Apple, Huawei, Amazon Gain in Sluggish Tablet Market: IDC, Strategy Analytics

Apple, Huawei, Amazon Gain in Sluggish Tablet Market: IDC, Strategy Analytics


  • Overall tablet sales dropped to 37.9 million
  • Apple saw nearly 15 percent boost in iPad sales from a year ago
  • Samsung remained the number two vendor with a 15.8 percent market share

Apple, Huawei and Amazon boosted tablet sales over the past quarter, despite the ongoing slump in the overall market for the devices, surveys showed Thursday.

Overall tablet sales dropped 3.4 percent from the same period last year to 37.9 million, according to a survey by research firm IDC.

Apple’s nearly 15 percent boost in iPad sales from a year ago gave it a 30 percent share of the global market, IDC said.

IDC said Apple’s gains came from consolidating its lineup and introducing new tablets, including a 10.5 inch iPad Pro, which encouraged some consumers to upgrade.

Samsung remained the number two vendor with a 15.8 percent market shares as sales dipped one percent, the report said.

China-based Huawei meanwhile bucked the overall trend with a strong 47 percent gain in sales, vaulting to the number three position with an eight percent market share, the research firm reported.

IDC estimated that Amazon – whose sales figures are not reported – boosted its Fire tablet sales by 51 percent from last year to capture the fourth spot at 6.4 percent.

China’s Lenovo was fifth with a 5.7 percent market shares as sales dropped 14.6 percent from last year, the report said.

A separate survey by Strategy Analytics estimated a seven percent decline in global tablet sales to 43.8 million units in the April-June period.


Beyond open data: Insights through analytics

city analysis (Who is Danny/

The federal government is taking big steps to share information and make data more free and open. Thanks to legislation like the Digital Accountability and Transparency Act, agencies are now required to post standardized spending data on the site. Other initiatives, like the Government Publishing Office’s, let citizens use full-text searching and metadata to sift through decades of digitized content. It seems as if we are entering a new chapter of open data. But what, exactly can governments do with this data on hand? How do citizens and public officials make the most of this unprecedented level of access to information?

Analytics are what allows government to use “data as a flashlight, not as a hammer,” according to “A Practical Guide to Analytics for Governments,” recently produced by the team at the SAS Institute and published by Wiley.

The book celebrates information sharing and the wide range of data available on the municipal level in particular — from smart streetlights that also collect info on pedestrian foot traffic to rail equipment outfitted with sensors so that repairs can be made as needed, rather than on a maintenance schedule. (An innovation that Washingtonians inconvenienced by D.C. Metro’s months of “SafeTrack” repairs might envy). Overlaying of municipal code enforcement and police activity data reveals unexpected correlations between property neglect and crime, and having studied algebra in high school is connected to markedly higher income achievement later in life.

“Armed with insights” from shared data, officials in Arizona’s Pinal County used the strength of analytics to more effectively understand already-existing health data in a way that would better protect the public from heat stroke. Investigators were surprised to discover that analytics revealed the highest threat of heat-related illness was not found among the elderly — as had been expected — but instead, among the young people of this Arizona community.

Small agencies can benefit from analytics as much as larger ones.  The book’s authors make the case that smaller cities may be best positioned to take advantage of technology advances because there is “less infrastructure to retrofit.” Since only 300 U.S. cities have populations that exceed 100,000, they add, the opportunities for data-driven innovation are substantial.

State-level open-data success stories are also hailed, most especially the example of  North Carolina, which “opened its 2017 budget for citizen scrutiny” with a new visual analytics tool.

But more important than making data itself available, the authors argue, is recognizing the challenge of melding data into analytics. After all, they assert, “typical government IT projects are built in a siloed approach,” which means that while agencies have torrents of data, often not a drop is shared. Teachers are not given the opportunity to proactively provide remedial attention to students. Police don’t have background information to help them approach a suspect with either greater caution or more compassion.  The book also looks at applications in transportation, public health, child welfare, prescription drug abuse, fraud prevention, and it methodically lays out both the depth of missed opportunities and the possibility of a brighter future.

As government at every level updates its IT assets, the book warns CIOs that “[a]cquiring technology for technology’s sake … rarely achieves the expected outcome.” Instead, the book makes the case that the emphasis should be on “building an analytics-driven government” and leveraging data to “build stronger analytics capabilities.”

“A Practical Guide to Analytics for Government” lives up to its title and concludes with a specific suggested solution. Establishing an official center of analytics, the authors write, can help agencies create a keen awareness of the importance of “building common competency … [that] enhances government analytic success through shared experience.”

Some cities have begun to work in that direction, and the City of Boston’s Citywide Analytics Team and the New York City’s Mayor’s Office of Data Analytics are hailed for seeking “innovative ways to leverage data.”

Such efforts could even unite an otherwise polarized political community, the authors suggest, since “both Republicans and Democrats value opening the public’s business to citizens.” Indeed, they contend that during a time when the citizens increasingly distrust political leadership, “open data can . . . promote legitimacy.”

More importantly, though, the authors stress that governments at all levels should be “breaking down barriers to sharing and accessing information … to ensure frontline workers, management, and policymakers have the knowledge they need.”   After all, as Shawn P. McCarthy, research director of IDC Government Insights, is quoted as saying about this book, “in many ways, modern government is information.”


Smart Master Data Management Will Power Your Customer Analytics And Insights


It’s supposed to be a post omni-channel world. A time when personalization is the norm, but as customers we know this is not the case. When customers contact companies, the companies often don’t recognize who the customer is, and they certainly don’t know what the customer’s preferences are. The truth is customers rarely get the personalized and tailored experiences they desire.

Well it’s not a shortage of data about those customers.

So why is it that when customers contact companies, most companies seem completely unaware about anything specific regarding that customer, let alone tastes and preferences. The reason is delinquency when it comes to the aggregation and management of the customer’s data inside the company.

Companies have many challenges when it comes to using customer data, particularly data that is supposed to help the company make better decisions in real-time.

David Rowley, CTO of IAC Publishing Labs, and former executive at customer experience software vendor Sprinklr, said, “the notion of a centralized repository for key business data is an important aspect of providing a more comprehensive customer experience.”

But many companies don’t have this today.

This information is called master data. “Master data” can be about products, employees, materials, suppliers but also may include documents and sales. Rowley said, “A centralized master data store (the repository for the master data) can improve real time decision making and analytics, if information about the entities (e.g. customers) is stored centrally. This provides you with a single trustworthy source of truth about the customer.” Rowley added that you can report directly on that data, rather than having to aggregate customer data from separate systems. He said, “A central view of the customer, for example, keeps various systems in sync with what the customer has done.”

Vic Bhagat, CIO of Verizon Business Services, said, “master data is more important now than ever – the amount of data being generated today will pale in comparison to what will be generated in just a few more years.” He added, “Having a master data management strategy clearly defined — early on — enables a faster approach to analytics to deliver a more proactive, predictive, prescriptive outcome that customers expect today.”

As we established, companies struggle with their current state of their customer data management, but what happens when the business becomes a little complicated, such as with a merger or acquisition. How does the company merge all the new data with all the historical data? Master data management can also help with this.

We addressed the fact that many data systems that aren’t integrated. Sometimes data problems include the actual data sources, data duplicates, no data governance and no standards. Data is not always shared efficiently within the organization. Many of the world’s biggest companies operate like separate islands. Customer service and sales do not share a crm. The company does not collaborate around the customer to ensure a powerful customer experience. The company’s various departments operate in silos. More often than not the people working inside the companies do not even know one another in different departments, let alone use data that spans across the organization.

This is a challenge for organizations who need to create easy and elegant experiences for customers. Companies need to leverage data in real-time to make better decisions. Customer analytics are not helpful when one can’t trust the accuracy of the data. And with many companies today data is often too little too late.

“Leveraging Master data enables enterprises to clean, integrate, and supplement their data to ensure a complete 360 view of their customer. With this view, companies can make informed decisions, align different departments, and create world class online and offline customer experiences leading to sales growth,” says Rishi Dave, chief marketing officer at Dun and Bradstreet, a data and analytics company who provides master data.

End-to-end master data management helps clients make marketing campaigns 30% more efficient, improve upsell and cross-sell rates by 60% and increase loyalty members’ spending by 20%, according to Informatica.

Neopost Uses Master Data For A Transformation Initiative

Neopost, a market-leading global provider of mailing solutions, digital communications and shipping services, found that master data was critical for a transformation initiative. The company wanted a way to more competitively know and serve their customers. As Neopost focused on modernizing its offerings and delivery for the digital age, strong data management has played a key role.

“Besides developing the actual software we sell our customers, we’ve got to build an infrastructure where data quality is paramount,” says Steve Rakoczy, Neopost’s North America CIO. “You can make a mess really quickly in the electronic age if you don’t have your data right.” After using master data, Rakoczy said, “We can successfully manage our Salesforce environment for the first time in over a decade.”

Master data management can help your company create more compelling customer experiences, but first the company must decide on a strong data approach.

For more from author Blake Morgan sign up for her weekly customer experience newsletter here.


Guy Yehiav of Profitect: Prescriptive Analytics Gives You Next Best Actions to Go with Insights

Guy Yehiav shares with us why prescriptive analytics goes beyond identifying insights to recommendations for the actions you should take next.Small Business Trends: Maybe you can give me a little bit of your personal background.

Guy Yehiav: I have about 22 years’ experience in supply chain applications. Built up a company called Demantra that does demand management, trade promotion management, supply chain optimization. Implemented a lot of different projects with different types of companies and industries; retail, CPG, complex manufacturing, consumer electronics etc. Then I sold the company to Oracle and was the head of supply chain strategy for four years. And then I went back to entrepreneurship which I love.

What we did here with Profitect is try to be a different software company helping our customers in a much more efficient way and be much more effective, bring them live quickly, and focus on their problems mostly.

Small Business Trends: Everywhere you look today we’re talking about analytics, machine learning, artificial intelligence, deep learning. What you’re talking about is prescriptive analytics. Can you tell us exactly what that is and how that may differ from some of the other areas that we’re hearing about?

Guy Yehiav: All of those technologies that you just illustrated, Gartner aggregated all of them and said those are smart machines. They ingest data. They run different mathematical models, different dimensions and then they spit out stuff to generate value. And if you talk with customers they say hopefully it generates value. Sometimes they don’t. Sometimes I need to hire a lot of talent to then generate the value out of those smart machines.

All of those smart machines at the end of the day generates typically a report, a title of a report, a workflow sending those reports etc. Prescriptive analytics is based on all the years of working with a lot of different types of companies. We’ve learned that we would like to democratize the data differently. Giving not the data itself to a lot of users and people, but guiding them on the actions. And this is prescriptive analytics.

We have a smart machine that ingests data as you can imagine, use machine learning algorithms to automatically cluster things based on behavior, looking for anomalies etc. etc. But the beauty of it is when we find the value, what we call the opportunity, we then send it to the right users using workflow with a descriptive insert in plain language. So we tell them what they need to know and then we also prescribe an action; what they need to do in order to present an optimal outcome.

Small Business Trends: So it’s like you find the insight, and then not only go over the insight but deliver the next best action.

Guy Yehiav: And it could be multiple steps. Let’s say that we identified that the hottest Halloween costume is being sold in a specific store on an average every four hours. If it’s been about 8 or 12 hours since this costume was sold, and it was sold in other places, the engine will identify what is the probability of that costume not being available for consumers to pick it up from the shelf. That’s ultimately what the statistics tell you. In the next four hours you should on average sell at least one. If it’s been eight hours — ‘hey you’re lagging’.

Now, since we triangulate all of the information the machine knows … you do have it on hand. So how come you have it on hand? Other stores are selling it. You used to sell it and suddenly you stopped selling it. There’s a probability that it’s not available for consumers to pick up and so the action would be very simple — ‘Hey Mr. associate this specific costume you do have on hand for some reason you’re not selling it. Can you check that it’s on Aisle eight, shelf three? And if you don’t see it on the shelf, you do have it in the back. Go and move it to the front.

Now that allows people that are not analytically savvy to generate value using a smart machine that is very analytical and relies on statistics. But the outcome — the user interface, is in layman’s terms for everyone to understand. It will tell you what [is] the action you need to do and what’s being said.

Small Business Trends: We’re coming up on Halloween which is a big season for people to buy things from a retail perspective, with about seven billion dollars spent annually on Halloween related products.

How does using prescriptive analytics impact a retailer who’s trying to sell right now, versus a retailer doing things without having all these insights and next best actions available to them?

Guy Yehiav: When we speak with the store managers during the year, they always tell us that they’re getting tons of reports; shelf availability reports, average price reports, labor spending reports, based on budget etc. When they get all those reports, they either go in the back room and start analyzing it or they’re asking … different associates to analyze it and they will come with ideas on how to solve them. What happened is now you’re spending time in the back of the room and what you’re not doing is you’re not converting traffic to happy customers. Retail traffic is a major term that is being heard all across the net because you have more and more online traffic or online consumption of goods. And then you have people going to the malls and going to the stores which everyone is saying over the last four or five years traffic in malls is going down.

However, when you look at analysis, most of the traffic going into stores go with a better intent. When someone goes to the store rather than buying online they have an impulse buy. They are going to buy something, and you can even convert an upsell even more using impulse buy. And so if you do have enough people in the store to give the right service, rather than read reports in the back room, you actually have a better way of improving your sales.

More than that, if you use prescriptive analytics you’re not relying on talent. You will have better service and you will have better on shelf availability.

During the holidays and especially on Halloween, there’s a lot of pop up stores. Usually the retailer is just renting a certain square for about a month or two months for the whole of the holiday season and then they hire temporary labor in order to sell and service the customers.

Now the temporary labor is not familiar with the culture of the specific retailer. And you can imagine that different retailers, like any company, they have mission statements and values and they have different type of cultures that makes them who they are. So if you have a temp, they don’t know retail, they don’t know what is on shelf availability. You’ll give them a report. They will not know what to do with it.

The whole idea here is to just tell them what the insight is and what they need to do, because they do want to do the right thing. So now during the holidays it’s even more important, because you want to convert the traffic if you want to be able to create the online orders as fast as you can. You want to service the customer the best, but on the other hand you continue to get a lot of those reports.  And so prescriptive analytics during the holiday season is even more critical than the average day of the year.