Next Best Action
Next-best-action is the ability to convert all the information you know about a customer into actions or interactions that make sense to the customer. This increases customer loyalty and value, while optimizing revenues and profitability.
The most accurate and reliable way to find the optimal marketing touchpoints, or work efforts for your operation, is to use machine learning algorithms that quickly search through your own data. These algorithms learn by example, finding common patterns within the data, in order to predict sales or work efforts that will drive the highest revenue and profitability.
Product recommendations tailored to a user’s profile and habits are more likely to result in a successful sale and increased customer loyalty. Gaining a complete understanding of each customer and then taking a customer-centric approach to next best action is the way to take the right action at just the right time.
Next best action models can be complex and time-consuming to build internally.
Bossa Nova Data Solutions machine learning tools can help your organization better judge customer spending habits and guide marketing efforts toward connecting with customers to increase sales and customer satisfaction, without the need to develop the infrastructure internally, or make significant capital investments. Our tools utilize customer insights to encourage cross-sells, increase customer retention, and determine customer value. We work hand-in-hand with our client’s team to solve their business problems.
Contact center clients can benefit as well. With machine learning solutions, contact center clients increase effectiveness by providing agents with recommended next best work effort. This enables them to more effectively use their limited capacity and increase collections or sales at a lower overall operational cost.
We process and examine our client’s historical data, identify what is meaningful, select the right algorithms, and optimize a customized model that is just right for each client’s unique circumstances and requirements.
Step 1: Define the objectives. Is it to increase sales, or to drive recoveries, for example?
Step 2: Determine what data is available
Step 3: Data clean-up, organization, and segmentation
Step 4: Train and test the machine learning model
Step 5: Implement, monitor, measure, and refine
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