Today, most analytics tools offer Predictive Analytics in some form or the other, i.e., they use data to guess what might happen in the future, like trends, outcomes, or events. 

By analyzing historical data, these tools try to find patterns and connections that can help businesses make intelligent guesses. Whether it's figuring out what customers might do next, how the market might change, or how well a company might perform, Predictive Analytics is critical for businesses across industries as it helps organizations plan and get ready for what lies ahead.

There’s just one small problem. 

Most of these tools stop at the level of predictions, never making it to the next critical and vital step, i.e., providing a continuous stream of intelligent recommendations using Prescriptive Analytics.

What is Prescriptive Analytics?

Prescriptive Analytics is like a GPS for your business decisions. It not only tells you where you are and where you're going but also suggests the best route to take based on current conditions and future objectives. By doing that, it effectively removes the guesswork from your decision-making process. 

But, why is this important?

Let’s take a look at some of the findings of a new study (by Oracle and Seth Stephens-Davidowitz) on 14,000 employees and business leaders across 17 countries. According to the study, 70% of respondents find it too challenging to gather and understand data. About 78% of business leaders admitted that people often decide first and then seek data to back up their choices. Additionally, 74% of employees think that companies often prioritize the opinions of higher-paid individuals over data, while 24% believe that most business decisions lack rationality.

To better understand the difference between Prediction and Prescription, let’s picture this: let’s say that you are the owner of a retail organization trying to figure out the best prices for your products. Predictive Analytics tells you what sales might look like based on past pricing data.

But, Prescriptive Analytics takes it a step further. 

It says, "Hey, if you tweak your prices like this, you'll make more money and stay ahead of the competition." This means that by analyzing historical sales data, market trends, and competitor pricing, prescriptive analytics recommends specific price adjustments for different products to maximize profitability while maintaining competitiveness.

In other words, the end game of Prescriptive Analytics is a continuous stream of insight-driven recommendations. Seen that way, it effectively functions as a recommendation engine for all data users in an enterprise. 

Identifying the Next Best Step with MECBot’s Prescriptive Analytics

Introducing MECBot

MECBot by FORMCEPT is a leading data excellence platform for just-in-time decision-making. It is designed and fully orchestrated to solve key enterprise data challenges in an end-to-end manner and enables insight-driven decision-making without relying on the underlying databases or the structure of the data. This makes MECBot the go-to data analytics platform for several leading Fortune 1000 clients across the globe in industries like Banking, Insurance, Retail, Sports, Healthcare, and more. 

Here is a quick video that explains what MECBot can do: MECBot Product Video

MECBot’s Unique Architecture Makes Prescriptive Analytics a Cakewalk

MECBot uses cutting-edge Prescriptive Analytics to streamline the process of discovering insights, promoting data exploration, and facilitating more informed decision-making. By leveraging the power of AI and advanced analytics, it empowers users to extract maximum value from their data and drive business success.

MECBot also helps businesses explore unfamiliar datasets or uncover hidden insights by suggesting relevant dimensions, measures, or visualizations to include in their analysis. This proactive guidance not only saves time and effort but also empowers users to make more informed decisions by surfacing relevant information they may not have considered otherwise.

This means that MECBot’s recommendation engine automatically identifies and surfaces insights from large datasets, helping users uncover valuable information without the need for manual exploration. This capability accelerates the decision-making process and enables users to focus on interpreting insights rather than searching for them.

Moreover, MECBot’s recommendation engine can adapt and improve over time based on user feedback and interactions. As data users interact with the recommended content and provide feedback on its relevance and usefulness, MECBot’s recommendation engine can continuously refine its suggestions to better meet the user's evolving needs and preferences.

From Prediction to Recommendation with MECBot

How MECBot Uses Prescriptive Analytics to Fuel Just-in-time Decisions at Scale

MECBot can analyze large volumes of data from various sources, including internal databases, customer interactions, market trends, and competitor information, all in real time. By processing and interpreting this data, MECBot can identify patterns, correlations, and insights that are crucial for decision-making, and present actionable insights just in time.

This way, it takes Prescriptive Analytics to a whole new level. By training models on changing environmental factors, MECBot can recommend dynamic actions that are likely to yield the desired results or achieve a desired business goal. 

MECBot can understand and extract valuable insights from unstructured data such as customer feedback, social media posts, and industry reports using NLP (Natural Language Processing) and linked data concepts. By analyzing text data, MECBot can uncover valuable information about customer preferences, market trends, and competitive dynamics which are then used to extract and present personalized recommendations to each data user.

MECBot can also provide personalized recommendations tailored to the unique needs and objectives of each business. By considering factors such as industry dynamics, competitive landscape, and organizational capabilities, MECBot can suggest strategies that align with the specific context of the business.

Lastly, by monitoring the outcomes of recommended actions and adjusting its models accordingly, MECBot can adapt to changing circumstances and optimize decision-making processes. It uses the insights gained from the analysis to simulate different scenarios and assess their potential impact on the desired outcomes. This way, it continuously iterates and refines the approach based on real-time feedback and evolving business conditions.

Conclusion

While predictive analytics offers valuable insights into future trends, prescriptive analytics takes it a step further by providing actionable recommendations for informed decision-making. With MECBot's cutting-edge Prescriptive Analytics, businesses can navigate complexities effortlessly, fueled by intelligent suggestions tailored to their unique needs. By leveraging AI and advanced analytics, MECBot empowers users to extract maximum value from their data, driving success across industries. Say goodbye to guesswork and hello to strategic decision-making with MECBot's transformative capabilities.

Learn more about MECBot here, or request a demo to learn more about how we can address your data needs.