Insightfully picturing your customer base, enabling optimized decision-making for enhanced customer experiences and sustainable growth
For some time, segmentation schemes have been the means of uncovering characteristics that define different personas in enterprise customer bases, often conceived as the cornerstone of customer centricity. Segmentation schemes may be formed by business rules depicting an organization’s fundamental customer management strategy, or get developed leveraging algorithms’ unsupervised techniques, thus uncovering hidden insights regarding habits, needs and preferences. They can be based on customer value, behavior, or both. Being the data driven answer to customer understanding, customer segmentation schemes are the first step of an analytical-CRM roadmap, while the output of their combination with other customer analytics assets, leads to efficient campaigning practices, forming attractive journeys that convert to enduring customer loyalty.
Utilizing data and analytics methods facilitates measurable and repeatable customer acquisition approaches, leading to a consistent market addressing. As a result, the target of attracting the right customers is met, gradually decreasing the average cost of acquisition. The combination of insights generated from customer behavioral segmentation schemes and open market data, drives customer base growth of higher quality, foreseeing increased customer lifetime value.
In the digital era, enterprise databases are full of customer footprints generated from tracked customer journeys. More than ever, companies have the chance to grow customers acquired. Data and advanced analytics techniques can facilitate customer growth by all means, from serving personalized onboarding journeys, to offering products that fit customer needs and driving customers to higher value tiers. Advanced analytics methodologies vary, from cross/up-selling propensity models used to boost specific offerings’ sales, to association rules used to drive combo or sequential purchases, SNA to leverage virality and more. The combination of such analytical assets’ scores, with segmentation schemes’ outputs, guides for higher campaign performance.
A universal accustom: acquiring a new customer costs more when compared to retaining an existing one. Although customer retention is usually linked with the long and commonly used churn prediction models, the value of leveraging data and advanced analytics methods gets drastically multiplied when used to uncover the labyrinthic paths leading to attrition in each industry. Preventing churn in several industries can become a tricky exercise, as accurately predicting customers that show tendency to fall in a specifically defined churn event, doesn’t necessarily mean considerable reduction in overall attrition rates. A well designed, implemented and deployed analytical solution for churn management goes well beyond a propensity model and can hugely enhance a company’s retention strategy, driving market leadership.
Within a Next Best Action mechanism, lies a company’s “go-to customer” strategy, combining growth and retention initiatives, in a personalized approach. An enterprise’s next move towards a customer, whether triggered real-time or on batch mode, takes into consideration an event - or a set of events - a customer has “touched” on, while at the same time gets differentiated according to each customer’s scores available by offline analytics assets, like segmentation schemes and propensity models. A well defined NBA solution, enables CRM teams to set business rules, triggering campaigns based sociodemographic, customer experience and geolocation data, apart from the classic behavioural data generated by customer transactions.
The Customer Life-Time Value metric adds financial elements in the Customer360 view generated by analytics applications, like segmentation analysis and propensity models for cross/up-selling or/and churn predictions. A customer’s current and potential value can be only partially informed by product ownership and the likelihood to buy new products, or to attrite from existing ones. Elements like cost-of-acquisition, after-sales customer handling costs, a customer’s tenure and more, are important factors for the calculation of CLV. Being a financial projection, CLV requires informed assumptions, in order to estimate the profit margin an enterprise expects to realize out of its end2end business relationship with each customer.
RISK MANAGEMENT ANALYTICS
Risk Management is an indispensable and ingrained component of the Financial Services industry, as the modern economy’s climate has exemplified the adverse implications of non-performing loans, underscoring the critical importance of credit risk assessments, such as Probability of Default (PD), Loss Given Default (LGD), Early Warning Systems (EWS) and others.
To ensure lean everyday handling of risk management and regulatory requirements’ fulfilment, financial institutions require well-structured and dynamically updated data and KPI universes, ready to feed predefined algorithms and all sorts of scoring processes, as well as ad-hoc analyses, covering the fields of IFRS9, ESG, Stress Testing, ICAAP and more. Looking at the wider Credit Risk Management landscape, our specialized team provides design and implementation services, covering credit risk assessment and analysis, portfolio risk management, credit risk modeling, stress testing, scenario analysis, capital adequacy assessment and more.
Our long-standing experience in the frameworks of IFRS9 and Basel, positions us to serve as a trusted partner of financial institutions seeking respective compliance. In this context, we recognize the importance of ESG factors in the modern scheme of credit risk management, assisting our partners in incorporating such factors into their decision-making processes.
Financial obligations’ defaults – whether related with loans or bills – are inevitable, but there are ways to recoup a portion of outstanding balances through effective collections management.
Achieving quantifiable progress, while directing resources towards high-priority and high-potential areas, necessitates developing an impactful plan for handling delinquent accounts efficiently.
Optimizing debt recovery efforts, while making astute financial decisions, should be at the heart of such operations’ goals. Via the application of clustering and propensity modeling techniques, Payment Cure Modeling methodological approaches, enable bad debt management through differential treatment, shifting customer bases to improved billing performance, rather than to bad debt provisioning and write-off. Cash-flows rationalization is met, leveraging deeper insights in customer willingness vs. ability to pay.
Fraud detection is the process of identifying known or unknown fraudulent events within organizations’ transactions’ streams and is an essential aspect of risk management, particularly in sectors such as banking, insurance and government, where the potential of financial loss is high.
The primary goal of an effective fraud management strategy is to detect and prevent fraudulent activity before significant financial losses occur, while maintaining customer service and experience at the highest possible level. The utilization of advanced analytics techniques, combined with industry/business rules, forms an important accelerator for successful fraud management practices, enabling officers to constantly focus on true “red flag” cases, driving low risk decision-making and appropriate actions.