Whether it’s social media, digital advertising, buyer personas, content marketing, or any other new influencer solutions, today's marketers need to engage with prospects and customers on an increasingly personalized level. Making broad marketing strategies, less impactful than before. That’s why forward-thinking companies are turning to machine learning and artificial intelligence (AI) to predict both customer and prospect behavior at an individual level, resulting in improved customer relations, operations, and return on investment (ROI). This allows innovative marketing teams to uncover business insights hidden in large sources of data like customer surveys, website traffic, purchase behavior, and more. With machine learning, marketers can enhance targeted campaigns, boost revenue for the organization, improve customer service, and optimize operations throughout the department.
- 1. Predict Churn and Lifetime Value (LTV)
- 2. Predict Net Promoter Score (NPS)
- 3. Funnel Modeling
- 4. Next Best Offer, Cross-Sell, and Up-Sell
Determining the value of each customer, including likely future spending and the resulting profit margins, is a vital factor in deciding where to aim personalized marketing efforts. Lifetime customer value models determine the likely profit each individual customer will generate over the entire customer relationship, as well as when a customer is likely to churn; allowing marketers to focus their efforts on those likely to be most profitable eventually reducing churn and increasing revenues.
With data from customer satisfaction surveys, ScoopML can uncover the factors contributing to a customer’s net promoter score. Eventually by applying these predictions to customers not included in the survey, companies can predict those customers' likely scores and focus their efforts on those likely to be most loyal. These insights allow marketers to understand the true drivers of customer experience and effectively focus their resources on its improvement.
Marketers want to know how likely website visitors are to provide contact information, ask for further product details, or purchase a particular product. ScoopML's predictions can offer unprecedented insight into visitor behavior, allowing businesses to optimize their web design and product offerings accordingly.
Retaining and developing existing customers is much more cost-effective than acquiring new ones, and marketers that effectively encourage purchases from existing customers get the most lifetime value from each customer. Using customer demographic and purchase data, ScoopML can identify which customers will be interested in other products and which customers will be interested in upgrading existing products. Allowing marketers to determine which touch points will likely result in the desired action.