Organisations today are collecting data at every interaction, via digital clicks, mobile app usage, social media interactions and more, all contributing to a unique data fingerprint.
Yet at some (not-too-distant) point in history, the idea of customers sharing information such as what they ate, what time they woke up, or where they went on holiday, would have been considered completely bizarre.
However, customer social norms have changed and with that expectations have escalated. In the wake of National Data Protection Day 2017, Yasmeen Ahmed, Business Analytics Leader and Practice Partner at Teradata, outlines the five ways organisations can use data and analytics to drive positive business outcomes, while still maintaining and facilitating the highest standards of data protection and privacy.
Anticipating Needs & Proactivity
Organisations today are under increasing competitive pressure to achieve sustained growth and not only acquire customers but also understand their customers’ needs and wants in order to optimize customer experience and develop long term relationships.
By sharing their data and allowing relaxed privacy in its use customers expect companies to know them, make interactions relevant, and provide an easy, consistent, seamless experience across all touch points. The goal is to provide a seamless customer experience for designing interactions and reacting consistently to individual behaviours for the purpose of meeting or exceeding customer expectations, thereby, increasing customer satisfaction, loyalty and brand advocacy. Customers expect companies to know them across touch points and as a result, companies need to capture and reconcile multiple customer identifiers such as cell phone, email and address, to one single customer ID.
Customers are increasingly using multiple channels in their interactions with companies, hence both traditional and digital data sources must be brought together to understand customers’ behaviours and needs. Customers expect and companies need to deliver real-time, contextually relevant experiences.
Ensuring a secure environment – Mitigating Risk & Fraud
Security & fraud analytics encompasses the execution and optimization of all activities involved in protecting all physical, financial and intellectual assets from misuse by internal and external threats. Efficient data and analytics capabilities will deliver best-in-class organisational security and fraud prevention.
Effective deterrence requires mechanisms that allow companies to quickly detect potentially fraudulent activity, identify and trace perpetrators and anticipate future activity. Use of statistical, network, path, and big data methodologies for predictive fraud propensity models leading to alerts will ensure timely responses triggered by real-time threat detection processes and automated alerts and mitigation.
Data management alongside efficient and transparent reporting of fraud incidents will result in improved fraud risk management processes. Furthermore, integration and correlation of data across the enterprise can offer for a unified view of the fraud across various lines of business, products, and transactions. Multi-genre analytics and data foundation provide more accurate fraud trend analyses, forecasts, and anticipation of potential future modus operandi and identification of vulnerabilities in fraud audits and investigations.
Delivering Relevant Products & Offers
Products are the life-blood of any organisation securing a relevant and sustainable future and are often the largest investment many companies make. The role of the product management team is to recognize the trends that drive strategic roadmap for innovation, new features, and services.
Effective data collation from 3rd party sources where individuals publicise their thoughts and opinions, combined with analytics will help companies stay competitive when demand changes or new technology is developed as well as facilitate anticipation of what the market demands to provide the product before it is requested.
Read more: Big data: It may be the new oil but have public failures damaged adoption?
Product data management is the strategic process of analysing market performance to define, design, source, assort, flow, price, promote, present and manage the sales and profitability of products that customers want to buy. It provides a foundation for incoming revenue, profitability and market share growth. Core to brand building are distinguishable products that will help attract customers and maintain loyalty.
Ensuring a Personalised Service
Vast amounts of data are being generated by customers across multiple channels, and companies are eager to capitalise on this information to deliver a more personalised experience.
The challenge is that companies are still struggling with structured data and with deploying a useful analytics framework based on their customer relationship management (CRM) systems, as well as consolidating different internal and external data sources. Organisations need to be extremely responsive to cope with the volatility created by customers engaging via digital technologies today. Being able to react in real time and make the customer feel individually valued is only possible through advanced analytics.
Big data offers the opportunity for interactions to be based on the personality of the customer, by understanding their attitudes and taking into account factors such as real-time location to help deliver personalisation in a multi-channel service environment. Advanced and multi-genre analytics can be utilised to drive new customer insights into behaviours, experiences, journeys over time and satisfaction. These new insights can then be leveraged to drive customer interaction, develop and execute strategies, supported by customer insights, to acquire new customers and then manage and grow profitable relationships over time. Customer interaction management delivers revenue and business efficiency by providing consistent, engaging and relevant experiences for customers.
Optimising the Customer Experience
Poor management of operations can and will lead to a myriad of costly issues, including a significant risk of damaging the customer experience, and ultimately brand loyalty.
Applying analytics for designing, controlling the process and optimizing business operations in the production of goods or services ensures efficiency and effectiveness to fulfil customer expectations and achieve operational excellence. Advanced analytical techniques can be deployed to improve field operations productivity and efficiency as well as optimize an organisational workforce according to business needs and customer demand.
Optimum utilisation of data and analytics will also ensure that continuous improvements are instigated on an on-going basis as a result of end-to-end view and measurement of key operational metrics. For example, many organisations, inventory is the largest item in the current assets category – too much or not enough inventory can directly affect a company’s direct costs and profitability. Data and analytics can support inventory management by providing uninterrupted production, sales, and/or customer-service levels at minimum cost.
The use of data and analytics can provide transparency into current and planned inventory positions as well as deliver insight into drivers of height, composition and location of stock and aid the determination of inventory strategy and decision making. Customers expect a relevant, seamless experience and for companies to know them wherever they engage.
