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Unlike one of my recent blog posts titled Social Listening - Social Analytics - Social Intelligence, the 3 bigrams in the sub-heading are not part of a continuum, they are synonyms.
Synonyms are words that do not necessarily look or sound alike, but they have more or less the same meaning, while homonyms are words which are spelled the same, although they mean different things.
For the social intelligence discipline, synonyms and homonyms are treated in a diametrically opposite manner: the former are included when gathering online posts whilst the latter must be excluded; failure to do so results in another bigram we so often use in the data analytics business: “Garbage-in…”!
Sometimes, depending on the popularity of the homonyms, more than 80% of the posts gathered - using a social media monitoring tool are irrelevant - referred to as “noise” (as opposed to signal). Only if we have a way to remove the noise can we avoid completing the popular saying mentioned in the previous paragraph with: “…Garbage-out”.
But I digress…
This post is about efficient and meaningful ways to integrate unstructured and eventually structured data sources as part of an organisation's customer experience (CX) measurement or customer management (CM) process - a relatively new more encompassing term gaining ground on CX - in order to discover actionable insights.
This new process of beneficial unstructured data fusion from multiple source types can be described in the following 8 steps:
First a quick reminder as to what constitutes unstructured data:
Text analytics is the easiest to perform (as opposed to audio analytics for example) hence the idea to transform all forms of unstructured data to text for easier manipulation.
One of the most useful sources of unstructured data for businesses is their call center audio recordings, with conversations between customers and customer care employees. These audio files can easily be transformed to text (voice-to-text) using specialised language specific machine learning models. An accuracy metric used for the transcripts produced is WER=Word Error Rate which should be lower than 10%.
Another popular source of insights are images e.g. posted on social media or shared on a business client community. A deep learning model adequately customised can produce a caption describing in text what is illustrated in each image (image-to-text).
When it comes to video, a combination of voice-to-text and image-to-text tech can be used.
When all sources of unstructured data are turned into text, they then need to be uploaded onto a text analytics platform, usually in the form of a JSON or CSV file.
If the same platform has the capability to provide data from additional sources, such as online posts (text and images) from Twitter, Facebook, Instagram, YouTube, reviews, forums, blogs, news etc. so much the better. It can serve as both a social intelligence and text analytics platform.
If needed, text from each source type can be uploaded or gathered and saved separately and merged at a later stage, so as to take a bespoke approach to cleaning and subsequently annotating the text using custom machine learning models for each source.
When it comes to client/user owned data they are all intrinsically clean (read relevant) since the source types are:
As for the data gathered from online sources – what is commonly known as social listening or social media monitoring – that is where a thorough data cleaning process is required. The problem as already indicated above is the homonyms. When a Boolean logic query is created to gather posts from social media and other public online sources, using a brand name like Apple or Coke or Orange as a keyword invites a lot of “noise” as you can imagine. The platform is required to offer easy ways to eliminate posts about apple the fruit, cocaine and orange the colour or fruit..
There are two ways to get rid of the irrelevant posts which sometimes make up more than 80% of all posts gathered.
If data cleaning is done properly, we can expect brand/keyword relevance over 90%.
Natural language processing is the umbrella discipline that takes care of this step in the process. Ideally the use of machine learning models to annotate text in any language works best, but sometimes a rules-based approach may be a shortcut to enhancing the annotation accuracy.
A good text analytics tool offers multiple options i.e. the ability to train generic & custom unsupervised machine learning models or using native language speakers as well as a taxonomy creation feature using a rules based approach.
Text can be annotated for sentiment, topics, relevance, age or other demographics of the author (if not otherwise obtainable), customer journey etc. A minimum accuracy of annotation should be declared and aimed for, and the users need to be able to easily verify the annotation accuracy themselves.
This step can happen before or after the merging of the various data sources, depending on their homogeneity.
For the longest time data fusion or integration or merging from different sources meant weeks or months of data harmonisation, so that the different sources could fit together and make sense. Merging 5-10 source types of unstructured data after steps 1-3 above only takes a few minutes, not months. It would take a few hours from start to fusion.
A powerful filtering tool is required for the user (data analyst) to be able to drill down into the data and discover interesting customer stories which might lead to actionable customer insights. For example, the user could first filter for negative sentiment, then for a specific brand, after that a topic and finally a source type before they start reading individual interactions to get an in-depth understanding of the WHY and the SO WHAT.
Once the data is cleaned, merged, annotated, and explored, it can be delivered in multiple ways such as:
Data visualisation via PowerPoint slides, drill down or query dashboards and alerts work best. Ideally the data formats should be flexible so that they can work with multiple data visualisation tools.
For now, data fusion included in a CX/CM program is a better fit for larger corporations, for two reasons:
Hopefully soon there will be versions of SaaS products that will make this process efficient and inexpensive enough for SMEs (SMBs) to be able to afford it.
That is what we call the democratisation of data analytics and market research.
The more data sources we integrate the more likely it is for a data analyst, the user of a tool such as listening247, to be able to synthesize actionable insights in their true meaning.
It seems that the biggest gain from this newly found ability to accurately annotate text in any language and fuse/integrate/merge from any source type in a matter of hours is in the discipline of customer experience (CX) measurement and management (CM).
CX and CM are increasingly seeking to encapsulate market research, business intelligence, customer care and other business disciplines and are meant to perfect the customer path to purchase, minimise brand defectors and maximise the number of advocates.
Blog
It was a lightbulb moment during lockdown; a few days after the team completed a piece of work, it suddenly hit me. Online posts from Twitter, Facebook, Instagram, blogs, forums, news, reviews and videos were fused with call centre audio files and survey verbatims in just 3 days, done for the first time and done right. Albeit from very different sources, involving both solicited and unsolicited opinion, this data had something in common - it was all unstructured data.
For the uninitiated market researcher or data cruncher unstructured data exists in different formats such as:
Structured data on the other hand is numbers in tables such as:
When I compare the amount of effort that is required to integrate structured data, with what we experienced integrating text and audio (unstructured data) during the “light bulb event” the contrast could not be more surprising!
If you are dealing with numbers in tables, you’re looking at column headings, product names, units and rigid time periods, so integrating various sources means that everything should be harmonised, for example:
Harmonising structured data to import it into one platform and then further manipulate it to integrate the various sources in order for meaningful analytics to be possible takes weeks, sometimes even months, compared to the 3 days to import, integrate, annotate, and explore unstructured data from various sources.
With unstructured data the integration process is simple; all data in text format can be annotated for relevance, brand, sentiment and topics in an automated way using machine learning models or taxonomies. Data in other formats (such as image or audio) can be converted into text in order for the same process to follow. This makes it possible to annotate call centre conversations or images from social media, just as easily as text in online posts and responses to open ended questions from surveys.
Fig. 1 Ingesting survey verbatims on listening247
The difference that makes all the difference (pun intended) when it comes to integrating structured vs unstructured data is that with the former the intelligence is already an added layer before the data fusion takes place, whilst with the latter the text is ingested and integrated before consistent intelligence is added to the dataset as a whole e.g. brands, sentiment/emotions and topics. Once the data is integrated it is already homogeneous (since it is all text) so it is straightforward to annotate it using custom or generic machine learning models and taxonomies - without having to worry about harmonisation.
Fig 2. Annotated online posts with brand topics and sentiment on listening247 Data Explorer
There are some obstacles to integrating and annotating unstructured data other than text such as audio that needs to be transcribed and images that need to be captioned with text; only when that happens can the accurate annotation of all the integrated data sources take place. There are even more obstacles if the data to be fused involves multiple languages.
Fig. 3. Image caption example, image-to-text
Thankfully, technology is available to enable voice-to-text and image-to-text transformation, as well as accurate annotations. Without accurately adding layers of intelligence, big data and especially text is not only useless, but with the wrong labels also harmful.
A data analyst cannot be expected to read millions of online posts, but what they can do is use a smart filtering tool to drill down and explore the annotated documents (e.g. social media posts or call center threads) and discover the “gold nuggets”, the elusive actionable insights.
The future of unique and actionable insights lies in data fusion of unstructured + structured data. Some of this data will belong to the companies e.g. sales data, and some they will need to procure e.g. 3rd party online posts or survey results.
Integrating unstructured data is more effortless and straightforward than you might think. You only need a good unstructured data analytics tool.
Social Listening – Social Analytics – Social Intelligence
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S-L-A-I
Social Listening, Social Analytics, and Social Intelligence - are they the same or are they integral parts of a sequential process, a so-called continuum?
Quite a few pundits have discussed this question in their articles, blogs, and essays. The most controversial of the three is social intelligence; if you Google it you will find its Wikipedia definition on first position explaining that it is “the capacity to know oneself and to know others”.
Of course, the alternative definition, the one that was coined after social media monitoring and analytics became popular only appears on page four of Google search - which you would only know if you are me and you search for social intelligence. In this secondary context, this bigram* means: the knowledge and insights that organisations can extract from online posts (mainly on social media platforms) published by their customers and other stakeholders.
The precondition to get to actionable insights is to avoid “Garbage-in” during the so-called data harvesting process, and to use appropriate machine learning models to maximise the accuracy of brand, topic, and sentiment annotation.
But let us look at the three bigrams one at a time,*combination of two words.
Social listening is short for social media listening, and to some a synonym of social media monitoring. Most people use social listening as an all-inclusive term for all online sources from which online posts can be gathered or harvested. However, in addition to popular social media platforms such as Twitter, Facebook, Instagram, YouTube, we also can harvest data from blogs, forums, news, and reviews. There are other sources that are country specific, such as Weibo for China and VK for Russia.
It is of paramount importance to harvest for all synonyms and avoid all homonyms (garbage-in) which can be over 80% of all harvested posts. A homonym is a word that is spelled the same way as the keyword for harvesting but means something entirely different, which makes it irrelevant for the project at hand. The classic example used to explain this problem is: wanting to harvest posts about Apple the company but ending up with lots of posts about the fruit or juice; let alone Apple Martin who is Gwyneth Paltrow’s daughter and if not excluded will invite a lot of noise in the dataset rendering it not only useless but dangerous for the user!
Social analytics is what happens after the posts are harvested from the various sources and irrelevant posts are removed as “noise”. For data analysis to take place, accurate intelligence needs to be added to the dataset using machine learning and/or rules-based NLP methods. The most common annotations added to each post or relevant snippet within the post (this is a longer story that requires its own blog post) are: brand, sentiment, emotions, and topics/subtopics/attributes.
These annotations can be added for text in any language, and the accuracy sought after – measured in precision, recall and F-score – should be over 75% in all cases. It is even possible to reach F-scores that are over 95% with focussed and context related training of suitable machine learning algorithms.
Social intelligence is the wisdom discovered by exploring the intelligent dataset. You see, adding brand, sentiment, emotion, and topic annotations to a dataset makes it “intelligent” but in order to find wisdom or “actionable insights” a lot more than just accurate annotations is required.
For the time being, an intelligent data cruncher and a powerful filtering or drill down tool is still needed to explore a dataset and find the gold nuggets.
We like to think of the listening247 unstructured data analytics platform also used for social listening, as the Google Maps of big data. It enables a user to navigate in a maze of millions of online posts - or other documents for that matter - safely and accurately from A to B; B being the destination or in our case the actionable insight. The path to finding the actionable insight is oftentimes a data story worth telling.
What is your Social Presence Score (SPS)?
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Most people prefer order to mess, hardly a surprising conclusion.
A score that enables ranking in multi-player environments provides order and the ultimate gamification. Ideally it should be a composite score. This is a score that combines multiple metrics in one all-encompassing index.
Gamification does not mean turning a serious activity into a game; it is using gaming techniques to provide participant motivation and make the activity more fun overall.
I do like to think of business as a game. Some people take it too seriously, to the extent that it has a pathological effect on them – it affects their health in a negative way. Business is not a life-or-death endeavour. Winning is fun, losing is dreadful (worse for bad losers like myself) but it is not the end of the world.
So, let us break down how the SPS is calculated and its benefits for brands and for individuals; starting with the business perspective.
Brands and their parent organisations are always looking for the ideal KPIs that will drive their performance. NPS was once hailed as the single metric a company needed to measure and predict its future performance. NPS stands for Net Promoter Score and it is produced via a single question to customers, usually delivered via a survey: On a scale from 0-10 how likely is it that you would recommend Brand X to your friends and colleagues?
All those who provide a score between 0-6 are considered detractors, whereas the 9s and 10s are considered promoters; the 7s and 8s are polite negatives or passives at best.
The calculation of this composite score is as follows NPS=((promoters-detractors)/all respondents) X 100, with scores ranging from -100 to +100.
A similar idea applies to the Social Presence Score (SPS) for brands; however, its calculation is not as straightforward. Several social intelligence metrics have been considered by our data scientists and our conclusion was to use the following:
Fig.1: SPS score of shampoo brands
The SPS can have a value between 0 and 1 (see Fig.1 above); it is calculated, for a certain period of time, as one aggregated metric for multiple brand posts; it can be offered as a single score from all source types, or for individual ones such as Twitter, Facebook, Instagram, YouTube or Tik Tok (see Fig.2 below).
There is a secret sauce that even if I wanted, I would not be able to adequately describe in a mainstream article, and that is the weighting of the above-mentioned metrics in the SPS. Not only that but also the entire process, starting before the online posts are annotated, to eliminate irrelevant posts due to homonyms i.e.clean the data and remove the noise.
The benefit of having a score like this as a brand is, not surprisingly, the ability to:
Fig.2: Shampoo brands ranked based on SPS by source
The motivation to track a social presence score is not that dissimilar for individuals. Influencers and other high profile individuals want to know how their personal brand is doing compared to others and where they rank.
The metrics we use to create the composite Social Presence Score for individuals are similar but not all the same as for brands (see Fig.3 below):
The benefits for a person to know their SPS are:
Fig. 3: Influencers ranked based on number of posted brand comments
It goes without saying that even if the SPS is perfectly synthesised with its composite metrics, the accuracy of the individual metrics included must be measurable and acceptable. It is always possible to reach over 80% accuracy for sentiment, topic and brand relevance annotations.
So, what do you think?
Would you like to know your SPS in relation to others?
I know I would; I miss having one since klout score ceased to exist.
Social presence score (SPS) comes to the rescue; it is the new single KPI for brands and individuals based on all online mentions - not just a sample – that can be used to measure the overall success of their marketing efforts.
The Complete Story of listening247's NSS™ Score and Its Strategic Imperative
As businesses seek to understand their standing in the digital conversation, listening247's Net Sentiment Score (NSS™) emerges as a pivotal metric. This proprietary formula quantifies online sentiment towards brands, transforming raw social media data and unsolicited customer opinion into actionable insights.
Let’s deep dive into the NSS™ Score and explore its origins, how it operates, and its significance in today's digital-first world.
The Net Sentiment Score™ was developed by listening247 to fill a crucial gap in social media analytics. In a landscape saturated with diverse opinions, it provides a standardised way to assess and compare brand sentiment. This metric is the result of advanced machine learning models that meticulously annotate sentiments as positive, negative, or neutral, ensuring a nuanced understanding of the digital conversation landscape.
Fig.1: Graphic of listening247's advanced AI annotating sentiments as positive, negative and neutral.
Simplicity lies at the heart of the NSS™'s effectiveness. By focusing on the balance between positive and negative mentions and considering the volume of discussions, the NSS™ offers a comprehensive snapshot of a brand's online health. This approach allows for the aggregation of vast amounts of data into a digestible, numerical format, empowering businesses with the clarity needed to navigate the complexities of online reputation management.
This seemingly simple calculation belies the complexity and sophistication of the technology behind it. listening247's algorithms analyse vast amounts of online content, from tweets and blog posts to forum discussions and reviews, employing natural language processing (NLP) and machine learning to accurately capture and categorise sentiments.
The process involves more than just keyword recognition; it delves into the nuances of language, picking up on context, irony, and even regional dialects to ensure the sentiments are accurately interpreted. The result is a score that ranges from -100 (entirely negative) to +100 (entirely positive), offering a clear, quantifiable measure of online sentiment.
Fig.2: Graphic of different online content displaying positive. negative and neutral sentiment
The implications of the Net Sentiment Score™ for businesses are profound. It serves as a vital indicator for assessing the impact of social media conversations on brand perception. Here's why NSS™ is indispensable:
Fig. 3: Graphic of the value that the NSS™ serves for assessing the impact that social media has on your brand.
The Net Sentiment Score™ by listening247 represents a significant advancement in the analysis of social media sentiment. Its creation marks a strategic move towards a more informed, data-driven approach to understanding digital conversations. As businesses continue to operate in increasingly digital environments, the NSS™ offers a vital metrics for navigating the complex dynamics of online brand perception. Through its precise, insightful analysis, businesses are better equipped to foster positive engagements, adapt to consumer needs, and ultimately, drive success in the digital age.
To explore the depth of insights that the Net Sentiment Score™ can bring to your brand, we invite you to reach out and request access to your brand health dashboard. The brand health tracking dashboard offers a comprehensive view of your brand's digital presence and sentiment, enabling you to make informed decisions that drive your brand forward.
Whether you're looking to enhance your social media strategy, improve customer engagement, or simply gain a better understanding of your brand's position in the digital landscape, our team is here to guide you through the insights our dashboard can provide. Contact us today to unlock the full potential of your brand's digital narrative.
listening247 In the Press
Michael Michalis, CEO of listening247, delves into how AI-powered unstructured data analysis can transform marketing strategies with greater nuance. In today’s data-driven landscape, the ability to gather, interpret, and act on data is crucial for business success. However, the sheer volume and variety of data—from meticulously organised databases to spontaneous social media posts—can be overwhelming. This data can be broadly categorized into structured and unstructured data, each with its own implications for decision-making and strategy.
The term structured data refers to data that resides in a fixed field within a file or record. Structured data is typically stored in a relational database (RDBMS). It can consist of numbers and text, and sourcing can happen automatically or manually, as long as it's within an RDBMS structure. It depends on the creation of a data model, defining what types of data to include and how to store and process it.
Unstructured data (UD) encompasses all data that lacks a predefined format or data model, making it distinct from structured data. Unlike structured data, which is neatly organized in tables and fields, UD includes diverse formats such as text, rich media, social media activity, and surveillance imagery. Its volume significantly surpasses that of structured data, making it a vast, often untapped resource for insights.
While structured data offers valuable numerical insights, it falls short in capturing the depth and nuance found in UD. This type of data, growing at an exponential rate, includes everything from social media posts to multimedia content. As IDG predicts that 93% of digital data will be unstructured by 2022, businesses that learn to harness this data will gain a competitive edge. Despite its potential, UD remains underutilized, often referred to as "dark" information due to its raw and unprocessed nature.
Recent advancements in AI and machine learning have revolutionized how unstructured data can be leveraged for marketing. By applying these technologies, companies can transform vast amounts of UD into actionable insights, such as sentiment analysis from customer reviews and feedback. This shift allows marketers to move beyond traditional demographic segmentation and gain a more sophisticated understanding of their market, revealing hidden opportunities and enhancing strategy through sophisticated "dark analytics."
In conclusion, as Michael Michalis highlights, the true value of data lies not just in its collection but in its interpretation and application. While structured data offers clarity, unstructured data holds the depth and nuance necessary for comprehensive marketing insights. Advances in AI and machine learning are making it possible to unlock this previously elusive "dark" data, providing businesses with richer, more actionable insights. Embracing these technologies can transform how companies understand and engage with their customers, revealing valuable opportunities hidden within the vast expanse of unstructured data.
Original Source: Analytic Insight
listening247 In the Press
London, County, United Kingdom, October 12, 2021: A leading tech firm that specialises in CX management, social intelligence and text analytics celebrates success in winning long-term contracts with top tier organisations in Eastern Europe, the Middle East, Southeast Asia and LATAM, having recently rebranded to ‘listening247’.
Headquartered in London, listening247 was established in 2010 to help brands carry out digital market research for insights into consumer sentiment through social intelligence and online communities. In 2012 it pivoted to develop its proprietary technology in the same space and focussed for the following seven years on purposeful R&D to solve some of the biggest problems in leveraging unstructured data: the annotation in multiple languages.
Having expanded and invested in its AI capabilities to include CX measurement and management, customer journey optimisation and alternative data available on Bloomberg terminal, the firm now delivers ‘AI driven insights’ to multinationals, agencies and corporate organisations across various industry sectors ranging from FMCG and retail through to finance and telecoms. listening247’s clients are primarily data driven organisations that want to leverage customer interactions they possess from calls, private chat messages, emails surveys and social media.
Its recent success in winning multi-year contracts comes as more organisations focus on enhancing the online customer experience in the wake of the COVID-19 pandemic, as noted by listening247 founder, and CEO, Michalis Michael:
“Over the last 18 months or so, businesses across the globe have had to adjust how they operate and interact with their customers as a result of the COVID-19 pandemic. Although many countries are now pushing towards normality, times have changed and so have consumer expectations. More organisations have realised this and are utilising CX measurement and management to understand the clear gap between what their customers now want and expect Vs the product quality or level of service they are receiving. Advances in artificial intelligence enable us to ‘plug this gap’ by delivering and analysing personalised consumer insights on every single interaction to then optimise the customer journey and increase both brand loyalty resulting to business growth. With technology continuing to advance at an exponential rate, the CX management/measurement and alternative data space is proving to be incredibly interesting, and both the team and I are excited to see what the next six to twelve months will bring.”
Supported by a high calibre advisory board, including Peter Nathanial, ex-Group Chief Risk Officer for the Royal Bank of Scotland, listening247 is forecast to continue its accelerated growth trajectory over the next few years. To maintain competitive advantage, the leading AI driven insights firm will be launching its pre-series A funding round in early 2022.
Original Source: NEWS WIRES
listening247 In the Press
In a data-driven world, the 'DIKW pyramid'—with its levels of data, information, knowledge, and wisdom—has long been a cornerstone of business intelligence. However, in the realm of social intelligence, this model is evolving, replacing 'wisdom' with 'insight,' a term often misused and misunderstood in the industry.
In a data-saturated world, the ‘DIKW pyramid’—which ranks data, information, knowledge, and wisdom—has become a crucial framework for business leaders seeking to add value at each level. However, in the realm of social intelligence, this model is evolving to place 'insight' at the pinnacle instead of wisdom. The term 'insight' is often misused, sometimes being equated with mere information or knowledge, which can dilute its true meaning and significance.
Fig 1. DIKW pyramid
To clarify, insight should be viewed as the apex of our revised pyramid, transcending both intelligent data and actionable information. While intelligent data and actionable information are essential, insight represents a deeper understanding that combines various data sources with intuition to deliver strategic value. Recognizing this distinction helps businesses leverage their data more effectively, ensuring that they are not just reacting to information but proactively using insights to drive long-term success.
Insight is often derived from data, but it is crucial to differentiate between raw data, intelligent data, and true insight. While adding intelligence to raw data—through annotation and quantification—enhances its value, this does not automatically translate to insight. Intelligent data, though useful, merely provides a more refined view of the raw information.
In the realm of big data, which encompasses vast datasets measured in gigabytes and exabytes, the process of adding intelligence through machine learning can yield actionable information. This includes annotating data with sentiment, emotions, and topics. However, while this can offer valuable information, it still falls short of the deeper, strategic understanding that constitutes genuine insight.
As we progress up the knowledge pyramid, it's clear that actionable information and true insight are often confused. While actionable information involves identifying and addressing specific issues—such as a customer pain point uncovered through various data sources like tweets or reviews—this type of information is typically used for immediate problem-solving and short-term improvements.
True insight, however, goes beyond merely reacting to identified issues. It involves a deeper understanding that informs long-term strategy and drives proactive decision-making. For instance, while solving a customer pain point is valuable, insight requires synthesizing data to predict future trends and shape strategic direction, offering a more profound and strategic advantage.
At the pinnacle of the knowledge pyramid, business insight is defined as a 'gold nugget' that emerges from synthesizing information across multiple sources and applying a measure of intuition. Unlike simple data points or actionable information, which can be derived from single sources or immediate problem-solving efforts, insight is strategic and requires a long-term approach to implement effectively. It involves a deeper level of understanding that leads to proactive decision-making and can drive substantial positive outcomes for a business.
This distinction highlights why insight is positioned above intelligent data and actionable information in the pyramid. While intelligent data and actionable information are crucial for addressing specific issues and reacting to opportunities, insight goes further by enabling predictive strategies and long-term success. Recognizing this hierarchy underscores the importance of not only collecting and analyzing data but also transforming it into meaningful insights, which remains a rare and highly valuable skill in today’s business landscape.
Insight, while emerging from data and actionable information, represents a higher echelon of strategic value. It transcends mere data analysis to offer predictive and proactive benefits, making it a rare and valuable asset at the peak of our pyramid. Understanding this distinction helps businesses leverage data effectively and grasp the true power of insightful analysis.
Original Source: Information Age
listening247 In the Press
Artificial intelligence (AI) offers significant advantages across various industries, from healthcare and finance to retail and logistics. A key benefit of AI lies in its ability to enhance decision-making by analyzing user data through methods such as text and image analytics and emotion analysis. Michalis Michael, CEO of listening247, delves into how organizations can leverage AI to optimize user data analysis, thereby improving business decision-making and driving better outcomes.
Absolutely. In fact, we can use data – hard numbers like profitability, for example – to demonstrate that data driven businesses are often much more successful than those who do not use data for decision making. Working on the basis of ‘gut feelings’ may have a place in business management, but it’s no replacement for the insights that data provide.
If the right processes are in place: instantly. Of course, not all insights are created equal, so some of the more complex kinds of insight will require a human expert to identify them.
With that caveat aside, there are now sophisticated automation and sharing technologies which can handle data collection, cleaning, annotation, visualisation, analysis, and insight discovery. With the help of online dashboards and alerts, these processes enable data to be easily shared with an organisation’s management and implemented swiftly.
They certainly do. As I mentioned earlier, data is an increasingly essential tool for success in business, and the use of that data requires a certain amount of strategising. Businesses will need to decide, for example, what data to collect and how often. Data is all around us, so we need to be discerning about what to look for.
Not only do businesses need a strategy for what kind of data to collect (there is a huge variety of unstructured and alternative data to choose from, after all), but they will also need to decide which software tools and platforms are the most efficient and cost effective to collect the data, how they intend to add intelligence to the data, and – lest we forget – what kind of delivery mechanisms to employ.
In the trading and investment sector, ‘alternative’ data means any kind of data that falls outside the traditional fundamentals of a listed company. Examples of this standard kind of data include revenue, profit and price/earnings ratios.
Alternative data, by contrast, is any other kind of information. Social sentiment gleaned from Tweets, app usage, and even satellite images are forms of alternative data – and they contain the potential for insight and foresight in the right hands.
While many companies can benefit from harnessing alternative data, those operating in the financial services sector – quantitative funds, discretionary funds, private equity funds, and similar entities – tend to gravitate towards the world of alternative data to discover “new alpha”.
More broadly, alternative data is useful to corporates that want to predict sales or purchase intent. In those kinds of scenarios, companies can use KPIs from social intelligence. These can range from net sentiment scores to purchase intent posted on social media: alternative data sources that fall far beyond the interests of ‘traditional’ fundamentals, but which clearly have a huge bearing on companies interested in predicting the future stock price movements or actions of their prospective customers.
The longevity of relevance can vary enormously – it all depends on the sector involved and on the type of data we’re talking about. Suppose, for example, a company is interested in opinions expressed online about a big, long-standing topic like Brexit. In that case, I’d expect to see extremely slow depreciation as the issues rumble on. By contrast, gathering data on an advertising campaign sees very fast depreciation: in that scenario, the data is only as good as the most recent ad.
Sometimes, of course, the situation isn’t this clear-cut. Looking into social sentiment in order to predict stock price fluctuations, for example, can retain relevance for days or weeks depending on the ‘hype’ surrounding the stock. Data relevance certainly can depreciate very quickly indeed – that’s why, as far as tracking customer data is concerned, it’s advisable to implement tracking on a daily or weekly basis.
The analysis of unstructured data, including text, audio, images, and video, will soon become ubiquitous – engagement with this kind of data is swiftly changing from novelty to necessity. As such, our voices as customers – as users of products and services – will always be heard and responded to.
There are two positive implications for this. Firstly, we will be able to directly impact product development in direct, concrete ways that will improve human lives. Secondly, we’ll be able to monetise our own behavioural data and opinions. These changes in data analysis indicate a rise in the value of the data we put out into the world: it’s a precious resource and will likely be treated as such.
In conclusion, the strategic use of AI and data analytics is reshaping the landscape of business decision-making and customer experience management. The ability to harness comprehensive data insights—from traditional metrics to alternative data sources like social sentiment—empowers businesses to gain a competitive edge and respond swiftly to market demands. As technology evolves, the integration of advanced data analysis will become even more crucial, enabling real-time adjustments and enhancing product development. Companies that embrace these innovations will not only optimize their operations but also enhance customer satisfaction, making data a vital asset for future growth and success.
Original Source: Information Age
listening247 In the Press
The exponential growth of Big Data is set to transform the insurance industry dramatically. According to Statista, global data consumption, which was 79 zettabytes in 2021, is projected to exceed 180 zettabytes by 2025. This surge in data will predominantly consist of unstructured data from various sources, including customer interactions and IoT devices. As insurance companies strive to enhance customer experience management (CXM) through comprehensive data analytics, they must adapt to manage and leverage this influx of information effectively. The integration of advanced technologies, such as edge computing and AI, will be crucial in handling the massive data volumes and improving forecasting accuracy, ultimately reshaping the industry's landscape. Our CEO, Michalis. A. Michael gives us a better understanding from his interview.
Globally, data consumption is expected to surge from 79 zettabytes in 2021 to over 180 zettabytes by 2025, with storage capacity growing at a 19.2% annual rate. Much of this data will be unstructured, stemming from customer interactions and IoT devices. This expansion offers a significant opportunity to enhance customer experience management (CXM) by incorporating comprehensive analytics across various communication channels and languages. Advanced AI can process and interpret this data, providing actionable insights regardless of language barriers.
IoT data – for example from wearable devices which measure biometrics or smart car devices that measure driving behaviour – will become standard for the insurance industry since they promote forecasting accuracy and competitiveness which is articulated in a given customer’s insurance premium.
The impact will be largely positive due to improved forecasting models. The data available currently and in the future is an actuary’s dream.
Rapid changes in data processing are anticipated in the coming years to handle the growing volume of data. According to Gartner, while only 10% of enterprise-generated data is processed outside traditional data centers or cloud environments today, this figure is expected to rise to 75% by 2025. This shift highlights that current cloud solutions will struggle with the vast amounts of data generated by IoT devices and other sources at the network edge.
Data centers alone will not be able to meet the demands for faster response times and higher transfer rates, leading to significant challenges for many applications. The solution lies in decentralization through edge computing, which brings data processing closer to the source. This approach will be crucial in managing the massive data consumption and ensuring efficient data handling and analysis.
Data privacy increasingly focuses on obtaining individual consent for personal data usage, and organizations are becoming more aware of information security. This heightened awareness extends to longer sales cycles for CXM programs due to stringent security protocols. Adhering to global standards like ISO27001 and ISO27002 and undergoing independent penetration tests helps identify and address vulnerabilities, thereby mitigating risks of data breaches.
The most significant shift is driven by AI's growing capability to accurately annotate and analyze unstructured data, which comprises over 95% of all data ever recorded. Unlike structured data, unstructured data includes text, audio, images, and video. This development has profoundly impacted customer experience management (CXM), enabling companies to access and understand every customer interaction, regardless of language, and effectively design and implement tailored customer service workflows and scripts.
As Big Data continues to proliferate, the insurance industry stands to benefit significantly from improved forecasting models and enhanced CXM capabilities. The rise of IoT data and unstructured information presents both challenges and opportunities, driving the need for innovative data processing technologies like edge computing. While the increasing volume of data raises concerns about privacy and security, adherence to global standards and rigorous security protocols can mitigate these risks. The future of Big Data in insurtech is poised for transformative change, with AI-driven analytics enabling insurers to gain deeper insights into customer interactions and refine their services accordingly.
Original Source: Global Banking & Finance Review
listening247 In the Press
Michalis Michael, CEO of listening247, recently appeared on Kalkine Media Australia’s Expert Talks for a live interview focused on the role of AI in analyzing unstructured data. During the interview, Michalis delves into the significance of leveraging machine learning and AI to interpret vast and diverse data sets, including texts, audio, video, and images. He emphasizes the transformative power of these technologies in extracting valuable insights from unstructured data.
In his discussion, Michalis highlights how businesses can benefit from this advanced data analysis by enhancing their customer experience (CX). He explains how sentiment and topic annotation can provide companies with a clear understanding of their position in the customer journey and their relationship with their brand. This insight allows businesses to tailor their strategies and improve customer interactions.
For a deeper dive into Michalis Michael's insights on harnessing AI for unstructured data analysis, watch the full interview here: How to use AI to produce accurate, actionable, timely intelligence to unstructured data?
Original Source: Kalkline Media
listening247 In the Press
Unstructured data (UD) is rapidly gaining prominence, with its importance set to soar throughout this decade. Recent reports highlight a surge in investment, with 45% of businesses prioritizing UD analytics software and 62.5% of IT leaders increasing their UD storage budgets by the end of 2021. Given that over 90% of global data is unstructured and IBM estimates a daily generation of 2.5 quintillion bytes, it’s clear that UD holds immense, untapped potential. However, the vast scale and complexity of UD necessitate specialized expertise to unlock its valuable insights and drive business growth.
Unstructured data (UD) includes diverse formats such as text, audio, images, and video, and stands in contrast to structured data (SD), which is organized into tables and easily searchable. While SD's organized nature allows for straightforward access and interpretation, UD's complexity arises from its lack of structure and its expression in various natural languages, making it challenging to process with traditional methods.
The sheer volume of UD generated daily—equivalent to ten million Blu-ray disks stacked as high as four Eiffel Towers—demonstrates its vast potential. However, unlocking this potential requires advanced AI technology and specialized expertise, highlighting the crucial role of companies that can effectively manage and analyze UD. This growing field, largely driven by start-ups and early-stage firms, offers significant business value and opportunities for innovation.
Businesses invest in unstructured data (UD) due to its unparalleled value in providing deep, actionable insights. In marketing intelligence, for example, AI can analyze vast amounts of customer interactions to reveal nuanced details about customer sentiment and experiences with a brand, service, or product—insights that are far more precise and actionable than traditional methods.
Additionally, UD plays a crucial role in trading and investment by analyzing news articles and headlines to identify factors influencing stock prices. However, the effectiveness of UD is contingent upon the accuracy of the analysis tools and human expertise involved. Poorly managed UD analysis can lead to costly errors, emphasizing the need for sophisticated technology and skilled professionals to extract meaningful insights and avoid misinformation.
The growing investment in unstructured data (UD) by IT leaders is no secret; it reflects the undeniable power of data in fields like marketing and alternative analytics. For investors aiming to harness this power, opportunities abound in supporting companies that excel at converting raw data into valuable insights. The true value of UD lies with the technology and AI experts who master its complexities, making this a promising area for investment.
Original Source: London Loves Tech
listening247 In the Press
Research World recently highlighted the perspectives of Michalis Michael, CEO of listening247, in an in-depth article exploring the evolving landscape of social intelligence. His insights delve into the future of social listening and analytics, shedding light on emerging trends and innovations in the industry. This comprehensive piece is divided into two parts: "Remote Research: The Future of Asking Questions, Part 1" and "Remote Research: The Future of Asking Questions, Part 2," offering a thorough examination of these critical topics.
Research World, a leading market research association, is dedicated to advancing the field of market, opinion, and social research. By focusing on innovation and best practices, it provides valuable updates on the latest tools, technologies, and methodologies in data analytics. This publication helps professionals stay informed about the cutting-edge developments shaping the industry.
Original Source: Research World (part 1) & Research World (part 2)
Webinar
Today, most brands are wanting to take increase opportunistic strategies in how they actively shape their brand's presence, especially when it comes to having a strong online brand presence. To truly understand and resonate with your audience, this session dived deep into helping brands understand their consumers behave online from recognizing their interactions, understand their needs, and delivering experiences that exceed expectations. With the help and organisation by our agency partner in Asia, this webinar was held On Tuesday June 6th at 9am UK time.
As brands want amplify their brand's presence and influence in the market across various social media platforms, social media listening has become of great importance in boosting your brand visibility and engagement, whilst dominating conversations and attract more customers online. This webinar offers a unique opportunity to find out why social listening is gaining importance so quickly in research, how good quality social listening is carried out and when social listening works for you and your clients.
Michalis A. Michael, CEO of listening247: Michalis is an expert in digital monitoring and social intelligence with extensive experience in transforming data into actionable business insights. Under his leadership, DMR has developed cutting-edge solutions like the Brand Health Tracking, which helps brands monitor their performance and reputation across various channels.
Phil Hearn, CEO of MRDC Software: Phil expert in the use of MRDCL, the no.1 data tabulation and analysis software and interviewed Michalis, offering a unique opportunity to find out how Social Listening is changing the way many companies are spending their research budgets.
1. Detect and Manage Brand Crises: Learn how to quickly identify potential issues and strategically address them to avoid unwanted high costs in correcting brand marketing mistakes.
2. Accurately Understand the Market: How using social media listening allows brands to tailor strategies that authentically resonate with their audience, enhancing brand relevance.
3. Build Online Share of Voice: Discussing how social listening and analytics offers you a comprehensive approach to boost your brand's presence and influence online.
1. Senior business executives where informed decision-making is key.
2. Organisations who wish to establish a strong online brand presence and influence.
3. Any brand managers and marketers who wish to leverage the power of data to make informed decisions and stay ahead of the competition.
Don't let timing hold you back from transforming your brand into greatness! Watch the webinar now.
P.S. Have questions about the webinar? Contact us at: info@listening247.com. We're happy to help!
Webinar
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Brands are increasingly seeking innovative ways to engage with their audiences. communities247 stands at the forefront of this movement, offering a dynamic and fully customisable DIY Online Communities platform designed for consumer engagement, brand advocacy, insights, and co-creation. With the help and orrganisation by our agency partner in Asia, MRDC Software, This webinar was held On Wednesday August 23rd at 9am UK time.
As brands strive for novel methods to connect with their audiences, communities247 emerges as a pioneering force, providing a vibrant and entirely adaptable DIY Online Communities platform crafted for fostering consumer engagement, brand advocacy, insights generation, and collaborative creation. This webinar offers a unique opportunity to find out how to make the most of Private Online Communities to reach consumer insights on demand.
Michalis A. Michael, CEO of listening247: Michalis is an expert in digital monitoring and social intelligence with extensive experience in transforming data into actionable business insights. Under his leadership, listening247 has developed cutting-edge solutions like the Brand Health Tracking, which helps brands monitor their performance and reputation across various channels.
1. Cost-Effective and Scalable Solution: The benefits of using online communities vs. traditional research methods.
2. Easy Cross-Departmental Reach: How using online communities can lead to the advantage of various departments within an organisation.
3. Integrated Customer Insights: Discussing how online communities can be used in conjunction with Social Listening, enabling a virtuous circle of customer insights.
1. Any researchers, brand managers, customer experience officers, and any professional involved in consumer engagement and product development.
2. Organisations that prioritise direct consumer feedback in their strategic planning and seek to build a loyal customer base through continuous interaction.
3. Organisations that wish to leverage on the ability to engage with users worldwide and in any language to break down the barriers to customer insight.
Don't let timing hold you back from transforming your brand into greatness! Watch the webinar now.
P.S. Have questions about the webinar? Contact us at: info@listening247.com. We're happy to help!
Webinar
We are excited to invite you to an exclusive webinar hosted by Michalis A. Michael, CEO of listening247, where he will introduce DataVinci, our advanced Generative AI (Gen AI). This event is designed for consumer brands interested in using data and AI to enhance their marketing strategy and make smarter, more informed decisions.
The webinar took place on Wednesday, 30th of October at 1:30 PM (UK time), and last s45 minutes, including a Q&A session and interactive polls. This online event will provide valuable insights on how brands can better understand their customers, improve engagement, and optimise their marketing efforts.
But that’s not all! We’re also excited to introduce our special guest speaker, Tom Molnar, Co-Founder and CEO of GAIL’s, the renowned British bakery and coffee shop chain.
During the webinar, Michalis A. Michael will explain how DataVinci combined with listening247’s Social Listening and Analytics, can change the way you use digital media.
Tom Molnar, CEO of GAIL’s, will provide a real-world look at how DataVinci is impacting GAIL's business.
Michalis will explain how listening247’s Social Listening and Analytics solution gathers data from platforms like Instagram, X, TikTok, Facebook, and more. It also tracks mentions in news articles, forums, and online publications, providing a full view of your brand’s digital presence.
With this information, listening247 tracks and analyses key factors such as brand sentiment and customer opinions.
DataVinci then takes this data and turns it into meaningful insights. Using advanced AI capabilities, it identifies patterns and opportunities within the data and translates these findings into detailed reports and strategic recommendations. These reports don’t just highlight trends; they also provide the major conversation drivers that influence how your brand is perceived as well as specific actions that your marketing team can take to improve engagement and align with customer interests.
But DataVinci doesn’t stop at analysis. It goes further by offering ready-to-go social media posts, complete with image briefs, designed for your brand to publish. This feature simplifies the content creation process and ensures that the messaging is data-driven and resonates with your audience. DataVinci also helps your team stay on top of trends and topics that matter to your customers, ensuring that your content remains relevant and impactful.
This webinar is tailored for C-Suite Execs of SMEs consumer brand leaders, marketers, and decision-makers who want to use data more effectively. It’s an opportunity to learn how Gen AI can help you make informed decisions, improve customer engagement, and stay competitive in today’s fast-paced digital landscape.
On November 8, 2023, Michalis Michael, CEO of listening247 and GIHE Research Fellow, spoke at the Montreux International Tourism Forum in Switzerland. He discussed the role of AI in analyzing unstructured data within the tourism industry. You can watch his full presentation here.
Event Highlights:
1. Human resources: what are the solutions to the current shortage?
2. Artificial intelligence and tourism – definitions and realities;
3. Unlocking the potential of AI for tourism SMEs: insights from Europe-wide research;
4. “Service recovery” in tourism in a world of multiple crises;
5. Tourism and sustainable development;
6. A life for tourism.
Ideal for:
1. Any researchers and any professional involved in the toursim sector.
2. Organisations that prioritise toursim strategies and operations that involves some form of A.I.
P.S. Have questions about the ebook? Contact us at: info@listening247.com. We're happy to help!
Event
On October 10, 2023, Michalis Michael, CEO of listening247 spoke at the Data & Insights Network’s online event, "AI for Marketing & Business." His presentation was presented in English. For more details, visit the event page, which can be translated using Google Translate if needed.
1. Gain a deeper understanding of how AI is transforming marketing and business practices;
2. Delve deeper into the opportunities and threats of generative AI,
3. Engage with fellow members and industry experts in a dynamic, digital setting;
4. Understand the potential challenges and ethical considerations of using generative AI in business;
5. Experience an online Education Day tailored specifically for members, providing an intimate and focused learning environment.
1. This session (entirely in English) is exclusively for (teacher) members of the Data & Insights Network and NIMA
2. Anyone who is interested in becoming a member, and is interested in the education sector.
3. All those who play a crucuial role in the education sector.
P.S. Have questions about the ebook? Contact us at: info@listening247.com. We're happy to help!
Use Case
This material includes a used case infographic, which presents the 20 Questions you should be asking when evaluating Social Media Listening vendors. It dives deeper into helping organisations understand the process in choosing the right tool for their organisation and whatever they are trying to achieve.
1. Overwhelming Market Choice: With over 1,000 social media monitoring tools available, selecting the right one for your organization can be challenging;
2. Suitability for Market Research: Not all social media listening tools are equipped to handle the specific needs of market research, making careful evaluation crucial;
3. Critical Evaluation Criteria: listening247 has identified 20 key questions to ask when assessing social media listening vendors to ensure they meet the necessary standards for accurate customer insights;
4. Accuracy in Customer Insights: The right tool must meet specific criteria to provide reliable and actionable insights, essential for making informed business decisions;
5. Guidance for Informed Choices: listening247’s 20 Questions framework serves as a valuable guide for organizations looking to choose the most effective social media listening tool for their market research needs.
1. Market researchers, CX managers, digital marketing teams, and business decision-makers who are looking to choose the most effective social media listening tool for their organization.
2. PR and Communication Specialists: Individuals focused on crisis management and public relations, who need real-time monitoring of brand mentions and sentiment to react swiftly and effectively.
P.S. Have questions about the ebook? Contact us at: info@listening247.com. We're happy to help!
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Use Case
This material includes a used case infographic, which demonstrates how text analytics alone can miss key insights as more people use images and videos to express their views on social media. To fully understand brand sentiment, a deep learning approach can be employed to analyze visual content, while speech-to-text technology converts audio into text for comprehensive analysis.
Used Case Highlights:
1. Beyond Text: Traditional text analytics can miss crucial insights as more social media users share feedback through images and videos rather than text;
2. Visual Content Analysis: Deep learning technology enables the detection of themes and context within images posted on platforms like Instagram, Pinterest, and Twitter;
3. Speech-to-Text Conversion: Audio content from platforms such as YouTube and Facebook can be converted into text, allowing for thorough analysis using existing text analytics tools;
4. Enhanced Brand Sentiment Understanding: Combining visual, audio, and text analysis provides a more comprehensive view of customer sentiment and feedback;
5. Adapting to Social Media Trends: As visual and audio content sharing grows, integrating these forms of media into analytics strategies is essential for staying relevant and insightful.
Ideal for:
1. Social media analysts and professionals in the marketing analytic field who need to capture and analyze the full range of content—text, images, and audio—shared about brands on social platforms.
2. Brand managers and strategist who are responsible for understanding and managing brand perception across all forms of media, ensuring that no valuable insights are missed.
P.S. Have questions about the ebook? Contact us at: info@listening247.com. We're happy to help!
Ebook
This ebook explores the evolving discipline of social listening, its many names, and the key questions organizations should ask before getting started. If you are new to social mediamonitoring or don’t have theanswers to these questions, readon...
1. See how web listening provides a unique consumer portal at a time when response rates for research projects are dropping and it’s getting harder to reach your key customers;
2. Understand why It is now more vital than ever for organizations to understand what is being said about them, so that they can effectively manage their brands and their reputation online;
3. Learn how findings from web listening can improve business performance, whether it’s communication, targeting or product development or using the findings to enhance data from existing sources.
1. Any organisation, brand manager and employees within the marketing department who specialise in the discipline of social media.
2. Market research agencies who want to learn about social media monitoring or web listening.
P.S. Have questions about the ebook? Contact us at: info@listening247.com. We're happy to help!
listening247 In the Press
The future of Customer Experience Management (CXM) is set to be transformed by the ability to analyze all customer interactions, regardless of their source or language. This shift is not speculative but rather a natural progression of existing technology. With AI-driven tools becoming increasingly capable of processing vast amounts of customer data, the industry is moving towards a universal approach that captures every customer opinion, whether solicited or unsolicited. As CXM professionals adapt to this new reality, they will face both challenges and opportunities in harnessing the full potential of this comprehensive analysis.
The future of Customer Experience Management (CXM) will be significantly impacted by the ability to access and analyze all customer opinions, particularly in tracking multiple relevant KPIs. While many companies currently rely on the Net Promoter Score (NPS) to gauge customer loyalty and satisfaction, this single KPI may not capture the full complexity and nuance that new technology offers by analyzing every customer interaction.
As CXM evolves, we can expect a shift towards using multiple, composite KPIs that synthesize data from various customer interaction sources, such as sentiment, purchase intent, and social media engagement. While these findings may still be presented in a simplified score, like DMR’s Net Sentiment Score (NSS) or Social Presence Score (SPS), the measurement of market performance will need to reflect the sophistication and depth of the available data.
Access to comprehensive customer interaction data will not only enhance market position insights but also revolutionize how companies address pain points. With all customer feedback at their disposal, businesses will be able to prioritize and address issues more effectively, both proactively and reactively. Reactive measures may involve refining call center protocols and complaints handling, while proactive solutions could focus on real-time operational fixes to mitigate issues before they escalate.
These advancements are already feasible with current technology and specialist expertise, enabling companies to convert customer opinions into actionable strategies. By tackling pain points and optimizing customer retention, businesses can significantly reduce churn. Future discussions will shift towards leveraging customer strengths and gain points to further improve CX.
The growing reliance on advanced technology for Customer Experience Management (CXM) will introduce new challenges, particularly regarding the accuracy of AI-driven annotations for sentiment and purchase intent. Ensuring precise analysis of millions of interactions across various languages is crucial for effective CXM. As the industry adapts to these technologies, a virtuous cycle may emerge: enhanced annotation accuracy leads to better analysis, which in turn improves CXM capabilities, promising robust business growth and a more satisfying customer experience.
In conclusion, the integration of advanced AI technologies in Customer Experience Management (CXM) holds the promise of transforming how businesses understand and respond to customer interactions. By leveraging precise, language-agnostic data analysis, companies can move beyond traditional KPIs and gain a deeper, more nuanced view of customer sentiment and behavior. This shift enables proactive and reactive solutions to pain points, ultimately enhancing customer satisfaction and retention. As the industry embraces these innovations, the emphasis on accurate data annotation and comprehensive analysis will drive significant growth and elevate the overall customer experience.
Origal Source: Finance Digest
listening247 In the Press
London, UK – 01/10/2024 – listening247, a leading provider of data-driven marketing solutions, is proud to announce the launch of DataVinci, a Generative AI (Gen AI) solution that is set to transform how businesses gather, analyse, and act on data. DataVinci adds value to listening247’s Intelligent data as a Service (IdaaS) offering, delivering actionable insights and helping brands unlock the full potential of their marketing strategies.
DataVinci sits in the middle of listening247’s suite of solutions, including listening247 for social listening and analytics, engaging247 for social media management, and communities247 for direct customer engagement through private online customer communities. Together, these solutions provide businesses with a comprehensive, end-to-end data intelligence platform that empowers them to stay ahead of their competition.
“DataVinci is a significant leap forward in the way we approach data and content creation,” said Michalis A. Michael, CEO at listening247. “We have developed a solution that not only identifies trends and insights but also turns those insights into action. DataVinci delivers fully automated recommendations, and what truly sets it apart is its ability to create ready-to-use content, complete with post copy and image briefs, helping businesses connect with their audiences more efficiently than ever.”
DataVinci: Powering Intelligent data as a Service (IdaaS)
At the heart of DataVinci’s innovation is its ability to turn raw data into precise, actionable recommendations. By integrating with listening247’s social listening and analytics solution, DataVinci analyses both structured and unstructured data from across digital channels to offer a 360-degree view of consumer sentiment and brand performance. It not only identifies emerging trends, customer emotions, and key purchase drivers but also generates content—from engaging copy to image descriptions—tailored to your brand’s voice.
“DataVinci is not just about reports; it’s about taking meaningful action in a very efficient way,” added Michalis A. Michael. “With DataVinci, businesses no longer need to struggle with interpreting complex data or creating content from scratch. Our AI-driven solution automates these processes, allowing teams to focus on what truly matters—engaging with their customers and driving growth.”
Enhancing Social Media and Customer Engagement
DataVinci works alongside engaging247, listening247’s social media management platform, to help brands manage their social presence with ease. While DataVinci creates high-quality, actionable content, engaging247 streamlines the posting and scheduling process, allowing businesses to maintain a consistent and impactful online presence.
Meanwhile, communities247 enables brands to gather solicited feedback directly from private, branded customer communities, offering deep insights into customer behaviour and preferences. With DataVinci, these insights are automatically analysed, and businesses receive clear, data-driven recommendations to enhance customer engagement and drive loyalty.
Industry Leaders Impressed by DataVinci
Since its introduction, CEOs who have experienced DataVinci in action have been thoroughly impressed. DataVinci’s ability to streamline data analysis and content creation has resonated strongly with decision-makers looking for smarter, more efficient ways to engage with their audience.
“After seeing what DataVinci can do, all CEOs who were involved in our “lean start-up” approach for customer-centric development were eager to sign up and get it implemented to support their teams,” Michael continued. “They appreciate how DataVinci simplifies the process of data analysis and automatically produces reports, actionable recommendations, and even content that’s ready to go. It’s not just about saving time—it’s about ensuring that the actions taken are always based on intelligent, data-backed insights.”
A Bright Future for Data-Driven Marketing
As more businesses turn to AI to gain a competitive edge, DataVinci stands out as a solution that combines innovation, authenticity, and ease of use. Its ability to deliver insights, automate content creation, and empower businesses with actionable recommendations sets a new standard for how brands can leverage the power of data.
“Our mission has always been to help brands better understand their customers and act on those insights,” said Michael. “With DataVinci, we’re delivering on that promise in a way that is more intuitive, more powerful, and more cost-effective than ever before.”
*Original Source: Global Tech Reporter
Ebook
This ebook aims to demonstrate how integrating Social Listening and Analytics data with survey data, and other traditional research methods, can help companies uncover some of the most accurate insights possible. A case study revolving around Health & Wellness, as discussed online in Arabic, was selected as an example to illustrate the untapped power of social listening analytics.
Ebook Highlights:
1. Find out how to uncover and synthesize accurate insights by integrating data from various sources;
2. Discover the untapped power of social listening analytics;
3. Understand why traditional methods are no longer sufficient or fully representative;
4. Learn about trends and ideas related to Health & Wellness in the MENA region.
Ideal for:
1. Any organisation within the health & wellness sector who want to enhance their market research activities to uncover some of the most accurate insights possible.
2. Market research agencies who want to learn about the importance of accuracy when it comes to social insights about trends and ideas related to health and wellness in the MENA region.
P.S. Have questions about the ebook? Contact us at: info@listening247.com. We're happy to help!
Case Study
A global company needed to enhance product perception and performance without increasing costs, as traditional market research methods were too slow and expensive. listening247 responded with a private online community platform that allowed real-time consumer interactions and feedback, offering a cost-effective and timely solution for strategic insights and marketing decisions.
A global company faced the dual challenge of improving product perception and performance without increasing expenditure. Tasked by the CEO to deliver actionable insights, the Marketing Director needed a solution that was both quick and cost-effective, as traditional market research methods were proving too slow and expensive for the dynamic market landscape.
To address these challenges, listening247 introduced an innovative solution: a private online community platform designed to serve as an advisory board comprising consumers. This platform facilitated real-time interactions and allowed the marketing team to conduct various activities like polls, asynchronous discussions, and video diary sessions.
1. Accelerated Decision-Making: The client rapidly acquired preliminary consumer feedback that informed critical marketing decisions, keeping them ahead in a competitive market.
2. Economic Strategy Refinement: The online community platform reduced overall market research costs while providing valuable insights, maximising ROI on marketing spend.
3. Real-Time Consumer Feedback: listening247 introduced a private online community platform that acted as a consumer advisory board, enabling the marketing team to gather immediate insights through polls, discussions, and video diaries.
4. Cost-Efficient Engagement: This virtual platform significantly cut costs associated with traditional research methods by eliminating the need for physical focus groups and reducing logistical expenses.
5. Privacy-Centric Interaction: Maintaining consumer privacy, the platform encouraged genuine feedback, ensuring that the insights gathered were both authentic and actionable.
Case Study
A leading luxury watch manufacturer sought to deepen their understanding of market dynamics and consumer perceptions but faced challenges with a limited volume of social media interactions. listening247® technology was deployed to analyse over 41,794 posts, refining the data to 4,423 relevant commercial interactions, which provided actionable insights and enhanced brand perception and engagement.
A leading luxury watch manufacturer needed to enhance their understanding of market dynamics and consumer perceptions. The challenge was to derive actionable insights from a limited volume of social media interactions using methods beyond traditional analytics.
listening247 implemented their proprietary technology to meticulously analyse social media data associated with the luxury watch market. The approach was comprehensive: beginning with the harvesting of over 41,794 posts and undergoing multiple rounds of noise elimination to refine the data to the most relevant 4,423 commercial posts.
1. Enhanced Brand Perception: Insights revealed the brand as 'elegant' and 'good value for money', crucial for refining marketing strategies and consumer communication.
2. Direct Consumer Engagement: The identification of leads facilitated direct interactions with potential buyers, boosting conversion opportunities and enhancing customer relationship management. Identifying key influencers and approximately 513,000 potential customer leads, listening247 enabled the brand to strategically connect with both market movers and potential buyers.
Case Study
Carlsberg’s YouTube campaign faced challenges with a noisy dataset of 22,448 posts, plagued by duplicates and irrelevant content. listening247 employed a three-step noise elimination strategy, refining the data to 177 relevant posts, leading to more accurate insights, cost efficiency, and improved campaign performance.
Carlsberg’s “Standing up for a friend” YouTube campaign initially struggled with a noisy dataset of 22,448 posts, predominantly filled with duplicates and irrelevant content. Traditional tools inaccurately processed this noise, risking flawed campaign evaluations.
listening247 approached the problem with a structured three-step noise elimination strategy, utilising their proprietary solution, listening247. The first step involved the removal of 3,354 duplicate posts, instantly refining the dataset. Subsequently, a taxonomy of noise terms was created with the assistance of specially trained human curators, which facilitated the identification and removal of 18,890 irrelevant posts. By the end of the process, the dataset was distilled to 177 relevant posts, ensuring a high level of data purity.
Using engaging247, the brand enhanced its social media management with features like the smart compose for scheduling posts, a unified inbox for tracking and responding to comments, and easy access to comprehensive reports. The seamless integration between listening247 and engaging247 allowed the brand to target the right audiences with the content and offers that they were expecting.
1. Informed Decision-Making: With purified data, Carlsberg gained accurate insights into audience engagement, enhancing decision-making and strategic planning.
2. Cost and Time Efficiency: The precise analytics saved significant time and resources otherwise wasted on sifting through irrelevant data, boosting operational efficiency.
3. Enhanced Campaign Performance: The insights derived from high-quality data enabled Carlsberg to optimise its marketing efforts, leading to improved campaign efficacy and a stronger market presence.
4. Precision Analytics: listening247's proprietary solution, was pivotal, filtering out 99% of the noise and isolating 177 pertinent posts, starkly contrasting with the results from other tools and solutions.
Case Study
A global FMCG company sought to identify emerging trends in Vietnam's market using digital data. listening247 provided a solution by applying advanced AI models to analyse extensive online content, enabling the client to enhance marketing strategies, adapt swiftly to market changes, and make informed decisions through detailed insights and interactive reporting tools.
A global FMCG company needed to uncover emerging consumer trends in Vietnam’s dynamic market. They required a solution to interpret vast digital data—from blogs to social media—without direct consumer interaction.
To address these challenges, listening247 implemented a comprehensive social intelligence strategy using advanced AI models. The approach involved collecting data from a wide range of digital platforms using 60 generic keywords categorised into areas such as 'lifestyle', 'appearance', and 'career'. This data was meticulously annotated with information about brands, sentiments, and topical discussions.
1. Enhanced Marketing Strategy: The insights gained enabled the FMCG company to refine their marketing strategies, ensuring alignment with real-time consumer trends.
2. Agile Market Response: Access to the listening247 platform allowed the client to monitor changes and adapt quickly, maintaining a competitive edge.
3. Informed Decision-Making: The strategic insights led to better decision-making, driving effective consumer engagement and increasing market penetration in Vietnam.
4. Interactive Reporting Tools: listening247 provided an intuitive dashboard and detailed reports via a partner agency, facilitating seamless insight dissemination to the client.
Case Study
In a collaborative project with a telecom client, listening247 faced the challenge of integrating social listening analytics with existing consumer surveys amidst managing over 620,000 social media posts and aligning diverse Net Promoter Scores (NPS™) and Net Satisfaction Scores (NSS™). The project demanded advanced analytical techniques to sift through noise, categorise data effectively, and correlate consumer sentiment with market events.
The collaborative project between listening247 and a Telecom client sought to integrate social listening analytics with existing consumer surveys. A significant challenge was managing over 620,605 social media posts, with about two-thirds rendered irrelevant due to noise issues. Aligning the monthly NPS™ with variable NSS™ scores also presented complexities, necessitating an advanced analytical approach to synthesise these diverse data streams.
listening247 addressed these challenges by involving extensive social media listening over a six-month period and the deployment of sophisticated data curation techniques. This included the creation of a detailed hierarchical taxonomy to categorise the data effectively and the use of refined search term combinations to improve the relevance of the data collected. listening247's solution also involved superimposing NPS™ and NSS™ data over a calendar of market events thereby providing a dynamic analysis that captures the pulse of consumer sentiment and its impact on brand perception.
1. Enhanced Strategic Decision-Making: The integration of NPS™ and NSS™ allowed telecom client to gain a dual perspective on customer loyalty and social sentiment, offering a holistic view of brand health and consumer satisfaction.
2. Improved Customer Insights: The project uncovered specific consumer sentiments not captured in traditional surveys, enabling telecom client to tailor their customer engagement and service strategies more effectively.
3. Informed Operational Adjustments: The insights provided led to better-informed operational decisions, enhancing brand’s responsiveness to market changes and customer needs, ultimately strengthening their market position.
4. Dynamic Data Analysis: By overlaying NPS™ and NSS™ data with a calendar of market events, listening247 provided a nuanced analysis that captured real-time consumer sentiment fluctuations and their impacts on brand perception.
Case Study
A leading fashion brand in South Africa faced the challenge of understanding true consumer sentiment despite widespread online mentions. Traditional analytics fell short, prompting the brand to partner with listening247, which used advanced image analytics and deep learning to decode the visual narrative of the brand’s online presence.
A leading fashion brand in South Africa found itself facing a perplexing conundrum. Despite being mentioned across various online platforms, deciphering the true sentiment and perception surrounding its brand proved to be an uphill battle. Traditional text analytics fell short in capturing the richness and depth of consumer engagement, leaving the brand grappling with a fragmented understanding of its online presence.
Recognising the imperative to adapt to the evolving landscape of social media, the fashion brand turned to listening247 for a transformative solution. Leveraging listening247's cutting-edge image analytics capabilities, the brand embarked on a journey to unravel the visual narrative surrounding its products and brand identity. Through the deployment of advanced deep learning machine technology, listening247 provided the brand with the tools to decode the complexities of consumer-generated content, including images, videos, and other unstructured data.
1. Holistic Brand Perception: By harnessing image analytics, the fashion brand gained unparalleled insights into how consumers perceive and interact with its products online. From identifying key brand elements within visual content to discerning emerging trends, the brand now possesses a comprehensive understanding of its online image.
2. Strategic Agility: Armed with a newfound understanding of consumer sentiment, the fashion brand seamlessly integrated image analytics into its strategies. This strategic pivot not only unlocked new revenue streams but also positioned the brand at the forefront of innovation within the fashion industry.
3. Rapid Insights Generation: With listening247's image analytics solution, the fashion brand can now decode vast amounts of unstructured data in mere minutes. Whether it's analysing video clips, social media posts, or online community chatter, the brand gains actionable insights at an unprecedented speed, enabling agile decision-making and strategic planning.
4. Emotion Intelligence: listening247's proprietary AI models go beyond surface-level analysis, delving deep into the intricacies of consumer emotions. By dissecting both positive and negative sentiments expressed in visual content, the fashion brand gains a nuanced understanding of consumer sentiment, driving enhanced engagement and brand loyalty.
5. Intelligent Consumer Insights: With listening247's advanced machine learning models, the fashion brand gained unparalleled access to consumer behaviours, requirements, and preferences. Armed with this knowledge, the brand can tailor its marketing strategies and product offerings to resonate more deeply with its target audience.
