This hard drive offers 1TB of storage for photos and an app that lets you edit on the go

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For anyone who suffers from a long wait while attempting to upload large video and photo files onto their computer, there’s a new Kickstarter for a portable hard drive that says it can back up your files and offer faster on-the-go file uploading services you can use right after taking photos. The company suggests you can do away with bringing your laptop to outdoor photo shoots and instead use its mobile app for editing files and uploading to Dropbox.

The Kickstarter is for a device called the Gnarbox 2.0, which lets you insert your SD card into the hard drive and use it as an additional backup tool. It’s an upgrade to its predecessor, the Gnarbox 1.0, which already offered a mobile large file storage and uploading solution also through…

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How Comcast’s Xfinity Home is using Analytics and more to Drive Business Decisions

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xfinity home

By: Comcast’s Shuvankar Roy, vice president, Xfinity Home and Neeraj Grover, director of business analytics and reporting, Xfinity Home

Q: Why are analytics right to use to drive business decisions?

A: Analytics can help to identify the actual pain points in many parts of the business, including the customer journey. By using analytics, it’s easy to prioritize initiatives and avoid making decisions based on hypothesis, with no evidence to back up your work. Data offers significant insights into your business and can help you to course correct, as needed. Additionally, data can help you to define goals, make forecasts based on trends, patterns or the season.

To give an example, we here at Comcast are hyper-focused on customer satisfaction. For us, it is imperative to gain insights into our customers’ interactions with our front-line employees, such as technicians and customer service representatives, to help serve them better. Measuring the net promoter score or NPS at all levels including transactional, products, and employees help us to identify pain points that customers may experience from our service and products. Without these insights, we would not be able to improve or change processes and better serve our customers at every level.

Q: What type of analytics are essential?

A: To a large extent it depends on the maturity of the organization. To start with, you need Business Intelligence tools to identify and measure leading and lagging indicators. With this level of analysis, you will be able to understand what is driving an uptick and what may be causing a downward spiral.With Business Intelligence, your organization will be able to start predicting what is happening and provide corrections as needed.

As the organization matures and starts collecting more and better quality data, consider digging deeper beneath the numbers by using machine learning algorithms. These can provide further insights into top factors impacting your KPIs and help come up with a few “if-then” solutions that may help improve your metrics. We recommend testing each solution separately to see if the outcome actually enhances the indicators you want to influence.  The tests help foray from predictive into prescriptive analytics.

A particular type of Analytics can also be used for specific applications. For instance, Artificial Intelligence when paired with chatbots can offer your customers immediate access to assistance, and the text analytics can also help provide insights into areas that may need improvement.

Adoption of technology that leverages analytics to solve issues could become the critical differentiating factor to improve service delivery.

Q: With so much data/analytics that exist within a company, what are some best practices to narrow down the data that matter the most?

A: We see three factors of success for any projects: rely on extensive domain knowledge, be very skeptical of visible past trends and don’t  overanalyze,

Providing enough domain knowledge at each step of the way is key to identifying the most relevant data for any analytics study. The structure that works the best for us is having the analytics and business intelligence teams work carefully (or at times embedded) within the business units. This ensures that there is participation from subject matter experts to provide real-time feedback as insights are provided. The input and validation from SMEs ensure that the right data elements are being considered.

The other factor is to be very skeptical of apparent past trends as market conditions evolve. Lastly, avoid over analysis. In some cases getting 80 percent, accurate data is sufficient to understand directional and correct patterns which can help you make timely decisions. Avoid the pitfall of ensuring 100 percent accuracy as you may miss the opportunity to course correct in time.

Q: What type of business decisions can be made based on analytics?

A: Many decisions can be made based on analytics, but it’s important to look at the whole picture such as market conditions and what’s going on in the world. As you begin to make decisions based on analytics keep in mind these key points:

Analytics may be wrong sometimes as correlation is different from causation so take immediate corrective actions when you realize the change that was implemented is not working. Don’t be afraid to pivot and move on to another solution.

Prioritize analytics initiatives based on business goals. You can get a lot of data, and there may be many areas that need to be fixed, but you can’t do it all so narrow in on the few that will make the most significant impact to your business and go from there.

At times, be sure to complement data with other approaches. Sometimes it’s essential to conduct a few focus groups or review processes to find the triggers leading to the lagging data.

Q: Can you share an example where you made some critical business decisions based on the analytics?

A: Losing customers or churn is a measure that is key to most businesses. A while back our team leveraged decision trees and other machine learning algorithms to predict the type of customers that may have a high propensity to churn, and we identified key factors that led to it. The outcome of the machine learning algorithms identified customer engagement – the lack of activity and usage with the service – as the most impactful predictor of customer churn.

The importance of this factor led our teams to dig deeper into customers’ engagement with their services, their tenure, the services they have subscribed, and their preferred channels of engagement with us. This led to further insights into how our customers engage with each product and what service delivery steps could help drive customers to have a better experience. Ultimately, we found that customers who participate with or used the product(s) regularly led to more satisfaction with their service, which lowered churn.

Xfinity Home touch-screen

As the IoT space expands with more and more devices in a secured and connected home, the value for AI to help further improve customer service will be imperative.  Machine learning supported chatbots will become more sophisticated as they can scan for any system issues or other similar customer issues and quickly help to resolve and respond to customers. This level of customer care and service can be provided at an increased scale and response time will be quicker without adding to the cost of operations –the cost to provide the best customer service may even decrease.

The post How Comcast’s Xfinity Home is using Analytics and more to Drive Business Decisions appeared first on ReadWrite.

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Google Drive will help you figure out who needs access to a file

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One of the tricky things about coordinating an email chain is always making sure everyone has access to the right files, but Google said it's making that easier in Drive. The next time you go to share a document or other file through email or a calen…
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Drivers May Soon Have to Pay to Drive in Traffic-Choked Manhattan. Other Cities May Be Next.

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Honking. Pollution. Stunted economic growth.

Traffic sucks. It hinders every major metropolitan center worldwide. Now New York City, the planet’s second-most-congested city, might have a solution. If they do it right, it might catch on.

The idea is simple: congestion pricing. Want to drive in a busy part of the city? Pay a fee.

This is a win-win. It would discourage tight-fisted drivers from going there  or maybe from driving at all — which would reduce emissions. For drivers that do pay, the city can spend the money it collects on projects that reduce congestion. In New York, for example, that would mean using the funds to improve the city’s subway and bus systems, which, let me tell you, are in dire need of a cash injection.

There are a few cities worldwide that already have congestion pricing systems in place. But it hasn’t caught on yet in the U.S., in part because it’s not all that appealing to the average voter.

“It is almost universally acknowledged among transportation planners that congestion pricing is the best way, and perhaps the only way, to significantly reduce urban traffic congestion,” wrote a team of urban planners from UCLA in a 2016 issue of Access Magazine. “Politically, however, congestion pricing has always been a tough sell. Most drivers don’t want to pay for roads that are currently free, and most elected officials—aware that drivers are voters—don’t support congestion pricing.”

In NYC, the idea has been around for more than a decade. In 2007, then-Mayor Michael Bloomberg proposed the idea of congestion pricing in New York. His proposal never made it past the state assembly. Last summer, New York Governor Andrew Cuomo revisited the concept, telling The New York Times it was “an idea whose time has come.”

In October 2017, Cuomo assembled a state task force, Fix NYC, to draft a proposal on how congestion pricing would best work in New York. In that proposal, the group suggested charging vehicles a set price to enter busy areas of Manhattan depending on the vehicle’s size and intended use: $ 11.52 for passenger cars, $ 25.34 for trucks, and $ 2 to $ 5 per ride for taxis and for-hire vehicles.

According to the Fix NYC proposal, the city could implement the fee for taxis and for-hire vehicles within a year, with truck and car fees following in 2020.

Fix NYC delivered the proposal to state lawmakers in January. Those lawmakers are currently working to deliver the state’s new budget by April 1, and they could decide to include congestion pricing within it.

congestion pricing
Image Credit: Assy / Pixabay

If it doesn’t happen in NYC, other cities in the U.S. may give congestion pricing a go. Portland, Oregon, is also exploring congestion pricing, and a group in Los Angeles is promoting the idea as well (San Francisco tried, and failed, to pass its own congestion pricing system in 2010).

If New York does decide to take a chance on congestion pricing, it could start a domino effect across the nation.

It seems as though the biggest hurdle will be simply implementing the system. That’s at least what happened in Stockholm. “The closer you get to implementation, the more the drawbacks stand out,” Stockhold transportation director Jonas Eliasson told StreetBlog NYC. “If you survive this valley of political death, and people actually see the benefits, and also realize that, in addition to the benefits, it’s actually not as bad as you thought — it’s not so hard adapting to this — then support starts going up again.”

His advice to New York? Just do it.

 

The post Drivers May Soon Have to Pay to Drive in Traffic-Choked Manhattan. Other Cities May Be Next. appeared first on Futurism.

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Video: As Dropbox IPO goes live, should you consider switching to iCloud Drive?

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As Dropbox goes public, AppleInsider examines if it is time to consider whether you should stay put with iCloud Drive, or fully embrace Dropbox for your cloud storage needs.
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9to5Toys Lunch Break: $300 off iMacs, Infinity Blade for iOS $1/ea., SanDisk 128GB Metal Flash Drive $22, more

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Keep up with the best gear and deals on the web by signing up for the 9to5Toys Newsletter. Also, be sure to check us out on: TwitterRSS FeedFacebookGoogle+ and Safari push notifications. more…

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Pluto TV Teams Up With SpotX To Drive Advertising Demand

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MMW can now confirm that SpotX, a video advertising and monetization platform, today extended their strategic partnership with Pluto TV, the leading free OTT television service in America. As part of the renewed collaboration, SpotX will match media buyers to Pluto TV’s premium inventory and power the programmatic monetization of Pluto TV’s free, over-the-top (OTT) content.

So how does it all work?

Pluto TV offers over 100 free live channels and on-demand programming and has partnerships with TV networks, movie studios, publishers, and digital media companies. The company currently entertains millions of monthly unique users who tune-in to watch premium news, TV shows, movies, sports, live events, cartoons, and trending digital series on a free, ad-supported service.

The partnership opens up more opportunities for media buyers who have been increasingly trying to break into the OTT game. According to SpotX’s recent research report entitled, “TV is Total Video: Predicting OTT and the Future of Video Advertising,”

Conducted by S&P Global Market Intelligence’s Kagan, 67 percent of advertisers expect to spend between 21 and 40 percent of their ad dollars on OTT platforms within the next 24 months. SpotX’s Demand Facilitation services will bring a diverse group of buyers to Pluto TV including auto, retail, CPG, and entertainment, adding more variety on Pluto TV, enhancing the viewer experience, and enabling more advertisers to reach untapped OTT audiences.

In recent months, Pluto TV has been heavily focused on its advertising technology stack, which employs server-side ad insertion (SSAI) technology to combine content and ads into a single video stream that can be easily distributed across all devices and platforms.

We’re told that Pluto TV also runs dynamically-generated videos that promote upcoming content by automatically pulling clips and programming information together into promotional spots that run on-air, driving viewership. Its ad platform integrates with any industry-standard (VAST) ad-serving system without any additional customizations.

“As viewership continues to grow in OTT, many advertisers are unsure about what to buy in the space, or how to effectively buy it,” said Mike Shehan, co-founder and CEO at SpotX. “Pluto TV offers a seamless, TV-like viewer experience, and we’re excited to help create a more powerful offering by connecting advertisers with their inventory.”

The post Pluto TV Teams Up With SpotX To Drive Advertising Demand appeared first on Mobile Marketing Watch.


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OWC ships Mercury Elite Pro Quad four-bay external drive with USB-C connectivity

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Aftermarket Mac upgrade vendor OWC has launched the Mercury Elite Pro Quad, an external hard drive enclosure which connects via USB-C that is capable of offering up to 48 terabytes of RAID storage when all four of its bays are occupied with drives.
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App Marketers Turn AI and Machine Learning To Drive Growth

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Did you know that 80 percent of users churn within three months of downloading an app? That’s because most apps are marketed to the masses and not necessarily to the right customers.

Oftentimes, the goal of app marketing is to reach as many consumers as possible with the hopes of recruiting en masse and converting at better-than-average ratios. But part of the challenge for marketers is that many of today’s strategies are driven by metrics that don’t link to advanced user targeting and growth.

More specifically, app marketers aren’t using available data strategically to deliver productive user experiences that ultimately drive greater business profitability.

Now more than ever, marketers must shift from tracking traditional vanity metrics to measuring the very things that contribute to retention and growth. More and more, successful companies are investing in customer-centric metrics such as CLV (customer lifetime value) to gain intelligent, consumer-centered insights that not only identify the most valuable customers but also key behaviors and preferences to continually improve consumers’ experiences and journey.

Next-generation marketing and CX are about identifying and engaging valuable consumers

CLV is more important than apps in isolation. It helps apps and other touch points work together to deliver value-added, cohesive experiences.

CLV measures the value a consumer represents to the business across all interactions over their lifetime, not just a single transaction or touch point. That is ultimately the definition of customer experience. It is the sum of all moments a customer has with your brand throughout their life cycle. Marketing and customer engagement is now a cross-functional mandate.

Not all app users are the right users. If you use the Pareto Principle, you can assume that 80 percent of business value is attributed to 20 percent of your active consumers. While these percentages aren’t by any means a standard, they do emphasize the need to identify and cultivate the important customers who drive your business.

Instead of casting a wide net and attracting as many users as possible in the hopes of retaining a reasonably active base, CLV tied to artificial intelligence (AI) and machine learning focuses marketers and also developers on targeted engagement and growth. The idea is to drive profit by investing in more value-added user experiences and personalized offers. Doing so intentionally cultivates meaningful relationships with key customers.

Next-generation customer engagement is about cross-functional collaboration and data sharing

Unfortunately, customer experience today is largely siloed. Marketing, mobile, in-store, e-commerce, digital and so on are not collaborating nor operating against the same customer and market data. But that’s all about to change with the proliferation of AI and machine learning tied to smart CLV initiatives.

When the goal is to deliver targeted and integrated experiences, not just in-app, but across each touch point and the life cycle overall, companies create a truly customer-centric approach. AI then helps brands get a more complete, shared view and understanding of customer behaviors and expectations.

Additionally, AI-driven customer-centricity fosters cross-functional collaboration and data sharing that, by design, boosts customer experiences, along with CLV and business growth.

Identify highest-value customers and deliver targeted experiences

AI/machine learning platforms offer intelligent insights when pointed in the right direction. Successful brands study how much revenue highest-value customers drive over their lifetime and how much it costs to manage those relationships. And they examine CLV across all channels to get a holistic view of high-value behavior in all interactions. When the system can analyze important traits of high-value users, it can learn how to optimize CLV.

For example, to reach potential high-value customers, AI/machine learning uses data from existing high-value customers to optimize campaigns and touch points. In a study by Bain aimed at retail banking, it was found that it costs banks $ 4 every time a customer calls or visits. However, if consumers can complete the transaction via an app, it costs only 10 cents.

The key is to deliver capabilities in ways that consumers prefer and appreciate. Imagine how much AI and machine learning could additionally uncover when tasked with identifying friction points and new opportunities.

AI and CLV call for a new customer-centric playbook

You’ve probably heard time and time again that it costs more to acquire a new customer than to retain one. Brands that are winning prioritize CLV and AI and are drafting the playbook as they go. They:

  • develop a customer-centric mindset.
  • open doors between silos around in-store, digital and mobile so teams can focus on one clear business goal, rather than individual metrics (such as engagement or clicks).
  • align customer-facing groups to a business outcome such as CLV and promote cross-functional collaboration and data sharing to assemble a holistic view of the customer across all touch points.
  • understand who their highest-value customers are, how much revenue they drive over their lifetime and how much it costs to manage the relationship — across all channels.
  • focus on measuring and communicating clear business goals rather than individual or vanity metrics.

AI and machine learning improve both by using existing data without cognitive bias. The more the system learns, the more it optimizes.

In the end, not all customers are created equal. By identifying those who drive value, how and why, you can learn how to design and deliver personalized value to them and enhance customer engagement and experiences to grow your business now and over time.

The post App Marketers Turn AI and Machine Learning To Drive Growth appeared first on ReadWrite.

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Keep Your Mac Running Smoothly with Drive Genius 5 [Deals Hub]

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Believe it or not, your beautiful Mac requires regular maintenance, much like any other machine that has ever existed. Knowing the exact and detailed ways to get your Mac up-and-running can get overwhelming with the amount of knowledge you need, as well as the amount of applications you have to open. Now you can keep your Mac in the utmost tip-top shape with a Standard License from Drive Genius 5. It’s on sale now in the iPhoneHacks Deals Hub. Continue reading
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