HUMAN-CENTRIC INTERACTIONS

How much can you tell about your customers?

By 2025, 90% of customer interactions will be supported by AI. Clearly, companies are investing hugely in Ai to get to know their customers better, build loyalty and make more sales.

But are they seeing a return on their investment?

A recent report by Gartner found that 85% of AI projects fail to deliver. There are myriad reasons for this, but we believe that there are 3 fundamental issues that can lead to such high failure rates:

1. Deployment Complexity can skyrocket costs

Putting accurate and successful machine-learning projects into production is complex. The tools to create predictions, build recommenders and configure experiments are costly, complicated and require expertise.

Moving to a low-code, specialised deployment environment reduces these costs immediately, and gives business users more involvement throughout the lifecycle of the project.

2. Current predictions don’t account for ever-changing human behaviour

The human in the system is seldom accounted for. Even though machine processes are largely designed to target, speak to, and engage with humans; most of the patterns and correlations it identifies don’t tell you much about their behaviours. Using machine learning to understand the impact of shared rituals is poorly lacking in most deployments.

Ai needs to advance and factor in human behaviour and context. The extra layer of meaning this adds to the data allows companies to communicate more effectively. As well as offer more relevant recommendations, and understand what customers truly want.

3. Reliance on historical data doesn’t provide real-time insight

58% of successful machine learning projects take more than several months to deploy. Delays happen, which means that at the point of deployment, the data being analysed and predicted on is from the past. As stated, this can’t account for a customers changing behaviour, and crucially, it doesn’t factor in their most recent transactions.

Can a deployment based on historical data really predict what customers are going to do in the future? Consider the data from the last 3 years when the Covid pandemic influenced an un-referenceable series of data points. Can we reliably use this data to predict what customers want this year, or the next?

The platform is built on 3 foundational pillars:

Low-Code Environment

Our low-code platform increases your speed to market by reducing complexity costs

The platform has a no-code environment Workbench, as well as low-code environment Notebooks. Designed to be accessible to everyone in a business - from the Businessperson who needs to drive success. To the Data Scientists who require uncomplicated tools to untangle machine learning complexities. Aswell as Technologists who require flexibility in the form of code-based customizations that are easy to implement and integrate. ecosystem.Ai’s multi-faceted technology provides a single platform that works within your own environment, or in the cloud. It facilitates open collaboration for all team members to work on the same project, and keep track of all deployments.

Behavioural Analysis

Engage with people more authentically with pre-configured behavioural models

Generic data science answers the “how” through analysis of data points and trends. But ecosystem. Ai use Computational Social Science to take this a step further by asking the “why” and “what next”. Uncovering this deeper level of understanding reveals a multitude of untapped data patterns to identify real human behavioral nuances. One way to achieve this is by applying a digitalpersonality*. Segmenting by personality provides a better way to know an individual’s wants and needs, in a more personalized way than traditional segmentation.

Every digital interaction leaves a transactional trail that gives insight into the behaviours of a human. Analysing this trail as it plays out in realtime, helps you make better predictions about that individual. Companies can structure offers and engagements based on the rhythm and actions of a customer, while continuously testing and experimenting with novel offers to spark interest.

Real-time Predictions

Capitalize on emerging trends and ritualistic patterns with real-time analysis

Companies need to evolve. There must be frequent continuous cycles of experimentation to keep pace with changing behaviors. Our real-time engine makes this possible by giving your machine-learning models more accuracy and relevance, right now. Allowing you to change configurations, tweak engagements, and tailor offers and messages as customers interact with you.

Real-time means no more waiting for batch processes to analyze performance.You can let your customers reactions guide decisions as you view their real-time interactions. This is a game-changer as it takes the element of guesswork out.

With predictions there is benefit to using historical data. But in real-time you don’t necessarily need data to get started. Our experimentation process means you can start building relevant predictions with no data.

Experiment to get faster, more reliable insights into production

According to Gartner, only 53% of projects make it from prototype to production. But what if you could run live experiments with your predictions? Having no data does not mean you cannot make good predictions. Whether your data is tied up, behind red tape, unusable or nonexistent, experimentation is the key.

Our Data Science Experimentation Module helps you detect real-time changes in human behaviour without data. It uses an experimentation-based methodology, built with sophisticated functionality that takes time dependence, seasonal effects, and human rituals into account.

Using our experimentation modules, you can start testing while the traditional data science process is being completed. Furthermore, you can run multiple experiments in production to amplify the success of your predictions. Dynamic Experimentation is a framework that goes far beyond general AB testing. While the one focuses on testing few options in the hopes of finding the ‘one-size-fits-all’ solution. Our Dynamic Experimentation means you can find the right fit for all of your customers, by giving them exactly what they want, and not just the most popular opinion.

With Dynamic Experimentation, you can identify tangible evidence of offer takeup in a personalized customer basis. Learning from each customer which option suits them best, and which one they are most likely to engage with. This allows you to lower the costs associated with failed deployments and unpopular offers.

Continuous behavioral tracking and interventions through experimentation will catapult your business to success. The experimentation’s powerful real-time feedback system continuously learns from user interactions to enhance both system knowledge and actionable outputs. Therefore, you no longer have to decide on a single option to test.

Recommend the right things at the right time to the right people

Recommenders are commonly used by all major companies like Amazon, Spotify and Instagram, to provide offers, filter content and make suggestions. These have already moved on from the early days of ‘people who like this, also like these’, to now incorporate more personalization. But are they getting it right yet, and what’s missing?

ecosystem.Ai are advocates for appealing to the human as a changing being. We have structured our recommenders to always remember the human, by offering fresh, new options to customers to introduce an element of novelty. Our recommenders offer further enrichment to businesses by providing accurate and timely view of customer takeup. Real-time information combined with human behavioural analysis, allows for more personalized, targeted offers at pivotal times in the customer journey.

Engagements are enhanced, as the customers behavior determines the design, structure, amount of information, and wording that can be used. The recommender can then offer the best options depending on feedback from the customer in real-time.

Multiple recommenders and experiments can be run at the same time, all easily managed from one central location - the ecosystem. Ai Platform. Companies should always be experimenting. When you increase the frequency of experiments and deployments, you stay more current and more competitive.

*a digital personality is derived from digital interactions and provides a view of a human’s behaviour with your company, not their human personality.