How can you use prescriptive analytics to lower operational costs?

Prescriptive analysis is a way of using data and algorithms to find the best solution for a business problem. It can help lower operational costs by improving efficiency, quality, and profitability in various domains, such as transportation, manufacturing, and healthcare. Prescriptive analysis uses mathematical models, machine learning, and artificial intelligence to analyze data and recommend the optimal course of action. Unlike descriptive analysis, which tells what happened, or predictive analysis, which tells what might happen, prescriptive analysis tells what should happen. Prescriptive analysis can also enable scenario analysis, which allows businesses to compare the impact of different decisions and improve resilience to disruptions.

It’s always important to track down key levers of business performance, and plan ahead with potential scenarios based on (mostly) external (PESTL) scenario; once identified, (SWOT) the internal perspective comes into play to fine tune variables, impact & model how they will be affected & how they can be improved. ARIMA models are useful to plan scenarios and their impact - highly relevant on this matter to optimize opex or costs.

Prescriptive analysis is a way to offer solutions to existing problems. It contributes to healing damaged processes, improving, and adjusting existing portfolios to new realities and to leveraging operational efficiency, using data to find the most suitable solution. It can add value and improve profitability. Like a medicine, during the prescription It is essential to have strict control and management of the solutions implemented, so it can be effective.

Prescriptive analytics can lower the operational costs by providing actionable insights and we have to identify the inefficiencies of the existing operations and optimise processes by using the effective AI tolls and reduce the human intervention as much as possible and automate the repetitive tasks in the existing operations which can decrease the cost l, so we can maximize the profits.

Prescriptive analytics will help you to have a better understanding of your situation. Then you can clearly choose your best scenario, optimize your strategy, define the practical models and have a better workflow for your business. Depend on your business, you can automate your processes, have a useful CRM or any other activity that can help you to lower or better to say optimize your operational costs.

Using prescriptive analytics to automate and streamline processes is a game-changer in reducing operational costs. It's like setting up a highly intelligent system that not only performs tasks but also adapts to new conditions. This approach transcends traditional automation by integrating smart, data-driven decisions into everyday operations. For instance, applying prescriptive analytics to automate scheduling or routing can significantly boost efficiency, saving time and labor. Moreover, it enhances productivity and quality by constantly fine-tuning these processes based on real-time data and performance metrics.

Imagine a retail shop as a dynamic puzzle where decisions impact everything from pricing to inventory. Generative AI acts like an assistant, learning from past data to understand customer behaviour, market trends, and inventory patterns. While predictive analytics foretells what might happen, generative AI doesn't just say, "Here's what's coming," but adds, "And here's what you should do about it." For example, during a seasonal shift, the AI might suggest not just adjusting inventory but also recommending targeted promotions based on individual customer preferences. This human touch in data-driven decision-making empowers your business to respond with agility, ensuring that each move aligns with your goals and keeps costs in check.

????Prescriptive analytics is a game-changer when it comes to optimizing operational costs, and automating and streamlining processes is where it truly shines. ??By leveraging data, algorithms, and machine learning, prescriptive analytics can help you create intelligent systems that not only predict outcomes but also suggest the best course of action to take. ?In essence, prescriptive analytics empowers you to make smarter, data-driven decisions that streamline your operations, leading to significant cost savings and efficiency improvements. It's like having a crystal ball that not only predicts the future but also gives you a roadmap on how to navigate it for the best possible outcomes.

Conduct a thorough analysis of your existing processes and identify tasks that are repetitive, rule-based, and time-consuming. Prioritize processes that have a significant impact on operational costs, such as scheduling, routing, dispatching, and maintenance operations.

Now more than ever, it’s important to leverage tech breakthrough on AI to automate, streamline & improve in terms of efficiency “repeating-like” tasks. Funnels are a huge opportunity to analyze and streamline to improve conversion probability, to improve lead management (maturing processes & hunting) and so on.

Retention vs. acquisition is a huge discussion. At an early stage company, acquisition is key to build a customer base and generate awareness (hence market penetration) and this is a push strategy. Once a critical mass is reached, it’s important to optimize retention rates (critical to cascade incentives properly to shift frontline focus) since LTV becomes somewhat more important than CAC for profitability (as CAC is the acquisition cost, once a client is retained, you are not spending a dollar to have them happy, but operational excellence - where perspective analysis and data deep dives play a HUGE role - and CSAT is key to deliver high retention rates) Retention is long-term focus while acquisition is short-term;both must be balanced.

To concisely convey the value of prescriptive analytics in enhancing customer engagement and reducing operational costs: - Customization: Utilize customer data to personalize offerings, boosting satisfaction and value perception. - Proactive Support: Anticipate service needs, averting issues and conserving support resources. - Retention Focus: Pinpoint and counteract churn drivers, economizing on customer acquisition. Incorporating these tactics leads to a delightful synergy between customer contentment and streamlined operations, ultimately reflecting the smart and spirited application of analytics in business strategy.

Prescriptive analytics holds transformative potential for businesses. For example, a telecommunications firm could leverage prescriptive analytics to predict and avert service disruptions, thereby mitigating churn and lowering acquisition costs. However, an excessive reliance on algorithms risks diminishing the value of human touch in customer interactions. Customers who appreciate personalized experiences may feel alienated in such scenarios. Ethical considerations surrounding data privacy and security are paramount, necessitating robust safeguards to uphold customer trust.

In hospital financial management, prescriptive analytics transforms customer service and retention. By understanding patient needs, preferences, and behaviours, hospitals can offer personalized solutions, fostering loyalty and reducing "acquisition" costs. While leveraging analytics is crucial, maintaining a balance with the human touch is essential to prevent alienating patients who value personalized experiences. Ethical considerations, including data privacy safeguards, are paramount to uphold patient trust.

Prescriptive analytics enhances customer service by providing insights and recommendations to improve the customer experience. It helps with customer segmentation, predictive modeling, real-time decision-making, resource optimization, and churn prediction and prevention. By leveraging analytics, companies can personalize service, address customer needs proactively, make informed decisions, optimize resources, and retain customers.

Prescriptive analytics, beyond its notable cost reduction benefits, proves pivotal in mitigating risks and navigating uncertainties, fostering resilience in an ever-evolving landscape. Unlike predictive analytics, it recommends actionable strategies to effectively address uncertainties, enabling organizations to streamline operations and proactively manage risks. It identifies weaknesses in processes, preemptively addresses issues, optimizes resource allocations to avoid disruptions, and allows organizations to anticipate fluctuations, adjusting operations proactively. These examples illustrate that embracing prescriptive analytics is a strategic leap toward operational excellence, ensuring adaptability and efficiency amidst uncertainties

Businesses can utilize dynamic supply chain optimization with real-time adaptability, implement comprehensive financial portfolio management considering macroeconomic variables, and employ predictive workforce management for strategic talent development. The risk is to over-rely on predictive models, data privacy and security concerns associated with extensive analytics usage, and the challenge of integrating prescriptive analytics into existing processes due to resistance, technological gaps, and skill shortages. Finally, a robust data governance framework is essential to address privacy and security concerns arising from the extensive use of sensitive information.

In hospitals (financial management), prescriptive analytics minimizes risks by anticipating and addressing potential threats. For example, it aids supply chain risk identification and financial portfolio optimization. However, over-reliance on predictive models, data privacy issues, and integration challenges may pose hurdles. Balancing analytics usage, addressing (medical) privacy concerns, and overcoming integration challenges is crucial for effective risk management.

Utilizing prescriptive analytics to reduce risk and uncertainties involves leveraging its predictive capabilities to anticipate potential issues and market fluctuations. By analyzing historical data and current trends, prescriptive analytics can forecast future scenarios, allowing businesses to prepare for various outcomes. This foresight enables proactive decision-making, reducing the impact of unforeseen events or market changes. Furthermore, prescriptive analytics can suggest optimal actions under different conditions, helping to mitigate risks associated with operational decisions, financial investments, and strategic planning, thereby enhancing overall business resilience.

Prescriptive analytics can detect possible risks and uncertainties in business operations by examining past data and outside variables. After that, it can offer doable suggestions to reduce these risks and enhance decision-making. Prescriptive analytics, for instance, can evaluate market trends and suggest investment plans that reduce exposure to market swings in the financial operations domain. Prescriptive analytics helps businesses make informed decisions by automating risk management processes and offering real-time adjustments. This lowers the likelihood of costly errors and unanticipated disruptions.

A huge bottleneck with new ideas is the huge costs that come with them, and it’s aggravated by the risk associated with them, the lack of certainty of success of the idea. What could be better than to forecast the outcomes of innovative ideas, and know beforehand where to put your money ??. Predictive Analytics can save millions of dollars for companies by guiding them in the right direction. But here comes the catch, the limitation is the accuracy of data and ML models, which is why most companies today are data hungry.

Prescriptive analytics often drives innovation, lowering operational costs. It enables testing new ideas, models, and solutions to enhance operations and gain a competitive edge. For instance, hospitals can use it to design and assess innovative healthcare delivery models or cost-effective technologies, positioning the institution as forward-thinking. Integrating prescriptive analytics not only optimizes processes but also fosters a culture of real innovation, ensuring adaptability in a dynamic healthcare landscape.

Using prescriptive analytics to innovate and experiment involves leveraging its insights to identify opportunities for new solutions or improvements. By analyzing complex data sets, prescriptive analytics can reveal hidden patterns and trends, suggesting areas ripe for innovation. It enables businesses to test various hypotheses and scenarios in a controlled, data-driven manner, reducing the risks associated with experimentation. This approach not only fosters a culture of innovation but also guides the development of more targeted, effective solutions, driving forward-thinking strategies and maintaining a competitive edge in the market.

By analyzing data and predicting potential outcomes, prescriptive analytics can guide organizations in exploring and implementing innovative approaches to their operations. For example, in manufacturing, it can suggest optimized production processes or recommend the adoption of new technologies that improve efficiency. This proactive guidance enables businesses to experiment with strategies that have the potential to reduce costs over the long term. Moreover, prescriptive analytics allows for real-time monitoring and adjustment, facilitating a continuous cycle of innovation and improvement.

Forget Complex Models, Usable Data is Key to Driving Efficiency: While advanced analytics has its place, often the key gap is effectively reporting data to decision-makers. —> The challenge? A gap exists between data designers (who understand data & tools) and operators (who need actionable insights). —> Solution: Bridge the gap! Hire data translators that bring analytical mindsets and operational experiences and can help turn the data into usable insights for everyday decisions. This can unlock the true power of prescriptive analytics and drives real results. #dataanalytics #usabledata #prescriptiveanalytics #operationalexcellence

I see a lot of prescriptive commentary on prescriptive analytics, but not enough emphasis on the basics i.e. what causes operational costs to increase or inflate? In my experience as a strategist, the biggest failure is lack of measurement and monitoring. Many businesses fail to measure unit costs at every level i.e. cost per service, cost per component, cost per task, cost per call, cost per ticket, cost per code, cost per box, cost per delivery, cost per meeting etc etc. Reducing anything, any-cost requires benchmark data for baseline measurement. Hence I say, prescriptive analytics or not, unless you can clearly measure cost per unit, you cannot objectively reduce it.

Cost modelling and scenario analysis - 1.To develop cost models using prescriptive analytics to understand the impact of various operational scenarios . 2.Conduct scenario analysis to identify the most cost - effective strategies and make data driven decisions .