Data-Driven Customer Success

Qlik AutoML for Sales, some fundamentals to think about..

Sales Pipeline Forecasting:

Predicting win/loss probabilities in the sales pipeline is crucial for businesses to allocate resources effectively, prioritize leads, and optimize sales strategies.
By leveraging AutoML, businesses can analyze historical data and various factors influencing sales outcomes to generate accurate forecasts, helping them make informed decisions and improve sales performance.

Customer Churn/Retention:

Customer churn (loss of customers) is a significant concern for businesses across industries.
Identifying factors leading to churn and predicting which customers are at risk allows companies to implement targeted retention strategies.
AutoML simplifies this process by automatically selecting the best-performing machine learning models and features, enabling businesses to proactively address churn and enhance customer retention efforts.

Customer Prospecting/Targeting:

Identifying potential customers who are likely to convert or engage with a product/service is essential for marketing and sales teams.
AutoML streamlines the process of building predictive models to identify high-value prospects based on historical customer data, demographics, behavior patterns, and other relevant factors. By accurately targeting prospects, businesses can improve marketing ROI and increase conversion rates.

For example, these highlighted use cases are fundamental because they directly impact a company’s revenue generation, customer satisfaction, and overall business performance.
By leveraging AutoML technology, businesses can automate and accelerate the process of developing predictive models, enabling data-driven decision-making, and gaining a competitive edge in the market.