The Traps of Big Data

With all the buzz around big data nowadays, it is easy to get carried away with collecting as much data as possible. People think to themselves that with more data they will be able to better improve their business making decisions. This is true… but to an extent and with caveats. Over at Semeon, we pride ourselves in our ability to make use of data for the companies we work with. This has been our mission, and since our inception, we have been able to help brands such as P&G, Honda, Toyota, Pepsi, Sephora and many more leverage their data for actionable insights.

Along our journey, we have encountered a countless number of firms who have fallen into some of the traps of big data. Some of the major pitfalls we see are as follows:

1. Vanity Metrics

This is the number one problem we see with companies today: they focus on data that doesn’t matter. It takes time, but it is important to determine what data matters to your company. What data is going to help increase user retention? What data is going to help you better understand the needs of the customer? What data is measurable and consistent? Focus on quality over quantity. Too many companies love the idea of having data that they forget what the purpose is for. The purpose is to create insights that allow you to make better, more informed decisions.

2. Un-Actionable Insights

This leads right into our next point: actionable insights. This problem is deeply correlated with vanity metrics but requires its own mention due to its importance. Many companies we have interacted with collect data for data’s sake. Perhaps this data can be analyzed ex-post but it is much more effective to know why it is being collected, ex-ante. If you can’t summarize what the data is being used for in a few lines, it is time to reconsider whether or not it is providing any use to the company.

3. Lack of Statistical Significance

Insights can’t help you if your results aren’t valid. When you find trends and interpret data there is the question of whether your results are statistically significant. This can be figured out with little knowledge of statistical analysis and is necessary to confirm trends. Using data sets that are small in size can lead to a misalignment of strategic priorities based on information that isn’t true.   

4. Not Leveraging it enough

I’m sure we don’t have to explain to you the importance of data. Chances are, if you are in the workforce, you have either dealt with analyzing data firsthand or at least contributed to data entry. Economist Intelligence Unit reported that 60% of the professionals they quizzed feel that data is generating revenue within their organizations and 83% say it is making existing services and products more profitable. The uses of it are seemingly limitless and continue to be improved upon, yet still, there are copious amounts of companies not leveraging it to their full capabilities. As the famous manager, Peter Drucker says, “What gets measured, gets managed”. Our suggestions to companies that use data is to evaluate what else they could be measuring. Is there anything in the company that needs to be improved upon? If so, is it measurable? Will measuring it interfere with workflow? These are just some of the questions companies should be asking themselves on a regular basis.

5. Misinterpreting it

“It is much easier to maintain and bolster views that we currently hold than it is to create and develop new ones” – Daniel Kahneman.

Confirmation bias is one of the biggest dangers to big data. It is a cognitive bias in which one possesses a hypothesis and actively seeks out data to support it – ignoring the data that reject it. The worst part is that people do it unconsciously. Over at Semeon, we have been able to statistically show some companies that their firmly held assumption were false.

We have also seen a decent amount of firms mistake correlation for causation. Just because data suggests two events are interconnected, it doesn’t mean that one definitively caused the other, even if it makes logical sense.


We hope you enjoyed the article! If you are interested in receiving a free consultation call on how to improve your company’s use of data, we are more than willing to assist and offer insight. Contact us by requesting a demo on our main page.

Utilizing AI to Understand your Customers

Noam Chomsky, a central figure in the study of linguistics from the late 1950’s, said that language “is a process of free creation; its laws and principles are fixed, but the manner in which the principles of generation are used is free and infinitely varied. Even the interpretation and use of words involves a process of free creation.”

Clearly then, language is complex. By Chomsky’s logic, conjuring and deciphering language is seemingly infinite. In linguistics, semantic analysis is the study of the structure and meaning of speech. It’s the job of semantic analysis to understand language from the point of view of the encoder. This eliminates the process of ‘free creation’ that comes from interpretation. It does this by relating syntactic structures from the level of phrases, clauses, sentences, and paragraphs to the level of the writing as a whole. This involves removing features specific to cultural contexts  and deciphering key elements from idioms, and figurative speech.

For many brands nowadays, the complexity of language is proving to be an obstacle. The fact that there are so many ways to encode and decode messages makes understanding the customer difficult. Online content is rife with spelling mistakes, acronyms, initialisms, and emoticons. In addition, the mechanics of tracking exactly when and how people are interacting with one another is non-trivial (for example, are they in a series of threads in a forum, are they retweeting or replying to one another on Twitter?). Also, once the boundaries of people’s conversations are well understood, the correct interpretation of the meaning behind words is handled very poorly. Most analytics companies’ attempts to interpret conversations are based largely on frequency driven metrics. As a result, we often see that interpreting meaning is based on the number of times any given word is mentioned in online posts being analyzed. This misinterpretation can lead to a misunderstanding of customer sentiment and cause a multitude of problems. A study from IBM and eConsultancy found that 81 percent of companies say they have a holistic view of their customers. And yet, only 37 percent of customers agree that their favorite retailer understands them. This is a huge discrepancy and highlights the challenges associated with language.

But despite the complexities, it is not impossible to understand your customers. Like Chomsky said, there are basic laws and principles that need to be followed. These rules make it possible. Over at Semeon, we have made it our mission to understand the meaning underlying what the customer is saying. It started with the realization that customer and brand alignment is essential to maximizing ROI. So, using artificial intelligence, we have created a software that figures out the context behind the consumer’s speech instead of identifying keywords. This allows us to provide proper analysis and gives brands the capabilities to be in the 37 percent companies that truly know their customers.

I hope this article provided some insight into the complexities of language and the problem is poses for brands. Furthermore, I hope that if you represent a brand that it makes you stop and think about how you are evaluating what your customers are saying.

The Call to Action

At the least, stop with the glorified post counters. We need to start focusing on the metrics that really matter: the customer sentiment. What are your customers actually saying about your brand? Are they content? Do multiple customers have the same problem?  

At the most, check out what we have to offer over at Semeon! We are here to help you and make your lives easier. Our software can help take your brand to the next level.

Let’s Skip The Small Talk Within Big Data!

As the world is moving into a symbiotic state with technology, specifically with their handheld devices, there is much buzz around ‘Big Data’ as a means to understanding societies and individuals. However, there is an important hurdle, algorithms must face in order to begin decoding our condition – the complexities of the human language.

Originating across the globe, our language constantly evolves to carry culture, tradition, sentiment and overall sentiment at a given time. The traditional analysis uses simple word clouds that divide text content through elementary rubrics such as positive/negative words, frequency tags. Nonetheless, in a society where sarcastic memes surf large waves of the internet, it is crucial for big data analysis tool to stop comprehending like a detached machine and view text like a human would.

So, where do we go from here…

The Birth of NLP (Natural Language Processing)

Natural language processing (NLP), a component of artificial intelligence, is the ability of a computer program to understand human speech as it is spoken.

The development of NLP applications takes on bigger challenges as computers traditionally require humans to “speak” to them in a programming language that is precise, unambiguous and highly structured or, perhaps through a limited number of clearly enunciated voice commands. In contrast, Human speech is not always precise — it is often ambiguous and the linguistic structure can depend on many complex variables, including slang, regional dialects, and social context.

Hm, only if there was an example of how this can be used in business…

Leveraging of the Concept Cloud for an Influencer

The excerpt below is a Concept cloud that has been plucked from a report created for Sugar Sammy, a Canadian comedian, actor, writer and producer from Montreal who conducts his routines through a mixture of fluent English, French, Punjabi, and Hindi.

This analysis poised Sugar Sammy in a better position to pick his events, content material and the correct means to connect with his fans, as it provided him insights on how his fans were reacting to his performances.

Fans are engaged and want to praise Sugar Sammy online and the death threats seem to drive negative sentiment. Moreover, there is a connection made with Montreal Canadiens that has had a significant impact on Sugar Sammy’s brand identity as well.

A legitimate data-driven approach, that surpasses the human linguistic complexities, allows brands, be it large consumer product goods organizations or individual influencers, to understand the real conversations that their audience are having. This allows them to connect directly to their audience while reducing inefficient decision making on the content/project roll out, thus helping their overall return on investment.

An extremely small cohort of companies, such as Semeon Analytics, focus all its efforts on research and development of the NLP technology through its product and services. Please feel free to comment below, or reach out if any of this work interests you!



BACK TO THE BASICS: Understanding Online conversations with Big Data, Sentiment Analysis, Concept Clouds, and Semantic Analysis

Harness Big Data for Big Insights

With the availability of resources online being limitless, Internet users’ each have their own unique browsing habits: visiting social media pages, shopping online, reading about a topic, etc. Naturally, internet users are also having conversations online; it is now easy for someone to voice their opinion online and be heard by thousands.


To put things bluntly, the practical opportunities from a business standpoint are fantastic; online conversations are the equivalent of massive unbiased focus groups and can be leveraged to the benefit of a business.

Take for example Lenovo’s success story: after sifting through thousands upon thousands of online conversations (social media, blogs, forums, etc.), Lenovo created new products in response to the voice of the public. These new products achieved fantastic sales simply because they responded to customer needs that were previously unanswered.

We are now able to gather massive amount of data points (referred to as Big Data) and the ability to analyse them now propels companies to a whole new level of customer experience.

For further insights, we recommend our guide to leveraging your influencers:…/branding-technique-101-leveraging-your…/



Gathering data for consumer behavior used to be time consuming and an expensive endeavor. Today, data can be collected en masse through various channels and networks available on the internet, hence the term Big Data.


There are other important factors besides the obvious time and cost reductions that make Big Data attractive. For starters, the massive amount of data grants businesses the ability of gathering insights with statistical validity, meaning that the voice of consumers is correctly represented. Furthermore, gathering data that is already available online is different than asking for opinions; the data gathered is less likely to be bias, so the true emotions and opinions of consumers will be depicted.

For example, Hertz used to handle customer satisfaction surveys locally for all their 8600+ locations! Not only was it inefficient, but the insights gathered were unable to truly depict the needs, wants and behaviors of the customers because of the surveys being isolated from one another. By switching to Big Data, Hertz went from throwing 8600 small sized fishing nets with difficulty, to easily throwing 1 giant sized net + leveraging new customer data channels previously inaccessible. The value that Hertz received following the implementation of Big Data is easily in the millions.

For further insights, we recommend how big data is revolutionizing how Ad Agencies operate:



We’ve established that online conversations are pivotal for businesses to improve their customer experience. However, having to read every bit of conversations manually and extracting insights is humanly impossible. This is where sentiment analysis come into play.

Sentiment Analysis at its core is the process of analysing conversations and gauging the strength of the emotions behind them in an attempt at understanding a) if conversations are positive, negative or neutral, and b) what aspects of the conversation are positive, negative or neutral.


Sentiment Analysis is a fantastic way for companies to track their brand or products and understand how their consumers react. The main benefit from sentiment analysis alone is the ability to understand the reception of a brand or product in the face of change, for example: after a new product release or a marketing campaign.

The perfect example to highlight the strength of sentiment analysis would be Whole Food’s Asparagus Water situation. After releasing the product, customers were quick to express their discontent online. Had Whole Food implemented sentiment analysis, the whole Asparagus Water situation would have been killed. However, since they did not leveraging sentiment analysing, they were ridiculed by the media, continuously criticised by consumers and their stock dropped in the process until the product was removed.

For further insights, we recommend reading on the trends that are happening in social media analytics:



Powerful insights can now be automatically extracted by understanding the meanings of words and sentences of online conversations, assigning meanings to words be known as semantic analysis.


While Semantic does a wonderful job assigning meanings the words and sentences, the perfect sidekick and the true hero in this story is contextual concepts cloud; the ability to portray and identify the driving forces that influence public opinion. Having the possibility of identifying the elements that bring value to customers, to observe what they care about and see the language they use, are fantastic ways of improving the customer experience.

When Apple released their Apple Watch, the company was keen on gathering the initial feedback from their users. Concept clouds were able to identify that the top concepts were “Apple” and “Watch”. This methodology offers little insights because no meanings can be placed after observing single words with no context. On the other hand, semantic analysis paired with context concept clouds was able to identify that the battery life in the apple watch was a problem that consumers cared about and something that they would want improvements on.

The ability to paint a full 360 degree view enables companies to attain new heights and identify opportunities that would have been impossible to observe without contextual concept clouds.

For further insights, we recommend understanding customers with the use of intent analysis:

Optimizing your Resume-filtering process to improve Quality-of-Hires

Talent Match: Filtering the Best Candidates

A day in the life of a Recruiter usually includes a large portion of reading and reviewing resumes. Since it is now much easier to mass-apply to job openings online, on average corporate job openings attract 250 resumes each! While the number of applicants are continuing to increase, the Quality-of-hire are still inefficiently low. The gap in quality long-term hires starts with the errors at the resume-reading stage. From spending too little time on each Resume, to mismatches between the candidates and the open positions, to the human error layered throughout the reading and matching process. Ask any professional Basketball player to make 100 shots in a row, even they make mistakes and they are the best in the world at what they do. Imagine you are required to read resumes all day long, even the best get tired, distracted, “hangry”, leading to decreased accuracy and even bad employee-position fits.

Here are three Pain Points that decrease accuracy of hires and increase the cost-per-hire:

  1. Latent Human Bias embedded in the resume-reading procedure
  2. Accents/capital letters/differences in language/document formats which are usually automatically disqualified in most Resume Parsers because the Parsers are only Keyword-based
  3. Showcasing why you chose the candidate, without gut-decisions, while ensuring compliance for the Diversity of hires

Machine Learning-powered Resume Parsers such as Talent Match solves these issues with its context-based analysis process. Through the analysis and categorizing of resumes in any format, Talent Match rates candidates based on their whole resume, compared to keyword-based searches. This ensures an accuracy rate of more than 85%, an increase of quality-of-hires, and a decrease in time-to-hire. When looking to integrate Parsers into your ATS or resume databases, make sure the parser can analyze different document formats, otherwise you will lose a large portion of candidates to incorrect digital formats.

Global Recruiting Trends show that the average time to hire is between 1-2 months, this can be greatly improved with a strategy that includes automating the assessing and classification of incoming resumes and organization’s
database of resume. If you are looking to increase your quality of hire while decreasing your time to hire, Talent Match will find you the right candidate faster than ever.

Contact us today for a demo showcasing the benefits of automating your talent matching, leading to higher quality of hires in less time than ever before!

Microtargeting: The Elections, and what Marketers can learn to boost Customer Experience

Trump and Microtargeting

Since the 2000 elections, Microtargeting has been an essential piece in winning the American elections, that is 5 elections in a row that are influenced by this data-driven tactic. Let’s start with a simple definition, what is Microtargeting? Microtargeting is the use by political parties and election campaigns of direct marketing datamining techniques that involve predictive market segmentation (aka cluster analysis). Thanks Wikipedia, us Marketers can learn a lot from how political parties divided up their audiences into segmentations from the data collected online. 17 years later and modern Marketers are now implementing similar techniques to their digital channels.

This data-driven approach starts with collecting data from their multiple channels; whether it be polls, social media data, and demographic interests by state. The next step is the most difficult to execute and regarded as the most important; finding the relevant patterns. Once teams understand the patterns in the data they divide (the demographics) and conquer. In 2004 Karl Rove, considered the “architect” of presidential election by Bush Jr. himself. What Rove realized is each micro-demographic, as in young Latina families in California, have majorly different political agendas. Once these micro-demographics were defined, specific and targeted messages were crafted for each group. For example: These young Latina families mentioned earlier may care most about public schools, while families from the same heritage and state may care more about work opportunities if their children are older. This division of message, is a major technique used in every election, especially this last one by Trump’s political team. Trump, whether you love or hate him, tapped into the voter interests and preferences and possibly most effective was voters’ fears.

In 2017, successful brands can tap into the interests of each relevant demographic, and provide actionable messaging that drives sales and Customer Experience. Consumers have much more power than they did, say 30 years ago, this is due in part to the accountability provided by Social Media networks and the increase in competition over the years. Crafting effective, engaging messaging starts with a robust data analysis strategy.

Semeon provides organizations with the tools to turn consumer data into digestible and actionable Insights, leading teams to customer-focused strategies. Align your marketing campaigns with the relevant audiences to boost ROI and increase Customer Experience. Contact Semeon today for your free Demo today!

Semeon Analytics

How to Get the Most out of Facebook Analytics

“Highly Data-driven organizations are three times more likely than others to report significant improvements in decision making”. The amount of data at company’s disposal is no longer the road block, rather creating Actionable Insights that fuel business decisions from the ocean of data is holding companies back from the full potential of Big Data. Two of the biggest businesses in the United States, Facebook and Google, are Ad Tech. This should showcase how many millions of data points are fuelling advertisement decisions already, and the types of analytics continues to grow. Just recently Facebook updated their Facebook analytics tool to improve the benefits for Marketers and Product Managers, by leveraging Facebook’s user data.

Demographic insights are going to be very important in the next few years within the analytics industry. The amount of data is no longer the problem, but the type of insights created from your business data is. Demographic insights will be fuelled from data coming from multiple sources, to create very clear and descriptive perspectives of the clients throughout the sales lifecycle. Facebook is now incorporating their own anonymized audience data so users can learn what Facebook pages their users like, among other insights, so users can be attuned to their customers and provide products that are aligned with their clients. The other massive feature update from Facebook allows users to compare their audience segments side by side. This is huge for Marketers who are trying to compare and learn what search engines provide the most traffic, or which age group is more likely to engage with video content for example. For marketers, this means the power of asking the right questions, which will lead to the “a-ha” moments. As I have said earlier, successful marketing strategies are at the axis of creative and scientific.

Leveraging your data analytics should be involved in every part of the marketing decision, including the planning, implementing, and especially looking back on past campaigns to learn for the future. Like your history teacher in high-school always said: “if you do not learn from the past, you are doomed to repeat it” Ask your data questions every chance you get, this will lead to new perspectives on your consumers, your digital channels, and your marketing campaigns. Marketers need to be empathetic enough to care about their customers’ wants and needs and how they go about learning and eventually buying their products and services. In this age, we can learn a great deal about our customers and use that to fuel better business decisions across leadership teams, product teams, and especially sales and marketing teams. Modern Marketers need to be able to leverage data to uncover who their clients are, by searching the data to find user patterns that lead to improved communication and product strategies. With the increase in Big Data, marketers will be expected to know more, but with the right data analytics strategy in place they can perform their research and implementation in less time but with more accuracy that ever before.

Semeon Analytics is here to help you make the most of your campaigns analyzing Facebook and Social Media data to fuel better business decisions! Contact Semeon to request a free demo surrounding your digital marketing strategy today!

Big Data & Ad Agencies: Giving Reason to Gut Decisions

Big Data & Ad Agencies

“an overwhelming 83% of [Ad Agency] clients are looking for unique skill sets and specialized capabilities not found in most Ad Agencies or media buying firms.” Two of the areas with the most negative ratings are; Managing the Data explosion (70% were not satisfied) and analyzing data to create personalized experiences (only 29% gave positive reviews). There is not a marketer today who doesn’t want to know more about their customers, but for most teams this is next to impossible based on exclusive data silos, proprietary labels, and the different types of unstructured text. Big Data has allowed Ad Agencies and Brands to proactively learn about their customers from many digital channels, the biggest road-block is transforming the ocean of data into Actionable Insights.

It is said that genius lies at the intersection of creativity and science. Creating engaging, aligned campaigns, comes from a statistics-backed creative process. Netflix and Spotify have fantastic use cases that showcase the benefits of leveraging consumer data to create customer-centric products or advertisements. Netflix has been using Consumer-data from their own streaming service to create products that have high-chance of success in terms of viewership. Using viewer habits, they could make purchase decisions with high predictability rates based on what directors, genres, and the actors’ viewers watched most. Spotify used consumer data to create an engaging campaign surrounding the users’ listening habits in a fun and unique perspective. Another aspect of leveraging data comes from digital channels accelerating the feedback-loop process for testing campaigns before purchasing media space.

Social Media is an effective testing ground for campaigns before they hit the national stage, quickly gauge public perception and proactively align with customer-centric data. Listening to digital channels allows teams to prioritize the channels that help them optimize their customer experience. Ad Agency teams and Brands require a solution that is robust enough to tackle data from any source, transform the sea of data into relevant insights, and analyze textual data instead of counting the frequency of keywords. Semeon Analytics can help Ad Agency teams win more pitches by helping them become industry experts by turning customer data into Actionable Insights quickly. Using customer data, teams can align their campaigns with their clients’ consumers to improve engagement and boost ROI.


Make sure your gut decisions have statistical support, leading to winning more pitches and showcasing your ROI more clearly. Contact Semeon Analytics to learn how you can proactively align your next campaign with the customers that matter most!

Fuel your Business Decisions with Dynamics 365 Data

Dynamics 355 - Leverage DataWhat is the main business tool that Sales and Marketers both use on a daily basis? CRM, the business platform that collects and houses an immense amount of your customer’s data. Third-party applications make CRM tools more powerful, and increase the amount of data being fed into the system. Add-ons such as Marketing Automation Software allow for automated email campaigns from the CRM system while collecting user data from email responses. How are you optimizing your sales and marketing efforts without first listening and understanding your customers? Effective campaigns are customer-driven, and what better to use than the data already at your team’s disposal.

Semeon Insights platform employs the power of Artificial Intelligence to sift through all of your customer data to create insights that fuel better business decisions. The Holy Grail is aligning your campaigns with the wants and needs of your customers. Market leaders can respond to customer issues quickly if they are listening to all their channels, and proactively adjust their latest communication strategies by gauging public opinion. Consumers are overexposed to content coming from brands, and this leads to less effective messaging compared to reviews coming from the industry Influencers. Leveraging your Brand influencers provides your consumers with product updates and incentive to purchase from the highest respected opinions in the industry. If you are listening to all your communication channels effectively, your teams will optimize brand influencers with the concepts and ideas that mold public opinion the most.

To get there you need to analyze and understand data coming in from multiple sources, such as Social Media data, Blogs, comment sections, and to combine this with your CRM data to create a 360-degree view of your customers. Combining data from your CRM and your social channels, the interactions between customers and between the customers and your company, allows you to stay ahead of your competition with Actionable Insights.

The type of data Semeon can analyze from CRM includes; sales notes, email responses, customer service interactions, competitor information, basically text that is either structured or unstructured. Semeon Analytics allows you and your teams to quickly understand public opinion, to drive higher engagement and close more deals. Semeon handles any type of textual data, whether it be structured (in forms) or unstructured (free-form such as blogs) and transforms this wealth of data into Actionable Insights you and your teams can use everyday. If you are ready to optimize your sales and marketing efforts contact us today for a free consultation today!

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.

Social Media Analytics Trends in 2017

Social Media Analytics Trends 2017Welcome Marketing Professionals and anyone else realizing the power of analytics. There are many exciting aspects of the market to look forward to, and trends to follow that will lead your business to an effective Marketing strategy. Semeon Analytics has been in the Text Analytics space since before 2012 and have seen all the high’s and low’s, all the passing fads and the features that truly benefit their clients. To give you a sense of where this market is going, the worldwide Social Media Analytics Market is set to grow to $5.40 Billion by 2020, at a Compound Annual Growth Rate of 27.6%. (

There is no surprise that with the maturation of the market, the features are becoming infinitely more effective, while the tools in general have become more intuitive and user-friendly. Truth be told, many of the products just a few years ago were either light on the analytics, or far too complex for the non-Data Analysts. Today, tools like Machine Learning are quickly becoming an essential piece to the Analytics puzzle, allowing platforms to continuously learn while refining their results. Similarly, how Machine Learning is helping Siri answer your questions, it is also helping Analytics tools provide better answers to Marketing teams. Another emerging trend is integrating Text Analytics tools with CRM databases, this can be extremely effective if performed properly. Combining Data Silos such as CRM data and Social Media data, you are creating a 360-degree view of your customers and sales cycle which will provide your teams the insights to drive more sales online and off. In 2017 Influencer Marketing is going to continue to grow. As Brands and companies overload News Feeds and Social Feeds, consumers are becoming weary and fatigued from all this content, leading to lower engagement and decrease in brand trust. Consumers are looking elsewhere to receive their product reviews and information; Industry Influencers have popped up everywhere and have become an essential piece to online branding. More than 75% of consumers identify word-of-mouth as a KEY influencer in their purchasing decision (

With the new Influencer analytics features within the market, brands and marketing teams can keep track and leverage their key industry influencers! Creating strong relationships with these top influencers will lead to stronger social engagement, positive brand perception, and trusted advisors within the industry as your Brand ambassadors, not to mention the free advertisement whenever they post about your products. Content creators can benefit immensely from the influential topics and ideas that some high-end tools provide. Imagine being able to create blogs that resonate with your audience, every time. This is what’s possible when you are analyzing the most influential topics within your industry or surrounding your products and services, providing your team with the tools to grow engagement and sales. 2017 is the year to stay clear of advertising on Social Media channels, people will see right through your efforts and it will only hurt your engagement. Social Media channels are a place to engage your followers, keep them up to date with thought leadership, and nurture your relationship between your customers and your brand. As millennials grow their purchasing power, brands will need to communicate to them anywhere and everywhere. With chat bots and integrated chat features with Instagram and Facebook, personalized customer service will become more essential to any consumer products or services. A strong analytics strategy will provide teams with the insights needed to engage with their consumers intelligently.

Social Media Analytics is a massive ocean of products and services, getting deeper by the day, if you have any questions feel free to ask the experts at Semeon Analytics. Semeon Analytics is a leader in Text Analytics solutions and can provide organizations with Actionable Insights, straight from the consumer, leading to better business decisions. Contact us at to learn more about how we can optimize your digital marketing strategies.

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. Semeon Analytics