Though many businesses understand the importance of Big Data Analytics and its potential to impact business growth in the areas of marketing, finance and operations, not every organisation knows how to achieve these benefits. One key to unlocking value is harnessing the power of Advanced Analytics.
“Advanced Analytics is the autonomous or semi-autonomous examination of data or content using sophisticated techniques and tools, typically beyond those of traditional business intelligence (BI), to discover deeper insights, make predictions, or generate recommendations.” – Gartner.
Data mining, machine learning, predictive & prescriptive analytics, pattern matching, neural networks and location intelligence are just some of the categories that make up Advanced Analytics. Whilst the applications of Advanced Analytics are many, here are five ways it may help your business.
#1 – RISK MINIMISATION
All businesses have some level of risk, including possibilities of fraud, intellectual property theft and ransomware. Fortunately with advanced analytics, these risks can be measured, identified and acted upon.
“Advanced analytics capabilities enable clearer visibility into the challenges associated with managing the many types of risk in such key areas as operations, regulatory compliance, supply chain, finance, ecommerce and credit. By using analytics to measure, quantify and predict risk, leaders can rely less on intuition and create a consistent methodology steeped in data-driven insights.” – Deloitte.
#2 – INCREASING CUSTOMER LOYALTY
“With improved customer experience and service, and more efficient operations leading to increased customer acquisition and retention, companies are realising what advanced analytics can do for their operations. And as these data-driven strategies take hold, they will become an increasingly important point of competitive differentiation.” – Tribridge Connections.
Advanced analytics is changing the way we engage with customers. We are now able to use data to predict consumer buying behaviours that help with micro-targeting, up-selling and churn management.
#3 – EFFECTIVE PROMOTIONAL STRATEGIES
Ensuring that marketing efforts are effective requires an organisation to invest in promotional strategies that are based on data rather than theory. Today’s business environment requires data to support effectiveness claims and seeks marketing results that are not usually achieved without the sophistication that advanced analytics enables.
Predictive analytics based on machine learning technologies can help in this regard, as the various predictive models can be used for customer segmentation, analysing customer engagement, collaborative filtering, up-selling and prioritising leads.
“Predictive analytics appears to have the potential to double marketing success measures in customer engagement and targeted sales across B2B and B2C industries.” – Daniel Faggella: CEO & Founder of TechEmergence.
#4 – BETTER DECISION MAKING
Data-driven decision making derived from Advanced Analytics enables businesses to decrease costs, increase revenue and achieve regulatory compliance.
“Companies that make better decisions, make them faster and execute them more effectively than rivals nearly always turn in better financial performance. Not surprisingly, companies that employ advanced analytics to improve decision making and execution have the results to show for it.” – Bain & Company.
#5 – IMPROVING EFFICIENCY
“A Big Data and Analytics Implementation can help companies uncover ways to make operations more efficient and effective by improving asset efficiency and streamlining global operations.” – IBM.
Predictive analytics tools such as Optimotive, Infer and SAP Predictive Analytics allow businesses to optimise operations and be better prepared to respond to changes in the marketplace.
Companies that are actively analysing and using data are experiencing the rewarding benefits of staff and operational efficiency. For example, companies can now build forecasting models that accurately predict sales volumes, optimise preventative maintenance or perform optimal resource scheduling. These models are swiftly trained, self-optimise and can accommodate highly complex input considerations, or computations at great scale. This allows businesses to consider the use of data types they’d not previously have been able to harness, such as detailed data from customer website use or use of assets such as machines and vehicles.
To discuss Advanced Analytics and other topics, please contact the team at Contexti – +61 2 8294 2161 | firstname.lastname@example.org
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