Category: Insights

The Situation

Recently, True Interaction lead backend developer Darrik Mazey had experienced some troubling hardware issues – this time, it was outside of his hectic True Interaction project schedule.

His family’s 2-year old LG refrigerator went down, requiring multiple visits by the repairman to diagnose and fix, pulling him away from professional commitments, and generally making life annoying as he tried in vain to keep his family’s perishables from spoiling.

Leveraging Social Media, the ‘Old Fashioned’ Way

During this process, he did learn the benefits of leveraging social media to improve his customer service experience with LG.

When Darrik began tweeting about the fridge, LG offered a $50 food reimbursement, and extended his warranty. But this was just the beginning of further product woes, which eventually became the catalyst for an awesome solution.

Problems with the refrigerator persisted after the first “repair” job, and Darrik eventually had to rent another fridge while further diagnosis continued. In all, he estimated the cost due to missed work and equipment rental to be over $1500, no small amount for a hard-working family man.

Implementing a Real-time Solution with Xymon

Being the veteran coder that he is, Darrik resolved to ensure that, should the hardware fail again, he would be informed about it immediately via Xymon. In a nutshell, Xymon monitors hosts, network services, and anything else you might configure it to do via extensions. It can periodically generate requests to network services — http, ftp, smtp and so on — and record if the service is responding as expected.

In a recent personal blog post, Darrik recounts his refrigerator woes, and how he configured a controlbyweb temperature module, connected it to the fridge, networked it via wireless AP and monitored the fridge and freezer temperatures via Xymon 4.3.10.

Darrik monitors his Xymon network via its own Twitter account which conveniently tweets him with status updates, and / or if something is awry. Now Darrik receives tweets as soon as the fridge goes above 40 degrees.

Maximizing the Value of Social Media in the SPIME Era

Learning from his recent foray into using social media to improve his customer service experience, he’s now configured the system so that — should the LG fridge malfunction again — it will @LGUS, LG’s official Twitter account, with a status message, the current temperature inside the fridge, and a #fail hashtag.

We are living in an age where, with a little work and ingenuity, products can be configured to actually hold their parent company accountable — in public — regarding their quality, and the customer service surrounding them. We’re nearly touching upon what futurist Bruce Sterling (who recently shared his take on Google Glass) describes as the “spime era“. Sterling uses the term SPIME for connected, information rich objects that operate and communicate in an internet of things.

Since the onset of Friendster in 2002, social media has evolved from a form of informal communication, to the hottest marketing platform since TV advertisements, to a very viable channel for consumers to elicit and receive customer service. Certainly entrepreneurs and small business owners need to understand that their products and services live and die by consumer sentiment (robotic or otherwise) on social media. While this sounds like a precarious situation, the flipside is that with not too much effort, entrepreneurs and SMBs can preemptively mine social media regarding their products and services in order to unearth incredibly valuable intelligence. This includes discovering not only general consumer sentiment, but also other perspectives on their products and services they might never have considered, such as creative suggestions on improvement and unique new use cases, as well as using social media as a dragnet for discovering bugs, service gaps, or product flaws.

With that in mind, entrepreneurs and SMB owners should establish their social media intelligence network as soon and as thoroughly as possible in order to monitor relevant SM channels and get into the trenches to interface directly with their consumers. Utilize customers for the intelligence that they can provide, and reward them – even if it’s just recognition or a “thank you” – when they provide insight on the product or service, even if it’s not directly to the organization. When businesses step in, get involved and interact, and consumers discover that they are listening and care about their opinion, they’ve just converted a customer for the long-term.

The Future

So what’s next? What happens when consumers can make decisions about the products they choose to purchase, based upon products themselves sharing their real-time data on a social media channel? What happens when brands are judged by their ability to manage the self-communicating products they’ve birthed? And considering all this, where will manufacturer responsibility for their products end or extend in the near future? All of this is fascinating and mind-boggling stuff to ponder for a moment. I’d love to hear your thoughts.

by Michael Davison

I recently blogged about blockchain where I described the technology and why it could be a revolutionary development, and also explored its various applications across a number of industries. Today, let’s take a closer look at blockchain in entertainment and media: its various applications, how it is transforming the industry, and its implications for the near future.

Quick Recap

A blockchain is type of data structure that can be purposed to create a digital ledger of transactions that can be shared among a distributed network of computers. By using cryptography, each account on the network may access and manipulate the ledger securely, without the need for any central authority or middleman – this is the supreme concept to take away. Blockchain is:

Reliable and available. Because a wide circle of participants share a blockchain, it has no single point of failure and is designed to be resilient in the face of outages or attacks. If any node in a network of participants fails, the others will continue to operate, maintaining the information’s availability and reliability.

Transparent. Transactions on the blockchain are visible to its participants, increasing auditability and trust.

Immutable. It is nearly impossible to make changes to a blockchain without detection, increasing confidence in the information it carries and reducing the opportunities for fraud.

Irrevocable. It is possible to make transactions irrevocable, which can increase the accuracy of records and simplify back-office processes.

Digital. Almost any document or asset can be expressed in code and encapsulated or referenced by a ledger entry, meaning that blockchain technology has very broad applications, most as yet unimagined, much less implemented.

Given these characteristics, blockchain technology will likely be a catalyst for transformative innovation in nearly every industry.

Blockchain as purposed in Entertainment and Media

Payments

One of the most obvious applications of blockchain in the media is its ability to support micropayments that can be processed without the need for an intermediary payment network or its fees. Generally speaking, without blockchain, intermediary payment fees are too cost-prohibitive to enable micro-payments of values less than $1. Chris Dixon explained this wonderfully in an an article by venture capitalist Marc Andreessen:

Ostensibly very little would change from a consumer standpoint; consumers are used to purchasing songs for $.99. But behind the transactional curtain, everything would be transformed.

Without a middleman, when digital objects can be cryptographically associated with their creators, then there is no need for distribution channels. Think about that for a moment. This technology can put all kinds of media directly in the control and management of their creator, obviating the need for iTunes, Netflix, or Amazon. Or record labels. Or publishing houses. Blockchain has the potential to erode away the financial paradigms that conglomerate media companies have been using for the past century. Says Bruce Pon:

Today, we are already seeing startups that are exploring new payment models through blockchain technology that are focused upon bringing more value to content creators. Ujo, in their own words, is a “home for artists that allows them to own and control their creative content and be paid directly for sharing their musical talents with the world.” The Ujo platform uses blockchain technology “to create a transparent and decentralized database of rights and rights owners, and automates royalty payments using smart contracts and cryptocurrency,” says Phil Barry, founding partner at Edmund Hart, which oversees Ujo. “We hope that it will be the foundation upon which a new more transparent, more efficient and more profitable music ecosystem can be built.”

Scarcity

Generally digital objects can lose value because they are easily copied. We see this especially in the area of pirated music, movies and TV. But because blockchain makes it possible for creators to register origin of work and set sharing permissions, structure the means of exchange that they’re willing to accept, it is possible create conditions for “digital scarcity”.

Consider a situation where an artist creates a piece of music in .mp3 format, and programs the ability for only 1000 people to listen to it, with the price of the .mp3 increasing with each new listener.

Relationship to consumer

Because blockchain precludes the need for a middleman, the technology creates new opportunities for large corporations to get closer to their customers and consumers. Because the playing field allows for individual creators to connect with their consumers directly, the onus on the bigger media companies is to operate more nimbly, and to offer more varied and interactive pricing models for their content based upon every individual consumer’s actions and purchases. And because provenance, payment, and distribution becomes simpler and less expensive to manage with blockchain, bigger media companies can concentrate more on creating quality content itself.

Reflection

While we haven’t seen it just yet, blockchain technology promises to transform a myriad of industries – especially media and entertainment. To the media consumer, it likely means more access to more content, from more creators, on a much more personal, secure, and granular level. For content creators, it means a much simpler attribution, payment, and distribution system, and the ability to be creative with payment models and the concept of “digital scarcity”. It will be exciting to see what happens.

If you are an SMB owner and want to learn more about blockchain, check out my first post on the subject. And by all means, get in touch with an expert. The time is now to begin exploring implementation of blockchain technology in your business. Consider these statistics:

– A billion dollars in venture capital has flowed to more than 120 blockchain-related startups, with half that amount invested in the last 12 months.1
– Thirty of the world’s largest banks have joined a consortium to design and build blockchain solutions.2
– Nasdaq is piloting a blockchain-powered private market exchange. 3
– Microsoft has launched cloud-based blockchain-as-a-service. 4

We’ve got the Experience you Need
True Interaction has logged 100,000’s of hours of research, design, development, and deployment of consumer products and enterprise solutions. Our services directly impact a variety of industries and departments through our deep experience in producing critical business platforms.

We Can Integrate Anything with… Anything

Per the endless variable demands of each of our global clients, TI has seen it all, and has had to do it all. From legacy systems to open source, we can determine the most optimal means to achieve operational perfection, devising and implementing the right tech stack to fit your business. We routinely pull together disparate data sources, fuse together disconnected silos, and do exactly what it takes for you to operate with tight tolerances, making your business engine hum. Have 100+ platforms? No problem. Give us a Call.

By Michael Davison

It’s behind the Tesla autopilot feature. It’s your recommendations from Netflix. It’s when Siri recognizes your speech and serves you results. It’s the foundation for your credit card’s fraud detection technology. We see the application of neural networks and machine learning all around us today in nearly every aspect of life.

With the exponentially increasing volumes and varieties of data, the advent of cheaper and faster computational processing, and ubiquitous affordable mass data storage, neural networks aren’t just for Google and Microsoft anymore. It’s important for small and medium business owners to know what neural networks are, what they can do for their business, and also what their limitations are.

So What is a Neural Network?

Let’s grab a definition from Dr. Robert Hecht-Nielsen, an early pioneer in neural networks in the 1980s and 1990s. He defines an artificial neural network (ANN) as:

An ANN mimics certain features of the brain’s physical structure and information processing, with a web of neural connections that consist of myriad interconnected and layered simple processing elements. Akin to its biological sibling, in an ANN:

1. Each processing element (essentially a neuron) receives inputs from other elements
2. The inputs are weighted and added,
3. The result is then transformed (by a transfer function) into the output.

The transfer function may be a step, a sigmoid function (S-curve), or hyperbolic tangent function, among others.

ANNs are basic learning devices in the form of hardware or software. In both cases, the fundamental idea is to assemble several single simple processors that interact through a dense web of interconnections, which result in a network architecture that is unlike the sequential linear processing and architecture of conventional computer systems.

How do Neural Networks Differ from Conventional Computers?

Conventional computers are good at numerical computation; they apply formulas, decision rules, and algorithms instructed by users to produce outputs from the inputs. A neural network, on the other hand, is not a general-purpose problem solver. It is good at complex numerical computation for the purposes of solving system of linear or non-linear equations, organizing data into equivalent classes, and adapting the solution model to environmental changes. But it is not good at such mundane tasks as calculating payroll, balancing checks, and generating invoices. Nor is it good at logical inference – a job suited for expert systems. Therefore, business leaders must know when a problem could be solved with an ANN; moreover, to make an ANN work, it must be tailored specifically to the problem it is intended to solve.

ANNs, like people, learn by example. They improve their own rules; the more decisions they make, the better the decisions may become. Data scientist and entrepreneur Jeremy Howard describes this phenomenon quite colorfully:

An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Think of it simply as a branch of statistics, designed for a world of big data, where the most common application of machine learning is to make predictions.

Applying Neural Networks to Different Industries

Because a neural network must be built and tailored specifically to the problem it is intended to solve, you can’t just slap on a machine learning solution someone else did for their own context and set of data. The best way to determine if you can leverage neural networks in your own business and then reap the gains achieved by them is to learn and understand how neural networks intersect and function across a breadth of different industries; this will inform your own specific situation. I’ve shared several examples for you below.

Marketing

In marketing, we identify customers likely to respond positively to a product or service, and target any advertising or solicitation towards them. Target marketing involves market segmentation, where we divide the market into distinct groups of customers with different consumer behavior. Neural networks are well-equipped to carry this out by segmenting customers according to basic characteristics including demographics, socio-economic status, geographic location, purchase patterns, and attitude towards a product.

Unsupervised neural networks can be used to automatically group and segment customers based on the similarity of their characteristics, while supervised neural networks can be trained to learn the boundaries between customer segments based on a group of customers with known segment labels, for example: frequent buyer, occasional buyer, rare buyer. Machine learning can save your organization both time and money by ensuring that you avoid contacting customers who are unlikely to respond. One study showed that neural networks can be used to improve response rates from the typical one to two percent, up to 95%, simply by choosing which customers to send direct marketing mail advertisements to. Neural networks can also be used to monitor customer behavior patterns over time, and to learn to detect when a customer is about to switch to a competitor.

Retail & Sales

Neural networks are excellent in the realm of sales forecasting, due to their ability to simultaneously consider multiple variables such as market demand for a product, a customer’s disposable income, population size, product price, and the price of complementary products. Forecasting of sales in supermarkets and wholesale suppliers has been shown to outperform traditional statistical techniques like regression, as well as human experts.

Another important area where retail and sales can benefit from neural networks is in shopping cart analysis, such as gathering and inputting information relating to which products are often purchased together, or the expected time delay between sales of two products.

Retailers can use this information to make decisions about the layout of the store: if shopping cart analysis reveals a strong association between products A and B then they can entice consumers to buy product B by placing it near product A on the shelves. If there is a relationship between two products over time, say within 6 months of buying a printer the customer returns to buy a new cartridge, then retailers can use this information to contact the customer, decreasing the chance that the customer will purchase the product from a competitor.

Banking & Finance

One of the main areas of banking and finance that has been affected by neural networks is trading and financial forecasting. Neural networks have been applied successfully to problems like derivative securities pricing and hedging, futures price forecasting, exchange rate forecasting and stock performance and selection prediction since the 1990s.

But there are many other areas of banking and finance that have been improved through the use of neural networks. For many years, banks have used credit scoring techniques to determine which loan applicants they should lend money to. Traditionally, statistical techniques have driven the software. These days, however, neural networks are the underlying technique driving the decision making. Credit scoring systems can learn to correctly identify good or poor credit risks. Neural networks have also been successful in learning to predict corporate bankruptcy.

Insurance

The insurance industry can leverage neural networks in a similar means as the marketing industry: policy holders can be segmented into groups based upon their behaviors, which can help to determine effective premium pricing. And like the banking and finance sectors, the insurance industry is constantly aware of the need to detect fraud – neural networks can be trained to learn to detect fraudulent claims or unusual circumstances. Competition is fierce in the insurance industry, and when a policy holder leaves, useful information can be determined from their history which might indicate why they have left. Using machine learning to manage the offering certain customers incentives to stay, like reducing their premiums, or providing no-claims bonuses, can help to retain good customers.

Telecommunications

Machine learning offers telecommunications organizations a clear opportunity to ascertain a much more complete picture of their operations and their customers, as well as to further their innovation efforts. Some companies are using a series of neural networks to analyze customer and call data in order to predict if, when, and why a customer is likely to leave for another competitor. Many telecommunications organizations use machine learning to help predict the effects of forthcoming promotional strategies, as well as sift through and refine data to find the most profitable customers.

Other uses of neural networks in telecommunications include:

– Optimizing routing and quality of service by analyzing network traffic in real time

– Analyzing call data records in real time to identify fraudulent behavior immediately

– Allowing call center reps to flexibly and profitably modify subscriber calling plans immediately

– Tailoring marketing campaigns to individual customers using location-based and social networking technologies

-Using insights into customer behavior and usage to develop new products and services

Operations management

Neural networks have been used successfully in operations management, particularly in the areas of scheduling and planning. R&D regarding the scheduling of machinery, assembly lines, and cellular manufacturing using neural networks has been increasingly prevalent over fifteen years. Other scheduling problems, like timetabling, project scheduling, and multiprocessor task scheduling have also been addressed with neural networks.

The use of neural networks in various operations planning and control activities cover a broad spectrum of application, from demand forecasting, to shop floor scheduling and control. Neural networks have also been used in conjunction with simulation modeling to learn better manufacturing system design.

Operations management also benefit from neural networks in the area of quality control, as neural networks can be integrated with traditional statistical control techniques to enhance their performance. Examples of this include a neural network used to monitor soda bottles to make sure each bottle is filled and capped properly.

Neural networks can also be used in diagnostics, and have been used to detect faults in electrical equipment and satellite communication networks. Project management tasks have also been tackled by using neural networks to forecast project completion times for knowledge work projects, or to predict workloads and delivery times in software engineering and development projects.

Conclusion

It’s apparent that the application of neural network technology is having disruptive effects and is becoming more and more pervasive in common business operations – even across the SMB market – with every passing day. My advice for business leaders is to take the time to thoroughly learn and understand what the technolgy implies, so you may begin to identify use cases and scenerios within your own business ecosphere. Research and identify the possible opportunities or insights that may be gleaned by plugging into Big Data and/or employing data vendor systems. And by all means, seek the consultation of an expert – one that seeks to highlight and understand the key critical functions of your business, that identifies what parts and/or interaction points you need to improve, and then helps you articulate a realistic, cohesive plan to develop a scalable solution.

Sources

Chartiere, Tim. Big Data: How Data Analytics Is Transforming the World.
Lee, Eldon. Artificial neural networks and their business applications.
Smith, Kate and Jatinder, Gupta. Neural networks in business: techniques and applications for the operations researcher.

By Michael Davison