FinTech is about connecting with customers. They expect something different from classically understood banking. The more you know about your audience, the more you can offer them. It’s similar to prices – price optimization through machine learning is a great tool to grow your revenue. What can you learn from real-market examples?
Figuring out the best pricing model can be tricky. Especially with a newly developed product, when you have to convince people to set up accounts and trust you with both: data and money. That’s where machine learning algorithms come into place. By processing and analyzing big amounts of data, they can help you establish optimized pricing plans. How exactly?
Hire machine learning to make optimal pricing decisions
Solutions mentioned below will boost your product in real-time. They can help both: established companies and startups. Think of them as a multiple-step guide to designing your app with specific features and customer-centric solutions in mind.
This is how you can improve a pricing model:
Use machine learning to process data and discover services that need a boost. There are highly specialized FinTech applications that offer only one product; loans for example. There are, however, applications that are very popular and sell multiple solutions to the same audience. What product generates more money? Which solution is better? Do an A/B testing and find out. By going through data, you can figure out what works and what doesn’t. This solution can free up resources (money, employees’ time) to pursue more profitable features.Use automated pricing models to drive up revenue. The Boston Consulting Group created a study and it seems that revenue can be boosted up by 5% with this. The BCG believes that machine learning offers optimal pricing rules in revenue management systems. It also enforces contractual pricing.Generate insight on changing user’s behaviors through automated pricing solutions. It gives a highly valuable context on transactional data, providing the necessary perspective. One of the companies that offer interesting solutions is Vendavo. Their model and industry integrations work great with custom software development, powering your app. This combo of data and development solutions will help you make pricing decisions. Especially based on cross-border parameters.Use machine learning to figure out which customers are willing to pay for a product or a specific feature. You can pull information by linking spending or monthly fees in a software-as-a-service (SaaS) model with discounts, promo codes, etc. It’s especially valuable in the case of VIP pricing plans.Predict pricing impact with AI-powered user personas. Try to predict whether a first-time user or a paying customer will perform a certain and desirable action. Thanks to artificial intelligence propensity models, you can increase the customer retention and reduce churn.Use rule-based artificial intelligence (AI) models to establish the risk-to-revenue. Software development, specially dedicated to the B2C market, isn’t always fully predictable. Customers’ needs and the market itself change rapidly. Friction in user experience can be managed but what about mobile app development? You can use the customer even before you know about the issue. The price is not acceptable. The solution is brilliant, but underdeveloped. Microcopy inside the app doesn’t transmit the offers very well. User experience and user interface design are not attractive enough. Machine learning pricing algorithms won’t give you all the answers, but they can show you the right direction.
How to achieve your goals?
According to McKinsey, the estimated AI-based pricing solutions can have a global worth of $259.1B to $500B, globally. According to Mordor Intelligence, the AI market in the sector will grow from $7.27B in 2019 to over $35.4B by 2025. Those are, however, numbers you can’t use. As previously mentioned, 5% is something real. How to get to that? Use these factors to drive your decisions:
customers’ personasoperating costs and preferred marginsseasons and holidaysother, especially unforeseen, economic variables
Also, focus on something called a dynamic price. It’s adjusting prices, usually for a number of products, to react to the competition’s strategy. This model assumes frequent changes. It’s risky, unstable, and leads to churn. We have broken down the differences between price optimization, dynamic pricing, and price automation with machine learning.
Price optimization without machine learning is incomplete
Massive amounts of data and machine learning can generate pricing recommendations but you still have to base decisions on experience. Machine learning can and will give you a lot to think about, it can also free you from many mindless business operations. It can also be faulty.
As well as software development, which requires real specialists. Financial software development company can save you a lot of trouble and create a performing digital product worthy of your customers’ attention. Care to join the future?
Learn how genetic algorithms and machine learning can help hedge fund organizations manage a business. As well as bolster investor confidence and improve profitability.
As a hedge fund shareholder, you certainly want the best for your organization, right?
For instance, you want to generate effective AUM, NAV, and share value reports to improve investor confidence as a manager.
Or enable your company to produce maximum profits as a trader or employee, etc.
Well, it doesn’t need to be that difficult.
This article looks at how genetic algorithms (GA) and machine learning (ML) can help hedge fund organizations. For instance, to manage a business, boost investor confidence and increase profitability.
Let me walk you through these.
Ready?
Modern machine learning and back-testing; how quant hedge funds use it
1. Manage funds and make investment decisions
First off, hedge fund companies require sound investment decisions to enable profitability.
As such, over 56% of hedge fund managers use AI and ML when making investment decisions. And their percentage is expected to increase sharply over time.
They do so since investment algorithms are effective as they aren’t affected by opinions, emotions, and judgments like their human counterparts.
“Most of the hedge fund managers surveyed are leveraging advanced algorithms and human judgment to deliver smarter investment decisions.” This is according to Barclay Hedge founder and President Sol Waksman in his July 2018 statement.
2. Perform quantitative analysis
Similarly, hedge funds often use modern machine learning and back-testing to analyze their quant models. Machine learning has done a lot to help them improve financial trading. They do so to ensure that they’re in top form or of the highest quality.
Here, the models get tested using historical data to evaluate their profitability. And their risks before the organizations invest real money.
According to Insight FactSet, hedge funds can use ML to find patterns in data. As a result, it allows models that explain stock performance based on different factors, such as company activity and pricing.
Besides that, integrate advanced back-tests to test their algorithms from time to time to ensure that they’re in tiptop condition.
Methods of Algo-trading, machine learning tests, back-tests
These methods typically observe price shifts, channel flare-ups, moving averages, and associated technical indicators to decide. For instance, issue a purchase command when an asset’s price rises. And give out a sell order when the asset’s price falls.
However, they do not call for price forecasts or predictions, making them easy to implement.
3. Mathematical Model-based Strategies
Unlike trend-following approaches, these methods use time-tested and proven mathematical prototypes to enable combination-based trading.
Here, a method instructs a quant model to buy or sell stock when a specified mathematical condition is met. Or withdraw or deposit particular sums of money. An example of such is the delta-neutral trading approach.
Machine learning tests
As a routine, hedge funds usually test new and incorporated ML’s to determine their effectiveness to maintain competitive advantage.
To do so, they typically use the following evaluations:
1. Pre-train tests
They’re mainly carried out early on when developing a new ML to identify bugs to avoid needless training.
They include:
Tests that check an organization’s ML model output shape to ensure that it corresponds with the labels in its datasetEvaluations that check for label disclosure between an ML’s training and validation datasets, etc.
What makes the pre-tests unique is that they do not require trained parameters.
2. Post-train tests
The primary aim of these tests is to cross-examine the logic gained during training and showcase how the models are performing—as behavioral reports.
3. Invariance tests
They’re typically carried out to explain the sets of perturbations used. In addition, the tests are implemented to examine the consistency of the model predictions.
Backtests
Hedge funds usually carry out back-tests on historical data to:
Test if the quant models are working effectivelyEvaluate previous trading days or go through historical data to train new modelsCollect statistical data regarding the likelihood of the opening gaps getting closed within trading sessions
That said; some of the commonly used historical data analysis methods used by hedge funds include the use of:
Custom software such as Python and R;Robust trading platforms, such as MetaTrader 5 for hedge funds;Efficient back-testing software.
Genetic algorithm use case
Testing Expert Advisors on multiple currencies
Hedge fund organizations mostly use a Strategy Test to test and boost their trading techniques (Expert Advisors) before engaging in business.
This reduces the chances of making losses during actual trading.
Here, an Expert Advisor with its initial variables is first run on history data during the testing phase. And subsequently, run using different parameters during the optimization phase to identify the most suitable combination.
This reduces the chances of making losses during actual trading.
Final thoughts
As you can see, genetic algorithms and machine learning are being extensively used by investment organizations, such as hedge funds to improve profitability.
You can also tap into this revolutionary field by implementing similar strategies.
For instance:
Understand and implement ML when making investment decisionsLearn and use Algo-trading approaches
And
Routinely test your system’s ML capabilities
Doing so can significantly revamp your hedge fund business.
Expectations for both consumer and commercial products have changed. Consumers want products that evolve with their needs, adapt to their preferences, and stay up-to-date over time. Manufacturers, in turn, need to create products that provide engaging customer experiences not only to better compete in the marketplace, but also to provide new monetization opportunities.
However, embedding intelligence into new and existing products is challenging. Updating hardware is costly, and existing connected products do not have the capability to add new features. Furthermore, manufacturers do not have sufficient customer insights due to product telemetry and customer data silos, and may lack the AI expertise to quickly develop and deploy these features.
That’s why today we’re launching Intelligent Products Essentials, a solution that allows manufacturers to rapidly deliver products that adapt to their owners, update features over-the-air using AI at the edge, and provide customer insights using analytics in the cloud. The solution is designed to assist manufacturers in their product development journeys—whether developing a new product or enhancing existing ones.
With Intelligent Products Essentials, manufacturers can:
Personalize customer experiences: Provide a compelling ownership experience that evolves over the lifetime of the product. For example, a chatbot that contextualizes responses based on product status and customer profile.
Manage and update products over-the-air: Deploy updates to products in the field, gather performance insights and evolve capabilities over time with monetization opportunities.
Predict parts and service issues: Detect operating thresholds, anomalies and predict failures to proactively recommend service using AI, reducing warranty claims, decreasing parts shortages and increasing customer satisfaction.
In order to help manufacturers quickly deploy these use cases and many more, Intelligent Products Essentials provides the following:
Edge connections: Connect and ingest raw or time-series product telemetry from various device platforms utilizing IoT Core or Pub/Sub and enable deployment and management of firmware over-the-air and machine learning models with Vertex AI at the edge.
Ownership App Template: Easily build connected product companion apps that work on smartphones, tablets, and computers. Use a pre-built API and accompanying sample app that can incorporate product or device registration, identity management, and provide application behavior analytics using Firebase.
Product fleet management: Manage, update and analyze fleets of connected products via APIs, Google Kubernetes Engine, and Looker.
AI services: Create new features or capabilities for your products using AI and machine learning products such as DialogFlow, Vision AI, AutoML, all from Vertex AI.
Enterprise data integration: Integrate data sources such as Enterprise Asset Management (EAM), Enterprise Resource Planning (ERP), Customer Relationship Management (CRM) systems and others using Dataflow and BigQuery.
Intelligent Products Essentials helps manufacturers build new features across consumer, industrial, enterprise, and transportation products. Manufacturers can implement the solution in-house, or work with one of our certified solution integration partners like Quantifi and Softserve.
“The focus on intelligent products that Google Cloud is deploying provides a digital option for manufacturers and users. At its heart, systems like Intelligent Product Essentials are all about decision making. IDC sees faster and more effective decision-making as the fundamental reason for the drive to digitize products and processes. It’s how you can make faster and more effective decisions to meet heightened customer expectations, generate faster cash flow, and better revenue realization,” said Kevin Prouty, Group Vice President at IDC. “Digital offerings like Google’s Intelligent Product Essentials potentially go the last mile with the ability to connect the digital thread all the way through to the final user.”
GE Appliances, a Haier company, are enhancing their appliances using new AI-powered intelligent features to enable:
Intelligent cooking: Help cook the perfect meal to personal preferences, regardless of your expertise and abilities in the kitchen.
Frictionless service: Build smart appliances that know when they need maintenance and make it simple to take action or schedule services.
Integrated digital lifestyle: Make appliances useful at every step of the way by integrating them with digital lifestyle services – for example, automating appliance behaviors according to customer calendars, such as oven preheating or scheduling the dishwasher to run in the late evening.
“Intelligent Products Essentials enhances our smart appliances ecosystem, offering richer consumer habit insights. This enables us to develop and offer new features and experiences to integrate with their digital lifestyle.“ —Shawn Stover, Vice-president Smart Home Solutions at GE Appliances.
Serial 1, Powered by Harley-Davidson, is using Intelligent Product Essentials to manage and update its next generation eBicycles, and personalize its customers’ digital ownership experiences.
“At Serial 1, we are dedicated to creating the easiest and most intuitive way to experience the fun, freedom, and adventure of riding a pedal-assist electric bicycle. Connectivity is a key component of delivering that mission, and working together to integrate Intelligent Product Essentials into our eBicycles will ensure that our customers enjoy the best possible user experience.”— Jason Huntsman, President, Serial 1.
Magic Leap, an augmented reality pioneer with industry-leading hardware and software, is building field service solutions with Intelligent Products Essentials with the goal of connecting manufacturers, dealers, and customers to more proactive and intelligent service.
“We look forward to using Intelligent Products Essentials to enable us to rapidly integrate manufacturers’ product data with dealer service partners into our field service solution. We’re excited to partner with Google Cloud as we continue to push the boundaries of physical interaction with the digital world.” — Walter Delph, Chief Business Officer, Magic Leap
Intelligent Product Essentials is available today. To learn more, visit our website.
Today at Google Cloud Next we are announcing innovations that will enable data teams to simplify how they work with data and derive value from it faster. These new solutions will help organizations build modern data architectures with real-time analytics to power innovative, mission-critical, data-driven applications.
Too often, even the best minds in data are constrained by ineffective systems and technologies. A recent study showed that only 32%of companies surveyed gained value from their data investments. Previous approaches have resulted in difficult to access, slow, unreliable, complex, and fragmented systems.
At Google Cloud, we are committed to changing this reality by helping customers simplify their approach to data to build their data clouds. Google Cloud’s data platform is simply unmatched for speed, scale, security, and reliability for any size organization with built-in, industry-leading machine learning (ML) and artificial intelligence (AI), and an open standards-based approach.
Vertex AI and data platform services unlock rapid ML modeling
With the launch of Vertex AI in May 2021, we empowered data scientists and engineers to build reliable, standardized AI pipelines that take advantage of the power of Google Cloud’s data pipelines. Today, we are taking this a step further with the launch of Vertex AI Workbench, a unified user experience to build and deploy ML models faster, accelerating time-to-value for data scientists and their organizations. We’ve integrated data engineering capabilities directly into the data science environment, which lets you ingest and analyze data, and deploy and manage ML models, all from a single interface.
Data scientists can now build and train models 5X faster on Vertex AI than on traditional notebooks. This is primarily enabled by integrations across data services (like Dataproc, BigQuery, Dataplex, and Looker), which significantly reduce context switching. The unified experience of Vertex AI let’s data scientists coordinate, transform, secure and monitor Machine Learning Operations (MLOps) from within a single interface, for their long-running, self-improving, and safely-managed AI services.
“As per IDC’s AI StrategiesView 2021, model development duration, scalable deployment, and model management are three of the top five challenges in scaling AI initiatives,” said Ritu Jyoti, Group Vice President, AI and Automation Research Practice at IDC. “Vertex AI Workbench provides a collaborative development environment for the entire ML workflow – connecting data services such as BigQuery and Spark on Google Cloud, to Vertex AI and MLOps services. As such, data scientists and engineers will be able to deploy and manage more models, more easily and quickly, from within one interface.”
Ecommerce company, Wayfair, has transformed its merchandising capabilities with data and AI services. “At Wayfair, data is at the center of our business. With more than 22 million products from more than 16,000 suppliers, the process of helping customers find the exact right item for their needs across our vast ecosystem presents exciting challenges,” said Matt Ferrari, Head of Ad Tech, Customer Intelligence, and Machine Learning; Engineering and Product at Wayfair. “From managing our online catalog and inventory, to building a strong logistics network, to making it easier to share product data with suppliers, we rely on services including BigQuery to ensure that we are able to access high-performance, low-maintenance data at scale. Vertex AI Workbench and Vertex AI Training accelerate our adoption of highly scalable model development and training capabilities.”
BigQuery Omni: Breaking data silos with cross-cloud analytics and governance
Businesses across a variety of industries are choosing Google Cloud to develop their data cloud strategies and better predict business outcomes — BigQuery is a key part of that solution portfolio. To address complex data management across hybrid and multicloud environments, this month we are announcing the general availability of BigQuery Omni, which allows customers to analyze data across Google Cloud, AWS, and Azure. Healthcare provider, Johnson and Johnson was able to combine data in Google Cloud and AWS S3 with BigQuery Omni without needing data to migrate.
This flexible, fully-managed, cross-cloud analytics solution allows you to cost-effectively and securely answer questions and share results from a single pane of glass across your datasets, wherever you are. In addition to these multicloud capabilities, Dataplex will be generally available this quarter to provide an intelligent data fabric that enables you to keep your data distributed while making it securely accessible to all your analytics tools.
Spark on Google Cloud simplifies data engineering
To help make data engineering even easier, we are announcing the general availability of Spark on Google Cloud, the world’s first autoscaling and serverless Spark service for the Google Cloud data platform. This allows data engineers, data scientists, and data analysts to use Spark from their preferred interfaces without data replication or custom integrations. Using this capability, developers can write applications and pipelines that autoscale without any manual infrastructure provisioning or tuning. This new service makes Spark a first class citizen on Google Cloud, and enables customers to get started in seconds and scale infinitely, regardless if you start in BigQuery, Dataproc, Dataplex, or Vertex AI.
Spanner meets PostgreSQL: global, relational scale with a popular interface
We’re continuing to make Cloud Spanner, our fully managed, globally scalable, relational database, available to more customers now with a PostgreSQL interface, now in preview. With this new PostgreSQL interface, enterprises can take advantage of Spanner’s unmatched global scale, 99.999% availability, and strong consistency using skills and tools from the popular PostgreSQL ecosystem.
This interface supports Spanner’s rich feature set that uses the most popular PostgreSQL data types and SQL features to reduce the barrier to entry for building transformational applications. Using the tools and skills they already have, developer teams gain flexibility and peace of mind because the schemas and queries they build against the PostgreSQL interface can be easily ported to another Postgres environment. Complete this form to request access to the preview.
Our commitment to the PostgreSQL ecosystem has been long standing. Customers choose Cloud SQL for the flexibility to run PostgreSQL, MySQL and SQL Server workloads. Cloud SQL provides a rich extension collection, configuration flags, and open ecosystem, without the hassle of database provisioning, storage capacity management, or other time-consuming tasks.
Auto Trader has migrated approximately 65% of their Oracle footprint to Cloud SQL, which remains a strategic priority for the company. Using Cloud SQL, BigQuery, and Looker to facilitate access to data for their users, and with Cloud SQL’s fully managed services, Auto Trader’s release cadence has improved by over 140% (year-over-year), enabling an impressive peak of 458 releases to production in a single day.
Looker integrations make augmented analytics a reality
We are announcing a new integration between Tableau and Looker that will allow customers to operationalize analytics and more effectively scale their deployments with trusted, real-time data, and less maintenance for developers and administrators. Tableau customers will soon be able to leverage Looker’s semantic model, enabling new levels of data governance while democratizing access to data. They will also be able to pair their enterprise semantic layer with Tableau’s leading analytics platform. The future might be uncertain, but together with our partners we can help you plan for it.
We remain committed to developing new ways to help organizations go beyond traditional business intelligence with Looker. In addition to innovating within Looker, we’re continuing to integrate within other parts of Google Cloud. Today, we are sharing new ways to help customers deliver trusted data experiences and leverage augmented analytics to take intelligent action.
First, we’re enabling you to democratize access to trusted data in tools where you are already familiar. Connected Sheets already allows you to interactively explore BigQuery data in a familiar spreadsheet interface and will soon be able to leverage the governed data and business metrics in Looker’s semantic model. It will be available in preview by the end of this year.
Another integration we’re announcing is Looker’s Solution for Contact Center AI, which helps you gain a deeper understanding and appreciation of your customers’ full journey by unlocking insights from all of your company’s first-party data, such as contextualizing support calls to make sure your most valuable customers receive the best service.
We’re also sharing the new Looker Block for Healthcare NLP API, which provides simplified access to intelligent insights from unstructured medical text. Compatible with Fast Healthcare Interoperability Resources (FHIR), healthcare providers, payers, and pharma companies can quickly understand the context and relationships of medical concepts within the text, and in turn, can begin to link this to other clinical data sources for additional AI and ML actions.
Bringing the best of Google together with Google Earth Engine and Google Cloud
We are thrilled to announce the preview of Google Earth Engine on Google Cloud. This launch makes Google Earth Engine’s 50+ petabyte catalog of satellite imagery and geospatial data sets available for planetary-scale analysis. Google Cloud customers will be able to integrate Earth Engine with BigQuery, Google Cloud’s ML technologies, and Google Maps Platform. This gives data teams a way to better understand how the world is changing and what actions they can take — from sustainable sourcing, to saving energy and materials costs, to understanding business risks, to serving new customer needs.
For over a decade, Earth Engine has supported the work of researchers and NGOs from around the world, and this new integration brings the best of Google and Google Cloud together to empower enterprises to create a sustainable future for our planet and for your business.
At Google Cloud, we are deeply grateful to work with companies of all sizes, and across industries, to build their data clouds. Join my keynote session to hear how organizations are leveraging the full power of data, from databases to analytics that support decision making to AI and ML that predict and automate the future. We’ll also highlight our latest product innovations for BigQuery, Spanner, Looker, and Vertex AI.
I can’t wait to hear how you will turn data into intelligence and look forward to connecting with you.
Digital transformation is about gaining speed, agility, and efficiency. The faster and more easily your organization operates on a modern cloud platform, the sooner it can experience the benefits.
Today, we are excited to introduce Google Cloud Cortex Framework, a foundation of endorsed solution reference templates and content for customers to accelerate business outcomes with less risk, complexity, and cost. Google Cloud Cortex Framework allows you to kickstart insights and reduce time-to-value with reference architectures, packaged services, and deployment accelerators that guide you from planning to delivery so you can get up and running quickly. You can deploy templatized solutions from Google Cloud and our trusted partners for specific use cases and business scenarios in a faster, more cost-effective way.
Our data foundation release
In our first release, customers can take advantage of a rich data foundation of building blocks and templates for SAP environments. Customers can leverage our:
Scalable data cloud foundation to combine the best of SAP and non-SAP data to drive new insights;
Pre-defined BigQuery operational data marts and change data capture (CDC) processing scripts to take the guesswork out of modeling and data processing; and
BigQuery ML templates, which provide advanced machine-learning capabilities for common business scenarios such as Product Recommendations and Customer Segmentation.
See below for an example of some of these templates within BigQuery.
Together with plug-and-play Looker dashboard templates, customers can gain fast insights into sales, orders, products, customers, and much more. But this is just the beginning. We see Google Cloud Cortex Framework as a “content factory” that will expand to address new use cases, incorporate best practices, industry scenarios, and build on our cumulative experiences in enterprise environments.
“At Google Cloud, our goal is to make it as easy as possible for SAP customers to modernize in the cloud,” says Abdul Razack, VP, Solutions Engineering, Technology Solutions and Strategy, Google Cloud. “Google Cloud Cortex Framework is our latest innovation to that end. With readily available reference architectures and other tools, SAP customers now have what they need to design, build, and deploy advanced cloud solutions and accelerate business outcomes.”
Get up and running quickly
The Google Cloud Cortex Framework helps us answer a common question we hear from our customers: “How do I get started?” Google Cloud Cortex Framework can help customers with an off-the-shelf packaged approach that they can implement and customize to their own specifications and provides multiple benefits:
Accelerate business outcomes with easy-to-leverage, scenario-driven reference architectures and content that remove the guesswork from deployments. Expedite value with line-of-business and industry example solutions and packaged services from Google Cloud and partners.
Reduce risk, complexity, and cost with proven deployment templates. Deploy the industry’s most advanced cloud-native capabilities at a fraction of the time and cost of from-scratch, in-house efforts. Support business process improvement with accurate and relevant insights to quickly deliver differentiating capabilities to your customers.
Leverage a scalable technology strategy for future innovation by standardizing on a reusable data and analytics architecture. Easily identify and support the innovative technologies required to deliver a full range of current and future scenarios. Provide the building blocks and blueprints you need to prepare for the future, and upskill your team so they can deploy the technology you need to support your business objectives today and tomorrow.
Our partner ecosystem makes Google Cloud Cortex Framework possible
Today’s launch of Google Cloud Cortex Framework includes support from a large ecosystem of partners such as Accenture, Infosys, Palantir, C3.AI, Informatica, HVR, Qlik, Pluto7, ATOS, CapGemini, Cognizant, Deloitte, HCL, Lemongrass, NIMBL, PwC, SpringML and TCS who will be offering solutions and services to accelerate customer innovation. These partners are adopting and augmenting Google Cloud Cortex Framework to enable customers to more rapidly deploy and drive value for their organizations. With vast customer and partner interest in advancing data landscapes leveraging Google Cloud, we will continue to develop the ecosystem of Google Cloud Cortex Framework partners.
As foundational partners, Accenture and Infosys have been instrumental in our solution engineering efforts, leveraging their strengths in the data and analytics space.
“Organizations today rely on increasing volumes of data to quickly react and respond to change. To handle the high volume and variety of data from disparate sources, our clients need a modern data foundation that can respond rapidly to those growing demands. Google’s Cortex enables us to align our assets and industry solution models into a consistent architecture for our clients to drive business agility, customer intimacy, and real-time decision-making.” – Tom Stuermer, global lead of Accenture Google Business Group at Accenture.
“Infosys is excited to partner with Google Cloud to drive the adoption of Google Cloud Cortex Framework, unlocking value from SAP and non-SAP data and enabling insights-driven digital enterprises across multiple industry domains within our large SAP customer base. Google Cloud Cortex Framework complements Infosys Cobalt that brings together our extensive SAP, data analytics and Google Cloud capabilities to help clients fast-track their cloud adoption and accelerate their business transformation.” – Sunil Senan, SVP and Business Head – Data & Analytics, Infosys
Building on decades of innovation with Google Cloud Cortex Framework
To illustrate the opportunities that Google Cloud Cortex Framework will bring to our customers, we developed an initial release that combines multiple Google data sets with SAP enterprise data. By leveraging machine learning and other Google technologies, companies can deliver new analytics and gain new insights. An example of this is demand shaping.
Demand shaping will benefit line-of-business executives and supply-chain professionals, who can leverage the Google Cloud Cortex Framework reference architecture to improve supply-chain operations by improving business processes or accelerating time-to-insight with analytics. Chief data officers (or any executive responsible for data and analytics) will also benefit by saving time, building on reusable components, and following best practices to get innovative cloud solutions up and running as quickly, effectively, and efficiently as possible. Today’s enterprises can use Google Cloud Cortex Framework to create a reusable architecture that can adapt and expand to new scenarios to gain better visibility into signals that influence demand forecasts.
Of course, Google Cloud customers aren’t just interested in scenarios that apply to the data and analytics space. Future Google Cloud Cortex Framework offerings will help provide recommended approaches to better implement use cases in consumer-facing industries, including consumer packaged goods and supply chain and the delivery of improved customer experiences, as well as infrastructure and application workload management integration—all to drive insights to execution and improve automation of business processes. The common denominator will always be the ability to not only reduce the time and effort to deploy and manage each solution, but also to develop a technology strategy that can scale above and beyond an individual scenario or use case.
Are you interested in learning more? Watch our session at Google Cloud Next ’21 and fill out this form to connect with our solution experts on the latest content, deployment options and free tailor-made innovation discovery workshops.
If you enjoy working with data, or if you’re just interested in a career with a lot of potential upward trajectory, you might consider a career as a data engineer. But what exactly does a data engineer do, and how can you begin your career in this niche?
What Is a Data Engineer?
A data engineer’s job is to take data and transform it in a way that makes it easier or more useful to analyze. For example, imagine that you’re working for a company that’s creating self-driving cars. A central server is likely collecting tons of data from multiple sources; onboard measurements from the self-driving car, feedback from the driver, and external sources of data are all feeding into the system.
The company is interested in creating solutions that allow them to analyze car performance, customer satisfaction, road safety, and other concepts. Your job as a data engineer would be designing, creating, maintaining, and improving the systems to do it.
How to Become a Data Engineer
If you like to think logically, if you have a love of engineering, if you want to solve problems, or if you just want a sustainable career, data engineering could be the best path for you. So what does it take to become a data engineer?
Get a bachelor’s degree. It’s not strictly necessary to have a bachelor’s degree to begin working in data engineering, but it certainly helps. Some employers will specifically look for candidates to have a four-year degree in computer science, data science, software engineering, or a related field. If you have a bachelor’s degree in a non-related field, like English, that may help – but you’ll need to make up for that lack of degree with an abundance of experience. Master your software engineering skills with small projects. It’s a good idea to polish your software engineering and coding skills with small projects. You’ll need to be very acquainted with SQL, a foundational programming language in the realm of data science, and be at least somewhat familiar with other languages and frameworks like Python, Spark, and Kafka. Your small projects should also help you better understand things like machine learning, database architecture, and data mining – as well as commonly used platforms like Amazon Web Services.
In addition to boosting your skills, this step will help you assemble a portfolio of work to show off your talent. Depending on where you’re applying and what role you’re seeking, this could be crucial in helping you get hired. Join hackathons, groups, and other networking opportunities. Get involved with the community and start networking. Join hackathons, data and software groups, and try to meet other professionals in the industry whenever you get the chance. This is a great opportunity to learn new things, put your skills to the test, and have fun doing it. Plus, you’ll make a plethora of new connections, which may ultimately direct you to new job opportunities. Start applying for entry-level jobs. At this point, you’ll be ready to start applying for entry-level jobs. Try not to get too hung up about the job title, pay rate, or working conditions – what’s important at this point in your career is getting your foot in the door. If you’re not happy with this position, you can always move onto something else – and that transition will be much easier now that you have some experience.
Just make sure you’re professional and polite at all times during this process to maximize your chances of getting hired and preserve your connections; that means everything from sending a thank-you email after your interview to leaving on good terms. Earn new certifications. Getting an entry-level job isn’t the end of your journey as a data engineer; in fact, it’s barely the beginning. Whether you get hired immediately or not, your next objective should be earning new certifications to boost your skills and credentials. Talk to your mentors and other people connected to the industry to find out the best certifications for you – and the most relevant ones for modern employers. Pursue higher education. Most data engineers ultimately spend more time in education, pursuing a master’s degree, or even a PhD to fuel their career. This can be difficult to manage if you’re also trying to hold down a job while studying, but it’s worth it to maximize your career-long earnings and open the door to better opportunities.
The path to becoming a data engineer isn’t always straightforward, and you may have trouble getting started in this relatively new field. But once you have the skills and background necessary to be successful, you should have a bright career path ahead of you; demand for data engineers is unlikely to abate anytime soon.
Analytics technology has been invaluable to modern marketing. The market for web analytics is projected to be worth $9.11 billion by 2025. The utilization of analytics and big data in the marketing industry has played a massive role in this robust growth.
One of the most important benefits of analytics in marketing is with PPC marketing. More companies are using analytics to expand the reach of their PPC campaigns and improve their ROI. PPC marketing would be infeasible in 2021 without analytics.
Companies that intend to use analytics in PPC can find that it drastically reduces the CPA of lead conversions, while also increasing the number of leads as well. Lead generation is one of the most important roles that marketing plays in your business strategy, so you shouldn’t overlook the benefits of using analytics to accomplish it more effectively. Existing customers on their own won’t help you to grow your brand: you need to find new customers and leads to keep your business in motion.
Most (if not all) marketing activities can be effective ways of generating leads, but in this blog, we’ll be focusing on using analytics to improve your strategy for generating leads through Pay-Per Click (PPC) advertising.
Is an Analytics-Driven PPC Strategy the Best Approach for Lead Generation?
One of the main goals of marketing is to generate leads, so it stands to reason that most marketing channels are effective methods of lead generation. Those with a lengthy customer journey might find content marketing to prove particularly effective, as it creates more touchpoints to build up a relationship with a new lead. Many retail brands see great success on social media, as it gives them the chance to show off new items in a format that can be creative and fresh when done right.
PPC on the other hand, can prove to be a highly effective lead generation strategy for any business or budget. You’re ensuring that you’re appearing when users are searching for things you offer, and you can more easily control your message for different stages in the marketing funnel, helping to ensure you’re generating qualified leads. However, as with any marketing strategy or campaign, your success will be dependent on how well you’ve thought through your activity before beginning.
This is why analytics is so important in PPC marketing. You have a lot at stake, since you are investing such a large amount of money. Therefore, you are going to need to use big data and PPC together to get the best return on your investment.
5 Ways to Use Data Analytics Ensure Your PPC Campaign is Generating Leads
So, how can you ensure your campaign is doing everything it can to give you the most lead generation opportunities? Can data analytics help you get more out of your PPC campaigns? Below, we explore 5 key ways you can guarantee success.
1. Campaign Organization
Setting up your PPC campaigns for lead generation success means that you need to have a real handle on your campaign organization. For those new to PPC, structuring paid search ads can be quite daunting. With multiple layers of the campaign to work through and various targeting metrics to consider, it’s easy to get it wrong and waste your budget. Luckily, there are plenty of online tools, and of course experienced marketing agencies, to provide you with a helping hand.
For those starting out, the simplest way to structure a PPC campaign to ensure that it generates leads is to organize it in line with your goals. This means creating ad groups that are specific to the results you want to achieve. For example, you may want to drive traffic to a specific landing page, in which case you would use a targeted PPC ad group linking solely to this page. Setting up your campaigns in this way also gives you greater control; allowing you to adjust your budgets on a micro level and thus see incremental improvements to your ROI – especially if you use multiple groupings.
Data analytics is going to be very important for this stage. You are going to need to outline your overall strategies and make sure that they are backed by data analytics. Analytics technology will help you better organize customer demographics and estimate the profitability of different market segments, so you can decide which to focus on.
2. Extensive Keyword Research
Another way to ensure you’re generating leads through PPC is by completing extensive keyword research to identify as many relevant opportunities as possible. Keyword research is a crucial part of any PPC campaign; you want to ensure that you’re appearing whenever someone searches for the products and services you offer.
As you’re evaluating target keywords, make sure that they are both relevant to your business, and achievable. This way, you make sure you’re appearing in the search engine results pages (SERPs) whenever someone is searching for things related to your business, but you aren’t pushing yourself into more difficult and competitive waters.
Part of your keyword research will also involve highlighting the keywords that are irrelevant and perhaps even harmful to your brand to highlight as negative keywords. There will naturally be searches you don’t want to appear for: a business in another sector with a similar name, harmful terms, or even highly competitive keywords. Knowing which keywords to avoid is as much a part of your keyword research as knowing those you want to go for is.
New advances in analytics technology have made keyword research much more effective. You can use analytics tools that work with third-party keyword research platforms to estimate the monthly search volume of various keywords. You can also use analytics to evaluate trends to estimate the future cost of various keywords and gauge the potential profitability of given keywords based on projected conversion rates.
3. Draw Attention with Catchy Copy
Nothing draws in new leads like powerful copy, and the same is true in PPC. Eye-catching, interesting headlines and descriptions for your PPC ads help you to stand out from the crowd in the SERP, bringing you more clicks and generating quality leads. Consider what your USPs are compared to your competitors, and be sure to detail any special offers you might have on. This (alongside audience insights) will help determine the content you should be including.
Writing effective ad copy can be a difficult task: you’ve only got a limited number of characters to get all the key information across in a way that draws in new leads. A full-service digital marketing agency will be able to support your copy creation, and will also bring expertise, audience insights and industry best practices to support your lead generation strategies.
Analytics can be surprisingly useful in developing better copy. A lot of marketers are now using AI technology to automate content creation. This is a lot more effective if you merge your AI content generation technology with analytics tools that can see how various content has performed in the past.
4. Develop Highly Targeted Landing Pages
The ad experience doesn’t end when a user clicks on your PPC ad. To generate leads from your ad campaign, you’ll need to make sure the landing page users are sent to is tailored to the ad group you’re targeting.
Consider your landing page your big opportunity for converting visitors into leads. Your ad entices visitors in, but once they’re on the page, it’s the ultimate chance to seal the deal. Where possible, you want to be tailoring your content to the wants and needs of the audience you’re addressing. Try to keep your pages short and sweet, without too many distractions: the sole goal of these landing pages is to get visitors to complete the desired action, so don’t include anything that could prevent that.
Last, but certainly not least, you’ll want to maintain standard landing page best practices. Not only will this help push users to convert by giving them a great experience on site, but it will also satisfy search engines when it comes to determining your quality score.
This is one of the most important benefits of analytics in later stages of PPC campaigns. You can use analytics to see which landing pages have performed the best and optimize them for better ROIs.
5. Testing & Reporting: The Road to Continuous Improvement
Another major benefit of analytics is with testing your campaigns to make incremental improvements. With your PPC campaigns set up, you might be tempted just to leave them be. You’ve spent all that time researching and building, now should be the time to sit back and watch the leads flow in, right? Sadly, the work of a PPC manager is never done, and you’ll want to be monitoring your results and running A/B tests if you want to see continuous improvement.
Be sure to set up analytics reporting so you can see what activity is working, what’s bringing in new leads, and what isn’t performing as well. This information can then be used to optimize your campaigns to drive greater success. You can also add in new iterations of ads to be tested to see what gains you can make with tweaks in your copy or CTAs. To continue bringing in new leads, you need to continuously invest time and effort into testing and optimizing your campaigns.
Generating leads through PPC can be a highly effective strategy, and with these tips, you’ll be set to create and deliver well optimized campaigns. The most important things to remember are to keep an eye on your budget, and to keep testing to find the best ways to attract your target audience and bring in those leads.
Iteration syntax supports easier creation and better readability of workflows that process many items. You can use a for loop to iterate through a collection of data in a list or map, and keep track of the current index. If you have a specific range of numeric values to iterate through, you can also use range-based iteration.
Click to enlarge
Connectors have been in preview since January. Think of connectors like client libraries for workflows to use other services. They handle authentication, request formats, retries, and waiting for long-running operations to complete. Check out our previous blog post for more details on connectors. Since January, the number of available connectors has increased from 5 to 20.
The combination of iteration syntax and connectors enables you to implement robust batch processing use cases. Let’s take a look at a concrete sample. In this example, you will create a workflow to analyze sentiments of the latest tweets for a Twitter handle. You will be using the Cloud Natural Language API connector and iteration syntax.
APIs for Twitter sentiment analysis
The workflow will use the Twitter API and Natural Language API. Let’s take a closer look at them.
Twitter API
To use the Twitter API, you’ll need a developer account. Once you have the account, you need to create an app and get a bearer token to use in your API calls. Twitter has an API to search for Tweets.
Here’s an example to get 100 Tweets from the @GoogleCloudTech handle using the Twitter search API:
Natural Language API
Natural Language API uses machine learning to reveal the structure and meaning of text. It has methods such as sentiment analysis, entity analysis, syntactic analysis, and more. In this example, you will use sentiment analysis. Sentiment analysis inspects the given text and identifies the prevailing emotional attitude within the text, especially to characterize a writer’s attitude as positive, negative, or neutral.
You can see a sample sentiment analysis response here. You will use the score of documentSentiment to identify the sentiment of each post. Scores range between -1.0 (negative) and 1.0 (positive) and correspond to the overall emotional leaning of the text. You will also calculate the average and minimum sentiment score of all processed tweets.
Define the workflow
Let’s start building the workflow in a workflow.yaml file.
In the init step, read the bearer token, Twitter handle, and max results for the Twitter API as runtime arguments. Also initialize some sentiment analysis related variables:
In the searchTweets step, fetch tweets using the Twitter API:
In the processPosts step, analyze each tweet and keep track of the sentiment scores. Notice how each tweet is analyzed using the new for-in iteration syntax with its access to the current index.
Under the processPosts step, there are multiple substeps. The analyzeSentiment step uses the Language API connector to analyze the text of a tweet and the next two steps calculate the total sentiment and keep track of the minimum sentiment score and index:
Once outside the processPosts step, calculate the average sentiment score, and then log and return the results
Deploy and execute the workflow
To try out the workflow, let’s deploy and execute it.
Deploy the workflow:
Execute the workflow (don’t forget to pass in your own bearer token):
After a minute or so, you should see the see the result with sentiment scores:
Next
Thanks to the iteration syntax and connectors, we were able to read and analyze Tweets in an intuitive and robust workflow with no code. Please reach out to @meteatamel and krisabraun@ for questions and feedback.
We see data engineers use Dataflow for a wide variety of their data processing needs, ranging from ingesting data into their data warehouses and data lakes to processing data for machine learning use cases to implementing sophisticated streaming analytics applications. While the use cases and what customers do varies, there is one common need that all of these users have: the need to create, monitor and manage dozens, if not hundreds of, Dataflow jobs. As a result, users have asked us for a scalable way to schedule, observe and troubleshoot Dataflow jobs.
We are excited to announce a new capability – Dataflow Pipelines – into Preview that address the problem of managing Dataflow jobs at scale. Dataflow Pipelines introduces a new management abstraction – Pipelines – that map to the logical pipelines that users care about and provides a single pane of glass view for observation and management.
With Data Pipelines, data engineers can easily perform tasks such as the following.
Running jobs on a recurring schedule: With Data Pipelines, users can “schedule” recurrent batch jobs by just providing a schedule in cron format. The pipeline will then automatically create Dataflow jobs as per the schedule. The input file names can be parameterized for incremental batch pipeline processing. Dataflow uses Cloud Scheduler to schedule the jobs.
Creating and tracking SLO: One of the key monitoring goals is to ensure that data pipelines are delivering data that the downstream business teams need. In the past, it was not easy to define SLOs and set up alerts on those. With Data Pipelines, SLO configuration and alerting is natively supported and users can define them easily at Pipeline level.
Health monitoring & Tracking: Data Pipelines makes it easy to monitor and reason about your pipelines by providing aggregated metrics on a project and at a pipeline level. These metrics (both batch and streaming) along with history of previous execution runs provide a detailed overview of the pipelines at a glance. In addition, the ability to easily identify problematic jobs and dive into the job level pages makes troubleshooting easier.
Here is a short video that provides an overview of Data Pipelines.
Google Cloud joins the EDM Council to announce the release of the CDMC framework v1.1.1. This has been an industry wide effort which started in the summer of 2020, where leading cloud providers, data governance vendors and experts worked together to define the best practices for data management in the cloud. The CDMC Framework captures expertise from the group and defines clear criteria to manage, govern, secure and ensure privacy of data in the cloud. Google Cloud implements most of the mission critical controls and automations in Dataplex – Google Cloud’s own first party solution to organize, manage and ensure data governance for data across Google Clouds’ native data storage systems. Leveraging Dataplex, and working with the best practices in the CDMC framework, can ensure adequate control over sensitive data, and sensitive data workloads. Additionally, Google Clouds’ data services allow a high degree of configurability which, together with the integration with specialised data management software provided by our partners like Collibra, provide a rich eco-system for customers to implement solutions which adhere to the CDMC best practices.
The CDMC framework is a joint venture between hundreds of organizations across the globe, including major Cloud Service Providers, technology service organizations, privacy firms and major consultancy and advisory firms who have come together to define best practices. The framework spans governance and accountability, cataloging and classification, accessibility and usage, protection and privacy and data lifecycle management. The framework represents a milestone in adoption of industry best practices for data management and we believe that it will contribute to build trust, confidence and accountability for the adoption of cloud, particularly for sensitive data. Capitalising on this, Google Cloud is going to make publicly available Dataplex, which will implement cataloging, lifecycle management, governance and most of the other controls in the framework (others are available on a per product basis).
“Google Cloud customers, who include financial services, regulated entities, and privacy minded organizations continue to benefit from Google’s competency in handling sensitive data. The CDMC framework ensures that Google’s best practices are shared and augmented from feedback across the industry” Said Evren Eryurek, Google’s Director of Product Management at Google Cloud, a Key leader for Big Data in Google Cloud. אא
The organizing body of which Google Cloud is a member of, the EDM Council, is a global non-profit trade association, with over 250 member organizations from the US, Canada, UK, Europe, South Africa, Japan, Asia, Singapore and Australia, and over 10,000 data management professionals as members. The EDM Council provides a venue for data professionals to interact, communicate, and collaborate on the challenges and advances in data management as a critical organizational function. The Council provides research, education and exposure to how data, as an asset, is being curated today, and vision of how it must be managed in the future.
