Advanced Archives - Business Intelligence Info

Tag Archives: Advanced

Teradata is Selected by Brinker International to Enhance Advanced Analytics, Machine Learning and Data Science Capabilities

August 21, 2020 BI News and Info

Leading Casual Dining Restaurant Company Reinvests in Teradata as it Moves from On-Premises to the Cloud

Teradata (NYSE: TDC), the cloud data and analytics company, today announced that after an evaluation of other cloud analytics offerings on the market, Brinker International, Inc. (NYSE: EAT) has reinvested with Teradata, leveraging the Teradata Vantage platform – delivered as-a-service, on Amazon Web Services (AWS) – as the core of its data foundation to facilitate advanced analytics, machine learning and data science across the organization.

Brinker is one of the world’s leading casual dining restaurant companies and has been a Teradata customer for more than two decades. Founded in 1975 and based in Dallas, Texas, Brinker owns, operates, or franchises more than 1,600 restaurants under the names Chili’s® Grill & Bar and Maggiano’s Little Italy®. Over the past year, Brinker has been working to further increase its capabilities in advanced analytics and data science.

“Being a data-driven organization allows us to make informed decisions to create a better Guest and Team Member experience,” said Pankaj Patra, senior vice president and chief information officer at Brinker International. “As we looked for more flexible and cost-effective ways to manage and access our data, we evaluated quite a few cloud-native providers. After careful consideration, we decided the best course of action would be to migrate to Teradata Vantage in the cloud and take advantage of its as-a-service offerings to support our analytic goals.”

With Teradata Vantage delivered as-a-service, in the cloud, enterprises such as Brinker can focus on mining their data for insights that drive business decisions, rather than on managing infrastructure. By integrating Vantage’s machine learning capabilities, Brinker can now apply advanced analytics and predictive modeling to its business processes, enabling more accurate sales forecasting, demand and traffic forecasting, team member management, recommendation engines for customers and more.

“We’re proud of our ongoing relationship with Brinker and its long-standing position as a leader in the restaurant industry – a position due in large part to its culture of innovation in using data and analytics to streamline business processes, facilitate rapid decision-making and turn insights into answers,” said Ashish Yajnik, vice president of Vantage Cloud at Teradata. “Our collaboration with AWS and participation in the AWS Independent Software Vendor (ISV) Workload Migration Program has helped Brinker successfully move their mission-critical data infrastructure to the cloud. We look forward to expanding our relationship by powering their advanced analytics and data science capabilities through the scalable, clean and trusted data foundation that the Vantage platform provides.”

Teradata is an Advanced Technology and Consulting Partner in the AWS Partner Network (APN). The company brings proven processes and tools to make migrations to Vantage on AWS low risk and the fastest path to customer value through the AWS ISV Workload Migration – an APN Partner program that helps customers migrate ISV workloads to AWS to achieve their business goals and accelerate their cloud journey.

“Through the AWS ISV Workload Migration Program, Teradata was able to help Brinker migrate to Vantage on AWS securely and cost effectively. We are pleased to collaborate with Teradata and its long-standing customer Brinker to enhance their cloud practices,” said Sabina Joseph, director, Americas ISVs, Amazon Web Services, Inc.

Teradata Vantage is the leading hybrid cloud data analytics software platform that enables ecosystem simplification by unifying analytics, data lakes and data warehouses. With Vantage delivered as-a-service, enterprise-scale companies can eliminate silos and cost-effectively query all their data, all the time, regardless of where the data resides – in the cloud using low cost object stores, on multiple clouds, on-premises or anywhere in-between – to get a complete view of their business. And by combining Vantage with first party cloud services, Teradata enables customers to expand their cloud ecosystem with deep integration of cloud-specific, cloud-native services.

Webinar
Join Teradata for a live webinar on July 29th, 8:00 – 9:00 a.m. PT featuring Mark Abramson, lead architect, BI and analytics at Brinker International, and William McKnight, president of McKnight Consulting Group. The session will be moderated by Ed White, vice president, portfolio marketing and competitive intelligence at Teradata. Details below:

Webinar: Brinker’s Journey Back to Teradata

Wednesday, July 29th
8:00 a.m. – 9:00 a.m. PT /
11:00 a.m. – 12:00 p.m. ET

Registration is required and is open to Teradata prospects, customers, analysts, partners and Teradata employees.

This interactive webinar will highlight:

Brinker’s future analytic strategies and how Teradata will be part of its ongoing journey to lower overall costs and improve performance.
How Brinker embraces and drives benefits using Teradata Vantage on AWS, particularly to meet their advanced analytics and computing needs.
McKnight’s latest research into price-performance on modern cloud database management systems, including best practices.

About Brinker International, Inc.
Hi, welcome to Brinker International, Inc. (NYSE: EAT)! We’re one of the world’s leading casual dining restaurant companies. Founded in 1975 in Dallas, Texas, we stay true to our roots, but also enjoy exploring outside of our hometown. As of March 25, 2020, we owned, operated or franchised 1,675 restaurants in 29 countries and two territories under the names Chili’s® Grill & Bar (1,622 restaurants) and Maggiano’s Little Italy® (53 restaurants). Our passion is making people feel special and we hope you feel that passion each time you visit one of our restaurants or our home office. Find more information about us at www.brinker.com, follow us on LinkedIn or review us on Glassdoor.

Let’s block ads! (Why?)

Teradata United States

5 Ways Oilfield Services Companies Can Leverage Advanced Technology

November 3, 2019 NetSuite

5 Ways Oilfield Services Companies Can Leverage Advanced Technology

Posted by John Goode, Senior Director, Channel Marketing

Technology like NXTurn’s OFS Vertical Edition for NetSuite can help oilfield services companies improve efficiencies, streamline their supply chains and save money.

For many oilfield services (OFS) companies, the boom or bust nature of the business, requiring a focus either on responding to rapid demand or suddenly needing to cut costs has left them without a reliable technological backbone. Most are a long way from what some have termed the “digital oilfield.”

“Dependence on legacy systems and remotely scattered oil and gas fields are further complicating data-capture from all producing assets,” GlobalData Analyst Ravindra Puranik wrote recently, noting that full-fledged adoption of the digital oilfield is still a far way off. “Nevertheless, oil and gas companies have managed to achieve productivity gains through improved reservoir understanding, remote monitoring of operations and with logistics and supply chain optimization.” Learn how to implement these strategies in “Driving Revenue Growth and Streamlining Processes for the Oilfield Services Industry”.

Here are five more reasons why all OFS companies are implementing digital strategies today:

Gain efficiencies and improve profitability. Worth an estimated $ 63 billion as of 2017, the U.S. oilfield services industry has experienced some major fluctuations over the last decade. From 2012-17, for example, it experienced steep declines, losing an average of 13.6% annually. For the more than 21,000 firms that are currently active in the U.S. oilfield services industry today, managing market volatility has become a way of life. NXTurn’s OFS Vertical Edition for NetSuite helps streamline the dispatch-to-cash process, manages well activity, and provides a procure-to-pay process. Combined, these functionalities vastly improve the manual, paper-based processes that many OFS companies continue to rely on.
Maximize value across the entire supply chain. NXTurn’s OFS Vertical Edition for NetSuite takes a systematic approach to helping companies find the efficiencies in the services process, and then maximizing that value across the supply chain. The solution’s streamlined dispatch-to-cash process, for example, ensures that any service that’s been assigned will be billed.
Eliminate disconnected, legacy technology systems. Along with their paper-based systems, most are using a variety of different systems to run their businesses, including accounting platforms such as Sage, QuickBooks or SAP. When financial management, revenue management, fixed assets, procurement, order management, billing, inventory management and services delivery don’t “talk” to one another, this disconnected approach is inadequate at best for today’s growing businesses that seek real-time visibility into essential business data.
Integrate key functionalities. Like many industries, the oilfield services sector has historically relied on disparate, legacy systems that don’t talk to one another. This creates data, information and knowledge gaps that have to be “filled in” using manual processes. By integrating all functions into a comprehensive cloud computing suite, NXTurn’s OFS Vertical Edition for NetSuite improves command and control of an OFS business and simplifies field service. This unified operating system empowers employees to view and share one true version of data in real-time, leading to greater collaboration among departments, clear visibility for management, increased productivity across the business and greater revenue.
Gain from unexpected “wins.” For some OFS companies, the biggest “wins” that come from adopting a digital oilfield approach are the ones they weren’t even expecting. For example, NXTurn’s OFS Vertical Edition for NetSuite’s well and lease code database often fulfills an undiscovered need that the OFS company didn’t even know it had. The company that receives a wireline perforating job at a certain well in the Permian, for instance, can gather all of the pertinent information (i.e., revenue generated at that site, its latitude and longitude, etc.) right in its ERP. “The ability to see the history of a particular well site and services performed across divisions is a game-changer for a lot of our OFS customers,” said Joshua Bone, NXTurn’s Professional Services and OFS Manager.

Combined, these advantages can give OFS companies a leg up in a competitive industry where every dollar, minute, and man-hour count. Whether you looking to effectively manage supply inventory, efficiently dispatch technicians and track their activity, streamline invoicing, integrate with field service platforms, or anything in between, NXTurn’s OFS Vertical Edition for NetSuite is designed to help you achieve your business goals.

Read “Driving Revenue and Growth and Streamlining Processes for the Oilfield Services Industry” and view the infographic.

Posted on Fri, November 1, 2019
by NetSuite filed under

Let’s block ads! (Why?)

The NetSuite Blog

Keep Your Asset Management Firm Competitive with Advanced Analytics and Data Sets

September 23, 2019 Microsoft Dynamics CRM

Stay Competitive with the Help of Data Sets and Advanced Analytics

The asset management industry is more competitive than ever, and the most successful firms are taking advantage of unique data in order to stay on top. Staying relevant in the industry means keeping pace with these changes in data analysis.

Of course, asset management firms have been making use of large amounts of data for years. But what sets these new strategies apart is the combination of machine learning and advanced analytics with unique, nontraditional data sets from CRM systems, web traffic, social media, news reports, and other sources. Below, we’ll look at how firms can leverage data sets and advanced analytics to gain an edge over the competition.

More Sophisticated Targeting with Data Sets

With the emergence of technologies such as data lakes, organizations have access to larger amounts of data with lower processing costs. Combined with visual analytics, it’s now possible to gain valuable insights from these large amounts of data.

In the case of asset managers, the standard approach to this sort of data analysis involves looking for obvious sales opportunities. But the most competitive firms are now looking at data sets through a wider lens.

These kinds of data sets — offered by data brokers such as FactSet’s Market Metrics — typically include various financial adviser data such as their purchase history, market share, and information about their broker-dealer. While this data is helpful, it’s not enough to give you a competitive edge, particularly considering your competitors likely have access to identical data.

Improving Decision Making with Machine Learning

With the help of advanced analytics, you can get the most out of data sets. Advanced techniques such as natural language processing can provide investment insight from nontraditional data such as social media, news reports, and even conversational sentiments connected to your products and company management.

By combining these sorts of advanced analytics with data sets — along with human knowledge — it’s possible to analyze events in the news and project how they might influence various holdings. The result is a custom news feed that can help you manage risk and make investment decisions.

Data as Actionable Intelligence

Using data proactively as actionable intelligence is the key to increasing your revenue. Consider the following questions:

Do you have access to accurate, timely data?
Do you aggregate data from various sources, including your CRM and third-party data?
How quickly can you drill down into your data?
Does your data help your sales team?
Is your system easy for employees to use?

Consider the following six examples of how you might use CRM data alongside data sets and advanced analytics for actionable intelligence.

1) Taking Initiative and Improving Customer Satisfaction with Sales Data

Analyzing order data can lead to better and more strategic sales calls. Which advisers have placed the largest orders recently? Can your sales team quickly access this data to follow up with them?

2) Planning Visits Strategically

Visiting advisers takes a lot of time, and it’s important to be strategic. With the help of Power BI, your sales team can schedule visits in the most efficient and profitable way.

3) Marketing Campaign Analysis

Are you tracking the impact of individual marketing campaigns on your sales? With the help of actionable intelligence, you can determine which campaigns have been the most effective and improve the success of future campaigns.

4) Taking Action in Response to Changes

When an adviser places a large redemption order, is this information captured and relayed to the right wholesaler? Do you know why the client placed the redemption order? By identifying these sorts of changes right away, your team can act on them immediately and work to mitigate them.

5) Making Use of Sales Dashboards

Your wholesalers should have access to a dashboard with all their relevant data, both via mobile as well as on their computer. This dashboard should combine both in-house and third-party data. This makes it easier for your team to access the data they need, when they need it.

6) Taking Advantage of Competitive Information

Imagine you learn that a competitor is closing their large-cap equity fund to new investors. Can you take advantage of this information, building an alternative product and offering it to the right customers?

Leverage Your CRM Data Now

Want to learn more about how to leverage your CRM data in combination with data sets and advanced analytics? The experts at AKA Enterprise Solutions can help. Click here to contact us today.

[embedded content]

ABOUT AKA ENTERPRISE SOLUTIONS
AKA specializes in making it easier to do business, simplifying processes and reducing risks. With agility, expertise, and original industry solutions, we embrace projects other technology firms avoid—regardless of their complexity. As a true strategic partner, we help organizations slay the dragons that are keeping them from innovating their way to greatness. Call us at 212-502-3900!

Let’s block ads! (Why?)

CRM Software Blog | Dynamics 365

Intelligent Planning: Boosting Business With Advanced Analytics

August 10, 2019 BI News and Info

“Talent wins games, but teamwork and intelligence wins championships.” –Michael Jordan, NBA Hall of Fame

Basketball, like all sports, requires a strong plan before the game starts. But from the moment of the first tip-off, the plan needs to be assessed and adjusted. The same is true in business.

Every plan must start with analysis of past results. This can be used as the baseline to determine where the business is going and measure success in the future. Past results, as everyone knows, are not always indicative of the future.

Machine learning provides tremendous insight regarding market trends and business drivers. These factors include market propensity, consumer demand, economic factors, weather, and transportation costs. Many companies take these variables into consideration but provide limited or time-consuming analysis. This process limits corporate agility.

IDC learned from a survey in 2018 that 88% of responding organizations were still using spreadsheets as their primary planning vehicle. Most of these organizations were only able to create plans for the entire fiscal year and started the process over again in nine months for the following year.

Path to improved results

For years, the finance organization has heard the hype of analyzing Big Data. Despite 36% growth in this area, according to Dresner Advisory Services, the finance department is the laggard for adoption. The leading reason for that has been ease of use, or lack thereof.

Imagine if machine learning could predict what the most likely numbers should be based on past results. An example of this is “smart alerts for profit & loss.” This embedded capability identifies anomalies that are not correlated to the expected results and notifies the financial analyst. The analyst has the ability to override if necessary. The analyst could also interact with the system verbally. “Show me the variance….” Or “Show me the top 5 factors impacting this result.” In all of these scenarios, it is machine learning enhancing, not replacing, the financial analyst.

Intelligent Planning Intelligent Planning: Boosting Business With Advanced Analytics

How does it work?

Intelligent scenarios are embedded in modern cloud applications to provide the most up-to-date insight by analyzing the actual data being used by the systems of record. This enables users to react to analytic results in the context of their business process or workflow.

Intelligent apps can be developed to modify the embedded machine-learning scenarios and create insight from the harmonized data from various departments. This insight is then integrated into the corporate planning process.

Some practical examples

A luxury manufacturing company in Europe that specializes in windows and skylights determines the probability of failures in their products. These projections are integrated into their planning process to ensure that proper resources, both labor and parts, are in the budget. The company has not only realized cost savings from this process; it has also seen improved customer experience.

A leading retailer relies on machine learning to understand business drivers. This company is constantly monitoring its production data as well as store demographics. These factors are analyzed before, during, and after each quarter. The company cannot afford to wait until the following year to adjust plans. Understanding the propensity of potential customers within a specified radius is a key element of the plan. The faster the planners can determine the effect of business drivers, the more accurately they can adjust the plan. To them, planning is an ongoing process, not an event.

Airline and transportation companies have always had to deal with impending events outside of their control that impact their plans. Fuel is the most obvious. The price of fuel in the future can have a deep impact on profitability and thus the overall business.

Like the manufacturer example, predicting the probability of parts failing could also have a huge impact on costs, not to mention revenue. If the plane, truck, or ship is not available, there will be lost revenue.

Weather is even harder to predict but has an impact on all these industries. Manufacturers in the building supply business have improved their forecast accuracy by integrating weather and housing-start data into their forecasts. Combining this with analysis of regional stores has helped in creating create an actionable plan that improved sales by 10% in the first year.

In summary

The concepts behind machine learning and advanced analytics are not new. Technology has provided an opportunity to empower everyone in the organization. Machine learning, intelligent robotic process automation, and chatbots will provide tremendous efficiency benefits. The real value will come from the improved productivity of the workforce and better business outcomes.

Intelligent planning combines the power of embedded machine learning and analysis tools in the cloud. With these capabilities, today’s business user can accomplish in a fraction of the time what a team of scientists took months to do in the past. The benefit is satisfied customers, happier employees, and increased profits.

This article originally appeared in FP&A Trends and is republished by permission.

Did you know that SAP offers a personalized Business Scenario Recommendations report free of charge? Learn why and how to order one by joining our webinar on September 26^th.

Follow SAP Finance online: @SAPFinance (Twitter) | LinkedIn | Facebook | YouTube

Let’s block ads! (Why?)

Digitalist Magazine

Google makes Advanced Protection Program broadly available, launches Titan Security Keys internationally

August 1, 2019 Big Data

During Google’s Cloud Next 2019 conference in Tokyo this week, the Mountain View tech giant announced a slew of Google Cloud Platform (GCP) updates intended to bolster data, app, and user security. Among the highlights are the Advanced Protection Program’s broad launch and the expanded retail availability of Titan Security Keys, as well as improved anomaly detection in G Suite enterprise deployments and enhanced support for legacy apps in GCP.

“At Google Cloud, we’re always looking to make advanced security easier for enterprises so they can stay focused on their core business,” wrote director of product management Karthik Lakshminarayanan and group product manager Vidya Nagarajan. “Already this year, we’ve worked to strengthen user protection, make threat defense more effective, and streamline security administration through a constant stream of new product releases and enhancements.”

Advanced Protection Program

Google’s Advanced Protection Program — which is designed to prevent cyberattacks against business leaders, politicians, and other high-profile targets — will be available in beta in the coming days for G Suite, GCP, and Cloud Identity customers. Enterprise administrators will gain the option to enroll users most at risk of targeted attacks, such as IT administrators, business executives, and employees in security-sensitive segments like finance and government.

As a refresher, the Advanced Protection Program enforces the use of Google’s aforementioned Titan Security Key (or compatible third-party hardware) and blocks access to third-party accounts not explicitly approved by an admin. Additionally, it enables enhanced scanning of incoming email for phishing attempts, viruses, and malicious attachments.

Titan Security Keys

On the subject of Titan Security Keys, the sets of physical FIDO2 (Fast Identity Online) keys used to authenticate logins over Bluetooth or USB, they’ve hit the Google Store in Canada, France, Japan, and the U.K. roughly a year after launching in the U.S. Bundles ship with a USB key, a Bluetooth Low Energy key, and an adapter for devices with USB Type-C ports.

Above: Google’s Titan Security Keys.

Image Credit: Google

For the uninitiated, FIDO2 is a standard certified by the nonprofit FIDO Alliance that supports public key cryptography and multifactor authentication. When you register a FIDO2 device with an online service, it creates a key pair of an on-device, offline private key and an online public key. During authentication, the device “proves possession” of the private key by prompting you to enter a PIN code or password, supply a fingerprint, or speak into a microphone.

Since 2014, Yubico, Google, NXP, and others have collaborated to develop the Alliance’s standards and protocols, including the new World Wide Web Consortium’s Web Authentication API. (WebAuthn shipped in Chrome 67 and Firefox 60 earlier this year.) Among the services that support them are Dropbox, Facebook, GitHub, Salesforce, Stripe, and Twitter.

Machine Learning in G Suite

At the kickoff of Google Cloud Next this April in San Francisco, Google announced no fewer than 30 security-related upgrades headed to GCP in the coming months. Those were only the start, evidently — beginning today in beta for G Suite Enterprise and G Suite Enterprise for Education customers, admins can opt into automatic anomalous activity notifications in the G Suite alert center. They’re informed by AI models that analyze signals within apps like Google Drive to detect security risks, including data exfiltration and policy violations related to unusual external file sharing and download behavior.

Above: Anomaly detection in G Suite.

Image Credit: Google

The launch builds on Google’s ongoing efforts to block spam, phishing, and malware with sophisticated machine learning techniques. Google in February said that it’s blocking around 100 million additional spam messages every day for Gmail users thanks to its open source AI framework TensorFlow, all while ensuring the share of legitimate mail that inadvertently ends up in spam folders stays below 0.05%.

One-click access to apps

Lastly, Google today said that it’ll start rolling out support for password vaulted apps — i.e., legacy apps that require a username and password to authenticate — to Cloud Identity customers this week, complementing G Suite and Cloud Identity’s ecosystem of single sign-on (SSO) apps that tap identity standards like Security Assertion Markup Language (SAML) and OpenID Connect (OIDC).

“The combination of standards based- and password-vaulted app support will deliver one of the largest app catalogs in the industry, providing seamless one-click access for users and a single point of management, visibility, and control for admins,” wrote Lakshminarayanan and Nagarajan. “These new features will help strengthen protection and securely enable cloud workloads and business processes.”

Let’s block ads! (Why?)

Big Data – VentureBeat

TIBCO and Ping Identity Team Up to Deliver Advanced API Security to Customers

July 4, 2019 TIBCO Spotfire

TIBCO is excited to announce its new partnership with Ping Identity, a fellow Vista Equity Partners portfolio company. As partners, TIBCO and Ping Identity will provide cutting-edge API security solutions to customers.

Both firms are leaders in their respective markets, and this collaboration will help enterprises improve security measures, keep sensitive data private, and remain compliant with data privacy regulations. TIBCO Cloud™ Mashery®, a cloud-native API management platform with advanced API security features, will be integrated with Ping Identity’s PingIntelligence, an AI-driven API security platform.

Attacks on API infrastructures are increasing, as APIs are the foundation of digital businesses. The two offerings complement each other to provide businesses who are looking to drive digital transformation with a seamless security solution, including those in banking, travel, telecommunications, and similar industries that often are targeted by sophisticated security attacks.

TIBCO Cloud Mashery is the first cloud-native API management platform, providing rate limiting, advanced authentication, and parameters to control access by API consumers and detect threats using advanced techniques in order to protect APIs. By integrating PingIntelligence with Mashery, security is enhanced via decoy API deception, AI-driven threat-mitigation, and more.

Learn more about API security from TIBCO and Ping Identity at their breakout sessions during the TIBCO NOW Global Tour in London (September 25-26).

Let’s block ads! (Why?)

The TIBCO Blog

4 Advanced Ways Brands Use Data to Increase Online Sales

May 12, 2019 CRM News and Info

Data is one of the biggest advantages e-commerce brands have over their brick-and-mortar counterparts. Brands that sell products online can glean insights not only from sales figures, but also from impressions, click-through rates, bounce rates, and numerous other data points. They have a constant stream of visitors to help them optimize every image, ad and product description.

However, most brands are just scratching the surface of what they can do with e-commerce data, because they’re focusing exclusively on their own data. Manufacturers that want to maximize sales need to look beyond their own websites and tap into data from retailers that sell their products.

It sounds daunting, but without data from retailers, you’re missing the big picture of how people interact with your products online. As technology continues to evolve, the gap will only grow between brands that leverage the full potential of e-commerce data and those that don’t.

Right now, brands can use data from online retailers to improve their customers’ shopping experience, learn from buying behavior, monitor branding, and optimize their campaigns — all of which help you increase sales.

Following are four advanced tips for using e-commerce data to increase sales.

1. Streamline the Path to Purchase

No matter how good your product page looks on your own website, some consumers prefer buying from their favorite online retailer. Their loyalty to your brand shouldn’t be at odds with perks like these:

Free two-day shipping
Hundreds or thousands of verified reviews and ratings
Rewards programs
The convenience of buying online and picking up in store (BOPIS)

You can fight to keep people on your own website and risk losing sales, or you can embrace the diversity of the e-commerce ecosystem, and let your visitors decide where and how they want to buy your products.

What happens if someone goes to a retailer’s website, and your product is out of stock? Or the price is higher than what you listed on your website? They’ll probably either decide not to buy, or worse — they’ll buy a competing product.

That’s why more and more manufacturers have begun using software that automatically pulls pricing and stock information from their retail partners in real time, so they can display the data on their product pages. This lets consumers choose to buy your products in the way they prefer, while also ensuring that you don’t send them to a dead end.

By reducing the number of clicks and searches it takes to get to their products, these manufacturers have been capitalizing on people’s intent to buy. There’s less opportunity for their visitors to fall through the cracks or change their minds. Since these brands send people to retailers where they can actually purchase their products, they’re increasing dramatically the number of visitors who become customers.

2. Follow the Entire Customer Journey

Say someone clicks your Google ad or opens your email and clicks through to your product page. After doing some research about your product, say that individual decides to buy it. So the shopper hops over to Walmart.com and… what happens next?

Does the shopper buy your product?
Does the shopper ditch your product for a competitor’s?
What else goes into the shopper’s cart?
What complementary products does the shopper purchase?

Without data from online retailers, manufacturers can’t be certain how well — or how poorly — their marketing campaigns are doing. All you can see is what happens in the bubble of your own website, even though consumers might be buying from retailers as a result of your campaigns.

Manufacturers can remove the mystery by using tools that pull data from retailers’ websites. These tools allow you to follow a consumer’s path from your marketing assets to your website, to your retail partners, to checkout — so you actually can see how your campaigns are performing. This helps you focus your efforts on the campaigns that give you the biggest return on investment.

Additionally, some tools dgive manufacturers cart-level data, so you can learn what your customers are buying alongside your products, and how much they’re spending. Are consumers buying your drill with a competitor’s drill bits? Are they buying your home security cameras along with cribs, diapers, and other baby supplies? Insights like these can lead to bundling opportunities or even new product lines, diverting more sales from your competition.

3. Unify Your Brand Across Storefronts

Suppose someone is very interested in your product, but wants to purchase from a retailer to take advantage of a rewards program, or to read reviews. The shopper researches your product for a while on your website and then they goes looking for it on Amazon. Or Target. Or wherever. When the shopper reaches those other product pages, the images may be a little different. Or the name is displayed differently. Or the description isn’t quite right.

These might seem like minor discrepancies, but if there’s doubt that a page is displaying the right product, the consumer is going to click somewhere else. No one wants to pay for a knockoff or get stuck with an outdated model. All of a sudden you’ve lost a sale — not because your product wasn’t what the shopper wanted, but because your retail partner didn’t upload the new branding assets you sent a month earlier. Or — oops — you updated your own branding and forgot to send the new assets to your retail partners.

With the sheer number of products you sell and the many online retailers you work with, brand monitoring might sound like an impossible task — but it doesn’t have to be. Again, many brands today leverage data from online retailers to solve this problem and increase sales.

Using advanced technology, manufacturers can see all the copy, images, videos and other assets that appear alongside their products. More importantly, they can aggregate the data and see how well their retail partners are following their current brand guidelines.

By ensuring that your brand and products look and feel consistent wherever they appear, you help relieve any friction created by old images, incorrect copy and other discrepancies.

4. Prioritize Your Highest-Converting Retailers

Obviously, you don’t want to send people to a retailer that is doing a terrible job representing your product online or whose product page simply isn’t converting.

Maybe there are some abysmal reviews on your product page from people who had a bad experience with the retailer, or who objected to things that had nothing to do with your product. Sending shoppers there doesn’t just lose the sale on that page — it dissuades those consumers from considering you elsewhere.

Or maybe a retailer is promoting your competitors’ products on your product page.

On the other hand, some of your retail partners probably are doing an outstanding job. They have stellar reviews and current branding, and they always promote your relevant add-ons. Most importantly, they make sales.

You can use this information to decide which retail partners are worth investing in and promoting — and who you need to drop. If you have a “where to buy” widget on your website, you can elevate the retailers that convert and demote the ones that don’t. This ensures that you have the best shot at converting visitors who hit your product pages — and ultimately, at increasing sales.

Use Data to See the Big Picture

When you sell multiple products with multiple online retailers, the data you collect from your own website can take you only so far. If you can’t see which retailers have your product in stock, which represent your products the best, or if visitors even convert, you’re losing sales.

Conversion optimization platforms are developing new, more robust data solutions every day, and tech-savvy brands are finding more ways to leverage data and increase sales. In the world of e-commerce, the data revolution is far from over.

Anthony Ferry is CEO of
PriceSpider.

Let’s block ads! (Why?)

CRM Buyer

Persistent Debt Regulation and Advanced Analytics

May 7, 2019 FICO

The Financial Conduct Authority of the United Kingdom (FCA) has recently introduced new rules to avoid long-term indebtedness of credit card holders. As per this regulation, a cardholder is in persistent debt if payments against interest, fees and charges exceed amortisation payments over an 18-month period. The 18-month period is reset if the card balance falls below £200.

Persistent debt is a situation that tends to occur with customers who choose to make minimum payments only. With typical monthly interest rates between 1.5% and 2.5% (18% to 30% p.a.), a monthly minimum payment of 2.5% will systematically lead to a persistent debt situation. Some high-interest products even lead to persistent debt situations when more than the minimum payment is made.

While it seems to be the aim of the FCA to ensure that amortisation exceeds interest and fees, the persistent debt regulation surprisingly does not regulate minimum payments to exceed the interest rate by a factor of two. Instead, the policy requires card issuers to take a number of escalating steps, informing the customer after 18 and 27 months of persistent debt, and eventually going through a forbearance process before month 36, which might include actions like card closure and interest waivers.

My colleague Stacey West has recently blogged about some of the operational implications of the persistent debt regulation. Today, we will look at this topic from a decision analytics perspective.

Decision Options

For new accounts, card issuers can avoid the challenges arising from persistent debt entirely by setting the contractual minimum payment rate at least to double the value of the monthly interest rate. For products which accrue substantial fees, these might need to be considered for the minimum payment as well. In general, affordability checks will need to assess if customers can make sufficient payments avoid a persistent debt situation.

For existing customers, increasing the minimum payment can prevent persistent debt situations, but requires additional considerations. Customers making average payments in excess of the minimum payment and customers at low to medium utilisation levels might easily be able to meet higher minimum payment requirements. However, customers at high utilisation levels with low to minimal payment to balance ratios might not be able to make larger payments – whether or not an issuer increases the minimum payment terms, or the customer is implicitly required to increase payment levels to get out of the persistent debt situation. This also suggests that, for customers who do not have the financial capacity to stay out of a persistent debt situation, the credit limit provided might be too high considering the new regulation.

Persistent Debt Decision Tree Example

Persistent debt decision optimisation Persistent Debt Regulation and Advanced Analytics

Source: FICO Blog

For customers who typically make minimum payments only, this gets us to the following decision options:

Leave the minimum payment as it is and hope that the customer’s payment behaviour changes later or can be influenced by the mandatory persistent debt process. If unsuccessful, this might lead to forbearance measures, card closure or write-offs.
Increase the minimum payment to a level that prevents persistent debt situations. However, this might drive customers with insufficient financial capacity into arrears, eventually leading to account termination and write-off.
Decrease credit limit to a level where an increased minimum payment remains affordable for the customer even at high utilisation. However, this might negatively impact interest revenue and could trigger attrition if the customer wants to maintain the credit line or feels inappropriately treated.

When deciding whether to modify minimum payment and credit limit, the financial impact of the respective outcomes should be considered. If this decision is made at account level rather than at portfolio level, more appropriate decisions can be made, and unnecessary financial impact can be avoided.

How Analytics Can Help

Analytics can help to predict the probabilities of the respective alternative outcomes. For example, analytic models can predict the probability of a customer moving into the regulatory milestones at 18, 27 and 36 months, and hence can help to identify customers who should be contacted ahead of reaching these trigger points.

Analytics can also help to predict how customers are going to react to modified minimum payments and modified credit limits, and how such changes are going to impact revenue, attrition and delinquency levels. In that context, FICO’s Risk and Affordability Decision Suite can help issuers understand the financial capacity of customers, and hence their ability to make payments that get them out of the persistent debt situation.

Decision optimisation can help to balance the draw backs of the alternative outcomes, including revenue impact, losses and attrition. Combining multiple action-effect models into a decision framework, optimisation can simulate portfolio performance in alternative scenarios, and prescribe a treatment strategy that maximises or minimizes a given objective (such as profitability) under chosen constraints (such as attrition goals, revenue targets and operational capacity).

For example, decision optimisation can identify which customers should be subject to modified minimum payment and credit limits. Optimisation can also help to identify which customers in persistent debt should be contacted ahead of the regulatory milestones. In both cases, optimisation would help to understand the financial dynamics and trade-offs between revenue, losses and attrition.

When addressing persistent debt, don’t just look at it as an operational challenge. Treat it as a decision problem and gear up your analytic tools to address it.

Let’s block ads! (Why?)

FICO

AI Weekly: Nuclear fusion, neurology, robotics, and other frontiers advanced by AI

February 1, 2019 Big Data

Stop me if you’ve heard this before: Artificial intelligence (AI) will eliminate jobs, encode biases against ethnicities and genders, and automate war machines. And it might just lead to a third world war.

Those and other dire AI predictions resurface continuously in reports and speeches by analysts, celebrities, and prominent researchers alike — admittedly not without reason. If developed recklessly, without transparency and safeguards, AI stands to amplify humanity’s traits. But that’s the doomsday scenario. In the right hands, AI promises to advance scientific frontiers beyond what was previously possible.

In fact, it already is.

A report this week by The Verge examines TAE Technologies, a 20-year-old AI startup collaborating with Google to develop tools that’ll unlock the key to affordable, efficient fusion energy production. It’s not as radical as it sounds: In 2018, a panel of advisers to the U.S. Department of Energy included AI and machine learning in a list of technologies they believe could “dramatically increase the rate of progress towards a fusion power plant.”

Unrelated — but equally promising — recent work in the field of neuroscience involved a machine learning algorithm trained to decode signals from the human auditory cortex. A study published in Scientific Reports described a system that, with the aid of a vocoder (a synthesizer that produces sounds from an analysis of speech input), translated brain signals from epilepsy patients into intelligible speech. It comes just months after Canadian researchers detailed AI that digitally recreates faces test subjects have seen using electroencephalography (EEG) data.

Earlier this month, a team at the National University of Singapore tapped AI to derive neurological insights of a different kind: the cellular characteristics of various regions in the brain. Fed with data collected from functional magnetic resonance imaging scanners, which measure brain activity by detecting changes associated with blood flow, their model was able to estimate parameters that enabled neuroscientists to infer properties without having to rely on physical probes.

Yet another exciting development this week — this one robotic in nature — came from Columbia Engineering, where scientists created an AI system deployed on an articulated mechanical arm that, without foreknowledge of itself or its surroundings, produced a self-simulation it used to adapt to different situations and undertake unfamiliar tasks. Incredibly, the self-model was accurate to within four centimeters of the real-world arm, and directed the robot to grasp objects with 100 percent success.

“Philosophers, psychologists, and cognitive scientists have been pondering the nature self-awareness for millennia, but have made relatively little progress,” Hod Lipson, professor of mechanical engineering at Columbia University and director of Columbia Engineering’s Creative Machines lab, where the study was performed, said in a statement. “We still cloak our lack of understanding with subjective terms like ‘canvas of reality,’ but robots now force us to translate these vague notions into concrete algorithms and mechanisms.”

Algorithms and mechanisms, indeed. With any luck, it’s AI systems like these that will help realize realize the benefits analysts at the McKinsey Global Institute predict: a 1.2 percent increase in gross domestic product growth (GDP) for the next 10 years, and an additional 20 to 25 percent in net economic benefits — $ 13 trillion globally — in the next year alone. Here’s hoping.

For AI coverage, send news tips to Khari Johnson and Kyle Wiggers — and be sure to bookmark our AI Channel.

Thanks for reading,

Kyle Wiggers
AI Staff Writer

P.S. Please enjoy this video outlying a proposed 20-year roadmap for AI research from the Computing Community Consortium and National Science Foundation.

From VB

AI Weekly: Nuclear fusion, neurology, robotics, and other frontiers advanced by AI

Above: Google Assistant

Image Credit: Khari Johnson / VentureBeat

Google releases dataset to help AI systems spot fake audio recordings

In an effort to train systems to determine if an audio recording is real, Google has released a dataset of phrases spoken by its deep learning TTS models.

Read the full story

Dying pigs, AI, and the future of sound technology

The power behind a high-tech solution to save piglets — and many other AI applications — is durable IP67 waterproof microphones from Vesper.

Read the full story

Above: An illustration of how fingerprints are used to match audio.

Image Credit: Amazon

Why Alexa usually won’t respond when someone says ‘Alexa’ on TV

If all is going according to plan, Amazon’s Alexa won’t respond when she hears her name on a television broadcast. Here’s why.

Read the full story

Hanson Robotics debuts Little Sophia, a robot companion that teaches kids to code

Hanson’s new miniature robotic companion, Little Sophia, aims to teach kids the fundamentals of programming, science, and math.

Read the full story

iRobot unveils Terra, a Roomba lawn mower

iRobot has unveiled the Terra robot lawn mower. The Roomba for your lawn will be available in Germany and as part of a U.S. beta program later this year.

Read the full story

IBM releases Diversity in Faces, a dataset of over 1 million annotations to help reduce facial recognition bias

IBM today released Diversity in Faces (DiF), a dataset of over 1 million annotations that aims to reduce bias in facial recognition systems.

Read the full story

Beyond VB

We analyzed 16,625 papers to figure out where AI is headed next

Our study of 25 years of artificial-intelligence research suggests the era of deep learning is coming to an end. Almost everything you hear about artificial intelligence today is thanks to deep learning. This category of algorithms works by using statistics to find patterns in data, and it has proved immensely powerful in mimicking human skills such as our ability to see and hear. (via MIT Technology Review)

Read the full story

Engineers translate brain signals directly into speech

In a scientific first, neuroengineers have created a system that translates thought into intelligible, recognizable speech. This breakthrough, which harnesses the power of speech synthesizers and artificial intelligence, could lead to new ways for computers to communicate directly with the brain. (via Science Daily)

Read the full story

Learning Math For Machine Learning And Artificial Intelligence Programming

Last year, I started writing about my experiences taking courses on machine learning and artificial intelligence. One of the big, unexpected problems I ran into was calculus and linear algebra. I’ve found that many online courses say you don’t need much mathematics fundamentals to be a programmer, but inevitably, even in beginner courses, the underlying math was important to understand what was going on. (via Forbes)

Read the full story

U.S., China take the lead in race for artificial intelligence: U.N.

China and the United States are ahead of the global competition to dominate artificial intelligence (AI), according to a study by the U.N. World Intellectual Property Organization (WIPO) published on Thursday. (via Reuters)

Read the full story

Let’s block ads! (Why?)

Big Data – VentureBeat

Volvo and Luminar demo advanced lidar tech that gives autonomous cars detailed view of pedestrian movements

November 27, 2018 Big Data

Volvo and and autonomous vehicle sensor company Luminar gave a glimpse into the future of autonomous cars at the Automobility LA trade show in Los Angeles, after they demoed advanced technology that helps automobiles detect detailed human movement at distance and speed.

Luminar, for the uninitiated, is a San Francisco-based lidar startup that emerged from stealth in 2017 with $ 36 million in funding. Then in June this year, the company announced a closer tie-up with Volvo to provide lidar for its burgeoning self-driving vehicle efforts, while Volvo took a stake in Luminar via an investment from its Volvo Cars Tech Fund.

The lidar touch

For autonomous cars and trucks to traverse busy thoroughfares at high speeds, they must be able to identify and understand their environment to avoid collisions, a challenge that becomes even more difficult when you factor in conditions such as poor lighting and bad weather. Lidar technology surveys the environment using laser-powered light, and Luminar’s technology promises a significantly longer range and higher resolution imagery than others on the market.

Luminar actually has several OEM (original equipment manufacturer) partnerships in place, including one with Toyota it entered into last year, but it’s the Volvo collaboration that seems to be getting more of the limelight so far. Indeed, at the time of the duo’s partnership announcement in June, Luminar announced a new software suite which it called a “perception development” kit, which automates the process of annotating data from its lidar sensors. So, for example, if a black car ahead has stalled, Luminar’s lidars can not only detect that, but label that detection accordingly.

Volvo was the first OEM to take advantage of this new software.

Volvo and Luminar demo advanced lidar tech that gives autonomous cars detailed view of pedestrian movements

Above: A demonstration of Luminar’s perception development kit.

Image Credit: Luminar

At the Automobility LA trade show today, Volvo and Luminar announced further developments around its perception technology, which could have significant ramifications for the inherent safety of self-driving cars.

“Pose estimation,” as it’s known, is a facet of computer vision that tries to understand the position of various points in an object, such as the arms and legs of a human being. In the case of Volvo’s R&D team, Luminar’s lidar has enabled them to demonstrate how autonomous cars can understand a pedestrian’s body language. This, in turn, can help machines predict behavior and intention, for example a person walking by the side of a road waiting to cross.

Above: Pose estimation in Luminar’s lidars

Such technology could be used to identify that a pedestrian is on their phone, for example, and thus determine that their attention may not be as focused on the road as it should be. It’s just one more visual cue an autonomous car can use to make decisions, such as whether to slow down.

“Autonomous technology will take driving safely to a new level, beyond human limitations,” noted Henrik Green, senior vice president for research and development at Volvo Cars. “This promise to improve safety is why Volvo Cars wants to be a leader in autonomous driving. Ultimately, the technology will also create new benefits for our customers and society as a whole. Luminar shares our ambition in making those benefits a reality, and this new perception technology is an important next step in that process.”

In the fast lane

Today’s news comes hot on the heels of a number of Volvo announcements in the autonomous automotive sphere. A couple of months back, the company announced a new concept electric vehicle to illustrate how it envisions the car of the future, which positions the vehicle more as a mini living space where you can work, sleep, and be entertained on the road.

[embedded content]

Last week Volvo revealed that its first commercial self-driving trucks will be used in mining to transport limestone along five kilometers of roads and tunnels, representing a milestone moment for autonomous vehicles in industry.

The lidar market is estimated to be a $ 800 million industry in 2018, and this is expected to rise sharply to $ 1.8 billion by 2023. And that is why we’ve seen significant investment poured into lidar technology, with the likes of San Francisco-based AEye’s recently raising $ 40 million for a sensor that meshes camera and lidar data, while others in the space such as Israel’s Innoviz Technologies have also been raising big bucks.

The fledgling autonomous vehicle industry has already experienced its first fatal accident involving a pedestrian, the cause of which was apparently a confused “perception system” that was slow to figure out what the human “object” on the road was, or predict its path. Central to fixing these so-called “edge cases” will be improved technology, including lidar, which will be necessary to take self-driving cars from “mostly” safe to “absolutely” safe.

Let’s block ads! (Why?)

Big Data – VentureBeat

Tag Archives: Advanced

5 Ways Oilfield Services Companies Can Leverage Advanced Technology

Stay Competitive with the Help of Data Sets and Advanced Analytics

More Sophisticated Targeting with Data Sets

Improving Decision Making with Machine Learning

Data as Actionable Intelligence

1) Taking Initiative and Improving Customer Satisfaction with Sales Data

2) Planning Visits Strategically

3) Marketing Campaign Analysis

4) Taking Action in Response to Changes

5) Making Use of Sales Dashboards

6) Taking Advantage of Competitive Information

Leverage Your CRM Data Now

Path to improved results

How does it work?

Some practical examples

In summary

Advanced Protection Program

Titan Security Keys

Machine Learning in G Suite

One-click access to apps

1. Streamline the Path to Purchase

2. Follow the Entire Customer Journey

3. Unify Your Brand Across Storefronts

4. Prioritize Your Highest-Converting Retailers

Use Data to See the Big Picture

Decision Options

How Analytics Can Help

From VB

Beyond VB

The lidar touch

In the fast lane

Recent Posts

Categories

Archives