Artificial Intelligence & Data

Customer Lifetime Value (CLV) allows planning and performing decisions and strategies more efficiently based on the future value of your customer. You no longer have to look only at the past.

Thanks to a change in the retention process using CLV and Propensity to Churn models we improved client retention of a large Czech bank 4.9 times.

Solution

Customer Lifetime Value (CLV) represents the net present value of a future profit or revenue from a specific customer. Instead of measuring the customer’s past value, CLV focuses on their future potential that has a significant impact on business decision-making, for example in the following areas:

• Targeting marketing and retention campaigns;
• Determining the level of customer services;
• Design of new potential products/business plans;
• Planning and forecasts of the entire business; and
• Portfolio valuation.

Benefits

• Using CLV to target campaigns generates higher revenues.
• CLV has the potential to decrease the customer churn rate and allows to retain customers with a high future value.
• CLV improves the overall profitability of the organisation and return on investment of marketing campaigns.
• CLV can function as the primary indicator of business performance.

Method

At the beginning we define the purpose of the CLV model and the target. We discuss with the users what length of prediction horizon is the best, if we are more interested in the future revenue of a customer or the future profit (revenue net of costs). We are interested in the availability of reliable data on historical revenues with respect to the client and time unit as well as in historical costs with respect to the client and time unit. We need to know what instrumental variables should be included in the CLV models for the purposes of a what-if analysis.

In the second stage we start working with data that needs to be profiled, cleaned and accepted. We face the task of creating customer microsegmentation with the input of instrumental variables and predictors that will explain the present value. In this area we have achieved good results with regression trees with a high level of expert input. The right number of segments ranges from 10 to 100 based on the size of the data, so they really are microsegments developed specifically for CLV and not rough segments suitable, for example, for a service model. There are two segments with a special role: inflow (new customers) and churn (customers who have left). The quality of the microsegmentation determines the quality of the CLV model as a whole.

In the third stage we work with the matrix of transition between microsegments. In order to estimate the transition matrix, the whole client basis has to be retrospectively scored by the microsegmentation model for a historical period of time defined in advance. The probability of transition is estimated by comparing a customer microsegment in two consecutive periods and calculating the average for the entire historical period. The probability of transition to the churn microsegment represents the churn rate, which is a significant factor of customer value. The distribution of customers by microsegment is projected to the future using linear algebra and the theory of homogeneous Markov chains and their future value is discounted in the present. The setting of the prediction horizon and the discount rate are the two inputs from the client that determine how far into the future the CLV model looks. In other words, what importance the CLV model attributes to revenues from the distant future in comparison with revenues from the near future. The output is the allocation of discounted future values via the microsegment to the customer level.

In the fourth stage, CLV is visualised and interpreted using dashboards. At the same time a production scoring job is implemented that will calculate CLV changes and historise CLV development in time using fresh data. Now is the right time to use CLV for business decisions. For example, to build a business case on cross-sell, retention or collection. Using a CLV model we assessed the successfulness of the cancellation of little-used tariffs, the introduction of a new type of current account or the valuation of customer portfolio for the purposes of mergers and acquisitions.

The customer churn rate is a problem primarily in the highly competitive environment in saturated markets (finance, retail, energy). Many companies nevertheless underestimate the seriousness of this problem or incorrectly focus on the retention of unprofitable customers. There is, however, a verified and analytics-based road to success.

Thanks to proactive retention using the Propensity to Churn model we decreased the churn rate of one of the largest Czech banks by 41%.

Solution

Thanks to many completed projects in the area of retention, we have gathered knowledge of all factors that influence the retention of the right customer and retention success, and we have integrated this knowledge in the solution focused on customer retention. Our solutions are always adapted to specific client needs and may include management of retention teams, performance indicators, retention offers, predictive retention models and reports. We provide retention on an end-to-end basis and we have repeatedly helped many clients optimise performance and increase the focus on growth.

Other areas of use of retention

Cost of retention

We often encounter situations that companies are unable to assess how many customers they lose every year and how much money it costs them. This assessment is not trivial (although it may seem so), but it is a necessary first step to a well-managed retention process.

Retention dashboards

The customer churn rate must be not only measured but also regularly reported, trends must be measured in order to assess the efficiency of the retention process. This is what retention dashboards are for. We can design them, prepare data from them, create, deploy and train.

Competitor model

One of the most significant churn rate factors is the competition. It is necessary to know the competition and to identify the Company’s strengths and weaknesses in comparison with the competition. The competitor model calculates for how much a customer would get a particular product or service from the competition and how much they would actually pay for it (total cost of ownership) in comparison with what they really paid at “their” company. This calculation is a basis for smart customer communication, where arguments along the lines of “all that glitters is not gold” supported by numbers gain amazing persuasive powers.

Reactive retention

The first step on the long road towards efficient retention is usually the process of reactive retention, i.e. how to work with customers that are already about to leave or are threatening to leave. We look at the customer journey to see if the process covers all the leaving customers, on retention offers, on profitability by segment and offer and on communication channels. The typical output is usually the introduction of unified governance, enhancement of the range of retention offers, data consolidation, change in directing customers to offers and elimination of unprofitable offers.

Predictive retention

Predictive retention (or proactive retention) focuses on customers who have not left and are not threatening to leave, but many of them still are at various stages of the decision to leave. The goal is to identify the ones most at risk and select a strategy that will eliminate the risk of leaving as much as possible for the lowest possible cost. The key instruments are predictive models of propensity to churn (who will leave?), CLV (how much money would I lose?) and propensity to save (how difficult is it to convince them to stay?). It is necessary to realise that the risk of leaving does not concern only long-term customers, but the danger exists from the very beginning of the customer relationship. The predictive retention process therefore pervades the entire CRM process and should be well integrated therein.

Propensity to Churn (PtC)

PtC is a predictive model that is able to predict the risk of a customer leaving. It is built either as a binary classifier or as a survival model. It is a technical component of retention solution and it is not functional on its own. In order for the PtC model to work well, it is necessary to implement it in the client’s environment, connect the output to retention offers, deal with governance, set up reporting, train users and ensure regular maintenance of the model. We often come across situations where the client tried to develop a PtC model in the past without much success. Our recipe consist in correctly defining the modelling target and linking the output of the model to the execution of the retention campaign.

Propensity to Save

Propensity to save (PtS) is a predictive model that predicts the success of a retention event at the level of individual customers. What we generally perceive as success is when the client is saved, i.e. does not leave and stays active and profitable after a set period after the retention event has elapsed. PtS-type models have so far been something completely new for all our clients home and abroad, so we do not think it presumptuous to believe that Deloitte Advanced Analytics was the first in history to name, create and deploy this model. A PtS model is typically simple (only a small development sample is needed for its development) and must be updated quickly (the retention process changes dynamically throughout the project).

Customer Lifetime Value

The retention programme segments customers based on their value, propensity to churn and propensity to save. These three key indicators create the business case determining which retention strategy and which offer we will use for the particular customer. Customer value is based on the Customer Lifetime Value (CLV) model. Customer value is defined as the present value of all revenue (or profit) from the client over the period of the next several years (e.g. 5 or 10 years). CLV is the result of fine segmentation combined with Markov chains. Just like PtC, CLV is a technical component of the retention solution that needs to be wrapped in the process and execution, but unlike PtC it has a wide range of uses outside the retention area.

Retention governance

The basic condition for any retention process to work is that is has to be owned. And that it has to be owned only by one person, not by everyone and just a little bit. Retention governance is nothing more or less than an organisational change that prepares the company for efficient operation of the retention process.

Call centre analytics

The call centre is an important and indispensable communication channel in the retention process. As part of the retention programme we had to make many changes in our clients’ call centres. For example, to create a retention team, set up motivation, capacity model, information flows, prioritisation and direction of customers to the individual retention events and operations, coordination with the branch network. Since this is an extensive area and we have already gathered significant experience, we now offer call centre analytics as a separate solution. 

Retention offers

Advantageous savings, cash back for payments at retailers, preferential treatment of payments for energy sent from current accounts, lowest instalment guarantee – these are just some of the examples of innovative retention offers that are attractive for customers. From the long list of offers, the best ones have been distilled using customer surveys and practice shows that they have really shaken the old-fashioned world of free accounts.

Churn triggers

How should we even communicate with a customer who wants to leave? What do we know about them and what of it can we use during communication? Churn triggers are customer events that increase the likelihood that a customer will leave. These events are data-defined, regularly recalculated and saved in a database and paired with the customer profile, which serves as basis for the call script. These triggers offer the operator ammunition for leading an efficient conversation with a customer and increase the probability of saving the customer.

Segmentation/clustering is an analysis processing customer characteristics, making segments with maximum internal homogeneity and external heterogeneity.

The first step to an efficient and successful sale of products and services and relationship with customers? Knowing your existing and potential customers. Customers have different consumer behaviour, use products and services differently, have different contact preferences and many other dissimilarities. Segmentation allows you to find groups with similar characteristics in your customer portfolio and enable you to manage your relationship with them more efficiently and cost-effectively. If you focus on what customers really prefer and need, they will certainly be happier and more loyal. In addition, customer segmentation can be beneficial and useful for other departments of your company as well.

Using behavioural segmentation, we have improved service models for exiting corporate clients of a major Polish bank and determined the most suitable service for new clients. Financial impact of EUR 4 million per year.

Solution

The Advanced Analytics Deloitte Team has experience with creating and deploying segmentation in the B2C as well as B2B customer environment. Segmentation can work independently or it can serve as a predictor in other analytical models focused more deeply on specific business problems, such as retention, acquisition, cross-sell etc. Whether the solution to your request will be segmentation on its own or if it will be part of another solution always depends on an analysis of the specific request and the problem at hand. We will always recommend, create, deploy and test the best solution with the best result. We make sure that the selected segmentation method is transparent and its result brings maximum internal homogeneity and external heterogeneity of the segments.

Areas of segmentation use:

Customer acquisition

We apply non-client segmentation developed on external data on clients and measure profitability, loyalty and growth potential for each segment. This allows us to assess the profitability, loyalty and growth potential also for non-clients and adapt the acquisition campaign accordingly.

Customer cross-sell

We use the segment-of-one approach to cross-sell. This means that a customer represents an independent unit. We achieve this by estimating PtB models in rough segments, but each customer has an individual score from the PtB model.

Retention programmes

In the retention programme we segment customers based on their value, propensity to churn and propensity to save. These three key indicators create a business case determining which retention strategy and which offer will be communicated to the customer.

Service model

Customer segments with different revenue potential and different communication channel preferences receive different messages via different channels at different frequencies. The number of segments is given by a compromise between the requirement of highly individual service and overall service costs.

Customer Lifetime Value

In modelling CLV using Markow chains, the key input is a sufficiently fine segmentation explaining short-term income. The probabilities of transfer between segments then determine future scenarios from which we infer long-term income.

Dynamic pricing

The dynamic pricing solution is based on price elasticity. Price elasticity is individual for each customer, but at the same time it can be estimated accurately only using a statistical set of many customers. Customer segmentation represents a good compromise. The dynamic pricing solution includes one more segmentation – segmentation of types of goods based on their price and the shape of the demand curve.

Challenge​

By using computer-managed source discovery, content extraction, semantic analysis and threat/opportunity scenario detection to collect and analyze more than 10 million sources a month in more than 50 languages. This gives us a view of the vast majority of the internet media content and allows us to “take the pulse of the planet.”​

Being able to derive actionable intelligence from PAI using the latest advancements in Artificial Intelligence (AI), Natural Language Processing (NLP), and Machine learning (ML), completely changes the way analysts have to interact with huge volumes of unstructured data in a meaningful way. ​

Solution

Traditional analysts can spend hours perusing the vast amount of publicly available information (PAI) on the internet and even longer reporting on it. This process is labor intensive, expensive, and process-driven rather than data-driven.​

The challenge is to analyze and digest PAI, in a data-driven way, to derive actionable business intelligence in near real-time, 24 hours a day, 7 days a week. ​

Benefits

• Provides tools for analysis and visualization of news data in real-time​
• Enables to access a majority of internet media content ​
• Combines various tools and processes to interact with big volumes of unstructured data ​

Our services

SAP Analytics and Information Management

SAP Ariba enables organizations to streamline their spend-management and supply-chain processes. It allows for making timely and accurate business decisions while protecting the organization from potential risks.

Big Data Engineering

Big Data Engineering is the end-to-end solution that uses analytical models and predictive analytics to enable organizations to have a single point of true data while focusing on revenue generation and expense reduction.

Cloud Engineering

Cloud Services give an organization competitive advantage by designing and implementing optimal scenarios, increasing the ability to quickly adjust business processes within the cloud environment and reducing the costs for third-party provider services.

Process Mining

Process Mining uses advanced analytics to visualize and assess data from ERP and/or IT systems, enabling organizations to efficiently identify and control potential risks and issues while improving KPIs and reducing costs.

Targeting customers using predictive models can increase the efficiency of your campaigns. They help concentrate your energy and resources on customers who are more willing to buy a specific product or service.

Solution

Predictive models created using statistical methods are able to identify customers with higher willingness to buy (Propensity to Buy). Based on the use of these models in cross-sell and up-sell campaigns, priority lists can be created that the company can use to concentrate its resources on the most valuable customers, for example. Using the Net Lift approach (incremental response), we ensure that client targeting has a visible impact and that marketing costs are spent efficiently.

By deploying a Propensity to Buy model on a pension fund product we increased the conversion of a campaign of a medium-sized bank by 48 %.

Benefits for the company

• Increases the success rate (conversion) of cross-sell and up-sell campaigns;
• Increases the profitability of the organisation and marketing activities;
• Improves the quality of customer experience by optimising offers for customers in a way that reflects their current needs;
• Allows more efficient allocation and capacity use of communication channels.

Approach

Our starting point is the CRISP-DM methodology. First, we define the business problem, select models that will solve the problem, specify their inputs and outputs and determine their role in the business process. Then we focus on data that has potential for production operation of Propensity to Buy and we build an “ETL process” (extraction, transformation, load). The underlying data is transformed into the form of the so-called predictors, i.e. quantities aggregated at customer level for a given moment in time. The key variable is the “target” – indicator if the customer did or did not buy the relevant product. The table with predictors and the target serves as an input for the modelling stage, where we use advanced analytics algorithms (see below) to develop a model, i.e. predictors and a functional relationship, that explains the target as best as possible. We iterate the model several times and assess its performance, stability and inner logic. In the last stage, we implement the model in production, set up reporting, integrate in the business process and thus close the circle.

Areas of use of Customer Targeting

Uplift modeling

Do you feel that your PtB scores high almost always the same customers? That the same group is always hit by campaigns and the rest of the portfolio is unexploited? Then you probably do not have Uplift PtB models. The uplift concept (net lift, incremental response) is based on the assumption that our target is not the customers who will buy the product, but the customers who will buy the product as a result of a marketing contact. The campaigns then purposefully avoid the segments “sure thing” (will buy even without being contacted”) and “do not disturb” (contacting decreases the likelihood of purchase). Uplift modelling increases marketing ROI, but it requires a very high quality of CRM data on customer interaction.

Target

Target is a variable at customer level that the PtB model tries to explain. Example: the customer has a new credit card in the month of M+2 that has been activated (M is the current month). The correct definition of the target is key for the business use of the PtB model and also fundamentally determines its performance. We have already seen several models where the incorrect definition of the target led the business to refuse the model, even though the model itself was crafted flawlessly. We therefore give appropriate care to target definition, which has paid off many times already.

Machine learning

Customers often ask us: and what model do you use? The answer is not easy. We use a relatively wide range of models, because each problem requires a different model and there is not a universal one. This is confirmed by our experience and it has even been scientifically proven (refer to the “no free lunch theorem”). We often solve the dilemma of model performance and transparency – the highest performing models may be so complex that they cannot be validated from a business point of view. We decide according to the use case model together with the user. Our favourite binary classifiers include logistic regression, decision trees, random forest, gradient boosting, SVM.

Correct use of the Propensity to Buy models

There are many models that ended up closed in a drawer, because a good model on its own does not guarantee good customer targeting – it needs to be used correctly. Our team therefore includes a campaign specialist who is able to connect the result of the model to the business and thus elevate our customer targeting to an end to end solution unique on the market. This change provoked an unexpectedly positive response from our clients and we are now more secure in the knowledge that we bring our clients real added value.

Predictor Factory

Our development of PtB models is quick and efficient thanks to Predictor Factory. Predictor Factory is a specialised software for the preparation of predictors for modelling. It works with a database that contains input data in many interconnected tables. For a particular table with observations and a calculated target (the target table), it calculates all the possible predictors that can be calculated based on the other tables (based on an expandable set of patterns) and selects a reasonable number (approx. 1,000) of the best ones for modelling. In comparison with the traditional preparation of predictors (manual SQL coding), predictor preparation using Predictor Factory is:

• Faster (saving approx. 20 man days per model);
• Of better quality (the resulting model performs better);
• Flawless (no coding errors);
• Documented (predictor catalogue including the SQL code); and
• Flexible (if input data changes, it simply launches again).

Predictor Factory is a unique technology, we have so far not encountered a similar tool of a comparable quality in the world. We have developed Predictor Factory in cooperation with the Faculty of Information Technology of the Czech Technical University in Prague.

Real time marketing

Today’s highest goal for marketers is real time marketing. This attractive but at the same time feared discipline is now a solution that we have tested and can recommend. This demanding project requires the involvement and coordination of technology experts, data specialists, programmers, testers, business users, modellers and marketers. But the result is worth it. Real time communication and orchestration of many channels is something that will perfectly set you apart from the competition.

Challenge​

With the growing amount of processes, enterprise leaders face the challenge of truly understanding what is going on in their businesses.​

We can help to expose, deep-dive, and tame process complexities with advanced analytics. We can find, assess, pinpoint and remediate process-driven problems to improve KPIs. ​

Need a clearer picture of your enterprise's processes?​

Solution​

Process Mining uses advanced analytics to visualize and assess data from any ERP and/or IT system. It helps to eliminate process complexity by: ​

• Revealing an end-to-end view of processes​
• Drilling into and quantifying process issues at the activity and user levels​
• Providing benchmarks for internal best practices​
• Establishing a single version of truth to improve decision making 

The solution enables enterprises to identify, analyse and control process issues, waste, and risk to optimize sustainably in order to cut operating costs and improve KPIs.​

Benefits​

• Cross-functional interface​
• Single source of truth with fully fact-based insights​
• End-to-end evaluation of processes across system boundaries​
• Company-wide internal benchmarking based on comparable process KPIs​
• Multi-dimensional advanced drill-down for root-cause analyses​
• Automated process conformance check​

Challenge​

Geospatial data analysis is challenging. Whether for environmental studies, construction, or energy analysis, it is prone to erroneous data, time lag and labour-intensive analysis.  ​

Consider:​

• Urbanization
• Deforestation and regeneration​
• Construction and deconstruction​
• Earthquake damage ​
• Manufacturing activity ​
• Mining and drilling activity​
• Excavated material measurement​
• Subsistence​

Whether it’s for due diligence, site monitoring, or audit, you need a historical record of the changing landscape to make sense of the problem.​

Solution

SpaceKnow is a complete solution to historical, geospatial data analysis. Leveraging data from over 300 satellites orbiting the Earth, monitoring and analysing everything from urbanization to large-scale human migration, SpaceKnow is conducting ultra-large-scale and historical analysis of continents in near real time. Clients in public and private sectors can receive the data they need to make data-driven, informed decisions when they need it.​

Whether you’re a land developer interested in project progress, an environmental watchdog interested in terrain changes over time, or have a financial interest for due diligence, the right data can keep you one step ahead. ​

Benefits

• End-to-end data integration with your existing data pipeline​
• Personalized dashboards, metrics, and platform development​
• High location coverage, multi-resolution capability with visible and nonvisible analysis​

Rapid and widespread change can be unnerving for any organisation – even those founded on disruption. Digital, data and exponential AI technologies threaten the status quo but also offer the best opportunities to respond to the changes afoot. We help companies with their AI&Data strategy to support new business models and realise improved business outcomes.

Why building organisational AI&Data capability matters

Deloitte has been tracking the measures that organisations take to develop their data, analytics, and AI capabilities since 2017. Our methodology allows us to assess where organisations stand on their data journey and what barriers they face. Crucially we understand how they can gain a competitive advantage by using analytical insights in their decision making. In 2022, the Deloitte Insight-Driven Organisation survey collected responses from 263 organisations from around the world. Ranging in size, respondents were also spread across business sectors. 

The Benefits of an Insight-Driven Organisation

We’ve discovered that a structured approach is essential for achieving both increased pace and quality while creating and adopting analytics solutions at scale.

Our Insight-Driven Organisation (IDO) methodology is an internal asset aimed at helping organisations build and mature their data and analytics capabilities. An IDO’s capability goes far beyond technology – it considers strategic alignment, talent and leadership, business processes, and the entire information lifecycle as well as the systems associated with it.

Deloitte’s IDO framework consists of several different assets and accelerators. These range from a standardized maturity assessment with hundreds of benchmarking data points from organisations in different industries across EMEA to analytics and data use case value-assessment tools. In its entirety, our IDO framework helps define your organisation’s target operating model.

AI&Data discovery sessions

To ignite a strategic discussion of AI&Data topics in your organisation, we offer an AI&Data discovery programme. Focused on company leadership, this programme provokes strategic discussion, inspires possibility, assesses best use cases, and uncovers barriers in the way of successful AI adoption. 

Challenge​

Supply chain planning is more difficult than ever before​ because of changing shopper preferences, continuing e-commerce growth and unforeseen disruptions.​

For mature organizations, planning is more than just creating a unit plan for replenishment. It could include creating insights into capacity or upcoming labour requirements, providing inputs into joint planning with suppliers, or making longer-term planning decisions on network capability.​

Solution​

The Algorithmic Supply Chain Planning (ASCP) platform is an end-to-end solution for retail organizations. 

The platform has an intelligent forecasting engine covering standard statistical forecasting as well as artificial intelligence and machine learning for predicting the evolution of sales. The platform supports the decision-making processes of stores or distribution network replenishments based on the forecasts, and it provides predictions of impacts on the whole business. The platform enables setting specific managerial goals for on-shelf availability at the company, at a unique store or at the level of a group of stores.

Based on those targets, the platform can optimize inventory levels and order quantities for each individual product. This results in higher on-shelf availability (service level), improved forecast accuracy, lower stock keeping levels and reduced ordering effort. Accurate prediction of all KPI improvements can be simulated in advance. Lastly, our platform can provide a connection between strategic decisions, different constraints and realization on the operational level in the form of long- or medium-scenario planning. 

Benefit

The five benefit areas are: revenue lift, margin improvement, On-Shelf Availability (OSA) improvement, inventory reduction and decrease of working capital.

Based on 100+ successful implementations, sales improve by  3-5%, margin improves by 2-7%, OSA improves by 99%+, inventory is reduced by 25% and working capital decreases by 20-30%.

A suite of AI-related managed services including managed analytics, RPAaaS, Insights as a Service, co-sourcing. Creates, builds and owns Deloitte’s Digital FTEs.

Our services

Smart Factory

Connecting all data, systems and devices opens up the possibilities to recognizing new opportunities for improvement.

Third Party Risk Management

TPRM enhances organizations' procedures with screening and monitoring of relevant data on a global scale to provide meaningful and high quality reporting focused on the organizations' strategic needs.

This is what we hear too often. We have helped our clients overcome those troubles and the lessons learnt are embedded into this unique offering for every company, no matter big or small.

Building Insight Driven Organization is a programme for companies willing to make a strategic advantage of data science. It focuses on its appropriate use, internal engagement, talent management and technology or data administration.

Solution

Data Science stands on firm links between business, data and analytics, three areas which are often too far from each other within the organization. Data science needs to have strong leadership that connects the analytical forces spread around your organization in a functional community.

Building Data Science is a project where we help you establish such a unit. We support you in the whole set-up by using our technical experts, data experts, architects and change-management experts.

The key areas are:

• Data: Are you able to leverage all of your available internal and external sources?
• Analytics: Are you able to analyze big data sets without using samples?
• Visualization: Are you able to visualize outcomes of your assignments to provide meaningful insight to the business?
• Team: Is your team well-structured with the right skills?
• Platform: Are you using the right platforms and are you using them well enough?

Many companies have made significant investments in HR technologies to manage their day-to-day transactions but just a small number of companies use these aggregated terabytes of valuable data to make meaningful data-driven decisions.

However, the situation is changing and a growing number of executive teams are adopting advanced analytical techniques. As part of a broad workforce planning strategy, analytics can be instrumental in creating a more fact-based approach to solving complex workforce issues. Our research shows that companies that are ahead of the game in this area are doubling their improvements in recruiting, tripling their leadership development capabilities, and enjoying 30 percent higher stock prices than their peers. (Deloitte: Global Human Capital Trends 2014)

In Deloitte, we believe that being a step ahead and successfully leveraging analytics and big data is essential for organisations seeking to enhance HR’s role in business decision-making and outperform their peers in executing their talent strategies.

Benefits of HR analytics

• Uncovering the hidden drivers of retention
• Understanding the characteristics of high performers and predicting the risk of their leaving
• Prediction of workforce demand
• Prediction of employee success and tenure
• Understanding the impact of pay increases in detail to make more scientific decisions about where to invest to maximise performance

📄Workforce Analytics – The three minute guide - Download PDF⬇️

Challenge​

Automation, robotization, sensors and scanners are nothing new in the world of manufacturing. However, they alone are not enough to create an interconnected, organic and ever-evolving eco-system like the Smart Factory is. Without such eco-systems, manufacturers will not be able to keep up with the competition and may face existential challenges. Many manufacturers recognize this fact, and therefore they are increasing implementation of Industry 4.0 initiatives

One of the main challenges we see in the CE region is the gap between the data companies are harnessing from their production and supply network and how they actually exploit the data to have a real time or near real time view of their supply chain and unpredictable costs combined with uncertainty in Return on Investment.

Solution

The Deloitte Smart Factory difference is in the approach of thinking big, starting small and scaling fast. We design a game plan with achievable phases and apply powerful technology that leads to quick wins and big insights.

Benefits

• Productivity increase
• Cost decrease (maintenance, warranty, scrap rates, downtimes, etc.)
• Capacity optimization
• Workforce shortage resolution
• Better product offerings
• Faster demand reactions

The share of energy costs in global GDP is approximately 10% and comprises a significant portion of variable costs incurred by companies in many industries. With an active approach to prediction, procurement and optimisation, companies may directly address these costs and reduce them.

Thanks to the prediction of energy consumption, we assisted a major railway company in decreasing sanctions for a deviation by 99%.

Solution

In combining client historical data and the knowledge of external factors, such as weather conditions or exceptional occurrences, it is possible to make use of mathematical and statistical models and make a precise estimate of the future energy requirements. In implementing this solution, we use a data warehouse for storing information, analytical software for statistical analysis and an application with a user interface customised to reflect the client’s needs, thanks to which the client may model and visualise potential future results.

Approach

Deloitte adapts the quantitative analytical consumption model to each client. The models may be implemented either in the client’s system or provided in the form of an automated message. Once the model is active, it may be easily adjusted to reflect the client’s or the business’s requirements as they develop.

Statistical modelling and customer analytics have been traditionally associated primarily with the B2C world where there is sufficient data and the law of large numbers may work well. However, it no longer holds true that there are only few customers and data in the B2B world. Data-managed decision-making and the data science concept are penetrating B2B ever deeper, with concepts tried and tested in retail also working well in respect of companies. Deloitte has lately recorded an increase in the demand for B2B analytical solutions and has brought one of its own.

A six times greater sales success rate and fifty times lower customer churn in the SUPERACTIVE segment as opposed to the NON-ACTIVE segment. Behavioural segmentation for the SME portfolio of a major Central European bank.

Approach

Our B2B analytical projects usually start with a discussion of the different analytical tasks on offer, the objective being to select a few with a sound business case. Data consolidation and visualisation, basic revenue portfolio segmentation, non-structured text mining from contractual documentation, propensity to buy (PtB) models, customer wallet models, credit limit optimisation (CLO) and credit risk models (PD models) are but some of the most frequent tasks. When tasks have been selected, the process enters the next stage, in which scoping takes place. Scoping entails the estimating of labour-intensiveness and the planning of resources, inputs and outputs. After this comes regular project management. A lengthy yet highly important phase of preparing, consolidating and cleansing data takes place, with algorithms subsequently applied to the cleansed data and predictive models created. The models are assessed in terms of performance and business logic. Based on the performance achieved, the business case is updated. After the resulting models have been accepted by the business, we implement them in production and integrate them in the process, and we develop reporting. Throughout the entire duration of the project, we hand over our knowledge to the client’s internal team – for more information refer to Building Data Science Capability. In the production phase, we perform what is referred as “baby sitting”, where we monitor the process as it unfolds, remove any imperfections and specify the business case based on actual results. When everything is running smoothly and no improvements can be made, we hand over the whole solution to the client.

Challenge

Today’s markets are more competitive than ever before and delivering tailored customer experiences and proposals are becoming the new battlefield.​

The modern customer experience is more about data than  about design. The idea behind personalization is to tailor the user experience from the moment it begins. ​

Solution

The personalization engine is scalable piece of software capable of suggesting highly relevant products as Next Best Offer (NBO). In terms of pricing, a combination of profit and affordability will be estimated. Ranking and calibrated probabilities are calculated, and the models are updated in real-time, therefore they do not get obsolete.

Deloitte’s end-to-end offerings combine access to world-class IP including the source code, an exceptional machine learning algorithmic portfolio and advanced analytics skills with expert support and training for tools, governance, process, talent and KPI.​

Benefits

NBO, Next Best Action (NBA), next best content, etc. (NBx) probabilities guide sales efforts, promotions, product and other recommendations, both in digital and physical touchpoints.​

Scoring, calibrated probability, affordability and profitability estimations can deliver a 3-5% revenue uplift, while simultaneously having a positive impact on customer experience.​

Challenge​

In a global economy, the risks are global as well. The risk could be an issue with one of your suppliers that is affecting production, or it could be the health and safety issues of one of your business partners are impacting your brand’s integrity. Events like these could have significant consequences for your company. We understand that businesses facing today’s market risks might only be equipped with traditional risk procedures. Though they may be robust, well-tested, and familiar, they may lack speed. Red flags might take months to alert you. In the fast moving world of today, timely insights can make all the difference for your decision making. ​

Solution

Enhance your existing procedures with screening and monitoring. We are able to vet new third parties that you are considering doing business with as well as continuously monitor your existing business partners. ​

We use around a half a million vetted online news portals to look for adverse media from global household names and small local ones. To make sure the search is as broad as possible, it is independently performed in twelve languages using machine learning and natural language processing.​

To make sure you only get the insights you need, we leverage a team of intelligence experts that will prepare reports for you. They work with machine learning models that enable them to focus only on relevant news and to provide the quality and speed that you need.​

Benefits

• Near real-time data collection – 1 million articles per day​
  
• Automated identification of threat events in the articles’ text​
• Many documents combined into one event, which reduces the time needed for intelligence analysis​
• Feedback loop into the threat models that enables constant improvement by providing only relevant data to the client and enables robust decision making​

Customer Retention

Customer retention can assist you in understanding your disengaged customers, help identify which have the highest probability of leaving and assist you in creating and managing your customer retention.

Solution

Customer retention is an end-to-end solution. It helps reduce customer churn and determine the cost of retention. It can also include preparing a retention dashboard or setting up a reactive or predictive retention using the PtC, PtS or CLV models.

The customer churn rate primarily is a problem in highly competitive environments in saturated markets (finance, retail, energy). Many companies nevertheless underestimate the seriousness of this problem or incorrectly focus on the retention of unprofitable customers. There is, however, a verified and analytics-based road to success.

Thanks to proactive retention using the Propensity to Churn model we decreased the churn rate of one of the largest Czech banks by 41%.

Customer Targeting

Many companies suffer from ineffective cross-selling campaigns. Customers are bothered by the offers, and the campaigns are not performing well.

The Customer Targeting approach enables company to get the best possible scenario with a fixed marketing budget. It includes decisions about which product the customer is most likely to buy, as well as the preferred communication channels.

Solution

Predictive models created using statistical methods are able to identify customers with higher willingness to buy (Propensity to Buy). Based on the use of these models in cross-sell and up-sell campaigns, priority lists can be created that the company can use to concentrate its resources on the most valuable customers, for example. Using the Net Lift approach (incremental response), we ensure that client targeting has a visible impact and that marketing costs are spent efficiently.

By deploying a Propensity to Buy model on a pension fund product we increased the conversion of a campaign of a medium-sized bank by 48 %.

Customer Lifetime Value

Not all customers are worth the same. The Customer Lifetime Value (CLV) is a crucial input for your retention offers, changes in your services model or changes in your product portfolio. CLV focuses on your customers’ potential for the future, not on their value and profit from the past.

Using CLV metrics improves the overall profitability of the organization and the return on investment of marketing campaigns.

Solution

Customer Lifetime Value (CLV) represents the net present value of a future profit or revenue from a specific customer. Instead of measuring the customer’s past value, CLV focuses on their future potential that has a significant impact on business decision-making, for example in the following areas:

• Targeting marketing and retention campaigns;
• Determining the level of customer services;
• Design of new potential products/business plans;
• Planning and forecasts of the entire business; and
• Portfolio valuation.

Thanks to a change in the retention process using CLV and Propensity to Churn models we improved client retention of a large Czech bank 4.9 times.

Challenge​

Analytics is being redefined as organizations adopt new business models and strategies. Existing analytics and BI alone cannot satisfy current business transformations towards data-driven culture. ​

To be competitive, companies from various industries (Telco, Automotive, Finance, Retail, Healthcare, Energy) are enabling new customer-oriented business cases using high volumes and different types of data from newly available sources. ​

Solution​

• Establish modern infrastructure with different tools and Big Data platforms, such as PaaS​
• End-to-end solutions using process, governance, incentives and analytical models​
• Design and develop a Data Lake in the cloud or on-premises​
• Data transformation application development​ and Data Cleaning​
• Stream analytics and event-based Big Data solutions​

Benefits​

• Big Data Platform targets new business cases and enables companies to have a holistic customer view.​
• Big Data Platform provides a single point of truth for companies’ data​
• AI, in combination with Big Data Platform, applies predictive and prescriptive analytics, which enables new sorts of cases that focus on revenue generation and expense reduction.​

Our Voice-to-Text Analysis helps you to understand how your call centre works. It informs KPI optimization and measures its impact on your business.

The solution makes possible:

• For call centre directors to sleep soundly at night;
• To increase the availability of call centre employees;
• To increase the success rate of calls;
• To speed up the solving of incoming calls;
• To decrease call centre costs;
• To increase the profitability of call centres;
• To optimise call time, call duration and the quality of communication; and
• To optimise the capacity of operators and the transfer of knowledge.

We have assisted the internal call centre of a major Czech bank in doubling the sales success rate of its incoming calls.

Solution

Our Voice-to-Text Analytical solution includes:

• Conversion of voice files to plain text data and metadata;
• Emotional speech analysis;
• Analysis of the unstructured text, conversion of free text to structured data;
• Content analysis - topic and pattern analysis based on advanced linguistics;
• Semantic analysis - sentence analysis, lexical structure, sentiment analysis, lemmatization (standardized form of each word), n-grams, term-document matrix;
• Annotations – identification of entities like date, time, amounts, names, phone numbers, etc., using regular expressions, composite annotations (matching ontologies).

Our solution delivers on textual patterns leading to successful calls with a new sale to, or retention of, a customer. We analyse the emotions of operators and customers during the calls, as well as the impact of emotions on the result of the calls. The final analysis includes call patterns about hesitations, interruptions and silence. All of this delivered in a graphical report.

Challenge​

Enterprises recognize the potential that lies in data analytics and AI. For the past few years  they have been heavily investing into analytical  teams and capabilities. However, the more mature an enterprise becomes, the harder it is to evolve. That is why executives seek market benchmarks and best practices on how to leverage Analytics and AI capabilities to succes.

Typical challenges:

• Overloaded with ad-hoc tasks that  do not generate ROI; 
• Poor data quality is impacting the results; Technology landscape is  becoming more and more complex; 
• Disconnect between analytics and the business; 
• Unrealistic managerial expectations and slow decision making.​

Solution​

An IDO builds a culture of asking the right questions and constructs an ecosystem of data, digital, cognitive and innovation capabilities to support an analytical culture. A truly insight-driven organisation is one that uses its data to underpin decision-making at every level, across the organisation. Raw data is transformed into insights that are embedded into the business’s processes and surfaced in the right way at the right time. This means they become integral to decision making – both operational and strategic. Deloitte provides complex support on journeys toward becoming IDO.​

Benefits​

• Transform a growing volume of data into measurable business value​
• Gain lasting competitive advantage from existing data assets ​
• Improve the speed and quality of decisions, while reducing the costs of decision making​
• Shift insight-driven decision making from an executive-level pursuit into an all-employee pursuit​
• Harness the power of AI and utilise it to generate tangible benefits​

Are you dealing with questions of where to locate a new branch, in which area to target new customers, or how to optimise business in the context of the space? We are happy to help you!

Challenge​

An increasing number of companies and financial institutions are moving their activities from physical offices to web-based processes. This trend has been strongly accelerated by COVID-19 and the consequences of the enforced restrictions. Documents previously verified and scanned by employees are now being uploaded directly online or sent via email.​

Such a remote process is prone to forgeries on the clients’ side. Some clients try to improve their credit score or even attempt to commit fraud by manipulating the electronic documents before sending them. The repetitive nature of document investigation leads to human errors, unproductiveness and tends to become unpopular among employees and firms at the same time. In many cases it is also impossible to detect advanced fraud techniques with a naked eye.​

Solution

The solution consists of machine learning and decision-based modules that calculate the overall "Trust & Risk Score" of a document. It automatically inspects digital documents for signs of manipulation and raises an alert when a modification, file corruption, or other anomaly is found as well as marks documents that can be trusted and do not require further investigation anymore. Results of the model are delivered in a few seconds via several channels such as GUI, REST API or UiPath platforms.​

Benefits​

• Increased fraud detection​
• Strongly reduced manual labor and repetitive tasks​
• No human error​
• Reduced costs and processing time​
• Very low number of false alarms​

Challenge​

Business and technology relentlessly propel each other forward. Cloud technologies have quickened that process, facilitating a continuous process of iterative change that can lead to competitive leaps. In this environment, market disruption is possible any day, every day. ​

The only question is: Will you be the one to disrupt, or will you be disrupted?​

Solution​

Some see cloud as technology. At Deloitte, we see it  as possibility — the power to put insight at the heart of your business, the agility to put invention into the hands of your people, and the speed to delight your customers. ​

Deloitte will help you to overcome barriers on your way to the Cloud. We analyse existing IT ecosystems, design and create optimal scenarios, design transformations into the cloud environment, and finally recommend optimal cloud providers.​

Benefits​

• Reduce cost of physical infrastructure by contracting for the service from a third-party provider.​
• Access to updated applications and setup.​
• Scalability – As requirements grow, the cloud can expand.​
• Higher productivity and capability to quickly adjust business processes, which has a major impact on a company’s gross income​

Customer Analytics

Having infinite options regardless of what product or service customers are looking for, it is crucial to understand their buying habits and behavior. Customer analytics enables businesses to deliver relevant offers that attract vast majority of their customers. It consists of three main pillars – Customer Retention, Targeting & Lifetime Value. 

Actionable Customer Segmentation

Many companies use segmentation to better understand their customers. Unfortunately, segmentations are often difficult to make actionable. Do you know what drives your customer from low-value segments to high-value segments? Actionable Customer Segmentation provides this kind of insight.

Building Insight Driven Organization

Building data science capability is a programme for companies willing to make a strategic advantage of data science. It focuses on its appropriate use, internal engagement, talent management and technology or data administration. 

Voice-to-Text Analytics

Do you struggle with the quality of your customer care, with how call conversion rate translates to sales and opportunities? Do you want to understand the topics of conversation, as well as sentiments from engagements with your customers?