We’d like to help! Here are the research offerings our clients are using to develop their Q4 deliverables and map out their 2024 planning.

Consumer and Category Segmentation

By using millions of data points we create robust and representative segments based on stated behaviors such as activities, food and drink preferences, values, and brand associations. This resulting segmentation is highly effective in identifying different target markets within a specific category or set of needs. 

What’s New? Differently from typical demographic or behavioral segmentations, we create segments that are not based on race or gender but rather defined by the link between behaviors, needs, and product benefits. 

Now what it enables is discovery! This approach allows brands to assess how their performance within each segment as well as driving attributes of this performance compares to those of their competitors.  It also enables the discovery of whitespace within segments, making it clear where the performance of brands is falling short in meeting consumer needs. 

Reputation and Sustainability Tracker

We have a rigorous Reputation Measurement Guidance System (PRISM) that fully leverages the power of real-time streams of unstructured text data together with advanced machine learning and proprietary measurement frameworks to provide rapid, deep, and actionable intelligence about client reputation through the lens of key stakeholders and consumers. 

Data is processed through a proprietary scoring system, and our leading NLP provides actionable scoring enabling tracking and analysis. 

We leverage dozens of prebuilt machine learning classification models that align to a core measurement framework that includes “trust,” Customer Experience (CX), corporate responsibility (ESG), leadership, and emotional connections. Custom attributes can be added as needed.

We have also created frameworks around sustainability and innovation. Our system excels at providing clear and actionable guidance when reputation dynamic shifts, identifying both risks & opportunities. It also uncovers emergent, “below the radar” topics and their impact on overall Brand Reputation. We achieve the cost/benefits of specific actions by enabling simulation modeling.

Category Trends *NEW*

Trend vs. Fad: how can you know the difference?  Converseon has analyzed millions of data points to understand how people segment, how their needs differ, and how segments differ in fulfilling these needs. From the foundation of human-centric product innovation, we have developed an approach to decoding trends and fads. 

Our unique system scores each trend or fad in a multi-dimensional way mapping not only how volume and sentiment is changing over time, but also how demand is shifting as people choose different ways of fulfilling their needs.

Innovation Landscapes and Whitespace

The opportunities and risk matrix enables brands to spot new innovation opportunities, that are either emerging in a category or result from poor performance of market participants. Each segment (customer type by occasion) has the share and velocity (growth) shown to assist in sizing the opportunity. All brands within the cohort performance are evaluated and assessed to highlight their strong, satisfactory, and weak deliveries. We highlight cells with poor delivery making them easily detectable. If it’s the client’s brands that are underperforming in these areas, this turns into risk. 

Other available reporting options include:

• Category Landscape Reporting
• Media Campaign Performance and Tracking 
• Shopper Insights/Journey 
• Customer Satisfaction and Experience 
• Ad Hoc Reporting

We also have a number of strategic engagements you can ask about – Get in touch to discuss any of these options or inquiries you have.

The post Converseon’s AI Quantitive Market Research Offering for Q4 appeared first on Converseon™.

In the era driven by technological advancements, distinguishing between trends and fads can be key to success or failure. As Bradley delves into the nuances of trends and fads you will learn how Converseon can enable you to make informed decisions with the help of our insights harnessing the power of trends and avoiding the pitfalls of fads. Our unique system scores each trend or fad in multi-dimensional way mapping not only how volume and sentiment is changing overtime, but how demand is shifting as people choose different ways of fulfilling their needs. Join us in webinar to learn more.

Register below to attend the webinar on October 20th at 1pm EST.

The post Insights Tech Showcase(GreenBook): Trend vs. Fad: How Can You Know the Difference? appeared first on Converseon™.

Congratulations to our Converseon team for being shortlisted for 2 prestigious AMEC Measurement and Evaluation Awards!

• Best multi-market Reporting: Global Brand Purpose Reporting (Mattel)

• Best use of social media measurement: Measuring Impact on Corporate Reputation (Walmart)

We’re so proud of this work and are so excited to be recognized by this highly respected organization.

Our team has done incredible work this year and to receive this recognition is very special. We’re very proud of what we’ve accomplished together.

Cheers to all the companies and the work recognized!

The post Congratulations to our Converseon Team – Shortlisted for 2 AMEC Awards! appeared first on Converseon™.

Beginning in 2008, Converseon pioneered the application of machine learning and natural language processing to unstructured social (and related) data. Since that time, we’ve gained extensive experience in building, testing, and deploying ML models using a wide range of algorithms, from decision trees to neural networks.  

Our approach has been to remain, in some sense, algorithm agnostic – we use whatever best solves the problem at hand. We incorporate new innovations as they demonstrate productive impact on our work and solutions. Critical to this is our ability to apply human-in-the-loop integration, validation, and subject matter expertise to solve specific challenges. Our focus on model performance and practical application is one reason Converseon was recognized by Twitter as its first “recommended” NLP partner in 2022. 

The emergence of LLMs (large language models), the underlying technology for applications such as ChatGPT, is the most recent and most significant example of innovation that we have evaluated and incorporated into our solutions. We do so with a sharp eye toward their overall effectiveness, and perhaps more importantly, their challenges, risks, opportunities and, of course, anticipated evolution.

The media frenzy over ChatGPT has many in the consumer, market, and social intelligence space, and beyond, seeing these developments as a suddenly disruptive force. But this technology, and our application of it, has been around for some years. More importantly, we must keep in mind that, from our perspective, the key challenge facing organizations today is true predictive intelligence. The ability to see ahead and peer around the corner at what is likely to come – while quantifying the value and impact of actions before you take them – is a key imperative, and provides a context for how we are exploiting this technology.

And it is through such a lens that we evaluate this and all new applicable technologies.

Topline Summary

The summary of our view, which we detail here, is that LLMs are indeed a powerful technology that can play an important, but specific, role in the “intelligence” industries. They are integrated into our broader solution where they provide the most value while mitigating their risks.

LLMs provide significant power as foundation models upon which we build reliable task-specific solutions incorporating subject matter expertise and human oversight. By contrast, in isolation, LLMs have broad but shallow knowledge, are prone to error, sometimes “make up” facts, and are retrospective (not predictive) in nature.

In short, they must be approached with the same level of diligence, focus, expertise, validation and human in the loop intelligence that has been consistently required for other AI and machine learning approaches. In fact, in some ways even more diligence is required given the intoxicatingly convincing yet false output these models can produce. They are an important component in the ecosystem but only when realized with accompanying processes and technologies that augment, complement, and transform their power into the predictive, quantifiable, trusted and actionable decision intelligence demanded by organizations we work with.

Some Detail

ChatGPT has created quite a stir recently, earning quotes such as “everything is going to be different going forward” from Box CEO, Aaron Levie. Hearing what some people say about it, or even chatting with it yourself can make one feel awe struck or even intimidated.  

Will everything really change? Or at least will everything in the digital world change and therefore how we acquire and use data to make informed decisions change?

This is the key question for many looking to use this new technology. Firstly, let’s address the sentience question and dispel, at least for GPT version 3.5 and all current, similar models, that ChatGPT (and its rivals such as Google’s Bard) are not alive, nor intelligent. It does not think, and it cannot reason. It is a large mathematical model of inputs, weights, memory and outputs based on those inputs and weights which means it is a tool, not a being or person. It has been trained on large corpuses of text, to predict what should reasonably come next and some very small percentage of this is with a human in the loop. It makes mistakes, and it can reflect the biases inherent in the texts upon which it is trained.

This means that like any tool, it has strengths and weaknesses, 

and with that, right and wrong applications.

This is where things get interesting. If we adjust our thinking, ground our amazement and see ChatGPT as a tool instead of some mystical AI, we begin to see how and when it can be useful in helping our businesses make better decisions.

Let’s look at the strengths and weaknesses first and then we will look at some applications in business. We will use two practical examples to illustrate and demystify. 

Example 1: The India Times asked ChatGPT “Can you tell me which is India’s biggest IT company?“ ChatGPT answered “Tata Consultancy Services (TCS) is one of the largest software companies in India. TCS is a multinational corporation that provides a range of IT services, including consulting, software development, and business process outsourcing. TCS is headquartered in Mumbai, India, and has operations in more than 50 countries around the world. In 2021, TCS was ranked as the third largest IT services company in the world by revenue, and it is one of the most valuable companies in India,” (bold added for some fact checking later).

Now that’s an astonishing answer. Or is it? It is a seemingly good reply in very natural sounding language. Has an infinite brain just been tapped into? Well, getting past the amazingly well constructed reply, we can see that although the question was localized as a comparison within India, the answer was about a company registered in India but in a global, not local context. So this isn’t actually a good answer. If your CEO asked “which product performs best in the USA” and you said “our salt and vinegar flavor sells the most world wide”, they would not be happy since you did not answer the question.

The next point to note is that the answer is short on facts. ChatGPT is great at combining data sources to produce a natural sounding summary. Primarily the system has learned to construct language correctly, whether this be code or a human language. But it doesn’t include many facts, because facts are not represented in language in the way it was trained. Remember, it is trained to predict the upcoming words and unless the entire training corpus was question and answer style, it wouldn’t learn facts as such. Another note on facts: given that these models have been trained with texts from many sources, applying probabilistic weights to inputs, the resulting “facts” are often wrong. 

Let’s illustrate by looking at a simple Wiki search for TCS which has: “Tata Consultancy Services (TCS) is an Indian multinational information technology (IT) services and consulting company with its headquarters in Mumbai.[6][7] It is a part of the Tata Group and operates in 150 locations across 46 countries.[8-2017] In July 2022, it was reported that TCS had over 600,000 employees worldwide.[9]”  (bold mine)

This brings us to the next problem with ChatGPT, when using the public model (i.e. not training a GPT 3.5 model yourself): you don’t get any citations and you get only old data from 2021 or earlier. You can follow up and ask for the sources it has used, but as some have demonstrated, this leads ChatGPT to change its answers. Yes, the system will actually change its answer as you go down the route of finding the primary source document. This means, no matter what the public ChatGPT model says, you always need to fact check any assertions made.

This isn’t good for decision making. You wouldn’t take at face value glib responses from a casual (human) acquaintance, nor should you accept the same from ChatGPT.

Driving home this point of facts and sources, which would you prefer as answer to the question of how many countries TCS operates in?

• ChatGPT: over 50 (pre-2021 data)
• Wiki: 46 (2017 investor relations data)
• TCS Website: (55 countries, Jan 2023)

Now, some reading this might be thinking “you are being too hard on the tech.” But remember, the context is business and making critical business decisions. If the data you have is wrong, the decision you make is wrong.

Now let’s explore how facts get commingled within ChatGPT. This is a common problem across NLP models. It is actually really hard to provide sufficient data so that minor sub-categories will have the right facts. This is because, at its heart, all current AI is really probabilistic, i.e. it infers “answers”, via weightings optimized during training to produce what would be the most likely answer to a specific prompt. 

To illustrate this commingling our next example uses the iPhone 14 example from MKBHD’s Channel. He is a tech reviewer who had ChatGPT write a script for a video. It was super interesting, but as he points out when ChatGPT shared the technical specifications for the iPhone 14, it indicated a 12mp rear camera, which is true of some previous models. But the IPhone 14 has a 48mp rear camera. 

This is not surprising and in truth is what we would expect to see. The model is learning from many different sources. It sees “12mp and iPhone” together 10x or even 100x more frequently than mentions of “iPhone” and “48mp” rear camera, because the iPhone 7 through the iPhone 13 had a 12mp rear camera. What the model couldn’t do was “understand” the adjustment that including 14 would make to the iPhone technical specs. 

This exposes a weakness and benefit

 If you are interested in the most popular opinion then clearly a model which gives higher weight to the most expressed opinion is going to work well. If you are interested in the hard facts, regardless of the “hype,” then this isn’t going to work for you. ChatGPT is summarizing in a style which doesn’t focus on the facts but rather, loosely put, the co-occurrence frequency of words across many documents. (This is inherited from training techniques that work to predict the missing words from, or the next words to, a sentence. The more frequently certain word combinations appear together the more likely they will be predicted when one of the words comes up in a question via the inputs).

Now onto the benefits. 

How can one best use a model like this? We need to look at the underlying public model and then specifically train it further with your own data. ChatGPT itself is an instance of the public model with some additional training added. At Converseon, we have been achieving excellent results by applying additional training (fine-tuning) public models with very carefully curated data, and careful analysis of results.

In a general public pre-trained model, you are able to get a summary on various topics, the current winds (from 2021), so to speak, of public opinion as published on the internet (including Twitter and some other social platforms pre 2021). It is high level but can help you to break out of your way of seeing the world. This use of the tool is very much like bringing in experts from other disciplines or outside of your team or organization for brainstorming. It will likely not be of the same quality, but it fulfills that function. 

Another use case is to get well-written text, in your language (but not culture – it ignores non-English cultures), of your own ideas. So if you have a particular idea and want to expand upon it, you are able to enter and guide the public model with your idea to produce something more verbose or complete. When it comes to making decisions, none of this is useful. It is old data, filtered through unknown biases, curated to some degree by unknown people. This can produce a different or aggregate POV but is that really what you want?

By contrast, with a public model which is fine-tuned using your own text data, you are able to get some powerful derivatives such as what Converseon has created with advanced sentiment, tone of voice, and topic classification. This means you can use the GPT 3.5 architecture to create more advanced tools to better do those repetitive classification tasks. Sure, you could also have it respond back in natural language, but if you are making decisions, often the hard facts, not the fuzzy colors of language nuance, are what is most needed.

What I would suggest you don’t do, is get it to write your next VP report or customer insights report summary. The weaknesses around facts and context will appear at random, and could catch you off guard. You will be guilty of sharing an unknown or unsourced opinion, instead of facts, within your organization.  

To conclude, as OpenAI, the creators of ChatGPT put it,

“this is for entertainment purposes only”.

The post A Perspective on the Impact and Role of ChatGPT (and LLMs) on Social and Consumer Intelligence appeared first on Converseon™.

The following message has been written by Converseon’s CEO & Founder, Rob Key.

Recently, the White House released the “Blueprint for an AI Bill of Rights” to identify principles to guide the design, use, and deployment of models/systems/AI to protect Americans in the digital age. As a leader in conversation-based decision intelligence through the use of autoNLP and advanced AI technologies, we would be amiss to not provide a statement.

As an organization who began applying AI (“machine learning”) to unstructured social data in 2008, we have some strong viewpoints on the ethical (and effective) application of the technology. We’ve advocated for several years now for greater transparency and agreement on how models are built — through input of multiple labelers with diverse backgrounds – how they’re used and, perhaps most importantly, how effective they work in the real world.

Most consumers of models find them to be blackbox and inscrutable as to how well they classify data (their accuracy). Many would say general purpose, one size fits all, automated sentiment, for example, isn’t very good (and we might agree). We then ask exactly how good (or poorly) it performs and often they are not equipped with answers. This is typically for one reason: most vendors of NLP do not (or cannot) provide scores (usually F1 — precision/recall). It’s simply not available.

Yet, in our view, the social listening (alongside of customer experience and voice of customer) industry has an obligation to “get it right.” To accurately reflect the need, wishes, wants and dreams that are expressed – and not mistranslate it though poor AI. After all, organizations are making decisions about ESG investments, improving access and customer experience, serving and communicating with diverse communities, and building new products based on this data.

With that being stated, we fully support the White House’s release of the blueprint. We specifically advocate the following: that every model (“classification”) comes with a clear performance score for those who request it; that those who build models, a process that not only embraces multiple viewpoints in labeling but works for the highest precision, are not just tested once but against new data it hasn’t seen to protect against algorithmic drift; that there is a clear human in the loop to intervene if things go awry and the clear ability to modify models when they do.

There is much talk in our category about “AI,” but far too little discussion on how to do it right and why that is important. I hope this will help spur further discussions on the topic and we can work together for the greater good (and greater impact).

You can read the full blueprint here: https://www.whitehouse.gov/ostp/ai-bill-of-rights/

The post Converseon’s Response to The White House Blueprint for an AI Bill of Rights appeared first on Converseon™.

2022 has been a year of significant innovation for us, focused on transforming “conversation data” into decision intelligence, which combines predictive modeling with business simulators that enable brands to understand risks/benefits of specific actions before they are taken.  

Our current solutions include:

• PRISM (Predictive Reputation Intelligence System): The first comprehensive reputation measurement system that ties social based metrics to business outcomes.  Includes ESG/sustainability measures, brand trust, customer experiences broken down by stakeholder voice.  It’s the essential navigation system for today’s polarized world and is able to clearly demonstrate communication/corp affairs and reputation management impact on the company bottom line (including sales and shareholder value).  Here’s a link to more detail.

• Social Brand Relevance Tracker: One of the most exciting breakthroughs in our long history of innovation, Converseon’s Brand Relevance Tracker uses our unique top down (known important attributes) and bottom up (consumer driven unknown unknowns) approach to give you a 360 degree view of your brand, how it is fitting with today’s consumers as well as how it will fit with tomorrows so you can know what action to take!  This pioneering decision intelligence brand solution will enable you to monitor, strategize and react to the market like never before positioning your brand for the future and hence greater revenue share and customer loyalty. View our latest webinar on our Social Brand Relevance Tracker here.

• Social ESG Index: A module available with or independently of PRISM, this solution scores stakeholder perceptions of brand ESG efforts, and ties those efforts to sales.  It’s an increasingly essential companion solution to more standard ESG measurement systems and, importantly, can help brands guard against greenwashing and related concerns. 

• Social CX Tracker: Our Social CX Tracker uses our unique top down (known important attributes) and bottom up (consumer driven unknown unknowns) approach. We have taken the very best of theoretical literature on touch points, journeys, loyalty, resonance and combined it with the mining of 10s of millions of social media posts. The result is a robust and holistic CX framework which reliably measures what matters for creating and sustaining loyal customers. Broadly the CX framework measures the customer response EQ (emotional) and IQ (functional) of your and competitor offerings.

• Inflation Analysis Report: Understand how inflation is affecting your consumer’s views of the price of your products and categories. With this report you are able to know which products have a value equation which is able to sustain the current pricing pressure, how you compare to peer brands and which are going to lose sales to competitors. 

• Conversus: Our autoNLP platform continues to evolve with new key features and solutions.  If your analyst or data teams want to take more control of your data classification and move beyond just Boolean-based solutions, Conversus is for you.  The most powerful tool for building, validating and deploying machine learning models for unstructured social and related data analysis, Conversus also provides a growing number of prebuilt models that can be used seamlessly with many social listening and BI platforms to improve your analysis. 

We can provide additional custom solutions for social and media analysis to help transform this data from “reactive and descriptive” to “predictive and prescriptive.”

Connect with us today through a request demo or contact us page for more information.

The post 2022 Year-end Solutions to Level Up Your Brand’s Social Intelligence appeared first on Converseon™.

Sam Schrager is JUST’s Director of Metrics and Data Analytics. He is joined in this collaboration by Converseon‘s Chief Innovation Officer, Glen Kushner, and Advisor, Larry Friedman. Converseon uses machine learning to track perception of brand purpose on social media using a combination of natural language processing, social intelligence, and advanced analytics. You can visit its Brand Purpose Intelligence Center.

The fight against the coronavirus pandemic is still far from over, and corporations are still determining how to safely serve their stakeholders, especially their workers and the communities they serve. In fact, as JUST Capital has noted, the pandemic has forced companies to take stakeholder capitalism more seriously than ever. As we consider where we are headed, there is value to looking at the lessons of the past year.

That’s why we decided to compare our complementary data sets for a look at how America’s largest companies responded to the pandemic and how the public perceived those actions. JUST’s data is drawn from its COVID-19 Corporate Response Tracker, a tool thoroughly measures the policies and actions of the country’s largest companies, and Converseon’s Brand Purpose rating, a perception score based upon AI-collected social media posts from the public, over the same time frame.

Below, we’ve taken 11 of America’s most recognizable companies and found that for the majority, the public was in alignment with how the company responded, but that for others, there was a sharp disconnect due to the impact of negative stories.

Divergence Analysis

Comparing our ratings yields one of three results for each company.

If there is a Negative Public Perception Gap, the public reacted more negatively on social media toward that company’s brand relative to the quality of that company’s public response. The team at Converseon believes that by analyzing public perception and reactions on social media, brands can identify what they feel are negative perception gaps, for the sake of better alignment.

A Perception Alignment indicates that the public’s reaction was in line with the relative quality of the public corporate response.

And then a Positive Perception Gap exists where the public was reacting more positively than the way the company’s public actions would otherwise indicate.

(Converseon)To illustrate these three categories, we’ll walk through the example of 11 companies included in the Forbes Corporate Responders list from May, powered by JUST’s data, which ranked how large corporations immediately responded to the onset of the pandemic in the U.S.

Of the 11 companies assessed, eight fall broadly within the retail sector (CVS, Home Depot, Kroger, Lowe’s, McDonald’s, Starbucks, Walgreens, and Walmart), while the remaining three are large banking institutions (Bank of America, JPMorgan Chase, and Wells Fargo).

Our analysis found Perception Alignment for seven companies (Bank of America, CVS, Home Depot, Lowe’s, Starbucks, Walgreens, and Wells Fargo) and a Negative Perception Gap for four (JPMorgan Chase, Kroger, McDonald’s, and Walmart).

(Converseon)

Reasons for Divergence: The McDonald’s Example

According to the Forbes Corporate Responders list, McDonald’s scored a 3.64 out of a scale of 5 on its corporate COVID-19 response, as of May, and a score slightly above zero for its Brand Purpose. Reasons for this score include executives voluntarily cutting their base salaries by more than 25%, an expanded paid sick leave policy to address employee wellness concerns, and about $50 million worth of “Thank you Meals” given for free to public safety and health workers. During this same time period, there was a significant push on social media supporting workers who were demanding a better paid sick leave policy and higher pay. It is also worth noting that JUST assesses McDonald’s corporate workforce, and public perception of the brand includes both its assessment of the corporate leadership and the leadership of the company’s primarily franchise restaurants.

(Converseon)

Brand perception dipped to its lowest over this period in mid-March, coinciding with strikes. There was also a high profile video feature a few days later from the New York Times Opinion team that highlighted workers calling for paid sick leave and more safety precautions. Brand perception rebounded a month later with McDonald’s announcement of Thank You Meals to frontline workers.

By tracking in-the-moment perception as events occur, aspects of the reaction against McDonald’s vs. its relatively positive corporate response become more clear. As Eater noted in March: “McDonald’s says over 90 percent of its stores worldwide are operated by franchisees, and notes that franchisees, while required to adhere to state and local sick leave laws, are considered independent business owners. The McDonald’s Corporation can recommend sick leave policies to its franchisees, but — according to the company — the franchisees do not have to implement them.”

In that initial period, McDonald’s corporate policies were relatively strong among other large companies in America, but the disconnect between corporate and franchisee requirements resulted in a Negative Perception Gap.

As we all know, the COVID-19 infection continued to ebb and flow throughout the year, and McDonald’s notably announced franchisee safety inspections in November after a meeting with franchise owners around health and safety. As we have seen, such a move was in line with correcting for what was the biggest pain point in the early stage of the pandemic.

Benchmarking and Engagement

Key to the guiding philosophy of stakeholder capitalism is that all stakeholders – workers, customers, communities, the environment, and shareholders – are taken into account in corporate strategy. The pandemic has dramatically shown that both effectively communicating how actions fit into an overall guiding purpose and carefully listening to what workers and customers are demanding are essential for leadership that benefits all stakeholders.

This, of course, is easier said than done when it comes to massive, global corporations. That is precisely why we need to make use of an integrated view of the marketplace, combining insights from diverse sources to understand perception gaps, and identifying investments that are most critical to make.

The post Examining the Public’s Perception of Early Corporate COVID Responses Offers Insight Into Effective Stakeholder-Driven Leadership appeared first on Converseon™.

The era of conscious consumerism has arrived. Ushered in by the COVID-19 pandemic, a wave of individuals highly focused on environmental action, sustainable efforts, and green initiatives now seek out companies that are putting their advertising promises into real climate action.

What is ESG?

As we know, ESG stands for Environmental, Social, and Governance. Marketing Business News takes this further and states, “ [ESG] refers to the three key factors when measuring the sustainability and ethical impact of an investment in a business or company. Most socially responsible investors check companies out using ESG criteria to screen investments.”

What initiatives are brands taking with ESG?

There are many brands that have stepped up to the plate to reduce their carbon footprint, decrease their electrical consumption, and produce more sustainable products. The following brands continue to step forward in their sustainability efforts …

• For Motor Company – more sustainable fabrics, focus on fuel efficiency, and more
• Disney – zero net direct greenhouse emission policies and reduced electrical consumption
• Fisher Investments – initiatives to help preserve California’s Redwoods
• Hewlett-Packard – reduced greenhouse gas emissions and cut back on toxic substances in products and cartridges
• Johnson & Johnson – waste reduction focus, sustainable products, and packaging methods

While these companies have focused on the “E” in ESG, there is data to support that focusing on social and governance is also an important piece of the larger pie. In 2019, Mckinsey Quarterly released “Five Ways that ESG Creates Value,” and they stated the following “global sustainable investment now tops $30 trillion—up 68 percent since 2014 and tenfold since 2004” These numbers alone show that investment into ESG initiatives isn’t just a fad but truly an investment in the future that is likely to grow.

Converseon’s CEO & Founder, Rob Key, presented at AMEC’s Global Summit on “Connecting Reputation to Business Outcomes,” where he presented data to support why ESG measures are so important to brands today.

Through Converseon’s Predictive Reputation Intelligence System (PRIS), Key presented data to the following: “social justice and equality are the biggest issues [in the QSR industry] but which are the most impactful on revenue?” A heat map pulled from the solution shows that leading QSR brands struggle with labor relations, employee compensation, and wokeness – this is indicated on the far right of the heat map in the red. These “signals” show that these brands need to point focus in these areas through investment and action.

This brand and sub-attribute heat map for QSR puts into perspective all the social justice issues that businesses are facing and helps to order efforts and investments. We can see that , for example, McDonald’s is strong in sustainable practices but struggles the most in employee compensation.

For the QSR industry, investing in environmental initiatives can generate or detract the most from revenue at $140 million a quarter. Coming in second for revenue generation or detraction is social justice action with $110 million a quarter. Through predictions and simulations based on social media data, our sub attribute analysis from the diagnose heat map helps us to determine areas of priority, focus, and action.

Regardless of the industry, investing in the environment shows that it is a good business concept that impacts the bottom line and improves the world we live in.

Our most recent eBook defines and resolves gaps in ESG measurement – you can download your complimentary copy here.

Learn more about our Social ESG Index solution that resolves the critical gap in ESG measurement today.

The post Conscious Consumerism: The Imperative of ESG Investment appeared first on Converseon™.

AI, or more specifically machine learning, can learn from human input and with enough proper training, can approximate (and sometimes exceed) human performance without explicit programming. As Dr. Philip Resnik, a Professor of Computational Linguistics at the University of Maryland (and Converseon advisor) says: “computers simply don’t have the brains to handle some tasks, unless they borrow ours.”

This is why AI is essential for effective language analysis. Indeed, the power of AI to analyze even nuanced text-based conversations at massive speed and scale to accurately collect critical business insights has made tremendous strides over the past few years. Yet the broad use of these technologies in the areas of social listening and analytics largely remains in its infancy due to several factors. One has been the uneven availability of the most effective technologies combined with user confusion on which technical approaches are most appropriate to specific needs. This is compounded by the fact that if you are also like most organizations, there has historically been a dearth of “AI experts” available to you to help guide decision making and vendor selection in this area….

For our full white paper please download it here.

©Converseon All Rights Reserved

The post How To Apply AI to Your Social Listening in 2019 appeared first on Converseon™.

We wish for all our friends, clients and partners a happy, healthy and successful 2019.   This year marks 11 years of immersion and innovation for us in machine learning based social listening and insights, and we fully expect 2019 to be the most exciting yet.

To those who know Converseon, we continue to expand and adapt our technologies and services to the emerging environment. We joke only half-heartedly sometimes that we are a 15-year-old startup. Our original birth as one of the very first pure play ‘social agencies” (at a time when “social media” was not a common term) led to being named Digiday’s Top Social Agency.  We evolved from that phase to concentrate on our social listening technologies in 2008 and was named Forrester Leader in Enterprise Social Listening in 2010, and Strong Performer in 2012/14. At that point, we did a slight pivot again in 2014-15 to focus specifically on our AI technology for processing and understanding the massive reservoir of consumer text, specifically in social and related voice of customer areas, where we were finding very high client demand. It was a move we certainly do not regret.

2018 was an exciting year for us as we have forged ahead again to be a front-runner in our category by helping our clients accelerate insights and drive business advantage by accurately and effectively mining this massive, complex unstructured consumer text data at human level precision at the speed and scale that only software can provide.  For many organizations, this unstructured content is poised to become 90% of all their stored data.  Yet most companies are processing less than 20 percent of it so far (according to Forrester Research), missing a powerful source of competitive advantage.

At the dawn of 2019, we are pleased to be positioned as a clear leading solution for all companies who want to get better insights, action, and value from this data.   And while 2018 was a year of rapid evolution here, 2019 is poised for even greater growth and success for us, our partners and clients. We not only provide custom/advanced language models (both prebuilt as a library for instant use and built to spec), we also are enabling our clients to rapidly build their own models through our “no code” machine learning as a service platform, Conversus.AI. In short, we provide the tools, platforms, and services needed to translate complex consumer and social text into meaningful and actionable insight at massive scale and with high accuracy.
Here are some key highlights:

• We had strong revenue growth in 2018 and achieved more than 95% client renewal, validating the value our AI-powered text analytics technology and associated programmatic insights are generating.
• Clients include some of the most respected companies in the industry, including IBM, Dell, Uber, Walmart, among many others
• Our revenue per client has increased by approximately 80% as our models drive greater value and adoption in client organizations
• In mid-2018 we took our first external Series A investment and successfully applied it to accelerate evolution and scale out of our technologies.  Even with the investment, we achieved profitability again this year.
• We were the first to launch a comprehensive library of pre-built machine learning models by industry and use (customer experience, customer care, brand tracking, etc.) for immediate subscription, use, and deployment.   This is critical to accelerating the use of AI and reducing costs to clients.
• We have transitioned from reactive, descriptive analysis to predictive and prescriptive “on demand” analysis/insights via our highly granular and accurate data and machine learning technologies.   We have demonstrated clearly that our machine learning powered data can predictive key business outcomes, including sales and the results of survey-based brand equity trackers, to just single out a few areas.   Predictive social is an area of clear focus in 2019.   See how presentation redid on how to predict the future with social data for a recap.
• We have and will continue to build out other bespoke models for our clients that can be immediately deployed programmatically with a wide range of key social listening/management and business intelligence platforms.  These include Brandwatch (the largest social listening solution after its merger with Crimson Hexagon), Sprinklr, and others, along with BI platforms like Tableau and Domo.    Our models are designed for more sophisticated users who demand high data quality and the ability to use machine learning  to classify language “like humans do.” Our partnerships are flourishing and we look forward to even greater focus here moving forward.   Ecosystems matter!
• We are and will remain at the forefront on model and data transparency, providing clear F1 and other performance scores with each model prior to deployment.   We applaud industry efforts in these areas including the IAB Data Transparency Initiative and have advocated for similar labeling requirements for social data. advocated for similar labeling requirements for social data. We hope to work with the industry more generally to move this issue forward and hope all clients will also demand this level of transparency and data quality to ensure the elimination of unintended bias.   What we do is too important to not get it right.
• We have expanded our models and support to more than 14 languages and continue to expand to others based on client needs.
• Beyond our model building and insights, our Conversus.AI machine learning as a service platform now puts the immense power of AI directly into our client’s hands, including subject matter experts who don’t have data science skills, to better take control of their data and application. This “no code” approach to machine learning puts human experts in the loop directly and is highly transformative by empowering our algorithms to directly learn from these experts and then project and apply that “intelligence” broadly to the enterprise.    Combining the best of human expertise and AI is a very exciting era of growth and we applaud the “no code” revolution and glad to be a part of it along with some others forging the way ahead.  In 2019, we anticipate a wider range of client experts involved in building models simply and easily in areas ranging from product development, to brand management, crisis and more.   Better yet, these models can be built and modified to meet the specific taxonomies and perspectives of clients.  As Mark Andreesen has said, “if software eats the world, models will run it.”   We are that model factory.
• We have evolved far beyond just sentiment and emotion, into areas like  “trust/distrust,” human motives, advocacy and more as we find and unearth those insights in human language that can help change business and drive competitive advantage.   And we’re making these easily and effectively available to use as needed and in real time.
• We are also expanding the applications of our models from social primarily to other sources including call center transcripts, long-form survey verbatims and more.   We seek to help companies understand their true voice of customer, from all sources, with one clear and accurate “truth.”
• We continue to evolve our solutions in 2019 to stay ahead of the game –  to deliver better, faster and cheaper.   Our models now outperform individual humans in most cases and we will be applying even more technology and techniques this year especially in the areas of transfer learning, adversarial model building and other approaches built on the tenets of openness, transparency, accuracy, fairness, and impact.

We want to of course thank all our clients, teammates and partners who continue to challenge us to grow and innovate on an ongoing basis. Their challenges make us better and allow us to build on our rich heritage of innovation and excellence as leaders in social and VoC intelligence. We can’t wait to get the new year underway and hope that we have a chance to work with all of you to further the evolution and value of the industry.
Perhaps most importantly, we wish you all a happy and prosperous New Year.

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