Customer Experience & Relationship Management (CX/RM)
Customer experience and relationship management is crucial for building brand loyalty and can be improved by understanding customer sentiment and painpoints better. Tools within this category combine customer feedback, owned data and social media conversations - often with a real-time monitoring element - to give you a complete picture of the customer experience and highlight areas of improvement.
CX/RM tools have advanced in recent years by leveraging ML and AI to collect and analyze data more effectively. Businesses can anticipate customer needs and proactively address issues with these technologies, which help them perform sentiment analysis and predictive analytics more accurately. For example, AI-driven chatbots and virtual assistants are becoming more sophisticated, providing personalised customer support and freeing up human agents (and social intelligence professionals) for more complex tasks.
Using NLP and machine learning, text analytics are improving significantly as well: a number of advanced text analytics tools can now detect subtleties in customer sentiment, including sarcasm or mixed feelings (to varying degrees of accuracy), providing a deeper understanding of customer opinions. As a result, brands will be able to identify specific areas of improvement and tailor their strategies accordingly.
As a bonus, Text Analytics tools in this category offer users the ability to create custom models, enabling businesses to tailor text analytics to their specific needs and context. For social intelligence professionals, this opens up a new world of possibilities; for example: a custom model can be developed to recognise industry-specific jargon and sentiment nuances (e.g., certain keywords may have a positive connotation in the entertainment industry, but totally negative in the context of healthcare and pharmaceuticals). By training a custom model with industry-specific data (or even market-specific data, if needed), users can achieve more accurate sentiment analysis tailored to the unique vocabulary and sentiment indicators of their field.
Customer Experience Management (CXM)
Social Customer Relationship Management (CRM)
Text Analytics
Customer Intelligence
CXM technology provides a centralised platform to manage and optimise your customers’ experience across different touch points. These tools use social listening to build upon other customer data sources (such as email, surveys and website chat) by adding insights and sentiment data from online sources that aren’t considered traditional customer feedback channels. By doing this, they provide a fuller picture of the customer journey which you can analyse to find ways to improve customer satisfaction and loyalty.
Omnichannel management: Coordinate customer interactions across different channels - website, mobile app, social media and in-store - to create a more connected and personalised experience with your brand.
Customer feedback management: Collect, analyse and respond to all customer feedback - whether from surveys, reviews or social media - in one place so that you can track customer sentiment, identify trends and improve the customer experience more easily.
Experience design: Use customer insights and journey mapping tools to design better customer experiences across all channels and touchpoints and encourage loyalty to your brand.
Performance measurement: Track key customer experience metrics, such as net promoter score (NPS), customer satisfaction (CSAT), and customer effort score (CES) to work out how effective your CX initiatives are and demonstrate the business impact of investing in customer experience.
Employee engagement: Give your employees the tools and insights they need to offer amazing customer experiences and encourage a customer-centric culture. This is good for your customers and improves employee satisfaction which is good for business.
Social CRM tools can integrate social media data with your traditional CRM systems so that you can get a better view of customer relationships. This will help you to engage with current and potential customers on their preferred social channels and also quickly respond to their questions or complaints in a more personal way.
Social listening: Monitor social media for mentions of your brand and relevant keywords so that you can understand customer sentiment, identify trends and learn more about your competitors.
Social customer service: Respond to customers’ questions or complaints on social media to provide quick and personalised support and improve customer satisfaction by talking to them where they hang out.
Social engagement: Join in with online conversations and share content to proactively build relationships with current and potential customers, increase brand awareness and encourage customer loyalty by establishing a strong and authentic online relationship.
Social advocacy: Engage with brand advocates and influencers on social media to amplify brand messaging and drive word-of-mouth marketing. Using this social proof to your advantage will help you expand your reach and build trust with potential customers.
Social analytics: Analyse the impact of social media interactions and campaigns on key business metrics, such as customer acquisition and retention so that you can show the ROI of this type of marketing and allocate your resources better.
Customer Intelligence tools use advanced analytics and ML techniques to help you segment customers and predict their behaviour. By collecting and analysing social data and combining it with traditional customer data sources (including direct feedback), you can learn more about your customers’ preferences and painpoints. These insights can help you design personalised brand experiences that drive engagement and growth, and boost loyalty.
Customer 360: Build a complete picture of each customer by combining data from multiple sources, such as CRM, marketing automation and customer service systems so that you can deliver more personalised and relevant experiences.
Customer segmentation: Identify distinct customer segments based on their demographics, behaviours and the value they bring so that you can tailor your content, messaging and product development strategies to improve conversion rates and customer loyalty.
Predictive analytics: Use ML to predict customer behaviour - such as how likely they are to purchase something or respond to a specific offer - and use this information to proactively reach out to customers about relevant products or topics.
Customer journey analytics: Analyse how customers interact with you throughout the whole buying journey so that you can fix pain points, improve processes and create more personalised experiences that increase customer satisfaction and loyalty.
Customer lifetime value (CLV) analysis: Calculate how much value a customer is likely to generate for your brand during their relationship with you, so that you can allocate resources better to prioritise the segments that bring the most value.
Text Analytics technology is a fundamental part of social listening as it helps you analyse huge amounts of unstructured text-based data available across the internet. These tools use NLP, ML and sometimes AI, to analyse data sources such as social media posts, customer reviews and support tickets. By doing this, you can generate insights around customer sentiment, consumer trends and buying intent which will help you to make better decisions that improve the customer experience.
Sentiment analysis: Analyse sentiment and emotions shared in text form to understand how customers feel about you and your products or services and discover potential challenges or areas for improvement.
Topic modelling: Uncover the most important topics and themes that get discussed in large volumes of text data, such as customer feedback or social media conversations, so that you can understand what matters most to your customers.
Intent detection: Analyse text data to work out why customers are doing certain things - like requesting information, making a purchase or cancelling a service - so that you can help customers more quickly and personalise your reponse.
Named entity recognition: Find specific names - such as people, organisations, locations or products - from text data so that you can get a more detailed understanding of customers' preferences, experiences and interactions with your brand.
Text classification: Automatically sort large volumes of text data into categories, such as sentiment, topic or intent, so that you can organise and analyse it to get to the insights more quickly.
To learn more about how SITech is evolving to tackle the challenge of data quality for predictive analytics, we spoke to one of the tech providers in this space, Converseon.
Converseon is an AI powered NLP technology and predictive intelligence company focused on social and related “conversation data” (est. 2008).
Our “Conversus AI” platform does two things. First, it solves pervasive data accuracy and quality problems through a new generation of precise LLM powered NLP technology. It provides advanced enrichment together with a suite of “trusted AI” features - to ensure the AI is precise, fair, unbiased, transparent and impactful. It fully integrates with several partner platforms for seamless and accessible enrichment.
Conversus PRISM connects this enriched data to business outcomes such as sales and shareholder value. It not only predicts the future but can simulate how changes in perception will likely impact business outcomes. It’s a predictive brand navigational solution with modules that include reputation, ESG, CX, Brand Health and Trends/Innovation.
We’re rolling out our new generation of 50+ prebuilt LLM powered models that are setting new accuracy standards for industry. These combine state of the art algorithms and technology with our 15 years+ of model development experience, and include advanced sentiment, consumer attitudes (14 models), “intensity,” content type and packages for ESG, reputation, CX and more. They’re available through API and through partners like Brandwatch and Meltwater.
ConversusNLP will continue to add features that enable users to take more direct control of their own NLP models. For example, advanced topic discovery, model performance observability and more. Our proprietary ConvQL query language “goes beyond boolean” so users can combine “rules” with advanced AI models, resulting in superior output.
Conversus enrichment will also become increasingly important for those looking to fine tune (or RAG) other LLM models for better and more accurate output.
And we will continue to expand the types of data we work with to encompass all forms of voice of the customer.
Our PRISM solution will introduce enhanced features like “perceptual attribution” analysis to measure (and quantify) the relative value of specific perceptions on business outcomes and provide “simulators” for brands to understand how changes will impact performance. We call this “decision intelligence.” And we will continue to offer this directly and through more integrations with partner platforms.
While most are focused on big, expensive LLMs such as ChatGPT and BERT, we continue to build on our long term AI leadership through SLM (“Small Language Model”) technology. We’ve been working on this since 2019.
SLMs are a more practical, cost effective approach to solve the accuracy and data quality challenges the industry faces. By combining our AI model development experience with SLMs and some patent pending technologies, we intend to strengthen our industry NLP leadership. We’ll continue to do so in partnership with leading industry platforms as our technology is complementary to their foundational models. We can customize to specific domains and brands where it’s impractical for larger platforms, and provide the essential trusted AI model observability, tracking, auditing and governance organizations need to comply with emerging AI standards. We intend to continue generating the required trust in this data and insights to encourage broader adoption.
We also combine NLP technology with predictive AI and econometric modeling to further strengthen the predictive capabilities of this data. This will help the industry cut through vanity metrics and measure what matters most to the c-suite.
And finally, we’ll continue to make our solutions easier to access - with us directly or through more partner platforms, by licensing the Conversus SaaS platform, and through API endpoints.
We’ve witnessed many platforms come and go over the years but a constant has been the need to prove that these data and insights can be trusted, and support the core KPIs of the business. Social intelligence still suffers from a trust deficit. We address this head on.
ConversusNLP solves data quality with transparency and accuracy. Conversus PRISM demonstrates that data can provide powerful predictive capabilities and actionability towards business outcomes. It cuts right through vanity metric concerns.
This transitions “social intelligence” into a better form of “business intelligence” that will hopefully contribute to the elevation of the industry to its full potential.
Customers will benefit from:
Greater trust through accuracy and transparency and ensuring alignment with standards like AI EU act.
Reduced operational costs due to human corrections of data. One large brand told us that the increase from 65% to 85% accuracy resulted in tens of thousands of dollars of cost savings per month (and faster time to insight).
A move from descriptive to predictive analytics for unprecedented foresight, tying metrics to outcomes (and avoiding vanity metrics). “Real time” isn’t always fast enough today.
Better and faster informed decision making. Insight is not the endgame, decisions are. Our simulators predict the future and demonstrate how specific actions, informed by insight, are likely to impact outcomes.
“Real time” isn’t always fast enough today
