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Topics - Shourov Saha

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How 6 Brands are Using Machine Learning to Grow Their Business

As technology continues to evolve, consumer behavior does too — and retailers need to stay ahead of the curve. And using data is one way to make sure you stay ahead of trends and give customers products that solve their problems.

With the Internet creating more data than ever before, big data has become an industry buzzword. Big data typically refers to “data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them,” and which used to be impossible for any but the largest, most technically savvy company to collect and analyze.

But simply gathering vast amounts of data alone isn’t particularly helpful. What is valuable is digging through all those numbers to find significant insights about trends, customer preferences, and even future predictions.

So, how are retailers actually utilizing the insights and data they gather? We’ve rounded up six examples of well-known brands that are using big data, artificial intelligence (AI), and machine learning to optimize their processes, anticipate their customer needs, and — in the case of one brand — even identify the early stages of pregnancy.

But before we get ahead of ourselves, let’s take a deeper look at the ins and outs of machine learning specifically and how retailers can leverage it in their business.

What is Machine Learning?

Machine learning is one of the key technologies that’s increasingly valuable for retailers as more and more businesses take advantage of big data.

Before going any further, it’s important to understand what is meant by the term machine learning — it is related to artificial intelligence (AI) but the two aren’t the same. AI, broadly speaking, refers to a computer’s ability to make decisions in a way that imitates human logic.

However, machine learning is the way in which a computer can “learn” these logical rules without simply being programmed to do things a certain way. In other words, machine learning allows a computer to continuously update its understanding of the rules as it sees more examples of how humans react to various external factors.

This type of technology has become more widespread as hardware improvements make it possible to handle the sheer volume of data and run complex algorithms. Basically, machine learning is much easier to use these days because of technology has evolved to make it easier for retailers and consumers to use.

The most well-known example of machine learning in action is the Google search engine (yeah, the website you use every day). Google uses each query (i.e. the phrases you enter into the search bar) that a person runs as a data point for teaching the algorithm about humans’ search behavior and intent. The more the Google search engine learns, the better it is at answering questions and offering relevant sites for your searches.

But machine learning isn’t just for multinational tech companies — it’s possible to use it in a retail context as well.

The Benefits of Machine Learning for Retail

Retailers can apply insights retailers from big data and machine learning in a number of ways, especially for optimizing the supply chain, product sourcing operations, and for marketing and customer acquisition.

For example, a recent McKinsey study discovered that “U.S. retailer supply chain operations who have adopted data and analytics have seen up to a 19% increase in operating margin over the last five years.”

The same study also noted that the biggest challenge U.S. retailers currently face is simply the lack of analytical talent and shared data across their company; the opportunity is there for those who can bridge this gap.

Some key ways you can use machine learning in a retail context include:

     * Offering highly personalized product recommendations for advertising and promotions (for automated upselling and tailored,
        complementary product suggestions based on previous purchases).
     * Optimizing your pricing strategy with real-time, dynamic prices. An algorithm can take key pricing variables into account, including
        supply, seasonality, and demand and offer you insights on how to adjust your prices accordingly.
     * Optimizing inventory planning and predictive maintenance. Systems can detect “freshness” of perishables and wear and tear on
        machinery, and predict demand in advance for ordering stock.
     * Optimizing routes for more efficient deliveries according to past data and behavior.
     * Sales and customer service forecasting systems to predict customer behavior and allow retailers to deploy sales and customer
        service staff where they will be most effective.
     * Website content customization: Personalize the online experience based on an individual’s location, purchasing history,
        demographic, and more.
     * Segment your prospective customers based on previous behavior rather than self-identification.

The greatest value of machine learning is its predictive nature — it allows companies to use past and present customer and operations data to predict future behavior and trends. For example, let’s look at a customer who is normally a modest spender but has bought expensive planning materials around the same time each year for the past three years. Machine learning models could predict the most relevant time to offer these products again rather than wasting ad dollars at a time when the customer isn’t likely to make a purchase. Or, for high volume shopping periods like Black Friday Cyber Monday, machine learning can help retailers estimate how much inventory to stock compared to the rest of the year.

As a result, machine learning models help cut back on typical waste (such as unnecessary advertising costs and spoiled inventory) while optimizing marketing efforts to anticipate customer needs — leading to increased revenue and higher profit margins. For example, Target Corp. (one of the brands featured in this article) saw 15-30% revenue growth through their use of predictive models based on machine learning.

Examples of Machine Learning in Retail

Here are six examples of machine learning in a retail setting, illustrating the variety of use cases in which this technology can provide value.

Target: Predicting Pregnancy

As a “one-stop shop” for everything from clothes to groceries to household items, Target wanted to encourage shoppers to buy a wider variety of items from them rather than their competitors. Research has shown that the most typical time for a shopper to alter their store of choice is during a big life change: graduation, marriage, childbirth.

Target hired a machine learning expert and statistician, Andrew Pole, to analyze shopper data and create a model which could predict which shoppers were likely to be pregnant. After cross-referencing women’s common purchases who later registered with the Target baby registry (providing their due date in the process), Pole was able to identify key patterns.

These trends not only indicated pregnancy, but could pinpoint the current trimester of a woman’s gestation period (for instance, if a woman suddenly started buying certain supplements, she was likely in her first 20 weeks of pregnancy, whereas purchasing a lot of unscented lotion indicated the start of the second trimester).

This case study also illustrates the caution with which retailers must proceed in utilizing this type of insight. Target used this data to send coupons related to pregnancy and parenting to customers whose buying patterns fit the model. That included a 16-year-old girl whose father found out about her unintended pregnancy when she received these targeted promotions. Target later adapted their strategy to mix other offers in with the pregnancy focused promotions after finding that their customers felt uncomfortable with this degree of personalization.

Walmart: Anticipating Customer Needs

Retail giant Walmart has also implemented new technologies to anticipate customer needs and optimize operations. In 2015, the company tested facial recognition software as an anti-theft mechanism.

However, the discount giant also plans to use this machine learning technology to upgrade its customer service. According to Forbes, Walmart’s patent application for the machine learning tech that customer service can “be very expensive to maintain sufficient staff to provide great customer service. It can also be difficult to establish an appropriate staffing level that will provide proper customer service without excess staffing.”

The facial recognition software has the ability to recognize the level of frustration of customers at checkout and trigger an alert for a customer service representative to speak with the frustrated customer.

North Face: Robot Sales Associates

Outdoors clothing retailer North Face has been using artificial intelligence and machine learning to offer website users a highly personalized shopping experience called “Shop with IBM Watson.”

After downloading the app, shoppers speak right into their phone to access Watson, an AI system from IBM. Similar to a human salesperson who might help you select the right option, the virtual assistant walks users through a series of questions and learns from your answers to offer you the most relevant products for your preferences and needs.

Alibaba: Making Big Data Accessible for Smaller Retailers

Alibaba, a Chinese ecommerce platform similar to Amazon, is by some accounts the world’s biggest ecommerce marketplace. Unlike Amazon, however, which has traditionally been in the business of order fulfillment, Alibaba relies much more heavily on its retailers and considers itself a “retail ecosystem.” Because of this, they have prioritized big data analysis and one of their major features is to make that data more accessible to the smaller retailers who sell through their service.

Their latest application brings big data to the offline retail world so merchants can understand the bigger sales picture. For example, shoppers can order online for delivery from the Alibaba-backed grocery store HEMA. Or they can shop in-store, scan barcodes as digital price tags update in real-time, pay via their app, and get free delivery for their in-store purchases.

This allows Alibaba to capture this “offline” shopping behavior via the mobile app, which can be analyzed alongside the online data to offer a complete picture of customer behavior.

Amazon: Personalization and Predicting Supply and Demand

Amazon has one of the most famous recommendations engines of any ecommerce retailer, and for good reason; their machine learning algorithms work so well that 55% of sales are driven by these machine learning recommendations.

But the recommended products engine serves a dual purpose. It’s not only valuable in driving additional revenue through upsells and suggested products; the insights gained by these machine learning algorithms can also help Amazon to forecast predicted demand for inventory, making seasonal and trend-based supply decisions simpler.

Netflix: Giving Viewers The Entertainment They Want

Since its inception, Netflix has been using big data and machine learning to understand how its users consume television and film content and deliver the content the viewers want. This data has informed strategic decisions such as the way in which they release full seasons all at once, auto-play the next episode, and offer recommendations for how likely you are to enjoy a related film or show (their “% match” rating is the latest example of how they offer this type of data-based recommendation engine). This data has also informed all the original content they produce.

According to estimates from Netflix executives, machine learning insights save them $1 billion per year.


IOT / The Internet of Things for developing economies
« on: March 19, 2018, 10:24:40 AM »
The Internet of Things for Developing Economies

The United Nations’ International Telecommunications Union (ITU) and Cisco recently released a joint report (PDF) titled “Harnessing the Internet of Things [IoT] for Global Development.”  It reviews a broad range of solutions and global deployments of IoT technologies for developing economies, spanning energy, healthcare, agriculture and natural disaster relief, to name a few.  It demonstrates how simple IoT solutions can make a dramatic impact on human welfare, but also offers broader lessons for CIOs' business initiatives, such as cost-effective process management and improvement.

The 58-page report, which has been submitted as a contribution to the UN Broadband Commission for Sustainable Development, was launched in Honolulu by ITU Secretary-General Houlin Zhao and Cisco VP of Global Technology Policy Robert Pepper at the 38th annual Pacific Telecommunications Council conference, which brings together a variety of academics, policymakers, and executives from network and IT service providers and vendors (Disclosure: I am on the program planning committee for the event and moderated the discussion).

Many people view IoT as a means for manufacturers and service providers to improve operational efficiencies, for example, through better asset utilization.  In my latest book, Digital Disciplines, I focused on these and other ways in which IoT—together with complementary technologies such as cloud and big data—can enable new business strategies that drive differentiated customer value.  For example, GE can help airlines improve their service quality by maximizing the availability of its jet engines through better predictive maintenance, by, for example, extracting inferences based on sensors that detect, say, variations in engine rotation speed or oil pressure.  IoT applications are not restricted to businesses, of course; consumers can benefit from a wide range of connected things ranging from activity trackers to intelligent thermostats to connected vehicles.

But in the developing world, as the Cisco/ITU report makes clear, IoT can dramatically impact human welfare, because the things we often take for granted in the developed world—food, clean water, electricity, access to healthcare, and timely and adequate responses to human disasters—may be sorely lacking. Simple solutions such as networked temperature sensors on refrigerators containing vaccines or medicines literally can make a life or death difference.  Moreover, as the recent Flint, Michigan water crisis shows, even developed economies can benefit from such IoT solutions.  In addition, in today’s borderless world, improvements in one region can benefit others, say, through sustainable fishing, management of pandemics, or renewable energy use.

I found the insights and solutions described in the report to be eye-opening, not only from the perspective of basic human compassion but also because they show the breadth of applicability of IoT in solving basic problems, thus helping CIOs to better understand the immense potential of IoT, ways to leverage IoT for internal and external challenges, and in many cases, how to create and benefit from new IT and IoT-enabled business opportunities.  Moreover, rather than over-engineered solutions, CIOs can learn from the ingenious, simple, affordable solutions being deployed globally in a variety of sectors.

For example, in healthcare, the benefits of remotely monitoring refrigerator temperatures to maximize the safety and efficacy of medicines are clear, but one could argue that an even simpler solution could also work, say, a label that changes color if exposed to too high a temperature.  However, through remote monitoring, not only can the cycle time for problem resolution for a given refrigerator be accelerated, but wide-scale patterns of issues in the so-called “cold-chain” of delivery logistics—such as power loss or equipment failures—can be analyzed and the entire supply chain permanently improved.

Connected solutions offer additional benefits.  In the case of Ebola, “smart” bandages can monitor patients, and speed medical response.  Patterns in migration and interaction based on analyzing cell phone data can also aid in the control of emerging pandemics.

For agriculture, devices that contain not only sensors to monitor irrigation levels, but also actuators to control water flow can enable family farms to become much more productive, maximizing yield while conserving scarce water resources.

Sometimes, such connectedness can enable new business models.  For example, in the developing world, utility-provided electricity is either unavailable or unreliable. While solar panels are becoming more cost-effective, the capital outlay required to deploy them can be prohibitive in low-income areas.  But, remotely monitored solar panels can be offered to customers with little or no front-end capital expenditure, with subsequent payments then based on actual usage.

The Cisco/ITU report delves into numerous other applications, such as smart hand-pumps to improve access to water in villages where children might walk for an hour or more to get water from a well, sensors to monitor water purity, tagged livestock to reduce hoof-and-mouth disease, tsunami warning systems, sewage monitoring, wildfire risk management, and natural disaster management.  The developing world offers a perfect storm of these compelling human needs with a unique constellation of enablers.  And, the labor intensity of these regions can benefit greatly from the use of information to optimize processes for efficiency and quality.

To accomplish this, however, requires low cost, ubiquitous connectivity.  Fortuitously, the last decade has seen emerging economies leapfrog a technological generation of wireline networks by deploying the latest wireless technologies. In fact, 95% of the world’s population now has access to wireless networks, making such networks much more widespread than electricity or water.

More subtly, the fact that spectrum has not been so fully allocated to, say, legacy television broadcasting, means there is more white-space spectrum available for IoT applications.  And, a variety of networks can enable collection and aggregation of data from sensors and remote control of actuators.  At the low end, simple text messaging might be utilized, at the other extreme, 4G and emerging 5G networks, and increasingly, next-generation infrastructure will play a role, based on emerging technologies—such as those being explored by Google’s Project SkyBender or the Facebook-led—utilizing solar-powered planes that will remain aloft for months at a time to provide affordable, global internet access.

In the developed world, we will at first marvel, and then take for granted, connected things such as refrigerators that will automatically order more milk, eggs, or steak when needed, delivered in near-real time by drones and no doubt improving convenience and quality of life.  But in the developing world, IoT solutions such as connected refrigerators that maintain the safety and efficacy of medicines are doing something more important: helping to ensure not just the quality of life, but life itself.

« on: March 19, 2018, 09:16:27 AM »

The debate continues as to whether digital marketing is overpowering and surpassing traditional marketing or not. Many think that for the most part, digital marketing has taken over and traditional marking barely exists, if at all. Recent occurrences such as the magazine giant, Newsweek switching to totally digital publications cause ripples throughout the marketing arena. Over the last year or so traditional marketing had fallen nearly 160% while in the same time frame expenses for digital marketing increased over 14%. Are there any real advantages to using digital marketing over traditional means? And what is the big difference between these two anyway?

Defining Traditional Marketing

There are many facets of traditional marketing and examples might include tangible items such as business cards, print ads in newspapers or magazines. It can also include posters, commercials on TV and radio, billboards and brochures. Traditional marketing is anything except digital means to brand your product or logo. Another overlooked means of traditional marketing is when people find a particular business through a referral or a network and eventually you build a rapport with them.

Defining Digital Marketing

The world of digital marketing continues to evolve and as long as technology continues to advance, digital marketing will as well. Examples of digital marketing include things like websites, social media mentions, YouTube videos, and banner ads. Specifically, digital marketing is similar to traditional advertising, but using digital devices. However, digital marketing is considered a form of inbound marketing and its goal is for people to find you. Businesses put content (or ads) out for individuals to find. People may conduct an organic online search, a paid search, find your business on a social network or by reading content that has been published online such as a blog or an article. The more they see you or your content, the more familiar they will become with your brand and they will eventually develop a trust and a rapport with you through this online presence.

Traditional Marketing’s Advantages and Disadvantages

Because of its longevity, people are accustomed to traditional marketing. Finding ads in magazines and newspapers, or reading billboards are still familiar activities and people still do them all the time. Most of the time, traditional marketing is reaching only a local audience even though it is not limited to one. One of the primary disadvantages of traditional marketing is that the results are not easily measured, and in many cases cannot be measured at all. In most cases, traditional marketing is also more costly than digital marketing. And perhaps the biggest disadvantage today is that traditional marketing is static which means there is no way to interact with the audience. It’s more like you are throwing information in front of people and hoping that they decide to take action.

Digital Marketing’s Advantages and Disadvantages

One benefit to using digital marketing is that the results are much easier to measure; and another is that a digital campaign can reach an infinite audience. It is also possible to tailor a digital campaign to reach a local audience but it can also be used on the web and reach the entire globe when appropriate. Digital marketing is also a very interactive means of reaching an audience since it makes use of social outlets. There can be plenty of direct contact between the audience and the business which means that the business can get some very valuable consumer feedback. One of the disadvantages to using digital media marketing strategies is that it can take some time to realize measurable success.

Is there a realistic balance between the two?

The world has transitioned into a very digital environment. Not only are magazines going digital, we perform many of our daily tasks such as banking online and much of our reading is done on e-readers. Because of the rise of the digital age, it just seems like common sense to invest in a digital campaign. Even though traditional marketing still has a place, it is diminishing in our digitally based world. For today’s businesses, it is imperative to have a website and use the web as a means to interact with their consumer base. There are some successful traditional marketing strategies, particularly if you are reaching a largely local audience, but it is important to take advantage of digital marketing so as to keep up in today’s world.


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