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Big Data Data Analytics

How Big Data is Changing The Way We Fly

Airline big data, combined with predictive analytics is being used to drive up airline ticket prices.

As airlines and their frequent flyer programs gather more intelligence on your day to day lifestyle, flying and financial position – they begin to build an airline big data profile.

Consumer interests, goals, psychometric assessment, your motivations in order to engage with a brand at any given every point throughout the day, what has driven you to purchase in the past – and most importantly – where your thresholds are.

To illustrate how data is playing a growing role within today’s flight booking engines I’ve broken down play by play how each piece of data collected about you can be used, analysed plus overlaid with other datasets to paint a picture of who you are, exactly what motivates and drives you to purchase a particular product.

Every day – trillions of calculations are number-crunched to transform this goldmine of data opportunity into real, tangible high-revenue opportunities for the airlines and their frequent flyer programs.

“When armed with key insights, a holistic overview associated with yours, and other customers’ detailed profiled information can be applied to direct booking channels which are designed to customize pricing for your personal situation at that very given moment. Here is…

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Big Data Data Analytics

Deep Learning: Past and Future

Heavy learning is growing in both popularity plus revenue. In this article, we will shed light on the different milestones that have led to the deep learning field we know today. Some of these events include the introduction of the initial neural network model in 1943 and the first use of this technology, in 1970.

We will certainly then address more recent achievements, starting with Google’s Neural Machine Translation and moving on to the lesser known innovations such as the Pix2Code – an application that is used to generate a specific layout code to defined screenshots with 77% accuracy.

Towards the end of the article, we will briefly touch on automated learning-to-learn algorithms and democratized deep learning (embedded deep studying in toolkits).

The Past – An Overview associated with Significant Events

1943 – The Initial Mathematical Model of a Neural Network

For deep learning to develop there needed to be an established understanding of the neural networks in the human brain.

A logician and a neuroscientist – Walter Pitts plus Warren McCulloch respectively, created the first neural network mathematical model. Their work, ‘A logical Calculus of Ideas Immanent in Nervous Activity’ was published, and it put forth a combination of algorithms plus mathematics that were aimed at mimicking…

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Big Data Data Analytics

9 Reasons Smart Data Scientists Don’t Touch Personal Data

The production of massive amounts associated with data as a result of the ongoing ‘Big Data’ revolution has transformed data analysis. The availability of analysis tools and decreasing storage costs, allied with a drive-by business to leverage these datasets with purchased and publicly available data can bring insight and monetize this new resource. This has led to an unprecedented amount of data about the personal attributes of individuals being collected, stored, and lost. This data is valuable for evaluation of large populations, but there are a considerable number of drawbacks that information scientists and developers need to consider in order to use this data ethically.

Here are just a few considerations to take into account before ripping open the predictive toolsets from your cloud provider:

1 . Contextual Integrity

Data is gathered over different contexts which have different reasons and permissions for capture. Ensure that the data you capture is valid for that context plus cannot be misused for other purposes. There could be unintended side effects of mixing public and personal data. An example is notifying other parties associated with location data without consent, as there are numerous examples of stalkers using applications to track others.

2 . History Aggregation

History is an important part of many efforts to defining…

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Big Data Business Intelligence Data Analytics

What Does the Salesforce-Tableau Deal Mean For Customers?

Salesforce Buying Tableau for $15.7 Billion

Salesforce will buy Tableau Software for $15.7 billion in an all-stock deal announced Monday morning. Salesforce is doubling down on data visualization and BI in the purchase of one of the top enterprise technology brands.

The all-stock deal will be the largest acquisition in the history of the San Francisco-based cloud CRM giant. It is more than double the amount Salesforce paid for MuleSoft last year ($6.5 billion).

The acquisition price of $15.7 billion is a premium of more than 30 percent over Tableau’s market value of $10.8 billion as of the previous stock market close. The deal is slated to close in the third quarter. The boards of both companies have approved the acquisition, according to the announcement.

The acquisition comes barely a weekend-after Google announced its massive $2.6 billion acquisition of Looker, which also makes data visualization software for businesses.

The deal is also expected to escalate the competition between Salesforce and Microsoft. The two are already fierce competitors in the CRM arena with Salesforce CRM and Microsoft Dynamics CRM. Salesforce, armed with the Tableau product suite, will now compete with Microsoft’s PowerBI data visualization and business intelligence technology. Tableau and Microsoft have been in a fierce fight the last three years, with Tableau’s stock under pressure.

At $15.7 billion, Salesforce buying Tableau is the largest analytics merger and one of the largest software deals in history.

It combines two leaders in their respective space, Tableau for Data Visualization, and Salesforce, leader in Customer Relationship Management SaaS software.

It’s not surprising Salesforce wanted Tableau. Salesforce, like any other large Saas company, stores a massive amount of business data supplied by its thousands of customers. Naturally, those customers are hungry for advanced analytics on that data, and have been telling Salesforce that.

The risk for Salesforce and the massive amount of data it holds is letting that data flow out of its systems to those of competitors – not for new CRM services – but for Analytics.

Customers desiring analytics for Salesforce Data have a multitude of choices, major players like Microsoft’s PowerBI or any of the hundreds of other analysis platforms. Google searches for “CRM Data Analytics” and its variants number in the thousands per day.

Over the past few years, it’s swallowed Analytics companies like goldfish at a 50’s frat party. Salesforce acquisitions in just the last 2 years included:

  • Mulesoft,
  • BeyondCore,
  • PredictionIO,
  • Griddable.io,
  • MapAnything.

Why is Salesforce Investing in Analytics?

Because data has massive value, both current and potential value in the future. Salesforce knows whoever controls the data inherits that value, and has much greater influence over the customer.

Salesforce isn’t the only one who knows this, many other cloud and SaaS players know this too. The new cloud “land-grab” is actually a data grab, which may prove much more valuable than land over time. Cloud companies are doing everything they can to direct as much data into their clouds, and keep it there. Analytics services a way to keep their customers’ data happily ensconced within their own platform.

In the cloud universe, it’s much better to be a massive player with a strong gravitational pull that draws data toward you, than to see data flowing away from you. That may sound simplistic, but that glacial flow of data, first from the company, then into a SaaS application, then onward to other cloud companies, is what makes or breaks these companies’ fortunes.

Salesforce has turned most of its purchases in Data Analytics into the Einstein platform, which has had a decent reception by the market. However, Einstein has not had the planetary effect of drawing in non-Salesforce data and exists mainly to offer insights on Salesforce’s captive CRM data. Its adoption has not broadened significantly beyond Salesforce data.

The acquisition of BeyondCore promised augmented analytics into the portfolio by way of Salesforce Einstein Discovery. In this regard, the Tableau acquisition is good for Salesforce from a product perspective, while also a good move for Tableau shareholders.

There is some obvious overlap in the product portfolios. Tableau had acquired Emperical Systems to bolster its augmented analytics, which will likely be slowed or sidelined. The immediate goal for Salesforce and Tableau will be to rationalize duplicate products and improve the integration. We wonder whether Tableau will become the face of the Salesforce analytics apps, which are full cloud products, since Tableau has continued to lag in its browser-based authoring. All this means that it is not necessarily good news for Tableau customers. The reactions on Twitter were decidedly mixed.

Winners and Losers: What does the Salesforce-Tableau deal mean for customers?

Definite Winner: Tableau Shareholders

Potential Winner: SalesForce Customers

Potential Losers: Tableau Customers, Salesforce Shareholders

The initial reaction in markets and on Twitter was strong. Markets soundly rewarded Tableau shareholders with a 35% share price leap the morning the news came out. Salesforce shareholders didn’t fare so well, with their shares dropping 8% on the announcement, but will likely recover as the news spreads.

Both companies have strong, mature cultures. Tableau was multi-platform and connected to multiple datasets. Salesforce, which did buy Mulesoft to connect to other data sources, is likely to maintain Tableau’s mission and approach, but it’ll have to prove it to some folks. However, Tableau has built up a very successful community around its brand, and includes millions of loyal users among its fanbase.

One response on the Tableau community forum likely sums up the concerns by some customers:

“Will we wake up on this date next year and see ‘Tableau Powered by Salesforce,’ and then the next year Tableau becomes nothing more than a checkbox on the Salesforce contract? I have staked my career on this wonderful tool the past few years and truly love it. I just don’t want to see it ruined or fade off into the sunset.”

It will be interesting to watch how Tableau’s roadmap evolves or changes due to its new ownership.

These two deals are just the latest in a series of acquisitions of data analytics companies over the past quarter or two. We’ll cover the others in Part II of this post.

For now, here are some takeaways about all these acquisitions:

  • The Analytics and BI market remains hot, valuations for these companies continue to go up.
  • It’s clear that most of the benefits of these deals will go to the shareholders. However, the CEOs and boards should also be doing their part to make sure the benefits are shared with the customers and loyal users of these technologies. After all, that’s what got them where they are.
  • This isn’t the first consolidation the Analytics industry has seen. In the late 2000s there was a wave of activity as behemoths like SAP, IBM and Oracle gobbled up Business Objects, Cognos and Hyperion, respectively. How did those turn out? Well, the fact that companies like Tableau were born shortly afterward signals that innovation in the bigger companies slowed down after those deals. This paved the way for newer, more agile companies (like Tableau) who listened to the market, and innovated to deliver what it demanded.

If you have a horse in this race, either as a customer, developer or employee of any of the affected companies, drop us a quick comment below to let us know how you’re feeling about this news, and how you think it might affect you.

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Big Data Data Analytics

NLP vs. NLU: from Understanding a Language to Its Processing

As artificial intelligence progresses and technology becomes more sophisticated, we expect existing concepts to embrace this change — or change themselves. Similarly, in the domain of computer-aided processing of natural languages, shall the concept of natural language processing give way to natural vocabulary understanding? Or is the relation between the two concepts subtler and a lot more complicated that merely linear progressing of a technology?

In this post, we’ll scrutinize over the ideas of NLP plus NLU and their niches in the AI-related technology.

Importantly, though sometimes used interchangeably, they are actually two different concepts that have some overlap. First of all, they both deal with the relationship among a natural language and artificial intelligence. They both attempt in order to make sense associated with unstructured data, like language, as opposed to structured data like statistics, actions, etc. However , NLP and NLU are opposites of a lot of other information mining techniques.

Source: Stanford

Natural Language Processing

NLP is an already well-established, decades-old field operating at the cross-section of computer science, synthetic intelligence, and increasingly data mining. The ultimate of NLP is to read, decipher, understand, plus make sense of the human languages by machines, taking certain tasks off the humans and allowing for a machine to handle them instead. Common real-world examples…

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Big Data Data Analytics

The Advantages of Automation for Wastewater Treatment

Automation is everywhere these days, helping us to make better use of labor, time and other resources, plus leading to the development of cleaner, future-ready industries. It’s not a surprise to see automation edging into wastewater treatment: this is one of the most critical industrial-level activities on planet earth today where public health is concerned. Here are some of the ways technology is making this process more efficient and cost-effective.

Lower Energy Costs

Not surprisingly, energy use is the single biggest expense for wastewater treatment plants. Automating infrastructure provides 1 way to reduce energy expenditures associated with a number of critical water treatment processes. One example is the blowers located in holding basins, which keep the water aerated. Some estimates say blowers account for up to 60% of a treatment plant’s total energy consumption.

Automation can improve cost-effectiveness in this area through data collection. Instead associated with operating the blowers constantly, at the fixed speed, plants can use information about effluent levels in holding basins to apply air and remove solids only when it’s necessary to do so. This reduces energy costs, helps maintain a steady flow and reduces wear and tear on equipment.

Constant Access to Data and Ongoing Sampling

In wastewater treatment and many…

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Big Data Data Analytics

How Artificial Intelligence Will Disrupt the Financial Sector

Artificial intelligence thrives with data. The more data you have, the better your algorithms will be. However , just having a lot of data is not sufficient anymore. You also need high-quality data, or in the words of Peter Norvig, you need better data:

“We don’t possess better algorithms, we just have a lot more data. More information beats clever algorithm, but better data beats more information. ” – Peter Norvig – Director of Research, Google

Nowadays, most organisations collect vast troves of data, but especially the financial sector is well-suited for also collecting high-quality data. Simply because of regulations and because a lot of data in the financial sector is structured data. There is also an abundance of data within public markets or even other external sources that can become linked for additional insights. As it seems, banks and insurance companies can benefit a lot from AI, if implemented correctly, of course.

Financial Institutions Have to Innovate

Besides, more and more consumers require financial institutions to innovate. They have become fatigued with overbearing fees to their manage capital and provide products such as credit. The below graph by State of AI clearly shows the difference in costs between traditionally managed wealth and automated management of wealth. As a…

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Big Data

How AI is Transforming the Aviation Industry

The aviation industry, particularly commercial aviation, is continually aiming to improve both the manner in which it works and its consumer loyalty. Keeping that in mind, it has started utilizing artificial intelligence. In spite of the fact that AI in the aviation business is still in the beginning stage, some advancement has been made as of now as certain leading carriers put resources into AI. To begin with, certain use cases are being achieved, for example, facial recognition, baggage check-in, client inquiries and replies, plane fuel enhancement and factory assignments improvement. Be that as it may, AI can conceivably go a long way past the present use cases.

Commercial airline travel is a financial engine which effected an expected $168.2 billion in operating income in 2016. Ticket fees spiked to 74. 5% of operating income or $125.2 billion dollars, and airline traveler traffic is anticipated to double throughout the following two decades.

Today, leading airlines are investigating how AI can enable them to keep pace with client demand and improve operational adequacy, speed plus consumer loyalty. The following are a couple of changes we have seen, and what’s in store sooner rather than later.

Baggage Screening

Baggage screening is a dull yet significant task done at the airplane terminal. In any case, AI has disentangled the procedure associated with baggage screening. Osaka Airport in Japan is intending to introduce the Syntech ONE 200, which is an AI innovation created to screen baggage for numerous passenger lanes. Such devices won’t just automate typically the procedure of baggage screening, in addition they help authorities identify unlawful activities. Syntech ONE 200 uses an X-beam security system and it increases the likelihood of identifying potential dangers.

In 2017, American Airlines led an application development competition with the objective of having an application created for making baggage screening simpler for travelers. The particular competition, named HackWars, was themed on AI, drones in addition to augmented reality and VR. The winner, known as “ Team Avatar, ” built up an application that would not just permit travelers to decide their baggage size before arriving at the airline terminal, but in addition, prepay any potential related costs.

Virtual Assistants

Artificial intelligence based virtual assistants help aircraft organizations improve the productivity and even effectiveness of their pilots by decreasing repetitive assignments, for example, changing radio channels, perusing wind forecasts, and giving position data on request, among others. These repetitive jobs can be taken care of by AI-empowered virtual assistants. Organizations, for instance, Garmin (US) offer AI-empowered audio boards, which are invaluable tools for pilots.

Virtual assistants are likewise utilized by aircraft organizations to improve client services. Artificial intelligence empowered virtual assistance can give instant answers to basic inquiries. Normal inquiries incorporate points like flight status or services/contributions (sound, video, Wi-Fi) on flights. This allows the human customer service ambassadors to take care of more  significant issues requiring a human.

Alongside that, virtual assistants are helping travelers book and plan their trips. A wide range of organizations are making their very own applications to enable clients to automate various tasks related to travel. Gone are the days when you needed to book your flights and hotels, rent a vehicle, check in, and plan your itenerary alone. Artificial intelligence and the virtual assistants inside these applications gather information from you through simple prompts, at that point automate the tasks for you.

Customer Assistance

United Airlines is utilizing Amazon’s Alexa to reply to routine traveler questions. In September 2017, United reported a collaboration with Amazon’s Alexa. The feature is known as the United skill. To begin, travelers should simply add the United ability to their Alexa application and after that begin posing questions. Alexa answers regular questions effectively, for example, the status regarding a trip simply by number, check-in requests, and accessibility of Wi-Fi on a new flight. The reviews so far have been mixed, which suggests there is still more learning and adaptation for this technology. It may be a few more years before AI can completely take over client assistance.

AI Maintenance Prediction

Airline companies are wanting to use AI innovation to predict potential failures of and plan maintenance on aircraft. Leading aircraft producer Airbus is taking measures to be able to improve the dependability of aircraft through enhanced maintenance. They are utilizing Skywise, a cloud-based data storing framework. It oversees its fleet in gathering and recording a massive quantity of real-time information.

AI in predictive maintenance analytics is also establishing patterns and best practice methodologies for how and when the airplane maintenance should be completed. Enhanced, more predictable maintenance means fewer unscheduled delays and a better traveler experience.

In the meantime, organizations are making changes to screen the “health and status” of their aircraft in real-time. Air Canada CEO Calin Rovinescu says advanced analytics are required to keep planes flying over 16 hours every day. AI frameworks could anticipate when maintenance is required even before a part fails, incorporating quicker fixes and avoiding downtime for the aircraft. So-called “wellbeing monitoring” of aircraft enables data to be examined more rapidly and precisely, enabling preventive activities to be quickly performed.

Data Management

Enormous data volumes are being produced and used.  As the aviation industry embraces AI, this volume will inevitably lead to some data confidentiality risks. The need to appropriately govern and secure information goes hand in hand with this increased adoption of AI benefits. Several breaches and events, such as one where Emirates, a leading airline, leaked client data to third parties without approval. It was discovered that key customer details: name, email, schedule, telephone number and even passport number were exposed to third-party service providers. Even though Emirates policy specifically expresses that there will be no information sharing, travelers need to be cautious.

Preventing future disasters

Possibly one of the most important applications of AI-based analytics, however, might be in identifying risks to the security of aircraft in front of a tragedy –such as the crash of Lion Air Flight 610, even when a failure of the automated control system onto a prior flight might have indicated a major security issue. NASA Ames Research Center at Silicon Valley is significantly engaged in aviation-related AI, and a few of NASA’s jobs there is focused on distinguishing”anomalous operations” within data from commercial aviation–events which could be precursors to possibly larger issues. Since commercial aviation’s safety record is so good–much better than driving, for example–it is much more difficult to recognize those few cases where there is an anomaly that might represent a safety problem.NASA has performed some first development of algorithms related to anomaly detection and episode precursor diagnosis, and it’s started the process for gathering feedback from experts in the area. The airlines upload some subsets of their flight-recorded information to Mitre, which performs analysis and provides feedback on possible problems. (The data is shared supplied by the airlines.)The hope for those analytics being developed at Ames is that the AI can discover patterns of anomalies in flight info that may be indicative of a systematic issue with aircraft. Analysts would love to discover as soon as possible and produce some kind of a reduction to stop it happening again. Up to now, instead of AI replacing humans in air, AI and human specialists have proven to be complementary–a venture that can save human lives.

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Big Data Business Intelligence Data Analytics Data Monetization

7 Ways To Grow Your Business with Data Monetization

It’s estimated that by the year 2020 revenues around the world for big data and business analytics are going to exceed $203 billion. With all this earning potential, it makes sense to want to get your business “in on it.”

One of the best ways to do this is with data monetization. After all, data is the new currency.

In the past, businesses in the information technology sector have always been deriving value from data. However, the ability to effectively use and monetize data is now impacting virtually all types of business.

This means that driving value from data is something you can implement in your own business strategy. What many people may not realize is that this process can be extremely challenging.

As a result, you need to learn some helpful tools and actionable steps you can take to monetize data for your business.

If you are interested in learning more, then keep reading.

1. Decision Architecture

When thinking about analytics, the majority of organizations want to know how their business is performing, along with what information is needed to answer various performance questions. While this can help to inform and to describe what is taking place in the organization, it doesn’t enable any type of action.

Instead, the goal needs to be to capture the decision architecture of specific business problems. Once this is done, you can build analytics capabilities to create a diagnosis that enables decisions and actions. Leaders need to focus on making decisions that are based on data, rather than just answering questions about what already happened.

2. Stop Revenue Leaks

Busy healthcare providers, clinics, and hospitals can easily lose track of the services being rendered. Every procedure has an assigned code and description. Each of these often includes errors.

By using analytics, the organizations can identify patterns associated with procedures and codes, flagging patient invoices for possible errors or even missing charges. Intelligent data use can also help the organizations improve the ROI of their collections process.

3. Data Aggregation

The method that is at the very bottom of the pyramid, but that represents the biggest opportunity to earn, is data aggregation.

This means taking data from various sources, including your business, and merging it together to create a larger, integrated picture. While the data sources on their own may be interesting, when they are combined, they become valuable.

An example of this would be your credit report. The information credit bureaus aggregate, such as the credit cards you have, if you have a mortgage, and if you pay your bills on time, can be sold for a profit.

By aggregating this information into a single report, the information can be sold to interested parties. While there isn’t a lot of money in this, it’s still money.

4. Infer Customer Satisfaction

Many organizations use social media and survey sentiment to understand the levels of customer satisfaction. By combining data from several sources, airlines can now infer how satisfied a customer is based on factors, like where they are sitting.

This process requires information to be aggregated from several sources. However, in the airline example, you can use the information to determine if a customer is going to fly with you again, and if not, offer a free upgrade or other incentives.

5. Embrace a New Revenue Model

Today, data is actively changing relationships companies have with customers. Manufacturers of tangible goods are now supplementing the products they sell with flexible software options and services to offer customers new choices and new revenue streams.

Additionally, these companies are providing much higher levels of personalization. Across several industries, new economic models are starting to be explored – like replacing an auto fleet with self-driving cars.

In this example, rather than selling data, people are going to pay you to solve a problem or to provide answers. This is a unique revenue model.

The value lies in the fact that you have married your data to the mission of a business and solving a problem that businesses have. This is what is going to generate revenue.

6. Detect Piracy and Fraud

Most online retailers sell products on several different websites. Supplemental sales channels typically include eBay, Amazon.com and other online marketplaces maintained by larger retailers, like Best Buy and Walmart.

Selling through these channels is extremely data-intensive, since the customer types, products, and pricing can vary greatly across the channels. In some case, the price discrepancies are so large that they signal possible piracy or fraud.

If you sell across dozens of e-commerce websites, then consider building databases of your own products and your unique pricing. You can then compare this to existing expected pricing data, allowing you to detect stolen goods or suppliers who are mispricing their goods.

With this information, it’s possible to go to the marketplace and make a report stating that they believe someone is selling stolen items.

How Can You Use Data Monetization Methods for Your Business?

Data monetization is an ever-evolving concept that offers opportunities to earn profits by providing information to others. Your business can take advantage of this by utilizing the tips and information here.

The fact is, there are already countless businesses, in all industries, that are currently using data monetization. Now is the time to begin doing so, too, as it offers huge revenue stream potential.

If you are convinced that data monetization is something you want to use for your company, then contact us. We can provide you with help and information about how this process works.

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Big Data Data Analytics

How Social Media Data Can Boost Your Sales

Social media data is one of the richest sources of information available to modern marketers, influencers, website operators and data scientists. One of the challenges, though, is finding the right way for your operation to harness that power. Let’s take a look at how social media data can boost your sales.

The Raw Data in Social Media

There are plenty of ways to deploy data analysis tools to both mine data and derive insights from it. These include looking at data points like:

  • Shares and likes
  • Mentions
  • Hashtags
  • Click-thrus to URLs
  • Addition and loss of followers
  • Demographic groups
  • Influencer networks

It’s important to not obsess about the vanity metrics, though. All the followers in the world don’t mean much if they’re not translating into sales. For example, tracking codes need to be embedded with URLs to verify that social media followers are moving into the marketing funnel. By using embedded referral codes specifically designed for your social media presence, you can keep tabs on whether followers are converting.

Finding useful sources of data is also important. There are plenty of free options, such as pulling marketing data from:

  • Facebook Insights
  • Google Analytics
  • Twitter Analytics
  • LinkedIn Analytics

Some social media companies, such as Instagram, also offer paid access to their data. In many cases, however, it’s possible to pull data using other solutions, such as web scrapers.

If your setup is properly configured, you should be able to track engagement as it moves through your marketing funnel. For example, your Twitter-specific referral code will show up in both Twitter Analytics and Google Analytics, making it easier to tie user behavior to particular campaigns.

Developing Insights from Social Media Data

The best pool of information means nothing if you can’t use data analysis tools to derive insights from it. Foremost, you need to know what goals your business is shooting for. You can make a checklist that covers things like:

  • Acquiring new customers
  • Developing a more widely recognized brand
  • Making decisions based on social media data
  • Responding better to customer concerns
  • Fostering a superior customer experience

Let’s say your business wants to focus on social media as a way to quickly identify customer complaints. One great thing about social media is that folks quitting your brand might not call your customer support hotline to express their discontent, but you can bet they’ll complain to their friends online about your company’s products and services.

One way companies take advantage of this is sentiment analysis. This is a data-driven decision-making tool that focuses on gathering data regarding positive, negative and neutral statements that people make about companies online. By regularly scanning social media, these firms are able to “read the room” at a global scale. Instead of letting customer anger fester out of sight, sentiment analysis allows companies to get out in front of problems.

There is also plenty of information hiding in the networks that folks form on social media. Marketing data can be developed by creating network maps of their social associations. For example, a retailer that wants to build an influencer campaign on Instagram wants to know which users are going to spread ideas the fastest. They can then supply those Instagram influencers with:

  • Early access to product details
  • Marketing and brand materials
  • Product demos and samples
  • Immediate access to top-tier customer and technical support
  • Opportunities to meet with key players
  • Invitations to company-sponsored events

Driving Business Decisions

Using marketing data should not be seen as a one-way street. There’s a lot that can be learned by monitoring the social media sphere. Trend analysis, for example, can allow companies to get ahead of what people are excited about. A clothing company might focus on analyzing trends coming into each of the fashion seasons, allowing them to handle ordering issues like:

  • Choosing quantities
  • Conveying customer demands to overseas buyers
  • Establishing transport times to put products in stores in time for trends to peak
  • Re-ordering items that are expected to sell out

It’s important to develop a data-driven culture at a company in order to make the most of social media data. Stakeholders and decision-makers shouldn’t be stuck wondering what the social media budget is actually doing. By deploying dashboards, data scientists at companies can provide real-time, engaging insights to those parties. In no time at all, folks who once questioned data and social media expenditures will be checking the dashboards on their cellphones to see how campaigns are unfolding.

Building this sort of data-centric business culture requires an investment. Infrastructure has to be put in place to ensure data scientists on your team have the servers they need to pull data, clean it up, analyze it and generate insights. Done the right way, though, building out this sort of infrastructure can help you get a better grasp on how customers interact with your brands, products and services.

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