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

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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.

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Strategy Analytics crowns Sony Xperia XZ2, XZ2 Compact as battery life kings

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Strategy Analytics normally studies the performance of various manufacturers, but at Sony’s request, the analysts took on a new role – testing the battery life of “leading premium smartphones”. Here’s the competition: Sony Xperia XZ2 • Sony Xperia XZ2 Compact • LG G6 Apple iPhone X • Huawei P10 • Samsung Galaxy S9 • Oppo R11 And here are the results. The Xperia XZ2 came out on top followed by the XZ2 Compact. The LG G6 narrowly beat the iPhone X for third. Here’s how the tests were performed: phones were used for 16 hours a day, including calls, texts,…

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HPE’s Aruba launches AI analytics for smart digital workspaces

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Hewlett Packard Enterprise (HPE) company Aruba Networks has launched NetInsight, an AI-powered analytics tool that’s designed to help organisations set up, monitor, and manage IoT-based smart workspaces.

“Monitor, analyse, predict”

IoT networks can be complex, and integrating kit from different suppliers can be a challenge. Managing and maintaining these systems can also be a problem.

That’s where NetInsight comes in, according to the company. The new system has been designed to monitor IoT devices automatically, and to deliver predictive analytics about how well they’re functioning.

Aruba claims NetInsight will identify performance lags, suggest what might be causing them, and recommend configuration changes. The aim is to find and fix these problems before they impact on workflows or system performance within networks and organisations.

Improving the user experience

IoT implementations are often deployed in dynamic and unpredictable environments. But while these have evolved over time, many of the tools that IT professionals use to manage them have changed very little, and lack focus on the user experience.

However, IT managers increasingly demand tools that give them better insight into both problems and fixes at a glance. This is why NetInsight has been designed within the Aruba ‘mobile first’ approach, which was conceived to deliver information that is both accessible and actionable, said the company.

One customer is the University of Washington, which has to manage more than 12,000 Wi-Fi access points and over 150,000 devices on its campuses, hospitals, and clinics. In that environment, the network management challenges of size, complexity, unpredictable usage patterns, and performance-sensitive applications are significant, according to David Morton, director of Networks and Telecommunications at the University.

“Using Aruba NetInsight, we have access to network data with flow visualisations and actionable analytics,” he said. “That helps us make critical decisions about where expanded and new coverage is needed – such as outdoor Wi-Fi for new constructions. We can also validate the ‘before and after’ impacts of network changes, so we can proactively deliver the best possible user experience.”

• Aruba has also announced an expansion of its Aruba Edge Technology Partner Programme to include new partners CBRE, Deloitte, and global furniture maker Herman Miller.

Internet of Business says

As the IoT spreads, analytics and management tools are a fast-growing area. And as ever with connected programmes, the subtext is always data – not just data about users, but also about how the system itself is performing.

Read more: SAS, Cisco claim first platform for IoT analytics at the edge

 

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New Research Points to the Power of Predictive Analytics

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Mobileum, a leader in roaming and traveler data analytics, has announced new research aimed at telecom service providers looking to understand key trends and identify new opportunities to transform and monetize roaming business in an increasingly disrupted marketplace.

A key finding is that initiatives in customer behavioral analytics, and new predictive models, can deliver accurate conclusions of roaming customers, with clear graphical representation of results. An analytics based approach enables operators to take immediate action to improve the customer experience for travelers.

A joint effort by the industry recognized roaming experts, Mobileum, and Juniper Research, the report explores the challenges currently facing operators, including the rise of OTT messaging, social media substitution and the silent roamer problem and offers strategic recommendations to enable operators to monetize roaming traffic. A key finding is that developments in data analytics can help increase ARPU and reduce churn, through new ways to analyze and present data and swiftly act on the findings to improve both network efficiency and end user experience.

By using analytics to detect customer context, operators can offer targeted bundles, tailored to the roamer’s individual sales history and usage. Service bundling increases overall revenues by reducing the silent roamer proportion. By switching to a business model predicated on big data analytics, operators can thereby maximize their future revenues. It involves a sea change in how businesses operate, but significant benefits await if operators take the right approach.

“With the increasing amounts of both network and traveler data available for interpretation and analysis, operators who ignore trends face customer dissatisfaction or customers simply turning off data altogether. Roaming is one area where they can immediately improve the customer experience and provide a mutually beneficial roaming relationship with their subscribers,” said Tim Moran SVP Product and Offering at Mobileum.

To access the full report, click here.

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First Look: oneAudience Integrates Mobile-Driven Audiences in Adobe Analytics Cloud

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oneAudience, a first-party mobile data provider that specializes in mobile app usage data, confirmed ahead of the weekend an integration with Adobe Audience Manager, part of Adobe Analytics Cloud.

The mobile audiences now available in the Adobe Audience Manager marketplace enables revenue-driven marketers to leverage mobile insights across all major industries such as automotive, consumer packaged goods, financial services, healthcare and retail.

As a Premier level partner in the Adobe Exchange partner program, oneAudience enhances brand and agency audience data by understanding consumers at the individual level through their mobile device behaviors and app usage, providing audience attributes and segmentation for increased consumer engagement, brand awareness and sales.

“The combined power of oneAudience and Adobe Audience Manager enables marketers to gain additional mobile insights by layering together their data and our mobile-first audiences,” said Ari Saposh, VP of Data at oneAudience. “Our mobile-first collection methodology captures behaviors and insights, such as what apps an individual is downloading or using, to enable a better understanding of today’s on-the-go consumers.”

According to the formal announcement, mobile is hailed as being the most personal, relevant channel used by consumers. According to Facebook, over 88 percent of its revenue was driven by mobile alone – a number that is increasing year over year.

“The combination of Adobe and oneAudience gives brands and agencies access to additional mobile insights, such as data on apps consumers are downloading and using,” said Cody Crnkovich, head of platform partners and strategy at Adobe. “Joint customers can now leverage the unique mobile ID management capabilities in Adobe Audience Manager and the mobile data from oneAudience to help maximize reach and relevance.”

Having oneAudience mobile audiences available in Adobe Audience Manager marketplace lets marketers “apply real-time mobile insights and build truly personalized campaigns,” the emailed statement concludes.

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Here are the New York Times and Observer stories that pushed Facebook to suspend Trump’s data analytics company

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Cambridge Analytica had profile information for some 50 million Facebook users, according to reports.

Now we know what prompted Facebook to suspend Cambridge Analytica, the data analytics firm the Trump campaign used during the 2016 election: The company was trying to get ahead of big stories about Cambridge in both The New York Times and the Observer.

Both stories hit Saturday morning, and claim that Cambridge Analytica had amassed a data trove with information from more than 50 million Facebook users it collected without their permission.

That’s a much larger number than Facebook reported last night, when it said that just 270,000 people “gave their consent” to hand over data to a third party researcher and University of Cambridge professor named Dr. Aleksandr Kogan.

How does that work? Back in 2015, Kogan, who also worked at a company called Global Science Research, created an app called “thisisyourdigitallife,” which used Facebook’s login feature that lets people join a third party app with their Facebook account, instead of creating a new app-specific account. Some 270,000 people logged into the app that way, granting Kogan permission under Facebook’s rules to scrape some of their profile data, including their identity and things that they’ve “liked.”

But that permission also gave Kogan access to data about the friend networks of these 270,000 people, which amounted to tens of millions of Facebook users, according to The Times. Kogan then shared that data with Cambridge Analytica, which was “building psychographic profiles” on American voters in order to target them with ads.

Here’s a key graph from the Times’s story:

“[Kogan] ultimately provided over 50 million raw profiles to the firm, Mr. Wylie said, a number confirmed by a company email and a former colleague. Of those, roughly 30 million contained enough information, including places of residence, that the company could match users to other records and build psychographic profiles. Only about 270,000 users — those who participated in the survey — had consented to having their data harvested.”

Kogan and Cambridge Analytica both certified to Facebook that it had destroyed this data back in 2015, but “copies of the data still remain beyond Facebook’s control,” The New York Times is reporting.

Cambridge Analytica claims that the data has been deleted, and that it had no idea it was collected in ways that violated Facebook’s terms of service.

“When it subsequently became clear that the data had not been obtained by GSR in line with Facebook’s terms of service, Cambridge Analytica deleted all data received from GSR,” a company spokesperson said in a statement sent to Recode. “We worked with Facebook over this period to ensure that they were satisfied that we had not knowingly breached any of Facebook’s terms of service and also provided a signed statement to confirm that all Facebook data and their derivatives had been deleted.”

“No data from GSR was used by Cambridge Analytica as part of the services it provided to the Donald Trump 2016 presidential campaign,” the statement added.

Facebook, for its part, is adamant that the company did nothing wrong — the data was collected appropriately under its terms of service, it was then abused by the collector. Facebook’s Chief Security Officer Alex Stamos said it bluntly on Twitter Saturday morning: “[Kogan] lied to those users and he lied to Facebook about what he was using the data for.”

It’s an illuminating look at how Cambridge Analytica and the Trump campaign “won” Facebook during the campaign — Trump’s Facebook strategy has been identified as a key factor in his surprising victory.

But the stories also leave a number of unanswered questions:

  • How helpful was the data in targeting U.S. voters? How much of a difference did it make?
  • Will Facebook change its policies to further limit the data that third parties can collect from its users?
  • How much of the data is still out there online, and is it being used by the Trump campaign today?

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Facebook has suspended Cambridge Analytica, the data analytics firm that helped Donald Trump get elected

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Cambridge Analytica used Facebook data it promised Facebook it had deleted, the company claims.

Facebook on Friday suspended accounts for Strategic Communication Laboratories, the parent company of Cambridge Analytica, the data analytics firm used by Donald Trump’s presidential campaign ahead of the 2016 presidential election.

The suspension was issued because Cambridge Analytica received Facebook data from hundreds of thousands of users in a way that violated Facebook’s guidelines, the company said Friday in a blog post.

The back story: In 2015, a third party app called “thisisyourdigitallife” used Facebook’s login API, which allows people to use their Facebook profile on third party apps instead of creating a new account, to collect data on 270,000 people, which is allowed under Facebook’s rules and guidelines.

The app, which was run by a University of Cambridge professor named Dr. Aleksandr Kogan, then shared the data with Cambridge Analytica, which is against Facebook’s rules and guidelines.

Facebook found out about it, and demanded that both Kogan and Cambridge Analytica delete the data. They promised that the data had been deleted, but now Facebook says that “several days ago, we received reports that, contrary to the certifications we were given, not all data was deleted.”

Why this matters: Cambridge Analytica, which The Washington Post describes as “specializing in using online data to create voter personality profiles,” was used by the Trump campaign during his run to the White House in 2016. The firm has been under scrutiny from Special Counsel Robert Mueller, who is investigating Russian interference in the 2016 election. Back in December, Mueller requested that Cambridge Analytica pass along internal documents.

What this means for Facebook: The company says it’s still trying to get to the bottom of what happened. “Several days ago, we received reports that, contrary to the certifications we were given, not all data was deleted,” the company wrote in a blog post Friday. “We are moving aggressively to determine the accuracy of these claims. If true, this is another unacceptable violation of trust and the commitments they made.”

Regardless, it puts Facebook in another awkward position as the company continues to try and determine what role it might have played in helping Trump get elected. We’ve already learned that Russian sources used Facebook to spread disinformation to millions of users, and even bought ads intended to create divisions between American voters. Now we’ve learned that Facebook data that was easily and appropriately collected under the company’s policies, may have been unknowingly used to help Trump’s advertising campaigns.

What we still don’t know is whether this data was used by the Trump Campaign, or if it was, what impact it might have had. While 270,000 people sounds like a lot, it’s a very small portion of Facebook’s user base, and would represent a very small portion of the potential U.S. electorate.

It’s also unclear if Cambridge Analytica is still working with the Trump White House, or is helping with his re-election campaign. Or what Cambrigde Analytica could do to be un-suspended from Facebook, or what Facebook would need to find in order to ban them entirely.


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How Streaming Analytics Has Quietly Become Essential

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From startups to large enterprises, analytics play a critical part in driving today’s marketing strategies. The amount of data available — and what can be done with it — has changed. Yet, just like in the past, analytics most commonly remains static. As Zoomdata explained in its master class on Streaming Analytics and the Internet of Things, analytics often has centered on historical data gathered by transaction systems or has been compiled in data warehouses.

By the time that data was pulled back out and examined, it was likely no longer relevant. Today’s organizations need to respond, act, or pivot in real time based on the most current data. Decisions should be based on the most relevant information available; what a company received last week may no longer apply to the decisions it has to make.

What to Know About Streaming Analytics

For most businesspeople, the concept of streaming analytics is new. Companies like Zoomdata understand the challenge inherent in adopting new technology. Tony Baer, principal analyst at Ovum, broke it down by explaining that streaming analytics is “all about analyzing and making actionable decisions on data while it is in motion.” It focuses on “data that has a very short half life.”

Retailers, for example, use beacon technology in stores to learn what customers are doing. That way, they can make on-the-fly decisions about what to discuss with that customer while he’s in the store. This can ultimately direct the customer toward other products of interest while he’s still available and open to that messaging. Once he leaves the store, the retailer can’t do much with that data.

Technology Led to Streaming Analytics

According to Baer, the convergence of many types of technology helped propel the development of streaming analytics. From cheaper bandwidth to smarter software to the IoT, technology in other arenas has made it possible for streaming analytics to blossom. And it’s only going to move faster: Ovum believes smart devices will account for most of the data on IP networks by 2019.

The potential to use streaming analytics touches many industries, not just the ones that enabled it — and this includes industries in which the IoT is just beginning to find its way into processes. For example, Zoomdata cited industries like manufacturing. “We have machines that are so heavily instrumented that we have basically a huge amount of data coming in split second time, and the idea in manufacturing is that the one thing you really want to avoid is unplanned downtime,” Baer says. “And the idea is that if you basically can monitor all this data in real time as it’s happening and perform these, basically these analytics, you can start to basically nip symptoms in the bud.”

Then, of course, there’s the connected car. Data-oriented vehicles are common today; in the near future, however, they’ll deliver a lot more data. This will happen as more vehicles become connected through wireless networks. These vehicles will send data back to car dealerships and manufacturers and link to smart city grids for streetlights and traffic pattern monitoring. For all of this to work seamlessly, streaming analytics will be necessary.

Streaming analytics’ impact doesn’t just end with physical machines. Cybersecurity can stop hackers and fraudsters before online transactions are completed. The type of real-time data delivery and analysis offered by streaming analytics could end denial-of-service attacks, phishing, identity theft, and other cybercrimes. Hands-on industries like healthcare could also use streaming analytics to improve patient monitoring. Stream processing can assist online capital markets, trading systems, and algorithmic trading. The applications, for now, appear endless.

Using Batch and Streaming Data

According to Zoomdata, it’s important to ensure the analytics tools your business uses support both batch and streaming data. According to Wayne Eckerson, founder and principal consultant at Eckerson Group, “Ideally an analytics tool can analyze both data at rest and data in flight. And ideally they can virtualize between the two.”

By incorporating both types of data in your business, you can visual historical patterns as well as current trends. Plus, streaming information can address short-term and long-term messaging, meaning it can positively impact strategic business decisions. With the advent of new analytical tools and platforms, it’s not as challenging to succeed at using both types of data for new insights — and a true competitive advantage.

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T-Mobile rolls out Ericsson Expert Analytics to more quickly identify and resolve VoLTE issues

T-Mobile has long placed a focus on Voice over LTE (VoLTE), and now T-Mo is rolling out a new platform to try and improve its VoLTE experience. T-Mobile has deployed Ericsson Expert Analytics, a platform that will gather data from thousands of multi-vendor sources and process all of T-Mo’s nationwide VoLTE calls and data sessions to produce more than 8 billion records every day. With Ericsson’s algorithms, T-Mo will be able to identify VoLTE issues … [read full article]

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Strategy Analytics: Apple scores 51% of global smartphone revenue in Q4 2017

Strategy Analytics published its quarterly report on the smartphone revenue between October and December 2017. According to the data, Apple snatched $ 61.4 billion or 51% of the total $ 120.2 billion. Second came Samsung with $ 18.9 billion with a share of 15.7%, while third was Huawei with $ 8.4 billion revenue or 7% of the pie. Morgan Stanley also published a research note for the same period, stating the iPhone market in China grew 12% on a quarterly basis. The Cupertino company managed to rake in three times more revenue than the next best manufacturer due to iPhone’s average…

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