The Business Behind That Perfect Party Dress

So we’ve been an entire month into the New Year, and while we were still in the post-party season detox mode, retailers across the US were already out with up to 80% discounts on the merchandise that was “fresh” and “new in” just before Christmas. The holiday season of 2016 saw an overall surge in discounts after the Black Friday/Cyber Monday sales; a shift in the dynamics of retail sales calendar in the States accounted to be driven by millennials who apparently shopped way early before Thanksgiving, and more later.

As Black Friday virtually stretched over for two months in 2016, e-tailers have been more cautious than ever in rolling out new stock. How has that affected the party-wear business? Intelligence Node investigates with its proprietary SaaS-based tool, Incompetitor™ analyzing data points derived by mapping 1 billion unique products across 130,000+ brands for more than 1100+ categories every day.

Traditionally party wear dresses are broadly categorized as ‘evening dresses’. Google search queries for ‘evening dresses’ decreased by 4% for the US and 3% for the UK in 2016. But it doesn’t necessarily confirm a declining interest in party dresses. ‘Cocktail dresses’ in the US went up by 13%  and ‘party dresses’ up by 13% in the UK. Off shoulder  ‘bardot dresses’ were searched 162% more this past year.

If we have to describe the mood for party wear business in 2016, we’d use “cautious”.

We looked at the generally favored timeframe for introducing new stocks in partywear, which is early December, just at the hem of Black Friday weekend in November. Our data indicates a mere 2% increase in average in stock dresses compared to Dec 2015, despite in stock in the apparel category going up by 35% overall. This could be attributed to Black Friday looming over since September and the fact that this is the year ‘see now, buy now’ business model was adopted in one form or another by a rising number of brands and retailers. It renders the time-frame for buying partywear hazy, giving rise to continual discounting and simultaneous collection launches since as early as September first week.

Jumpsuits and skirts fared slightly better, 3% & 5% respectively, giving how velvet overalls and metallic pleated skirts have been a rage in the evening wear category. Search queries for ‘jumpsuit’ went up by 49% in the US; zooming in, ‘black jumpsuits’ have been searched 51% more according to Think with Google Report. Pleated skirts were having a moment, with a 33% increase in interest by end of 2016.

Discounting trends across different price/market segments for the last four months show an interesting insight into how conventional retailers differ from fast fashion brands and luxury e-tailers when it comes to holiday discounts. Macy’s, JC Penny, Barney’s, Nordstrom etc. still give heavy discounts over Thanksgiving weekends, whereas the newer, more dynamic players like Zara waited it out until the 2nd week of January to push sales. Luxury e-stores had a foot on both sides of the Holiday season, extending discounts from Black Friday right up till the end of January.

Dresses have been discounted more than skirts and jumpsuits in 2016 compared to 2015. Overall the “occasion wear” and “evening wear” have seen more slashed price tags than last year. Discounts were up by 11% in dresses and 8% in skirts. For a 2016 trend, discount rates on jumpsuits were not very encouraging. We saw an increase of 10.5% from 2015; just as much as dresses, indicating a failure to spike enough, considering the pre-Fall confidence in the category.

On the upside, the Luxury segment did quite well in the dresses department. 97.9% were out-of-stock at full price by end of December. Significantly more than accessible brand stores (87.3%) and way more than fast fashion brands (36%). Overall 45% of the stock in dresses wasn’t discounted at all in the Nov-Dec holiday season.

Compared to 2015, e-tailers were well-stocked this year. Average out-of-stock items were down by 16.77% for dresses and 14.92% for jumpsuits. Skirts saw the average out-of-stock rate decreasing by 19%. Could this mean that 2016’s pleated skirts were more successful than 2015’s voluminous midis? Maybe, for we saw velvet dresses neck-to-neck with pleated metallic skirts to gain the top spot, even during a colder last week of December.

2016 has seen some very strongly defined trends, dresses being on of the favored categories. Velvet dresses, off-shoulder/cold-shoulder dresses, slip dresses, metallics, sequins & ruffles made a splash this fall and overlapped across occasion wear, evening wear, and casuals- making them prevalent everywhere you laid eyes.

The overlap was not just confined to categories. About 39% of styles had a combination of two or more of these attributes. Off-shoulder velvet dresses, sequined slip dresses, ruffled velvet dresses, metallic ruffled dresses- and so on. The 90’s influence was heavy this holiday season, with Kate Moss’s iconic slip dresses making their mark alongside velvet Bardot numbers.

While this holiday season has seen ambiguous trends in holiday discounting and sales, across the past four months, retailers have been able to cash in on a handful of styles in dresses and skirts, thanks to ‘star’ items that hadn’t been around the past few years. It would be wise to gear up for the coming year with pricing strategies that tie-in discounting seasons as well as full-price (virtual) shelves to hit the sweet spot in e-commerce buying trends. Take note that your customers will expect bargain deals all year-round, and will spend on new collections, albeit cautiously. Now is the time to get a dynamic pricing automation strategy to feed both the proverbial beasts.

Decoding Decisions with Big Data!

1st May is just a couple weeks away. And we cannot wait to see the way Myntra fares. Going mobile! What will be the result? The management and directors may have studied the ins and outs before making a decision that has the ability to set a new standard in the world of eCommerce in India;

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Big Data- Growth Story 2014…

Big data, it seems, has become the new mantra for all and sundry. Every industry you see has used or is planning to use data analytics to increase sales or get to know their customer base. Newer and better organizations are coming up because of data analytics becoming important. Also, new jobs have been generated in the name of data.

For the uninitiated, Big Data refers to a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. Data analytics is all about processing this data using softwares, tools or other such services. Continue reading “Big Data- Growth Story 2014…”

How Financial Services firms are leveraging Big Data?

Financial Services today is an umbrella that covers everything from bank loans to insurance services. There are so many functions involved in the financial services sector that it is easy to lose count. In technical terms, financial services are the economic services provided by the finance industry, which encompasses a broad range of organizations that manage money, including credit unions, banks, credit card companies, insurance companies, accountancy companies, consumer finance companies, stock brokerages, investment funds and some government sponsored enterprises.

Managing money or economics maybe highly theoretical, but it is also one of the most practical and important aspects of our world. From times immemorial, we have required some kind of financial service. Since we learnt farming, we have believed in the concept of barter. Then came the money lenders and the Templars who created an ancient version of the banks we see today.

Finally we have banks that are deemed a very important part of our civilization. So much so that today, we use the services these banks provide to even buy the smallest of gadgets. Banks and other financial institutions have been crunching numbers forever. But now this highly manual process is becoming digitized now.

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Semantics- Reading between the Lines!

Big data analysis has made all sorts of things easier. The power of data is finally being harnessed in spades for various functions. Marketers and retailers alike are using big data to understand their consumers or customers respectively.

There are various facets of big data analytics that give it all the more power. One of the biggest data points today is the social media. All marketers accept the fact that the social media is a powerful tool to capture consumer attention.

Messaging through social media, phones, etc has become a common trend. In fact, the Queen’s English is also changing with colloquial annotations all over the world, which is called as ‘texting lingo.’

This brings to light a very important part of data analytics- text analytics. Text mining, also referred to as text data mining, roughly equivalent to text analytics, refers to the process of deriving high-quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning.

text analysis with compass

Text analysis involves information retrieval, lexical analysis to study word frequency distributions, pattern recognition, tagging/annotation, information extraction, data mining techniques including link and association analysis, visualization, and predictive analytics.

The overarching goal is, essentially, to turn text into data for analysis, via application of natural language processing (NLP) and analytical methods. Text Analysis makes qualitative research faster and easier. The ability to analyze what your respondents say helps you gain insight into their attitudes, behaviours, concerns, motivations and culture.

The technology is now broadly applied for a wide variety of government, research, and business needs. Text mining is being used by large media companies, to clarify information and to provide readers with greater search experiences, which in turn increases site “stickiness” and revenue. Additionally, on the back end, editors are benefiting by being able to share, associate and package news across properties, significantly increasing opportunities to monetize content.

Text mining is starting to be used in marketing as well, more specifically in analytical customer relationship management. It can be applied to improve predictive analytics models for customer attrition.

There is also sentiment analysis, where they may analyse movie reviews for estimating how favourable a review is for a movie. Analyzing unstructured text data is said to hold great promise. There are many text analysis tools that provide information on the readability and complexity of a text, as well as statistics on word frequency and character count.

At IntelligenceNODE, we believe that text analysis is an indispensible part of data mining and here to stay! With evolving language, our understanding of words also changes!


Big Data and Food Production…

Big data has revolutionized the way we solve our many problems. It has completely changed the world’s perception of people and things. With the availability of precise and thorough information, making decisions has become easy. The magic of Big Data has spread everywhere. All possible applications of data analytics have been studied. Newer and better uses of big data are being discovered daily.

Data analytics will lead us to a connected world where everyone and everything has an online history. In today’s world itself, many countries face food shortage. Global warming has far reaching effects. One of these is the change in weather conditions all over the world. They may be comparatively mild, but we are feeling these changes all the same. As a result, food production is affected drastically.

According to the United Nations’ Food and Agriculture Organization, food production must increase with 60% to be able to feed the growing population expected to hit 9 billion in 2050.

So we step into the future of farming and food production with Big Data! Farmers today produce three times as much food as they did 50 years ago using just 12 percent more land, thanks to new technologies and better farming practices.
But the global playing field isn’t level. In Africa, farmers produce a fraction of what they could, according to the Forum for Agricultural Research in Africa, and most barely get by, struggling against infertile soil, drought, and diseases.

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Big Data analytics and its integration with Social Media

Big Data is the buzzword today… Everything is about gathering and predicting data. In every field that we hear of, big data has made inroads. Call it exploitation or what have you to… but Big Data is here to stay.

We’re told that the potential for beneficial insights mined from anonymous, adequately protected data is enormous. So where does all of this indispensible data come from? There are structured and unstructured data according to those who bandy jargon.

One of the most transparent sources of big data is the social media network. Social media connects everyone to each other. They may not even be acquainted in real life, but online they know each other very well.

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Big Data in Real Estate- Real Opportunity!

Data are now woven into every sector and function in the global economy. These days all work is becoming “data-informed” or “data-driven.” Big data enables better demographic insight into how products and services are used by enterprise and competitors.

The presence of big data analysis has revolutionized the way industries operate. It is no wonder then that the real estate sector is also affected. Big data gives the ability to shift from expense to opportunity.

Three different kinds of data that are important to the real estate industry: property data, customer behaviour data and market data. Customers who would benefit from big data analysis include brokers, banks, and insurance companies, housing corporations, investors, authorities and real estate project developers.

Now it’s time for the real estate industry to tap into the Big Data phenomenon. Up till now, real estate has been governed solely by brokers. But with big data, now it’s all about taking the power back from the broker. The rise of Big Data means brokers are no longer the gatekeepers of all information.

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Pet That! – Big Data in the Pet Industry

Dogs have ever been man’s best friend. Pet owners equate their canine companions with children and dote on them just in the same way. The list of products catering to the pet industry has become endless. New fashion trends, new hairstyles, you name it, they have already done it. These trends may cross into the realm of bizarre, but they definitely complement the growing revenues of the industry.

The pet care industry is comprised of retail pet stores, groomers and boarders, and sales from online suppliers and of course the veterinarians. Pet owners are spending more and more on pet care across a range of sectors, including pet health, food and toys. According to the 2011-2012 American Pet Products Association National Pet Owners Survey, more than 60% of homes in the US have a pet, which is close to 73 million households.

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