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Successful Marketing during the Pandemic (COVID-19): 5 Tips

Even before the World Health Organization declared COVID-19 a pandemic, it had taken a toll on businesses all around the globe. If you are lucky then you might not be one of those who had to close down their businesses or lost their jobs. This sounds even worse than contracting the deadly coronavirus disease, right? In these times of uncertainty marketing has become a challenge for small and medium size businesses. We want to share 5 pointers with you that will help you successfully navigate and level up your marketing strategies. 1. Reassess your campaigns: With restrictions on and off all around the world, you have to rethink and strategize your campaigns’ performance in different regions around the world. You need to do a fresh market research and reassess the situations every few weeks to make sure you're targeting right. You have to notice the unusual trends from your insights and Ad reports. The pandemic has also opened up new opportunities and markets that didn't exist sometime back. A thorough analysis will help you identify market trends and markets you can target and shift your Ad budgets to. Due to restrictions people are spending more money locally. Have you always neglected local audiences? This is the time to market and sell locally. 2. Content is King: You must have heard this a gazillion times. Content is one small part of content marketing but it still remains the king. Yes, even during the pandemic! To understand this we first need to know how people used to consume content earlier vs how people consume content now. Work from home has made lives different. Working from the kitchen while managing your children and yet keeping an eye on multiple other tasks has become the new normal. Content consumption is way different from before. Brands need to leverage such opportunities and help their audiences in these complex environments. We have seen brands like Nike instantly change their strategies and launch campaigns like “Play Inside”. They offered users healthy diet tips and exercise routines that could be done at home. Times are tense and brands that are helping people ease out are growing during this period. 3. Context is also king: To hit a home run you have to be at the right place at the right time. But how do you hit a home run on every ball that comes towards you? Understand the sentiments of your audience. Does your business talk about things that your consumers want to hear? Emotional content analysis through AI can help you determine if your content bears the relevance or not. It will tell you how your content will sound and what emotions it would evoke in the reader. This is how you can be sure of your campaign's performance even before your spend. You can optimize your campaigns before running them and thus run cost effective campaigns. 4. Contribute wherever you can: This is the time to show that you care. Analyze and come up with ways you can help the people and the planet in these unprecedented times. Turn these actions into marketing campaigns. It is proven that campaigns based on sustainability, helping the local community and the likes perform far better than regular campaigns. You might even get free coverage from the press and this is a big chance to up or even create a brand image that people will remember. 5. Be creative and shift priorities: Creative Ad copies can be a game changer during this time. Include humor in your content. Ads containing humorous content perform better than the ones that don’t. You might even need to hire people for this. Collaborate with content creators if you need to. Influencer marketing has already unfolded exponentially. You might need to change the priorities of your business and reassess your KPIs. Evaluate your budgets and re-channelize them. Many alcohol beverage producing companies started manufacturing hand sanitizers. Fashion giants like Louis Vuitton are selling masks too. You have to brainstorm on how your business can stand out. During stormy times like these there is no fixed way to navigate but these five pointers can help you move towards light. These guidelines will help your business evolve and come out stronger than ever before. -by Dhananjay Mukerji

Influencer Marketing on Social Media through AI

Influencer marketing is the freshest entrant in the digital marketing space that has opened up new avenues for brands and content creators. The exponential growth has brought it under the spotlight. Most brands including fortune 500 brands and marketing agencies are yet to figure out how to extract the potential out of this new form of marketing. Natural Language Processing(NLP) and Artificial Intelligence(AI) has already brought about a revolution in any industry or domain you can think of. Marketing is also one of them. If you are a small, medium or large size business, the three key challenges that you might face are: Creation of relevant and impactful content Identifying the right influencers that sync with your brand Posting the right amount of content To solve these challenges Artificial Intelligence(AI) based tools are being used by brands. The ultimate advantage of influencer marketing can be obtained through data science where large amounts of data are fed into the Machine Learning engine to give you the best outputs. Here’s how Machine Learning(ML) and Artificial Intelligence(AI) will help you extract the ultimate potential out of it: Searching for relevant influencers: One of the biggest challenges that marketers face is finding the right influencers. This research if done manually consumes a lot of time and is energy intensive, moreover, after all the efforts you still might not be able to find the right influencers for your brand. The social media space is flooded with influencers, finding the genuine ones is another task in itself. Research shows that an average person sees up to 1000 Ads or brand messages a day. The question is how do you stand out in the crowd? How will you ensure that your content empathizes with the audience? Do not worry, AI is here to help. AI analyzes the type of visual content you’re looking for, moreover, you can even feed the AI engine with your preferred hashtags, interests, audience and it will scout the right influencers for you within seconds. It always keeps a watch on updates and will also update you with fresh and potential accounts for future needs. How much should you be charged? There is no set industry standards yet for the amount an influencer charges for posting content. It might be really difficult to come up with a number since it depends on various parameters such as the engagement rate, number of active followers, audience reach, regions, demographics of followers and a lot more. It is also not feasible to verify this data manually as it can become very time and energy intensive, moreover this data is not readily available and would require external applications and tools. AI helps in collating and analyzing historical data of the influencers’ social media posts from their accounts and can tell you if a particular influencer is the right fit for your brand or not. Not only this will help you find the right influencers but the AI will also give you an estimate of the pricing pattern for relevant influencers to choose from. Predicting ROI of your Influencer Marketing Campaign: No influencer or advertising platforms guarantee you an ROI. It’s difficult to come up with a number. Even if you find an influencer that has great engagement, you might not get the ROI you’re expecting. Calculating social media metrics is one thing but converting them to revenue still remains a challenge for marketers. AI finds strong relations between the metrics of different posts to analyze the performance of your Advertisement. For a human this would take numerous years whereas the AI engine analyzes this in seconds. Only a machine can deal with huge amounts of data in an efficient way and draw realistic conclusions. The future of influencer marketing: It has been on the rise ever since it came into existence and brands have started shifting their marketing budgets from traditional marketing methods to digital marketing. There is also an internal shift in Ad budgets from Social Media Ads to Influencer marketing campaigns. AI is playing a prominent role in ensuring the success of these influencer marketing campaigns by offering remarkable accuracy and efficiency in the integration process and flow. -by Dhananjay Mukerji

Instoried’s Metrics for Panel Testing

Panel testing is a feature that has been recently added to the Content Testing platform offered by Instoried. The need for it comes from the fact that testing content using Artificial Intelligence, while effective, needs to be reinforced with real-world affirmation. After all, AI does not feel, it merely knows all that has worked before, and hence predicts what will work now. But it lacks the intuitive judgment that is exclusive to humans. So we set about creating a feature that would enable our customers to leverage this very important side to testing their content, all part of our endeavor to help them reduce their spending and maximize effectiveness for effort in the mammoth task of content-based marketing. The operation is simple, once you have analyzed your content using the AI model, for its emotional parameters, you submit a request for panel testing. At the core of this feature is a carefully curated checklist of marketing parameters. And that checklist is the subject of this post. We adopted a twin-pronged elimination mechanism to arrive at the final checklist. First, we listed down as many possible parameters as we could think of that could serve and assist the emotional analysis that our AI-model provides. The challenge was to find maximum compatibility with our pre-existing emotional parameters. The first step was to put this list in front of probable readers of marketing content. A few sessions with focus groups allowed us to narrow down our initial list of 50-odd parameters to a good, round number of 25. Then came the interesting part. We constituted a panel of well-regarded marketing experts and set up some meetings where they discussed(with each other, we were only curators in this exercise) which metrics would best help them analyze the content that they produce. After a few fiery discussions, and eventual reconciliation, we were able to arrive at the 15 best actionable parameters- to be rated on a meter between 0 to 5. We further classified these metrics into 3 buckets of 5 parameters each, as listed below. Bucket 1- Purchase Intent (Gauge the intent of a prospect/reader to buy on reading tested content) Relevance- How relevant is the content in the context of this particular reader? Remarkability- Is the content out of the ordinary? Is it different from what the reader usually sees in relation to similar services/products? This metric measures recall and impressionability. Off-Putting rate- Was the reader put off by the content? Did it insight a sense of cringe at any point over the course of reading it? Credibility- Does the reader feel that the content in question is credible? Do they find themselves questioning the source of the information related? Motivation to act- Measures how motivated to buy (the product/service on offer) the reader is upon reading the content in question. Bucket 2- Shareability (How likely is it that the reader shares the content with their respective information sphere?) Likeability-How much did the reader like the content? Would they leave a like for it on a social media platform? Uniqueness- Was the content original in context to the reader? Was it authentic? Arousal- Did the content create an impact on the reader? Did it elicit a sense of attraction? Interest level- How interested is the reader in this type of content? Gripping quotient- How intensely gripped was the reader by the content? Do they want to see more of this? Bucket 3- Value Proposition (Is the promise of value to the reader clear upon reading said content?) Story structure- Does the piece have a beginning, middle and end? Does it effectively enunciate the problem, analysis, and solution? Informativity- Does this content inform the reader of something they did not already know? Superficiality- Was the content shallow? Did it come off shallow, i.e, as if not a lot of effort was put into the creation process? Clarity- Is the content clean? As opposed to cluttered writing and hard-to-follow tangents. Consumer Alignment- Is the content aligned with what buyers of the product/service wants to see/read? This checklist of metrics is the final result of applying the exhaustive processes mentioned above. Beyond these the tool also allows for the testers to lodge a brief descriptive answer as to what they like about the content and what they do not. The objective, as it has always been, is to make your marketing content as effective as possible! -by Havaz Mhd

4 Things you need to know before you start Content Marketing for a Fashion Brand

Whether you are a multi-million dollar luxury fashion brand or a designer struggling to create a brand, these are some tips and tricks that would help you grow in the content space and drive ROI like never before. What is your goal? What do you want to achieve using content marketing? Do not worry if you are not sure. Ask yourself the following questions: Do you want to generate leads? Do you want to spread brand awareness? These are the 2 broad categories that will help you strategize your content marketing for different social media. Who is your target audience? Your target audience will also define your marketing strategy and budget. Say, If you’re targeting teenagers, your posts will have to vibe with their interests. You need to know what’s trending in that age group, be it a certain game or an app like TikTok. If you are a luxury fashion brand targeting women in the age group of 25-40 yrs, you would look for themes related to a luxury lifestyle like a yacht party or luxury stay and travel. Knowing the target audience will help you to empathize with your audience so that you are in their shoes and you know their desires. It also helps in mapping out their journey and knowing the nodes where they might be interested in buying what you are selling. Don’t always try to sell: Every interaction with the target audience should never be about sales, try to connect with your audience through content and sales will eventually happen. If you make your audience feel that you tried to sell them the latest designer t-shirt you’ve launched at every point, they would walk away and never return. Your content should vibe with them without giving them a hint that you tried to sell something to them. Use customer interactions as marketing content: In the fashion industry customers always love to share how stunning they look in the dress they just bought during the launch of the fall collection. Re-share your customers posts on your social media and encourage them to share posts and tag your brand’s social media handles. Giving your loyal customers a discount coupon would go a long way in nurturing customer relationships. Don’t forget to tag your customers, they feel delighted when a brand recognises them. It’s proven that this kind of marketing actually works better than the brand itself sharing their products. This approach delivers a higher level of authenticity to the fresh audience and they trust the brand more since they see people reviewing and using the products in real life. Case Study of a luxury fashion brand that has offline presence in over 300 stores worldwide but did not have a visibility in the social media space: Challenge: To create a brand image on Instagram and Facebook that differentiates from the competition and start a sales funnel through organic posts. Although the brand had stores all over the world, they did not have any online presence. Solution: We created a comprehensive content marketing strategy after deeply analyzing their product, goals and the target audience. We fine tuned the company’s message to give the brand an updated look and feel and created a brand image around sustainability, balance and best practices that the brand followed in the fashion industry like manufacturing 100% recyclable products, tracking down the supply chain and supporting local communities. Improved audience targeting through the use of relevant hashtags and tagging, using location tags for geographical targeting and sharing customer testimonials. Results: A significant boost in the post engagements. Free shout-outs from pages and brands that support sustainable and ethical fashion brands. Higher number of customers started tagging the luxury fashion brand in their posts after they were aware of the whole process that goes behind the craft. A boost in the number of followers in just a period of 1 month. Increase in the number of fresh website visitors. All these results were obtained with $0 investment in the campaigns. -by Dhananjay Mukerji

AI: Final Samaritan or Ultimate Threat?

It has been a couple of months now, since the release of GPT-3, the all-powerful language prediction and generation engine, developed by Elon Musk’s OpenAI. Applications of GPT-3 range widely- from generating code using natural prompts(which means that a layman can now create code with absolutely no knowledge of any programming language) to creating written content that is indistinguishable from the work of a professional writer. The latter use case is illustrated to great effect by this piece, published by the Guardian. The article’s prompt was written by the Guardian and fed into GPT-3 by a computer science undergraduate at UC Berkley. Reading this piece- that was actually 8 different results edited into one by the Guardian’s editorial team- gives the reader an eerie sense that it could just as easily be written by any of the million writers that we know and read with such ferocious frequency in today’s world of express information. But, on closer inspection, another striking factor here is that the AI model seems almost self-aware. That it understands what it is- as it states its own case- with nuanced and intellectually reasonable arguments. The writing has personality, like the best writers, it is empathetic to overarching causes that we did not know we even cared for. An ability that would be a powerful tool in any writer’s utility belt, but in the hands of artificial intelligence, it is a testament to the sheer scope and magnitude of this remarkable technology. It is, by and large, modeled after(~inspired by) the human brain. Except for the fact that a human brain cannot nearly match its speed or volume of computation. But the fact that it has generated such a piece, that seeks to appease humanity to its innocuous nature, is indicative of its rapid evolution. It is cause for us to revisit the many important people that have expressed concern- Elon Musk, one of the original founders of Open AI says- “I have exposure to the most cutting edge AI, and I think people should be concerned by it.” Stephen Hawkings, the celebrated physicist had more pressing concerns when he said- "The development of full artificial intelligence could spell the end of the human race….It would take off on its own, and re-design itself at an ever-increasing rate. Humans, who are limited by slow biological evolution, couldn’t compete and would be superseded.” But one of Stephen Hawkings’ peers, a detractor in many ways, Roger Penrose(an Emeritus Rouse Ball Professor of Mathematics at the University of Oxford) has a vastly different point of view on what it means to be consciously intelligent. He draws a non-definitive line between computation and consciousness, contending that our awareness of self, the thing that overlays our complex evolutionary drives, is a separate function from computation itself. Penrose, along with anesthesiologist, Dr.Stuart Hameroff, conducted studies with the premise that if consciousness is the “ON” state of the brain, then, while we are “unconscious”(vis a vis- sleeping) they would be able to observe experimentally the defining factor or underlying cause for consciousness. While these theories are yet mostly stipulated hypotheses without empirical consensus, Penrose’s view of consciousness brings the discussion of Hollywood’s Artificial Intelligence doomsday to question its own premise. If the AI cannot be self-aware(and hence not have any real needs or wants for itself) then where does the threat come from? From ourselves. The greatest threat to our own existence has always been us. From gunpowder to Nuclear Fission the technology that we created for scientific and societal progress has always been used by elements(malicious or otherwise) within us, as weapons that could oppress other sections of this singular, life-supporting world. So like all technology, like lightsabers can be either red or blue(or green or purple?) depending on the wielder’s chosen side of the force, the fate of AI and which side it serves will be decided by who uses it, and for what purpose. -by Havaz Mhd

VALUE JOURNEY OF CONTENT

This post will try to track the journey of a piece of content from its inception- through its worth to the business- and till its eventual death from viewership. Birth Content is born the same way manifestos come into this world- As an idea. In the writer’s mind, it incubates like the alien from the movie ‘Aliens’. The period of incubation is different in different cases, depending on the depth of concept, difficulty in implementation, or even just the necessity for further research. The idea is born out of preset parameters that are designed to bring value to a business. The more creative the route of sale, the better. Process & Route of Sale Once the idea has sufficiently broiled in the writer’s mind, he/she begins the process of putting pen to paper. Or really just fingers to keys. The first step while writing content to attract business (and find value for search engine optimization) is to make a list of keywords. How do we come up with keywords? By looking at search intent. Consider what a user might search for and how it is relevant to the idea that we are going to write about. Relevance is important here because you are up against google’s dynamic artificially intelligent engine that keeps a strict and watchful eye on how much value your piece will bring to the user searching for a particular keyword string. Once the keywords are decided upon, the next step is to write the piece. The best and most impact full way to write is in the form of a story. The basics are easy- and the best writers know this- they have abstract protagonists going through the eight-stage character development arcs while writing about the state of the financial markets. Value to business Information is the foundation of our world. Anything we want to know is just about a couple of keystrokes away. And the primary peddlers and curators of this information are search engines. So if a business wanted to sell a service or a product and wanted to let the online world know, they have to do it through the search engine. Of course, the advertisements and social media strategies can help as well, but neither compares to the traffic that search engines(mostly Google)can bring. Google has been around long enough now for users to be wise to when they are being advertised to. And most users know that businesses at the top of Google AdWords have the deepest pockets. Statistics indicate that a high organic ranking attracts 50% more click-through than the top AdWord listing. So SEO is a very important value aspect that a piece of content brings to the endeavor of growth for a business. But is that all? No. The true value of content is the scale of communication. Good content has relevance far beyond SEO and SERP rankings. It is the business'(read brand) primary vehicle of growth and development. It sits at the top of your conversational funnel, a singular effort akin to shouting out to the world from the top of a mountain- the business’ first round of engagement with any customer. Death, Revival and Sustained Value Our lives are tainted by the expectation and certainty of death. But, what of information, can it die? Like any good SEO specialist will tell you- Just look at all the content on the second page of google’s search engine results. Of course, content can die. The thing about relevance is that it changes. The context of the user changes. Their needs, their wants, and the things they search for change consistently. But does that mean all the effort a writer puts into a piece only has fleeting value? Of course not. The idea is to have a content strategy that regularly updates and republishes refurbished content. Another way to keep content alive and valuable is by constantly referencing older content in new pieces, making sure that everything is tidy and the flow of information is linear. Maybe we cannot defeat death, but we can very well make certain that we gain and retain control of the fate of the content we produce! -by Havaz Mhd

SEO: SEARCH INTENT AND ITS MANY FACES

What is Search Intent Search Intent, also known as Keyword intent or User intent, is what the user intends(or intuitively expects) to find on the search engine results page( SERP ) after they have entered a query. Let’s say you are a user, and you want to figure the quickest way to join the circus. You deploy a search on google for “Quickest way to join the circus”. It shows you many links for how to join, but most of them are long roads that will take you years. But on one website you see a quick route that would only take six months. So you stay on that page, read it, and engage with it. If enough people do this following a search for the same query, that website will soon find itself ranking #1 on the SERP. The Importance of Intent SEO IS intent. Of course keywords, credibility, and authenticity all matter on how you rank on the SERP. But google’s most primary challenge is to give its user a seamless experience. And you can bet they will use their considerable resources and high technology to achieve this. There is nothing worse for a search engine than not recognizing the intent of the user’s search. Google is an SEO specialist’s greatest friend as well as their greatest nemesis. It’s often touted expansive and ever-changing algorithm presents the truest challenge of SEO work- Matching search intent with results on the Search Engine Results Page(or SERP). There is no denying that a website’s position on the SERP will directly contribute to business volume. If your business was a house, then the content(and its resulting SEO) is the water that holds the cement together. Except your business is actually a plant- and it needs constant watering. How do we discern Intent As it turns out, 99% of all search terms fall under 4 different intent categories: Informational Navigational Commercial Transaction Data also suggests that search intent changes constantly. Nobody knows what the future of search is. Add to that google’s ever-dynamic search engine algorithm- makes SEO a tough nut. So if a business is selling a product online(which would be a commercial endeavor), the practiced logic is to construct long and short tail keywords around what words people would search for. Essentially the content creator is left to guess what keywords a searcher would use. Pen-swinging marketers from big corporations have already cornered most SERPs with extensive blog production and keyword stuffing. This coupled with the inherent uncertainty of search makes establishing a SERP presence an uphill task indeed. But it is not all doom and gloom for the creative marketer in this story. There is an inbuilt dues ex machine here- Google. As mentioned twice here already, google’s algorithms are ever-changing and use deep learning AI technology to dynamically discern the intent of a user, as well as the relevance of a website in any particular search scenario. How do you counter a deep learning algorithm that indexes your website based on search intent? You deploy your own deep learning algorithm at the production level. You reverse engineer intent using hard data. To do this, the content production process has to be refined and data-driven. Check out Instoried’s content analysis tool that uses Natural Language Processing and AI to augment your writing. Google does say that they prioritize fresh content, but that just means that you need to have a well developed content strategy that is updated regularly. But it also means that someone starting from scratch or building an online presence can have a fighting chance- As long as they figure out Intent. -by Havaz Mhd

Emotions- Key to a Successful Campaign

Emotions are one crucial element of digital advertising that supersede responsive ad formats, cross-device presence and etc. The real long-term connection between a brand and its audience is driven by emotions — meaning that brands must trigger a positive emotional reaction in a consumer to gain access to their wallets. While this concept is not necessarily new, the importance of understanding the sentiment that content on a page can provoke when placing digital ads continues to increase exponentially, and with good reason. Emotional ads outperform on almost all metrics, including profitability, and if they elicit strong feelings they are twice as likely to be shared on social media. By putting emotion at the heart of campaigns and ensuring that ads are well placed, brands can enhance engagement and forge a lasting bond with their Audience. The most successful brands have earned audience recognition and loyalty through their ability to convey a simple and powerful emotional message in advertisements from search to display and video. Coca-Cola, for example, has cut through the noise with one word: happiness. Its “Choose Happiness” campaign links the brand with a basic emotional need and empowers consumers to feel that they can achieve and spread happiness by purchasing its product. Despite the power of emotive ads, their message becomes redundant if they are not placed in the most relevant context to ensure maximum impact. For example, if a user searches for festive family activities, the most effective ads that accompany the results will be those that reflect the positive emotions associated with family bonding. The technology goes beyond simplistic keyword detection to uncover the true meaning of different words according to their context, allowing advertisers to assess not only the sentiment but also the emotional context of individual Web pages. Emotion is becoming the greatest currency in digital advertising. But no matter how emotive and creative an ad, consumers could bypass it if not placed beside content in the correct context. Understanding the emotion in ads and placing them in the most effective context is vital to creating a truly engaging and immersive advertising experience. Instoried Research Labs, a Bengaluru Based startup builds products using Artificial Intelligence which tests the effectiveness of your content and offers smart recommendations to create a smart copy which would maximize the impact of your ad campaign. Sharmin Ali, CEO of Instoried Research Labs says, "Content has come a long way since its inception. We currently live in an age where emotions have become one of the main drivers for buying behavior. My team has put together an AI enabled tool that can change the way you write and help you communicate better to your consumers than before. At Instoried, we test the effectiveness of content and help brands reach brand goals i.e. create a brand presence." Check out Instoried here. Sign up for their Demo. -by Vaibhav Venu

Effective Content Testing for a Fast Fashion Retail Brand to Drive ROI

THE PROBLEM A high -end fast fashion retailer from London was blindly increasing their annual marketing spends Y.O.Y. However, consumer views and interest on their digital media platforms was continuously falling. The Company reached out to us at Instoried to help them improve content consumption and drive ROI for multiple geographies. THE APPROACH We first tried to understand the company's basis of content creation and the corresponding content consumption rate by their target customers, across multiple content formats(text, images and videos). We found that all of their content creation was based on intuition and there was no customer -centric, data-driven approach employed to understand if their content would even stick with their audiences, in order to drive ROI. THE SOLUTION Using our content A/B testing approach, we designed a set of 5 metrics that were important for the company to understand user sentiments and reactions towards their content. The company was able to achieve a 3.5% increase in their click-through rate in less than a quarter. This drove ROI increase by 1.5X. -by Sharmin Ali

How to implement a Data-Driven Content Strategy

It is becoming increasingly obvious that in the information age we live in, data-driven businesses win. Naturally, that extends to content marketing as well. But how would it work? One very big problem that persists against a data-driven solution for content marketing is this- The writer is human, and so, inherently subjective in their writing. So it follows that content creation being a creative process, cannot fully be data-driven. But it can get pretty close to the finish line, thanks to huge advances in AI and deep learning technology. Here's a brief description of how. The first step in any Data-driven process is tracking. SME’s and enterprises churn out huge amounts of content every day in pursuit of their online marketing efforts. But how is this content performing? And why? Performance Tracking There are many tools in the market that enable performance tracking. Google Analytics being a primary example of these. A marketer needs to identify which performance metrics(KPIs) indicate success. This is dynamic and will change depending on what he/she is trying to achieve with the content. If they are writing a long-form blog post that intends to inform users/readers about a particular topic(and thereby achieve search engine optimization), a good KPI would be Time spent on-page. Click through rates(CTRs) would also indicate how much the user has engaged with the content. Conversely, if the marketer is trying to insight an emotional response from the reader, social shares would assess how effective the content is, and what kind of impact it had on the user. Here is an extensive list of metrics that Google Analytics provides to help track your content performance. Another good way for enterprises to measure performance is through conversion rates from marketing qualification to sales engagement. This works well as a measure of success for a B2B enterprise trying to establish an online presence, and strengthen its Top-of-the-funnel(TOFU) sales pipeline. So using internal KPI’s we have performance results, now, what do we compare that with? Pre- Publishing Analysis How do you measure the quality or worth of written content? Of course, there are quantitative metrics like word count, grammar, spelling, etc that measure the basic quality of writing. But these are too impersonal and removed from why a piece of content finds success. To find truly qualitative data that can be used to run comparisons, we need to quantify how words relate to each other and how people react to them. Enter Natural Language Processing or NLP At Instoried, we use NLP to measure emotion. Using five primary emotions(Joy, Anger, Sadness, Surprise, and Fear) and three tonal metrics(Positive, Negative, and Neutral) as the primary indices that digest a whole page of content into numbers that can be compared against each other or against preset benchmarks. Here’s a series of blogs on how we did this. Writing Augmentation: Instoried's tool analyzes the words used in the content and recommends contextually aware changes that affect the emotional valences, and thereby final performance. Comparative Analysis So now let's look at the data that can be available to us. Market Performance/Results (eg: Time Spent, Duration of visit, CTR, Bounce Rate) Pre-publishing Emotional Analysis (Joy, Anger, Fear, etc) Now we have both sides of the coin and this unlocks a coveted tool in business - Predictability . Using historical and industry-specific competitive data, we compare Market Performance against the emotional analysis and analyze and digest what emotional combination gives what results. And also arrive at benchmarks that can be repeated and scaled(by predicting performance) through change to the content(either manually, or by virtue of machine-learning enabled writing augmentation). Instoried uses an AI model that also tries to figure out why, i.e, how do these emotions affect market performance? But that is a very technical question, with a very technical answer and it is for another day. For our purpose here, we use the correlations between the two to scale and repeat our best performing content- Optimizing for our internal KPIs and maximizing business value for creative effort! -by Havaz Mohammed

Emotion Recognition and Analysis for Marketing Content

BACKGROUND ABC is a large FMCG company based out of Singapore. Growing at an extremely fast rate and increasing their content spends on a monthly basis, the company seeks to establish a strategy to increase their brand recall and customer’s purchase intent across their product portfolios. ABC wanted to find innovative ways to create better marketing content. ASSESSMENT As it works the company ABC used to analyze their content production according to its performance in the market(post-publishing). This made constructive feedback loops a nightmare because the written word did not have quantifiable metrics, meaning that the company had to rely on the individual, and hence, subjective, judgment of writers. This was unacceptable for this company because they wanted a data oriented approach, and put a lot of stock in their ability to apply and process their data. CHALLENGES FACED During the preliminary assessment, our team identified the following challenges at company ABC: Problem 1: First of all, their content creation was outsourced to a content agency. The process of creating content was based on intuition and not backed by any data. Problem 2: There is no technology available that can measure the emotional quotient of the content and help the marketer validate if their content will strike a rapport with their consumers. Problem 3: Thirdly, many of the metrics collected internally at the organization were inaccurate, which gave room for human errors, and other inefficiencies. Fig: Important Metrics for ABC CTR: Number of people who view an ad, actually end up clicking on it. CTR for ABC varied from 0.05% to 1% for Google Display Network and Search Network respectively which are relatively low as compared with industry standards. Second important metric is Bounce Rate. Bounce Rate for ABC was at 75% and Stay at 25%. SOLUTION Instoried’s emotional intelligence opens up the doors to quantify writing as a probability distribution (or a composition) of 5 primary emotions- Joy, Anger, Sadness, Fear and Surprise, each its own comparative data point. Including our sentiment analysis, this gives us unique pre-publishing metrics to analyse performance. Coupling this with the capability of our recommendation engine to suggest changes in the writing to influence the composition, allowed Company ABC to increase their CTR and decrease their Bounce Rate significantly. Fig: Process followed by Instoried RESULT As a result of the analysis performed and content created using our tool, company ABC witnessed an increase in CTR to 0.47% and 2.18% for Google Display Network and Search Network respectively in 3 months. New Bounce Rate for ABC dropped to 40% and stay increased to 60%. Fig: x- axis: Time, y-axis: CTR; Google Display Network Fig: x- axis: Time, y-axis: CTR; Search Network Fig: Earlier Fig: After - by Sharmin Ali & Havaz Mohammad

Instoried Tech - There is a Secret Sauce!

Introduction In Part-II of our ongoing discussion, regarding the technological capacity building at Instoried, we will be focusing on how we accomplished the goal of per- forming sentiment analysis and spellcheck for text written in Hindi. We will also be introducing a novel “readability” analyzer, which predicts how hard a particular sentence is to read, for an average reader. Finally, as stated in Part-I of this series, we will be mentioning some open source libraries our NLP team is working on and is under rapid development. Credit: Rashmi Ghosh. Dataset As with all things Deep Learning, our initial focus was on creating a large, context-relevant and diverse enough dataset. This is absolutely essential for Language Modelling (LM), wherein we train a model to understand the semantics, syntax, context, etc of the underlying language. We also created another tagged dataset for the classification task, i.e. sentiment and emotional analysis. The dataset for the LM task was scraped from a variety of books written in Hindi on a multitude of topics - history, politics, science, etc. The size of the dataset was ≈ 9.7 GB. The dataset for the Classification task was scraped from a number of sources including, but in no way limited to - movie reviews, news articles, twitter, product descriptions, etc. We finally tagged the ≈ 60,000 data-points for both the sentiment and emotion. Pre-processing Once the datasets have been built, they now need to be “cleaned.” Apart from removing stop words and special characters, we also wanted to ensure that there were no transliterated text in the dataset. This was done to ensure that all the data-points had only Hindi content. On achieving that, we focused our attention on NER and POS Tagging. After a lot of trials and tribulations, we were finally able to manage satisfactory results using a modified FLAIR multi-lingual model. This little breakthrough would prove to be instrumental in tackling the spellcheck functionality, as we were now able to efficiently handle the class of nouns. Finally, we experimented with a number of tokenizers - Punkt, TreeBankWord, SpaCy, etc - to find the one most suitable to handle vernacular (i.e. indic) languages. We found SentencePiece to be the most suitable candidate. Now that we had these pieces of the puzzle in place, it was time to fire up those GPUs! Language Modelling Since there are no pre-trained Hindi language models available online with a “permissive” (i.e. MIT, Apache) license and which have been trained on a large enough dataset, we decided to train our own. After having weighed the pros and cons of various language modelling architectures - BERT, GPT-2, XL-Net - we decided to continue with GPT. Some of the features which went in its favor are: (i) ability of generate embeddings from the trained language model, which would be useful in classification tasks; and, (ii) amenability of the model to trimming / pruning, so as to reduce inference time. Now, training ∼ 9GB of data, even after parameter optimization, is very computationally intensive. But once we started testing the final trained model, the exercise proved to be well worth the time (and money) spent! The results generated showed a very intricate understanding of the syntax and semantics of the Hindi language, and seemed to lack any bias. The perplexity score of the model was 37. Certainly a lot of scope for improvement, but once we had our Language Model in place, it was time to deploy it for the classification task. Classification There are two classification tasks which are of utmost importance to us - (i) Sentiment Analysis, i.e. positive, negative and neutral; and, (ii) Emotional Analysis, i.e. joy, surprise, anger, fear, sad. Continuing our desire to ensure uniformity of models / architectures across the different modules, we went about the task of training the GPT model on the tagged dataset, for the purposes on sentiment analysis. As before, we used Stochastic Gradient Descent with Nestrov Momentum. To ensure faster convergence, we used the 1-cycle policy, complimenting it with the results of LR-Finder for the optimal learning rate. Our final model had an F1-score of 0.81 and the AUC score was 0.77. We are continuously striving to improve these metrics. And in-order to find the words which have a high correlation to a particular emotion, we experimented with the CAPTUM Library in PyTorch. That resulted in us being able to properly interpret our models, thereby ensuring a higher (self-) confidence in our results. Readability Now, for the exciting part; what we believe to be a marked contribution for the progress of vernacular NLP - the Readability Analyzer! Using TF-IDF on a down-sampled version of the large dataset, having a size of 500MB, we plotted a (word-) frequency graph. This helped us create a table of words linked to their general usage (i.e. how (in-) frequent its occurrence is over a large enough dataset). This was used as a metric, along with others, to determine how hard it is to read a particular sentence, thereby helping a writer gauge the reading level required to understand their content. Finally, the same sampled dataset was used to create a corpus of unique words, which would facilitate our implementation of the spellchecker. These words were stored in a highly optimized dictionary, to enable deployment in real-time scenarios. Also, we were able to provide recommendations for misspelt words owing to the embeddings generated in-house whilst conducting the GPT-based language modelling. Don’t you just love it when such disparate things just fall so beautifully in to place! Conclusion So, as can be seen, we have made a lot of progress with respect to objectively understanding the Hindi language. This is a very positive first step in building a comprehensive tool for vernacular languages. As for the next steps, our readers can expect two major updates in the short-term for our Hindi tool - (i) emotional analysis of content, along with highlighting of words contributing to a particular emotion; and, (ii) basic grammar check, then subsequently providing a subset of possible corrections. For the English tool, two major updates in the mid-term our readers can look forward to - (i) added automation to assist users make optimized decisions with regards to the recommendations; and, (ii) an in-house developed speech-to-text analyzer, for any audio format uploaded on our tool. We’re very excited for what the future holds. In conclusion, I would like to mention two GitHub Repos which our NLP Team makes use of while doing their tasks: DVC-Download: which helps practitioners keep a track of their datasets (and other large files) using DVC and S3 buckets. Classification-Report: which helps you visualize the models weights, biases, gradients, and losses, during the training process. Multi-label Classification: which is a compendium of notebooks that guides users on working with datasets with multiple tags. Till our next blog, keep writing! And let the emotions flow. - by Sutanshu Raj #Hindi #Vernacular #DeepLearning #NLP #Readability #Sentiment #Marketing

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