can be challenging to operate in a world and at a time when consumer behavior and supply chains are so disrupted and volatile, and where entire retail teams had to go remote during the pandemic and beyond. 

We’re working with retailers to leverage artificial intelligence (AI) to improve consumer experience through chat bots or conversational commerce that solves problems for customers from anywhere. You can learn more about these offerings in our Conversational Commerce with Google breakout session, featuring Albertsons.

As the need for digital transformation continues to accelerate, Google Cloud is helping retailers stay ahead of the curve with solutions for digital and omnichannel growth, data-driven and customer-focused experiences, and operational improvement. For every era of cloud technologies, from the past into the future, Google Cloud is committed to providing solutions to retailers.

Read more about our solutions for retail, and check out additional sessions, including the CPG Industry Spotlight Session How To Grow Brands in Times of Rapid Change – Featuring L’Oréal at our Retail & Consumer Goods Summit.

Editor’s note: To kick off the new year and in preparation for NRF The Big Show, we invited partners from across our retail ecosystem to share stories, best practices, and tips and tricks on how they are helping retailers transform during a time that continues to see tremendous change. Please enjoy this entry from our partner.

If you put a pot of water on the stove, it doesn’t heat up instantly. It simmers slowly at first, eventually picking up steam to reach a rolling boil. The retail landscape is not so different. Capgemini’s research shows the sector has evolved over four generations, from the early days of fragmented outlets to omnichannel and customer-centric focuses, with a fifth generation on the horizon that promises to be centered on consumption.

• Generation 1: Fragmented outlets
• Generation 2: Chain concentration
• Generation 3: Omnichannel
• Generation 4: Consumer-centric
• Generation 5: Consumption-centric

Although incremental change allows companies to experiment and iterate during their digital journeys, the COVID-19 pandemic rapidly accelerated the evolution of online and contactless shopping. Evolution became a revolution, with most Consumer Product and Retail (CPR) companies still mastering the omnichannel generation of their digital transformation to create a seamless shopping experience. Companies that are more digitally mature are already aspiring to the consumer-centric phase, embracing opportunities made possible by technology such as personalization and automation.

What consumers want

Customers have more choices in how they shop and engage with brands. This has made it harder for brands to predict and anticipate needs across customer journeys. And that’s convincing some companies to innovate more quickly as consumer demand drives the need for speed and scale.

Think about your own online behavior. Say you’re shopping for an item and searching online for the closest store in your neighborhood. Google is likely your go-to for finding that information. Looking to troubleshoot an issue with a product or seek out a service? Again, you’ll likely hit up Google first, not even considering going directly to a brand’s website for answers.

Both scenarios point to a disconnect between virtual and physical worlds, a gap technology can bridge in numerous ways such as breaking down silos and integrating fragmented media channels. Interestingly, though, not everything will be centered online all the time. In our recent study on consumer behavior, The great consumer reset, Capgemini discovered 57 percent of shoppers plan to return to brick-and-mortar stores post-pandemic, which is basically unchanged from the 59 percent who often interacted with physical stores before.

But business as usual? Not even close. Consumers have come to expect a frictionless shopping experience (buy online, pick up in store) or an immersive one (products displayed online using augmented reality), and are not content to return to in-store lineups, empty shelves, or a one-size-fits-all approach. Moreover, customers want personalized interactions while ensuring their data and privacy are protected.

So the role of the store is changing. In fact, many online-only brands are opening brick-and-mortar establishments to drive customer experience. In our research on “smart stores,” we found the majority of consumers (66 percent) believe automation can improve their shopping experience by solving the challenges they face at retail stores.

From personalization to serendipity

Retailers must recognize that they have to win consumer trust and confidence. Many consumers believe retailers’ use of tech is focused on reducing costs rather than easing friction. And they’re right. That same Capgemini research found that only one-third (35 percent) of retailers consider “solving customer pain points” as the most important criteria when deciding which automation use cases to implement.

“Retailers are largely in the early stages of adopting automation, and that’s an opportunity to rethink how they’re using technology, not just to smooth out friction and engender consumer trust but to build unexpected consumer benefits,” says Neerav Vyas, Head of Customer First, Co-Chief Innovation Officer, Insights & Data, North America, Capgemini. “We’re trying to move towards this idea of delivering serendipitous experiences to bridge the physical and digital divide.”

The focus is not solely on shoppers seeking out a specific product. “When consumers are in an exploratory mood, retailers can recommend products and services customers didn’t even know they wanted,” says Vyas. For example, business teams that use personalization platforms as part of an integrated media strategy can optimize algorithms against outcomes such as improving conversion and driving engagement.

Vyas says the elevated experience from “personalization to serendipity” fosters trust in the ability of recommendation architectures to persuade and influence consumers’ choices in beneficial ways. A case in point: our research found that half (52 percent) of spending by millennials goes towards experience-related purchases. As always, the key is to meet consumers where they are. Even better, according to Vyas, is to anticipate and understand when signals like customer intent are changing.

How to create value throughout the customer journey

One solution companies can implement right now is an integrated media spend platform that incorporates reporting, planning, and strategy across the entire customer journey. This offers value throughout the customer journey by using technology to reduce friction along the way. Think of it as starting with the customer looking for a product (search and discovery), moving on to the purchase (omnichannel basket, “shoppable” screens) and pick up/delivery (QR code scan in store), and through to post-purchase engagement with the retailer (Google Contact Center AI).

Such a holistic approach also accelerates data acquisition, integration, and reporting using advanced analytics to break down silos and emphasize the importance of privacy and first-party data. This in turn guides end-to-end interventions across customer journeys that enable optimized media spend, empowers businesses to analyze their spend distribution, fine-tunes owned and paid tactics with agencies, and promotes stewardship to support audit efficacy.

A culture of experimentation

With this data-driven focus, CPRs can create a 360-degree perspective of the customer. That intelligence can be used to enhance and humanize automated shopping experiences by putting the customer in control, whether online, in store, or across company brands. Moreover, by using Google Cloud’s emerging technologies such as artificial intelligence (AI), we help companies accelerate value across the spectrum, from supply-chain optimization and customer innovation to consumer experience.

Building a culture of experimentation is a team effort. “It’s not ever just one person who had a big idea. It’s all incremental steps,” says Jennifer Marchand, Google Cloud COE Leader, Capgemini. “Finding the right use case and timing is everything.” Take Google Glass Enterprise, she continues. For greater consumer experience, it can enable in-store associates to better serve with hands-free checkout, customer personalization and recommendations, and special offers. At the same time, Computer Vision and Smart Shelves can help prioritize tasks for employees, notifying them of low stock or a spill in the store.

Marchand points out that companies and consumers alike might not be ready to fully adopt some technology like facial recognition, but since almost everyone has a smartphone these days, they can benefit from automation with ease. “What’s interesting,” she adds, “is the way Google thinks about these types of problems, solving them for the long term.”

The store of tomorrow

Imagine a truly frictionless shopping experience, where state-of-the-art computer vision and AI identifies the products you pick up, put back, and keep, allowing you to head home, completely bypassing the checkout, with a 99 percent accuracy rate and receipts sent directly to your mobile app. That utopian experience is already taking shape at CornerShop, Capgemini’s live experimental store in London, UK.

Jamie MacLoud, Transformation & Strategy Consultant at frog, part of Capgemini, describes the retail space, which runs on Google Cloud, as the store of tomorrow, and not the distant future. It’s an experiential space where retailers and brands can explore, develop, and test technological shopping innovations in real-time. The outcome is a clearer understanding of how digital innovation can enable new ways to progress the customer experience, improve in-store operations, and help consumers to rediscover the joy of in-person retail through new ways to shop and engage with brands.

“We build, test, and learn about store concepts of tomorrow that we believe could be implemented into actual stores in the next one to two years. Getting these experiences in front of real customers in the CornerShop allows us to generate tangible learnings that we can share with our clients and use to shape future store strategy” says MacLeod. CornerShop was opened to the public in two eight-week stints, which allowed real-time testing to see what technologies resonated with customers, which brands can adapt and scale. Frictionless checkout, not surprisingly, was a big win for customers, but the technology underpinning the “virtual try-on” of clothing was deemed more suitable for the store of the future.

So, unlike an innovation lab, CornerShop lets companies experiment risk free, speeding up the process from hypothesis to full-scale implementation. It’s also another step toward solving the challenges customers face, while delivering those serendipitous experiences that build brand loyalty and longevity.

Learn more about how Capgemini is partnering with Google Cloud to help retailers create next-generation shopping experiences today.

We would like to acknowledge Jamie MacLoud, Transformation & Strategy Consultant at frog, part of Capgemini and Neerav Vyas, Head of Customer First, Co-Chief Innovation Officer, Insights & Data, North America at Capgemini who supported with invaluable insights and subject matter expertise in the writing of this blog post.