ecosystem.Ai
Transforming Customer Engagement through Data-Driven Decision-Making
ecosystem.Ai helps businesses tackle this challenge with its low-code prediction platform that combines behavioral social science with real-time machine learning (ML). It enables firms to assess and predict human interactions, manage processes, boost engagements, and schedule campaigns through its platform. In essence, ecosystem.Ai assists with three primary issues; How can real human behaviors be translated into algorithms? How can companies predict behaviors and engage with consumers in real-time? How can someone employing behavioral algorithms be spared from technological complexity?
ecosystem.Ai provides predictive interactions based on consumers’ personalities and behavioral characteristics. It supports an end-to-end ML model using historical data to help businesses improve customer engagement in the telecommunications, banking, financial services, and insurance sectors.
Its low-code platform eliminates the need for coding expertise by simplifying the design and deployment of ML models tailored to business requirements. Minimizing the complexity that usually accompanies the development of custom-built algorithms, ecosystem. Ai provides efficient model serving and deployment of data-driven predictions in production.
“We assist organizations in analyzing human behavior using predictive technology, helping them adapt to evolving consumer preferences quickly and intelligently,” Jay van Zyl, CEO of ecosystem.Ai
Customer interaction processes typically comprise different role players with various demands. For instance, a CMO or sales manager is primarily concerned with KPIs like sales growth and wants the best tools for optimal cost. Alternatively, professionals in the data science community seek technology to swiftly develop algorithms, whether using in-house models or procuring third-party offerings. ecosystem.Ai acts as an interface that bridges these business facets, helping clients understand how to boost engagement while supporting their data science teams in collaborating with consumer-facing departments to configure and deploy an impactful campaign.
In addition, ecosystem. Ai provides various modules, including recommender systems, interaction science, productivity, enterprise solutions, and experimentation engine. Its platform offers 12 recommenders, enabling clients to handle messaging suggestions and deliver relevant recommendations on offers, engagement, and more based on a consumer’s preferences and recent activity. These are backed by an experimentation engine that allows companies to engage, measure, and analyze consumer behavior while enabling AB or multivariate testing on their products and services to evaluate how they interact with them.
These capabilities enable ecosystem.Ai users to seamlessly leverage a consumer’s identity and metadata elements to provide best-suited offers. This matchmaking process is powered by a scoring engine that trains new models for every customer, continually learning their propensities and interactions to deliver an ML algorithm that instantly produces offers aligning with specific demands. Unlike most classical ML implementations plagued by time lags, ecosystem. Ai scoring engine processes consumer and reward datasets.
Another pivotal ML system at the heart of ecosystem.Ai success is its behavioral engine, which detects a consumer’s personality type (intentional or experiential) to provide offers or messaging that creates an instant impression. This engine dynamically trains itself and tests the success rate of its evaluation capabilities before combining the insights with the prediction engine and recommender system to sell the right thing at the right time to the right people.
We assist organizations in analyzing human behavior using predictive technology, helping them adapt to evolving consumer preferences quickly and intelligently
Setting ecosystem.Ai apart from its contemporaries are its three core pillars; behavioral predictions, real-time deployments, and a low code environment. This multi-front emphasis enables it to facilitate continuous training for models along with the ability to customize them in a low-code interface without complexity. Through these competencies, ecosystem. Ai remains a leading player in the customer engagement landscape, designing technology that combines ML, behavioral science, and predictive technology to help businesses boost their consumer interaction, intervention, and engagement prowess.
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3 essential steps to detect customer happiness
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Company
ecosystem.Ai
Management
Jay van Zyl, CEO
Description
Ecosystem provides a low-code prediction platform that enables much key real-time interaction and prediction scoring approaches. The company supports both end-to-end machine learning model training life cycles where historical data is used and cold-start modeling using online learning and automated model training. Prediction cases include recommender systems, interaction, nudging, fraud detection, productivity enablement, etc. The company’s key differentiators are that it enables behavioral properties across predictions, use computational social science, and does all this in real time
Customer happiness is typically captured with customer satisfaction (CSAT) and Net Promoter Scores (NPS) measures. But research from Harvard Business Review shows that these ”fail to tell companies what customers really think and feel, and can even mask serious problems.”
One of the reasons for this is if we look at the concept of happiness in business terms, it’s measured largely on emotion. But emotions exist subconsciously and passively. So asking a customer to rate their satisfaction at a certain point in time barely scratches the surface behind how and why they feel like that.
Take for example a customer’s poor satisfaction rating for a product because they dislike elements of the advertising campaign. Or the influencer the company is collaborating with. The satisfaction score may have very little to do with the actual product itself. But the survey wouldn’t account for this or other external factors that could potentially impact their happiness.
If the customers’ happiness analysis took account of their behaviour and patterns, the real reason behind their poor satisfaction rating would be clear. This approach combines the quantitative aspect of emotion with the qualitative perspective of social sciences, specifically sociology.
Can we really rely on the accuracy of results from standard satisfaction surveys like these? And if not what’s the alternative?
Step 1. Track real-time customer behaviour
Consider the benefits of measuring your live customer data to identify their satisfaction levels. Receiving information based on your customers’ real-time behaviour and patterns, rather than their emotional responses to a written survey, can give you highly relevant insights and a deeper understanding of what your customers truly want.
This is the premise of dynamic experimentation which eschews the traditional focus of testing fewer options in the hope of finding the ‘one-size-fits-all’ solution. Why place limitations on the experiment from the outset?
Far better to present all options upfront and track customers’ behavior as it happens. Your customer happiness becomes evident from the rise and fall of interest. Allowing your business to rapidly adjust to real-time data analytics rather than use unreliable surveys based on people’s emotions at a certain point in time.
Step 2. Introduce novelty
It appears we’re all in the pursuit of happiness both in our personal lives and keeping our customers happy. But is too much happiness a good thing?
Don’t we need to feel different emotions in order to grow, learn and experience new opportunities that a perpetual state of happiness might miss?
By introducing novelty, we can create excitement and engage people far more. This increases customer happiness by instilling a strong sense of understanding and empathy. As humans, we’re programmed to be more inquisitive about using something shiny and new. Until we get used to it, and the novelty wears off.
This means you have to find the right level of novelty to keep people happy.
Step 3. Use low-code automation
Dynamic experimentation brings a new dimension to your understanding of how people engage and respond to things in real-time. The power to automate recommender systems and engagement digital technologies means you can learn at pace with people’s behavioural patterns. As people engage further with an experiment, recommenders use their feedback as input into the next contextual recommender. Meaning you can set layers of context and automate your engagement by just understanding a handful of initial behaviors in the community you serve.
Happiness is essential for a company to thrive and succeed. Applying a computational social science approach with machine learning application can catapult your company to the forefront of customer service, relations and sales.