How to Implement Decision Intelligence
Decision intelligence is a framework that uses data science to solve problems companies face. If you are familiar with algorithms that predict consumer preference for products and services, you already understand the basis for decision intelligence.
Decision intelligence is a critical component of business success in the digital age. While data science alone used to be enough to ensure sustainability and growth, it is now only half of the equation. Data science, as well as analytical and behavioral approaches to decision making, are how businesses will remain sustainable for years to come.
By 2023, more than a third of large companies worldwide will implement decision intelligence. Keep reading to find out how you can join their ranks.
1. Crunch the Numbers
Decision intelligence is implemented in two steps: creating the model and increasing its effectiveness. To create a good decision intelligence model, you need data: the more the better. For the algorithm to have more predictive power and more intelligence, it is important to recreate the scenario with different variables, test it and observe its results. Over time, the model will become more accurate because it has more experience to draw from. Like a person, the more it learns, the wiser it becomes.
To improve the model’s versatility, you must think outside the box. Anticipate what could go wrong in a customer journey, or test decisions you would not normally expect a customer to make. If you spend time playing out these scenarios, your business will be better prepared for a wide variety of outcomes. You will also be better prepared for crises should they arise.
2. Expect the Unexpected
At this point, your model will have a solid foundation to effectively function when faced with stress or unexpected results. However, the work is not yet done. Continue to improve your business processes by contextualizing each scenario. Why did a customer decide to opt out of your email list, despite being a subscriber for four years, for example? If you want your organization to maintain its success, you must challenge yourself and your thinking.
For most companies, customer satisfaction is the number one priority. A key to understanding what customers want is listening to what they tell us, whether that be through feedback or purchasing decisions. Decision intelligence can be leveraged to monitor customer sentiment. By using Natural Language Processing (NLP) and machine learning modeling, you can anticipate both positive and negative customer experiences more accurately. This will give you a more complete picture of the risk associated with various business decisions.
3. Leverage the Platforms that Matter
Understanding how to reach your customer base where it is most active is vital. Data science machine learning (DSML) will indicate which channels are most likely to perform well. It is important to integrate the web analytics data you retrieve into both CRM campaigns and social media management. That way, you will have a clear understanding of which platforms drove more conversions. Decision intelligence allows you to create a data science pipeline you can model, adjust and deploy into production.
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