Pizza purveyor Domino’s is dominating delivery sales around the world. Today, Domino’s is the most popular pizza delivery chain operating in the U.K., the Republic of Ireland, Germany, and Switzerland — and sales just keep growing.

In these regions in 2014, Domino’s sold 76 million pizzas and generated £766.6 million (1.02 billion USD) in revenue — a 14.6% increase from the previous year.

In the U.K. and Ireland, online sales are increasing 30% year over year and currently account for almost 70% of all sales. Notably, 44% of those online sales are now made via mobile devices.

Multi-Device Purchasing Means Fresh Opportunities

Domino’s is a consistent digital innovator. Much of the company’s success stems from early investments in ecommerce and mobile commerce platforms that help people easily purchase pizzas from different devices.

Domino’s sold its first pizza online in 1999. It then launched an iPhone app in 2010, quickly followed by apps for Android and iPad in 2011, and a Windows app in 2012. By late 2014, Domino’s customers could even order pizzas from Xboxes.

 The Domino’s marketing team had assembled a variety of tools to measure marketing performance, keeping pace with the company’s rapid innovations. Unfortunately, measuring siloed analytics and channel-focused tools restricted the team’s ability to fully understand all of the different paths to purchase.

 Find out how they worked around this challenge with Google Marketing Platform. Download the case study!

AI has the power to revolutionize every industry—from retail to agriculture, and education to healthcare. Yet many businesses still haven’t begun to adopt AI.

There are a number of factors, including the need for specialized talent and hardware, the right types and quantities of data for training and refining machine learning models, and of course, the broader complexities of introducing a new way of working inside an organization.

But above all, AI can be challenging to apply to very specific business needs.

Google wants to change that, which is why it’s introducing a number of AI solutions aimed at making it easier for businesses to use AI to address many of these specific use cases.

Google’s AI solutions fall into two categories. The first is a set of pre-packaged AI solutions that can be easily integrated into existing workflows. To make these solutions as easy to implement as possible, Google works with popular, trusted enterprise partners who have deep-domain expertise in these workflows.

The majority of Google’s pre-packaged solutions will be delivered through these partners, although it will be offering some directly to customers as well.

The second category of AI solutions is comprised of reference architectures that businesses can use to create highly-custom AI tools. These require more development work than Google’s pre-packaged solutions, but simplify complexity for enterprises that need highly business-specific, integrated AI deployments.

Additionally, businesses can build on these architectures and deploy them to market more quickly, helping your business take advantage of AI, faster.

Learn more about all Google’s AI solutions. Download the whitepaper now!

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 customer preferences and emotions, campaign performance, agent’s effectiveness and much more. Knowlarity’s programmatic speech analytics platform is built with Google Cloud to optimize contact center performance by transcribing and analyzing millions of calls to impact savings, operations, CX, customer loyalty and retention, and revenue generation.

Download the e-Book to learn more about Knowlarity’s speech analytics for your business’ contact centers and elevate your agents’ performance by leveraging ML, natural language processing (NLP) and AI capabilities.

To design and implement a successful machine learning (ML) project, you often need to collaborate with multiple teams, including those in business, sales, research, and engineering. Understanding the essentials of gathering and preparing your data is crucial to align teams, and getting a project off the ground.

Building an ML model that achieves your business’ goal, requires the kind of data that a model can learn from. If the goal is to predict a target output based on some input, you’ll need some data consisting of past input-output examples. For example, to detect (that is, to predict) a fraudulent credit card transaction (the target of the prediction), each example must contain information on a past credit card transaction and whether that transaction was fraudulent or not.

So what are the best practices in preparing and curating data for a machine learning initiative? The answer to this question lies within the guide. You’ll learn:

• To evaluate whether your data is suitable for ML
• What types of data you need–and which ones to eliminate
• How to prevent data leakage
• Tricks to speed up an ML project such as Google’s human labeling service, and Cloud Dataprep
• The importance of the granularity of data