OUR APPROACH

 DESCRIPTIVE ANALYTICS/REPORTING & BI

Our Reporting & BI suite provides actionable information through intuitive reports and dashboards to help executive and managers take informed business decisions. An E2E process starting from data collection, data validation and data quality and ending with effective data presentation and dashboards will provide you with a business intelligence layer and actionable insights.

Our solutions allow you to filter, sort, analyse and visualise data without involving BI and IT teams. The use of Business Intelligence and Business Analytics Market leaders’ tools and industry expertise combine to make reporting part of the strategic decisional process for several business issues and challenges.

The most common use-cases in Descriptive Analytics are:

ETL/DATA MANAGEMENT: move away from flat databases to meaningful information, to extract value from data and ensure the right business decisions are taken 

DATA ASSESSMENT: engage client stakeholders and explore client data sources to decide on the most relevant Business Use Cases and the most suitable analytics solution

DATA ENRICHMENT: use external and non-standard data sources to enhance the quality of the information owned and find the right customer profile 

MARKET BASKET ANALYSIS: find product affinities to build the right marketing strategy (price, promotions, advertising, …) and identify cross-selling opportunities

REPORTING/DASHBOARDING: our Reporting & Dashboarding suite provides actionable information through intuitive reports and dashboards, to help executive and managers to take informed business decisions

DIAGNOSTIC ANALYTICS/ROOT CAUSE ANALYSIS

This is a powerful methodology that allows you to optimise your processes by analysing the root causes of a given behaviour. Diagnostic Analytics consists of investigating a CSAT/NPS score or real behaviours like churn/complaints, revealing the root causes and quantifying the impact of certain variables. What-if scenarios are implemented with simulation tools to provide you with actionable insights that can increase NPS or generally improve operative KPIs. A consulting phase managed by our industry experts considers quantitative analysis findings to improve Customer Experience processes with long-term strategic actions.

The most common use-cases in Diagnostic Analytics are:

ROOT CAUSE ANALYSIS: seeks to uncover the root causes of customers behaviours and ensure the right business decisions are taken

MARKETING MIX MODELLING: estimates the (ROI) impact of Marketing Levers on Sales and implements the right Business and Marketing Strategies

TEXT ANALYSIS: extracts meaningful information (topics modelling and sentiment) from unstructured text, using a consistent, scientific approach and state-of-the-art algorithms

SEGMENTATION/PERSONAS: builds Customer-Centric segmentation and an ideal Customer identikit, to define the Company’s Positioning and Marketing Mix offer

PREDICTIVE AND PRESCRIPTIVE ANALYTICS

Our advanced analytics suite focuses on future events and behaviours to predict the effects of potential changes in business strategies. It helps brands in customer acquisition, up-selling, cross-selling, retention, complaints resolution and win-back.

We have technological tools and programmes for interactive forecasting and prediction of customer behaviour and preferences; we accordingly help brands align their sales and marketing strategy to increase sales, lead conversion, improve collections, reduce customer effort and retention cost. Predictive Analytics becomes Prescriptive Analytics with Teleperformance Knowledge Services because the prediction is targeted at the implementation of custom-oriented actions tailored to customer needs.

 The most common use-cases in Predictive and Prescriptive Analytics are:

Customer Behaviour Prediction: predicting Customer Behaviour (Up/Cross-Selling, Retention, …) to define the right strategies – tactics or strategic actions. Examples of applications are:

Sales Acquisition

Up-Selling

Cross-Selling

Complaints Management

Retention/Anti-Churn

Win-Back

 Volumes Forecasting: understanding the Root Causes of Volumes and improving Forecasting accuracy, to correctly plan the Workload

Survey Analytics / Bridging Models: enriching Predictive Analytics data sources and leveraging Market Research surveys to project information collected on a sample of Customers onto the entire Customer Base (e.g. purchase intention of a new product)

AI AND MACHINE LEARNING

Our Artificial Intelligence and Machine Learning expertise are primarily focused on developing computer programmes capable of performing human/agent-like computational tasks without human intervention, while serving the end customers along their customer journeys.

Thanks to our close collaboration and integration with the BPO operations of the Teleperformance Group, our early implementation of AI, ML and NPL was both in the development of AI-powered virtual assistants on voice and text-based channels, and in cognitive RPAs to automate customer-facing and back-office business processes.

As these technologies mature, we at Teleperformance Knowledge Services and Teleperformance Group are increasingly expanding the use of AI and ML in all customer and business application fields and are developing use cases in various industries.

Across all use cases, we are focusing on a small number of patterns that different clients can implement in combined mode for the most advanced applications of AI:

• Automation with cognitive RPAs and autonomous systems, in order to minimise human labour, e.g. collective memory, knowledge generation, data manipulation.…
• Conversational robots capable of interaction with humans via natural conversation and interaction, including through written forms, image, voice and text
• Decision-making and goal-driven engines to find optimal solutions to problems with a “try and learn” approach or to predict future outcomes based on learned patterns and insights from interaction, conversation data and other sources
• Personalisation aimed at developing a unique profile for individuals (e.g. employees, customers, users, …) in order to personalise the way brands and organisations engage them and market to them, in terms of caring, product recommendations, etc.
• Recognition of voice/speech, video, image and text or any other source of unstructured data, in order to identify, recognise and segment it for collecting insights