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With big data comes data analytics – the science of analysing raw data in order to make conclusions about that information.

Data analytics can reveal trends and metrics that would otherwise be lost in the mass of information. This information can then be used to optimise processes and systems to increase overall efficiency and efficacy. 

In order to do this, we inspect, clean, transform and model data with the goal of discovering useful information, informing conclusions and supporting decision-making.

The ability to derive prospective analytics out of your conversational data is a journey.  Our suggestion is a Pathfinder Project where you can identify how and why you would apply this.  
Subsequently, we can work together to capture the right data to support your corporate ambition.

We’re in a time where more people and organisations are interacting through the digital channel: conversation is the new User Interface.

The sheer volume of conversations with users and customers through messengers, smart assistants, smart speakers and the Internet of Things is staggering and the data overwhelming.

Conversational analytics is a primary need for organisations who want to get to know their audience and provide a better user experience.

As psychology and communications are so closely intertwined, we are able to not only looking at communications data but also psychology data.

Data analytics tends to be broken down into four basic types.

  • Descriptive analytics asks ‘what happened?’ – a retrospective view that tends to track traditional indicators (e.g KPIs)
  • Diagnostic analytics supplements descriptive analytics and asks ‘why did that happen?’ as performance indicators are further investigated
  • Predictive analytics are predominantly trend-driven as near-future predictions are made
  • Prescriptive analytics helps to make the decision about what to do next.  Using Machine Learning to spot patterns in large datasets, data-driven decisions can be suggested

We capture hard information from the first four analytic elements.  Prospective Analytics is based on watching for outcomes to show key decision makers all available options for changing the current state, as well as any associated consequences.

In short, while retrospective analytics are good at identifying problems, and predictive analytics are good at anticipating problems, the prospective approach delivers its value by validating the gut instinct of your people with real-time, evidence-based solutions to problems.

One advantage of prospective analytics is that it can integrate the multiple variables associated with each user and customer, and identify likely outcomes based on existing data and past analysis.

Whether this is sales, customer service, loans, or mental wellbeing, prospective analytics can give you a new level of insight.

Applying Prospective Analytics

Prospective Analysis permits the assessment of complex processes and systems that can be fraught with potential risk as a result of human error.

There are many ways in which your organisation can use conversation and psychology analytics:

  • Fraud & Risk
  • Security & Hacking
  • Transportation
  • Risk Management
  • Public sector budgeting
  • Customer interactions
  • City planning
  • Sales process management
  • Public healthcare
  • Public health strategy
  • Energy management
  • Employee benefits
  • Employee attraction, recruitment, retention
Litha invests in extensive research to create leading-edge conversational AI.  Whether it is through text or voice, you want to have an environment that greatly engages with end-users but also satisfies your own technical compliance.  Feel free to speak to us about our Conversational AI Platform and we will have one of our lead scientists to talk through the technology.