![]() ![]() “We export a lot of seafood to Japan - sometimes we even fall short of demand because of our limited capacity.”Īl Kabeer is banking on growth owing to lifestyle changes in the metros arising out of increased work hours and consumers with less time to do fresh shopping. The US is a good market, but extremely strict about the meat that comes in because of the fear of contamination due to mad-cow disease,” says Subberwal, adding that Japan is another growing market. “We want to export to countries with low-tariff barriers. The company also wants to enter more markets but is making selective choices. Aspi Dinshaw, chief general manager, Al Kabeer, says, “About 45% to 50% of our products are now vegetarian.” This caters to a large population segment in India as well as to T A DNAH C B A RAH customers looking for a healthy alternative to meat. India is Al Kabeer’s major revenue contributor at 35%, followed by Malaysia at 25% and countries like the UAE, the Philippines and Japan making up the rest.ĭiscussing the way ahead, Subberwal says that vegetarian offerings from the brand will see an increase. So far, point-of-sale material has worked just fine.Īmong its competitors, Subberwal counts Allana in India, Americana in the Middle East and McCain in France. More than TVCs or print, Al Kabeer relies on the last-mile connectivity which has become such a rage with marketers of late. “We do not have any fixed marketing budget but do whatever good work we can as and when the opportunity comes up,” says Subberwal. To learn more, see the privacy policy.For a brand so well known, Al Kabeer surprisingly hardly advertises. Please note that Describing Words uses third party scripts (such as Google Analytics and advertisements) which use cookies. Special thanks to the contributors of the open-source mongodb which was used in this project. As you'd expect, you can click the "Sort By Usage Frequency" button to adjectives by their usage frequency for that noun. The "uniqueness" sorting is default, and thanks to my Complicated Algorithm™, it orders them by the adjectives' uniqueness to that particular noun relative to other nouns (it's actually pretty simple). You can hover over an item for a second and the frequency score should pop up. The blueness of the results represents their relative frequency. If anyone wants to do further research into this, let me know and I can give you a lot more data (for example, there are about 25000 different entries for "woman" - too many to show here). In fact, "beautiful" is possibly the most widely used adjective for women in all of the world's literature, which is quite in line with the general unidimensional representation of women in many other media forms. On an inital quick analysis it seems that authors of fiction are at least 4x more likely to describe women (as opposed to men) with beauty-related terms (regarding their weight, features and general attractiveness). Hopefully it's more than just a novelty and some people will actually find it useful for their writing and brainstorming, but one neat little thing to try is to compare two nouns which are similar, but different in some significant way - for example, gender is interesting: " woman" versus " man" and " boy" versus " girl". The parser simply looks through each book and pulls out the various descriptions of nouns. Project Gutenberg was the initial corpus, but the parser got greedier and greedier and I ended up feeding it somewhere around 100 gigabytes of text files - mostly fiction, including many contemporary works. Eventually I realised that there's a much better way of doing this: parse books! While playing around with word vectors and the " HasProperty" API of conceptnet, I had a bit of fun trying to get the adjectives which commonly describe a word. The idea for the Describing Words engine came when I was building the engine for Related Words (it's like a thesaurus, but gives you a much broader set of related words, rather than just synonyms). ![]()
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