New Mob4Hire Report “The Impact of Mobile User Experience on Network Operator Customer Loyalty” Ranks Performance Of Global Wireless Industry

Mob4Hire, in collaboration with leading customer loyalty scientist Business Over Broadway, today announced its Summer Report 2010 of its “Impact of Mobile User Experience on Network Operator Customer Loyalty” international research, conducted during the Spring. The 111-country survey analyzes the impact of mobile apps across many dimensions of the app ecosystem as it relates to customer loyalty of network operators.

Read the full press release here: http://www.prweb.com/releases/2010/08/prweb4334684.htm; The report is available at http://www.mob4hire.com/services/global-mobile-research for only $495 Individual License (1-3 people), $995 Corporate License (3+ people).

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5 Inbound Marketing Analytics Best Practices

analytics best practices

How do know which metrics are important? What should you be tracking and how do you track it?

With the amount of data now available at the click of a mouse (or trackpad), many marketers find themselves overwhelmed by information. To make your life easier, we’ve compiled this list of five inbound marketing analytics best practices. Pay attention to these key metrics, and you’ll be able to adjust your inbound marketing strategies to be more effective.

1. Know Your Keywords and How You Rank

First of all, it’s important to know:

  • What keywords you want to organically rank for.
  • How you measure up against your competition for those keywords.

When it comes to finding keywords, in addition to Google’s Keyword Planner, check out Ubersuggest and SEO chat’s keyword suggest tool. You can also use these tools to see how your competitors are performing.

2. Know Where Your Organic Traffic is Coming From

It’s great that you’re getting organic traffic, but do you know where it’s coming from? Check out which terms you’re showing up organically for, and how you can use those as a springboard for future growth.

By consistently blogging and marketing great content, you’ll start ranking for many terms you may have never even considered. Make sure to keep this in mind during the writing process so that you’re using your time dedicated for content creation wisely.

3. Know What Content Produces Leads

You want content that increases your organic ranking as well as content that generates leads. If you’re a Hubspot user, you can create an Attribution Report to see what blog posts are generating the most leads.

attribution report

It’s also important to keep an eye on your visits to leads ratio. Increasing traffic to your site is awesome, but if that traffic isn’t producing leads then you’re attracting the wrong people.

4. Know Your Referral Traffic Origins

So we know we want organic traffic that generates leads, but what about referral traffic? We want to create content that provides so much value that others want to link to us.

Check out how your referral traffic is doing and where it’s coming from. You can build your referral traffic byguest blogging on other sites and by building relationships with others in the industry that may be interested in linking to your content.

5. Know Your Level of Social Media Engagement

It’s important to keep an eye on how many leads and customers are coming from social media. Check each social media site’s level of engagement and then focus your efforts on the sites that are providing the biggest ROI.

social-media-traffic

The most important metrics to measure when it comes to social media are interactions, clicks and visits.

Conclusion

Keeping track of all of your website metrics can be confusing, but staying on top of these five analytics best practices will help you better assess your website’s performance and make improvements where needed.

Note: This article originally appeared in Xoombi. Click for link here.

Originally Posted at: 5 Inbound Marketing Analytics Best Practices

How innovative tech is transforming the manufacturing industry

Technology has undoubtedly reshaped almost every part of society, changing how people communicate, travel and live, among other things. It also upended processes and practices in the manufacturing sector, including through these advancements.

Artificial intelligence can reduce unplanned downtime

It wasn’t long ago that artificial intelligence (AI) was something the mainstream public only knew about through science-fiction films and books. They’re now more familiar with it because AI is more accessible and starting to influence all parts of society.

The opportunities to use AI in manufacturing are seemingly limitless. One practical application is to deploy a predictive maintenance solution that gauges the likelihood of a single part of an entire machine failing and shutting down production. An AI-based system can warn factory managers of the need to schedule repair appointments before it’s too late.

Before that option existed, issues typically only became apparent once a piece of equipment started having problems, such as not starting up at the beginning of the workday. Predictive maintenance, on the other hand, can detect minute changes in functionality. It can also extend the remaining useful life (RUL) of a machine by sending alerts as soon as it’s time to set up a service call.

Unexpected downtime can cost hundreds or thousands of dollars per minute, depending on what are manufactured at a plant. Moreover, depending on the role a broken machine plays in making products promptly and how soon a technician can fix the problem, a breakdown could substantially impact a company’s ability to remain profitable. Predictive maintenance using AI algorithms can help manufacturing businesses respond more proactively.

Additive manufacturing can create products faster and with less expense

Additive manufacturing, also known as 3D printing, involves using a computer to specify the necessary design details about an item, then instructing a specialized printer how to operate and make the product. The printer usually deposits material layer by layer, gradually creating three-dimensional results.

This method already shows incredible promise, especially for getting things made more efficiently and at a lower price point. 3D printing also makes products available to people who formerly couldn’t access them due to living in rural areas. One hospital in Guatemala uses a handheld portable scanner to generate the files that 3D printers need to work.

Before 3D printing arrived, prosthetics were too expensive for many people who needed them, and the process took weeks, requiring many appointments. Digitizing the approach with help from additive manufacturing increases patient access, plus cuts down the overall expense.

3D printing also enables manufacturers to print extra parts for their equipment. That option is especially convenient if it might otherwise take several weeks before a piece comes in stock and gets shipped. Jeannette Song, a professor at Duke University, built a mathematical model that assists manufacturing companies in deciding which parts to keep readily available on-site and which to 3D print when needed. Her model shows that even when manufacturers don’t use 3D printing to take care of their extra parts requirements very often, they save massive amounts of inventory.

Now, it’s no longer necessary to keep lots of parts available, but taking up space when the plant might not ever use them. 3D printing also enables a manufacturer to take more control of its parts needs without relying on outside suppliers.

Data-gathering helps manufacturing companies have enhanced visibility

Most manufacturing companies have collected data for a while, but the process for doing it was extremely segmented. If a plant manager wanted to compare performance between two facilities in different states, they’d have to contact people at each location and request information such as quarterly reports or weekly spreadsheets.

The time and effort required to find and compile the data often meant that the people needing the information might not have it for months. The individuals preparing it may have to communicate with employees in multiple departments while carrying out the research. Things are different now, and that’s primarily due to the Industrial Internet of Things (IIoT) concerning connected equipment.

IIoT sensors are internet-enabled and collect data in the background throughout a workday. Then, they automatically send it to a software-based interface — often one that functions in the cloud. Manufacturers use the IIoT in a wide variety of ways, but one of them is to enjoy better visibility into the lifecycle of a product. Then, companies can ensure the items they make are high-quality and will last as long as customers expect.

Data-gathering can also help manufacturers pinpoint what’s going wrong after customers receive products. For example, a company could use a database to capture all the instances of people making warranty claims or repair requests. Then, it could investigate further and uncover any commonalities about those events. It may become apparent that a particular part is most likely to break.

This boosted visibility can also lead to process improvement. In one case, a company that mined precious metals scrutinized its data to determine which factors had the biggest impacts on yields. It used the resultant conclusions to make small changes that caused a 3.7% increase in average yield within only three months. Modern technology supports facilities in acquiring data, analyzing it and making beneficial changes much faster than older methods allowed.

Collaborative robots safely boost production rates

Before robots were available for manufacturers to invest in, companies had no choice but to hire more people and increase the number of shifts to raise production levels and keep clients satisfied. When the industrial sector initially beefed up its workforce with robots, the machines stayed behind barriers or surrounded by cages to protect humans.

Starting in 2008, however, the first collaborative robots — more frequently called cobots — arrived. Those are smaller, comparatively lightweight and equipped with sensors that make the machines stop moving when people get too close. These features mean employees can work alongside cobots on the factory floor without sacrificing safety.

Grand View Research anticipates a 44.5% combined annual growth rate for the cobots market from 2029-2025. That forecast shows companies are getting on board with this technology and are eager to see what it could do for them. Cobots excel at tasks such as handling or assembling materials. Also, these machines don’t get tired like humans, so they can maintain consistently reliable performance.

Toolcraft Inc, a precision machining shop, needed to automate a multistep process to satisfy the needs of a medical device client. It tasked a collaborative robot with securely inserting a part into a computer numerical control (CNC) fixture. This approach also integrated a pneumatic gripper with the cobot to suit specific steps in the process.

After making the cobot part of its production, the facility saw a 43% increase in throughput, plus went from producing 255 pieces per week to 370. Once Toolcraft Inc. used the robot for six months, costs went down by 23%, and the company anticipates a return on investment in approximately one year.

Technology will continue to help the manufacturing sector progress

People in today’s society increasingly expect manufacturers to cater to them without delays. They also have little patience for faulty or low-quality products. These examples show that technology has already helped the manufacturing industry thrive. People should look forward to these and other technologies having similarly positive impacts for the foreseeable future.

The post How innovative tech is transforming the manufacturing industry appeared first on Big Data Made Simple.

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Menlo Security Transcends the Almost Secure Cybersecurity Paradigm

Companies of all sizes, across all industries, and from every region of the world all seem to follow the same basic cybersecurity strategy. That would make sense if it worked, but businesses continue to cling to an outdated model of cybersecurity despite overwhelming evidence that it’s not very effective. There is an implicit acceptance that […]

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Source: Menlo Security Transcends the Almost Secure Cybersecurity Paradigm by administrator

Big Data in eCommerce: The new trends for 2020

Over the years, Big data technology seems to have transformed several industries across the globe. And it is showing no signs of slowing down. The following post emphasizes how big data makes a significant effect on the present eCommerce trends.

Data is everywhere. In today’s era, we gather and share countless data every moment. And it may interest you to know that each one of our actions is generating data right now. Although, we used to watch several sci-fi movies which now has become a reality. With us being surrounded by so much data, managing them has become very challenging. Big data came into existence! Now you must be wondering what big data is and how it is impacting the eCommerce industry.

Big Data is an alternate method of inspecting a voluminous measure of information to uncover shrouded designs, relationships, advertise patterns, buyer inclinations, and different bits of knowledge that can assist organizations with modifying likewise. Although the concept is nothing new as businesses have been using this process of manually analyzing their data earlier with Big data.

On the other hand, eCommerce is a successful and most-preferred sector in the industry, which has changed the way of buying and selling goods and services. According to Statista, 54% of millennials are now making online purchases compared to 49% of non-millennials. The way to using enormous information to set for your expectations you center around two things in your hover of fitness, the way the world works.

Big Data and Analytics will adjust the substance of eCommerce in 2020

#1 Enhanced Shopping designs

Big Data Analytics is an extraordinary method of understanding the client’s shopping conduct to foresee designs that will improve business techniques. Client’s inclinations, most well-known brands or item that individuals look for, any item that individuals are looking for various occasions yet isn’t offered by you, spikes in requests, what season do clients shop more, et cetera can be evaluated through enormous information investigation

#2 Effective Customer Service 

The Success of any eCommerce lies in how the client feels. Poor services are one of the major turn off for the customer in the long run, maybe forever. Here big data has been extremely helpful in offering e-retailers a golden opportunity to constantly monitor the shopping experience of their end customers so as to provide them better responses for their needs. Right from answering their query to keeping them well-informed about the latest offers and tracking their items. Additionally, huge information can possibly give extraordinary bits of knowledge on client conduct and socioeconomics that will take you the long route in eCommerce improvement domain.

#3 Personalized experience 

It’s been a cutthroat competition in the eCommerce realm and trust me offering personalized experience isn’t something that will set your business apart right away. More than 86% of consumers think that personalization plays an important role in influencing buyers’ decisions. Moreover, big data has the potential to give special insights on customer behavior and demographics that will take you the long way in eCommerce development realm.

With eCommerce big data, you can:-

  • Send emails with customized discounts and special offers to re-engage users.
  • Give personalized shopping recommendations
  • Present targeted ads, as different customers want different yet relevant messaging. Now you must be wondering how this works?
    • First, determine your best customers
    • Create a VIP customer group for those
    • When you launch any new product just ensure that you make those items exclusively available to VIP customers for the time being
    • Offer several discounts that no one else gets

That’s all for now!

eCommerce big data has been extremely helpful when you are planning to survive in this competitive realm. To make things work in the right manner you need to seek for the permission of the user to collect data, create smart programs that offer some value to the end customer, make sure to keep the data small and function within your area of expertise.

From comprehension and breaking down client inclinations and conduct, enormous information investigation makes ready to give better methods of giving upgraded client support. Large Data Analytics even encourages you to comprehend your own organization’s qualities and shortcomings, empowering you to think of better item structures, better-estimating techniques and better serious qualities. To top everything, Big Data Analytics helps the specialists recognize installment cheats using sites and cell phone applications. This encourages endeavors to pipe different installment alternatives using a solitary unified stage, to make installments increasingly helpful.

The post Big Data in eCommerce: The new trends for 2020 appeared first on Big Data Made Simple.

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From Data Scientist to Diplomat

Getting Agreement on “THE PATH TO DEPLOY” your… predictive model, big data application, machine learning based algorithm, segmentation, insights, optimization model…

Battle Scars along the journey to the truth….

When you took your first class in Data Science, R or Linear Regression either at your university or at Coursera did you ever imagine you would struggle with getting your recommendations or analysis implemented?  Did you know you were also joining the diplomatic Corp?

As a good Data Scientist you go through the process of:

  • Defining the problem and objectives for your model or insights project.
  • You gather your information/data for your analysis
  • And you do all of the steps that go into any best practices analytics methods approach. Data Cleansing, Reduction, Validation, Scoring and more. 

When you go to present your analysis or data science results to your clients you end up getting push back in terms of what we call “the path to deploy.”

Frustrating though it may be, hitting this first wall in your data science career, should be viewed, as a learning experience and your greatest teacher, not only in your current role but also for how to assess the organizations analytical maturity (including top management support) when searching for your next role.  Our greatest problems are our greatest teachers.

I have a few painful memories in this area as well, but since then the diplomatic corps has been after me to enroll, one was when I was working with my data science team on behalf of the CMO in a card business to build out a Credit Card Customer Card Acquisition model. The purpose of the predictive model was to make cross selling credit cards to a retail customer easier, more efficient and more profitable.  All Noble and correct goals by all appearances.   The CMO wanted to ensure we acquired customers that not only responded to our offer but use and activate their card.  This strategy made sense not only from a profitability standpoint but also from a customer centric point of view.  The CMO who had agreed with the Decision Science team on the modeling approach and the “path to deploy” was stymied by his boss the CEO who didn’t want to implement the model as the CEO was under pressure to grow the number of card customers and was afraid of not hitting his account/widget goal.  The CMO was very frustrated as was the entire team.  So after much debate we scaled back our full “path to deploy” approach, by offering to set the model up to score and be applied to a smaller netted down number of cross sell leads, so we could prove the value of the model by showing that we may get slightly less customers but they were 5 x more profitable then random.  I call this deferring for collaborative agreement. 

Some Key Learnings

  • So if you think that Data Science is only about science, and trust me we wish it was, then think again. Decision makers are biased by their educations, judgements and what they know about a subject and the more you can show up as helpful and supportive in terms of educating the executives about the statistics and how the modeling works the more supportive they will be.  Think back to when you were hired and someone said that you had “good energy” well what does that mean?  It was a value judgement about how well they liked you, your physical presence during the interview and your ability to articulate your value story.
  • View an initial “No” as a deferral and regroup with the team to come up with an alternative test and learn approach to deploying your analysis or model. Sometimes you need to crawl before you can walk and then run. 
  • Ensure that you ask this question up front of the client: Are you the ultimate decision maker?
  • When you present your analysis results consider presenting a variety of options for executives with more detail around how each option impacts the business. Use data visualization to make your case. 
  • Try and anticipate both positive and negative analysis outcomes upfront and list them as potential challenges in the initial project definition: this way decision makers can decide if they would even go forward with the project. 
  • Use influence maps to determine who can help with pre-socializing your analysis results to convince the decision maker that the outcome is worth testing.
  • Try and understand who are the data science and analytics champions within the company and request that they be your mentors or better yet invite them to key meetings if possible.     

In Conclusion:    Become known as the Data Scientist who can drive collaborative decisions through both horizontal and vertical thinking through:

  • Listening to your clients and or corporate culture is key.
  • Getting agreement on the “path to deploy.”
  • Using Influence management techniques to persuade the client or executives to test the analysis in some way. Don’t consider it rejection just consider it deferring.
  • Demonstrate how the analysis directly aligns to the mission in the organization. For example, many firms are talking about customer centricity and your very analysis is key.  You need to remind people that test and learn and innovation and risk taking is the key to driving toward this vision.    
  • Know and cultivate executive sponsors, evangelists and champions of analytics.  The best firms will have an executive sponsor.

We would love your thoughts and if you find this article helpful please feel free to reach out to our advisors on call or our coaches if you need a sounding board before you react to one of these situations.

Tony Branda

profile-of-a-data-scientistCEO, CustomerIntelligence.net

Originally Posted at: From Data Scientist to Diplomat