To understand what’s happening on a human level across the enterprise and in the market, organizations must gather qualitative and quantitative data. These data points can come from various sources, such as social listening, survey responses, customer conversations, and so much more. Mining journey analytics is immensely complex.
Asking the right questions is crucial in any data mining initiative. In doing so, companies can look beyond data as a one-off initiative and structure the organization in a more insights-centric manner. This approach empowers businesses to break apart layers of the customer and employee journeys and leverage behavioral data loops to make more informed decisions.
Data mining is about so much more than facts and figures. To produce valuable insights, organizations must understand how the data relates to the core of the business. This requires understanding how data aligns between brand and customer experiences alike.
The StoryVesting framework is designed to evaluate these complex layers and leverage data sets effectively to unpack complex data sets and layers. When done effectively, data mining can expose new levers to pull to make more strategic decisions and turn those insights into action items.
Data collection, mining and modeling methods offer the insights needed to personalize the customer experience. Organizations can get deeper sentiment mining and emotional insights by leveraging modern technologies and merging qualitative and quantitative data into one place.
Without these emotional insights, organizations may draw unintentional conclusions along the customer journey, leading to inaccurate predictions and incorrect growth practices.
As you strategize what intelligent operations could look like for your business, there are key frameworks to help define your processes and move away from operating from a data swamp.
Getting people to engage with a brand is the crux of the sales and marketing departments. Through data mining and leveraging machine learning, organizations can identify emotional and logical triggers of customers with a higher propensity to buy and then leverage those insights to fuel a more personalized marketing strategy.
Machine learning models can predict the customers most likely to yield the highest lifetime value for your business. Leaning into behavior-driven models helps organizations avoid inherent biases in the data sets and better predict what’s happening at the user level.
Leveraging analytics and machine learning models allows organizations to understand buyers’ propensity to churn. As new competitors arrive in the marketplace, this model is crucial for proactively keeping buyers vested in the organizations they’re already working with and innovating on ways to serve them.
Quality employees are one of the biggest assets to any organization. Running predictive analytics can help organizations identify when an employee has the propensity to churn and thus leverage employee data to get out ahead to rectify concerns before it’s too late.
Take the first step in unlocking your growth potential by scheduling a complimentary discovery call with our EVP Tom Jones. This introduction will better help us understand your pain points as you explore new data-driven strategies and uncover hidden growth opportunities.