Apple Watch and Digital Branding- Extracting insights from the online dialogue

Apple Watch - Consumer SentimentConsumer electronics is one of the most competitive industries, with the top technology companies duking it out to be the gizmo in your pocket or briefcase. Once the Iphone 3 came out back in June 2007, Apple products were the most used products in most markets. The Apple Watch came out in April 2015, to a much different public response. The number of devices purchased pale in comparison to the previous iPhone and iPad sales, so what changed?

People were talking about the small screen, the battery life, and the customization to fit their style. Here is where the problem lies; while consumers are eagerly discussing their likes and dislikes of the device, Apple wasn’t listening, and thus was not leveraging the concepts that influenced the markets the most.

The Apple Watch Year-over-Year growth is at a staggering -55% from 2015 to 2016. The overall brand health is dropping, while Apple is still the most valuable brand in the world, companies like Google and Microsoft are growing faster than Apple. Apple has enjoyed overwhelming market share for many years, thanks to its intuitive products and effective marketing. The conversation around Apple products has changed in the last few years, while positive sentiment surrounding competitors has grown. Apple has some of the most dedicated Brand Advocates

Semeon ran two different analyses, one as the Apple Watch came out, and one exactly one year later to see how the consumer opinions have changed. By gathering discussions that happen all around the world, we were able to dissect the ocean of conversations, and extract Insights that provide a 360-degree view of the public perception surrounding the Apple Watch and the Apple brand.

On average, the Apple Watch generated twice as many negative mentions as positive ones. The positive comments were mostly focused on design, rather than functionality or the abundance of apps. The Health and Fitness category is underserved due to the inefficiency of the heart rate monitor, and the poor battery life. These issues, along with the lack of visual variety compared to traditional watches added to the overall negative perception towards the Apple Watch.

Understanding consumer purchase habits starts with extracting the most influential consumer Intentions; such as Intent to Inform, Intent to Complain, Intent to Purchase, etc. Through the Semeon analysis, the most popular actions surrounding the Apple Watch were Intent to inform, which showed up twice as much as any other consumer behavior, and Intent to Complain. The analysis of consumer behavior found online can lead to more aligned Marketing Campaigns, an educated sales force, and a prepared Customer Service team ready to deal with the issues that matter most.

To keep up with constantly changing consumer habits and expectations, Marketing and Branding teams need to stay ahead of the online conversations that drive their brand health and in the end their ROI. The only way to do this is to understand the consumer sentiment, concepts, and behavior that surrounds your brand or campaigns. Semeon Analytics provides teams with Actionable Insights that leads to better business decisions based on the consumers themselves.

If you would to learn how Semeon Analytics can leverage consumer interactions to align your marketing and communication strategies, check out our website

Post-Election Pondering: What the Data did and did not predict

Trump and ClintonWas Big Data wrong about the American elections?

How can we trust Big Data with Medicine and our finances when it was wrong about the Elections last week?

Big Data Analytics, especially implementing Machine Learning to big data sets, has been a popular topic in Silicon Valley for the last decade. Just in the last few years, it has received celebrity status among popular media sites and dinner tables across the country. The issue lies in the umbrella term that encompasses Big Data, and our inability to take data results with a grain of salt. A popular saying in the Data science realm is “Garbage in, Garbage Out”. What this refers to is the results of Data Analysis depends, to a large extent, on the quality of the data feeding the analysis.

There is something to be said about the quality of data feeding the predictive Election forecasting systems this time around, or specifically the omission of certain groups. In 1968, the “Silent Majority” helped Nixon win his Presidential race, described by Wikipedia as “an unspecified large group of people in a country or group who do not express their opinions publicly”. An easier feat without the advent of the internet and more importantly Social Media as a means of creating a global discussion. Yet, if you perform a simple Google search, you will find a collection of news articles using this term to describe a group of voters who checked off Trump on their ballot.

With the growth of Social Media, there is now a wealth of information on public opinion to be tapped. If I were a man of Statistics, I would choose the sample size of 10,000 opinions over a focus group of 6 any day (margin of error, conformity bias, the list goes on). This being said, the majority of pollsters, data scientists, and predictors were wrong on Election Night. The results of the predictive analysis showed Clinton winning the popular vote. There were a few variables under-considered during the prediction process. The “Silent” Trump voters; those who were less vocal towards their views to save themselves from the moral shaming which was commonly found online and on Social Media.

Many predictive systems employ Machine Learning to perform the number crunching, which is usually based on historical data. The historical data did not prepare the analysis process to consider another previously invisible group, first-time voters. Trump was able to motivate voters who were previously jaded by politics in the past, those who felt their vote did not matter.

What does this say about Big Data and Machine Learning? In the end, these are tools that we can leverage. As businesses and brands alike start to implement a data-driven strategy for their Marketing, Sales, or Customer Service teams, they need to ensure their data solutions properly ingest and clean the data before analyzing. There is only a small number of Data Analytics companies that collect and cleanse the data in-house, Semeon Analytics being one of them. Like everything else concerning Artificial Intelligence, Big Data, and Machine Learning these tools are not meant to replace humans, instead these solutions are meant elevate the human process. The predictions towards the elections does not prove that Big Data Analytics is flawed, rather it should remind us that we still have work to do in adapting these tactics to businesses around the world.

About Semeon: Based in Montreal, Canada, Semeon combines the best semantic, sentiment, intent and statistical analysis. Thanks to its 100 person-years of experience with natural language processing systems, our team of experts has developed a unique platform that affords Semeon’s customers the ability to track what is being said about their brands, products, customers, competitors and helps them do so more rapidly and efficiently than with competing products.

Intent Analytics brings real Insights to Social Media and Big Data teams

Text Analytics drives ROISocial Media and Big Data analytics areinefficient business tools until they take advantage of purchase intent, sentiment, and consumer behaviour.

Without Intent, Social Media Analytics produces flawed and shallow results. Those who can harness the power of consumer intentions, will stay ahead of the competition, create a better relationship with consumers, and sell more effectively. Consumers have more choice than ever, and perform a large portion of their research online while marketers still advertise without utilizing the power of timely and effective user data.

Marketing is more and more about targeted and timely approaches utilizing data to predict purchase cycles for consumers. With Intent Analysis, companies have the ability to send out a targeted coupon to someone who has been comparing products online, or motivate a brand advocate who plans to speak the good word about your company. In the end it better helps you leverage data online and in your business tools and provides insights that benefit multiple departments in a company.

Intent Classifiers

Intent to Inform

“Hey have you guys tried the new Pokémon GO!? it lags but I’m already addicted!”

Intent to Compare

“What has more battery life, the IPhone 6S or the Samsung Galaxy 7?”

Intent to Purchase

“Planning to pick up a new road bike, any suggestions?”

Intent Analysis provides companies andbrands with rich information on not only what people are talking about but how they interact with your products and services in the past, but more importantly in the future. It is true that people do not outright, explicitly, communicate their intentions:

This is why Intent Analytics takes a Machine Learning and Semantic approach to the massive amount of data. Intent action items are key indicators on how your customers and prospects interact with your offerings, how they discuss amongst each other, and how they will interact with your products in the future.

Intent Analysis can also be applied to inbound emails sent to customer service departments, contact center feedback, and data stored in CRM systems.

Smart Analysts will utilize these results to help create effective Marketing Campaigns, personal and targeted advertising, and predictive customer service among other benefits.

Interested in learning more you can visit our website, follow our blog or follow us on social media

Elon Musk, and his very expensive Tweet

Elon Musk TwitterElon Musk grew up a self-proclaimed wimp, reading comics, and creating video games from scratch at the tender age of 12. Musk is now one of the most influential titans in many industries, and he is just getting started. Using the power of social media, Elon Musk is shaping the social environment, and empowering his customers to do the same. From researched rebuttals towards the New York Times, to increasing his companies’ stock by $900 million from one single tweet (1). Using Social Media Analytics, we are able to see the effects Elon Musk’s social media has had on his company, and vice versa. While only 10% of CEO’s use Twitter (2), Elon Musk is leading the pact while using Twitter and other social channels for PR, release dates, Marketing, HR, and more. Elon Musk has a unique ability to see what technological tools will help the human race move forward, no wonder he has attached his brand to social media. The social media market has grown substantially in the last 5 years, with predictions of aprox 20 billion in revenue in 2015 (3). Musk has believed in the effectiveness of technology in the everyday life, for decades now. While everyone was trying to get their high score on Minesweeper, Musk was creating online banking services in the form of PayPal and Zip2. In 2002 Musk saw the need for online payment services, and he executed a multimillion-dollar deal with eBay to show for it. He was soon to set his sights to sustainability in the form of electric cars, and solar panels.

Elon Musk is a human predictive analytics machine. He is able to spot global trends years before they happen. Musk has a rare ability to know when the technology, and the market are both ready for a new product offering. Not only this, but he is able to make these products sexy enough that the average consumer wants the products, but just as important, are able to purchase his products. This is all fine and dandy if you are arguably the real-life version of Tony Stark (By the way, he is way more like Bruce Wayne than Iron Man, but that’s an argument for a different post). Companies don’t have the luxury to bet on a feeling, this is where Predictive Analytics comes in. Brands and Marketing Agents are able to see what worked well in the past, what is working in your market from competitors, and what behaviour is most likely to lead to success in the future. Tesla and Musk are marketing kings, with just 35,000 car sales in 2014; they are in the headlines almost everyday, a marketing feat for even the largest companies. Tesla, SpaceX are two examples of content marketing that resonates with users. Through these marketing practices, anyone can increase brand recognition, and motivate your brand advocates to sing the song of your company. Elon Musk is buzz worthy by himself, but his celebrity status has helped improve recognition with his technology companies. Using Semeon’s Sentiment Analysis, we were able to decipher the concepts most relevant to Elon Musk. Understanding the most popular concepts (which you can see in the table below) allows Marketers to get to know their audience better, and allow them to curate content that is more targeted and effective.

Semeon Analytics was able to analyze public opinion towards Elon Musk and the companies he is part of. While Elon Musk’s personal accounts tower over his brands, in terms of followers, his social media channels have strong engagement. Our Sentiment Timeline was able to follow Tesla Motors and SpaceX closely and find out what was truly being said and why. We found that large spikes in posts (by Tesla and SpaceX) align with posts made by Elon Musk. While Social Media exploded with negative comments (6% Negative vs. 2% Positive) following the recent SpaceX rocket crash, Elon Musk’s brand was hardly affected. This is due to Musk’s intuition and his quick responses via Twitter and his social channels.




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Why counting “likes” doesn’t make any money

Facebook Likes

With developments in technology in tandem with the requirement for meaningful social media analysis, what makes good social media analytics in 2015? What distinguishes what can be done today, from what passed for analytics just a few years ago? Firstly, there’s depth and sophistication. It’s really all about the depth with social data analysis. Sentiment analysis is a powerful concept, but social media analytics takes into consideration factors such as semantics, context and content in addition to sentiment, to provide a depth and sophistication of insight. The second is real-time analytics. An hour is a long time in social media, so analysis of Human Data has to be real-time to be effective. What is relevant in the morning might not be later that day, so it’s imperative to analyze data immediately. Finally, all content holds value: although Twitter and Facebook are the two poster-children for social data, there is also immense value in other social networks, and also the comments that people leave on blogs and news articles. At Semeon we work with all social networks, blogs, consumer review sites and all other websites. We are able to skip a step by automatically, statistically and semantically analyzing data in real time. This creates true and accurate data analysis.

Another key trait of social media analytics in 2015 is that turn-key applications need to deliver instant insights to customers, powered by an intelligent platform for analyzing social media conversations and replacing traditional market research, Semeon’s platform provides the following:

  • Speed: Data is indexed from hundreds of thousands of social media sites and can be queried in real time;
  • Intelligence: Data can be analyzed across a multitude of languages with SDL’s natural language processing, machine learning, and proprietary machine translation capabilities;
  • Accuracy: Using intelligent algorithms in order to properly identify relevant documents and filtering spam, Semeon offers more accurate results.
  • Better Business Intelligence: Patented analytic applications measure key performance indicators that map to business objectives, including purchase intent and brand advocacy.

Thankfully social media analytics has come a long way. Although many brands are still stuck in the quantitative dark ages of counting likes and mentions. Semeon is looking ahead to providing what we believe to be the best social media analytics tool out there.

Semeon Analytics