Simplifying the Next Best Action Planning in Retail Using AI

Problem

The Hurdles of Determining the Next Best Action

Determining the next best action to engage customers effectively remains challenging in the dynamic retail sector. Whether it’s recommending a product, sending personalized offers, or providing after-sales services, making the right move at the right time is crucial as it directly impacts customer satisfaction, loyalty, and, ultimately, the bottom line. In this context, ZBrain Flow simplifies the process of identifying and implementing the best next steps, offering a professional solution.

Solution

I. How ZBrain Flow Transforms the Next Best Action Process

ZBrain Flow automates the task of determining the most suitable actions tailored to individual customer behaviors and preferences. Here’s a time comparison for the process with and without ZBrain Flow:

II. Necessary Input Data

For ZBrain to deliver precise recommendations, it requires the following data:

III. ZBrain Flow: How It Works

Step 1: Data Acquisition and Exploratory Data Analysis (EDA)

ZBrain Flow automatically aggregates customer data, including purchase history, items wishlisted, social media behaviors and past promotion responses from diverse sources. An automated EDA is then conducted to uncover anomalies, missing values, and data patterns. This process extracts essential insights into customer behaviors, interactions, and purchasing patterns.

Step 2: Embedding Generation

Next, textual data, such as feedback, social media interactions, and email responses, undergo embedding processes to transform textual information into numerical representations. This conversion allows for the capture of semantic meanings and relationships among different data points. These embeddings hold essential sentiments and purchasing inclinations, readying the data for actionable insights extraction.

Step 3: Query Execution and Action Recommendation

When a retailer seeks a recommendation for the next best action, ZBrain sources the pertinent data. This data is combined with the retailer’s specific query and processed by the OpenAI Language Model (LLM) to craft actionable recommendations.

Using the refined embeddings, OpenAI LLM identifies potential opportunities, preferences, and the optimal next moves. From this data, it devises a tailored action plan designed to boost engagement and sales.

Step 4: Parsing and Final Report Generation

Upon formulating the next best action in text format, it undergoes detailed parsing. This process extracts and structures pivotal components like recommended products, promotion strategies, and engagement methods. ZBrain’s thorough parsing technique guarantees that the final recommendations are both data-driven and presented in a straightforward, actionable manner, ensuring that retailers can act swiftly and effectively.

By integrating data acquisition, EDA, embedding generation, recommendation formulation, and parsing, ZBrain presents retailers with the optimal next-best-action. This cohesive process ensures maximized customer engagement and increased sales potential.

Result

Streamlined Retail Engagement and Sales Strategy

With the automation and valuable insights provided by ZBrain, retailers can make quick and precise decisions about their next steps. This results in heightened customer satisfaction, boosted sales, and a substantial competitive edge in the bustling retail industry. Trust ZBrain Flow to elevate your retail strategies and witness tangible growth in customer engagement and revenue.

Example Report