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Simplifying the Next Best Action Planning in Retail Using AI

Optimize Retail Sales With ZBrain

Simplifying the Next Best Action Planning in Retail Using AI

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.


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:

Without ZBrain Flow
Time Without ZBrain Flow
With ZBrain Flow
Data collection Manual ~6 hours Automated
Data segmentation and analysis Manual ~8 hours Automated
Action recommendation Manual ~8 hours Automated
Report finalization and review Manual ~2 hours Manual
Total ~24 hours ~5 hours

The table clearly indicates that ZBrain reduces the time spent on identifying the next best action from approximately 24 hours to just 5 hours, providing retailers with a competitive edge in timely customer engagement.

II. Necessary Input Data

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

Information Source
Retail CRM system Purchase histories, customer profiles, and feedback Always updated
Online shopping cart data Items browsed, added to cart, and wishlist Last 30 days
Loyalty program interactions Points, redemptions, and customer engagement Current cycle
Email and ad interaction metrics Click rates, open rates, and feedback Last 3 months
Past promotion responses How customers responded to previous sales and offers Last 6 months
Social media behavior Product likes, shares, and comments Last 3 months

III. ZBrain Flow: How It Works

Next Best Action

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.


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


Suggest the next best action to upsell the customer segment who is currently browsing winter coats.

Customer Segment Upsell Opportunity Analysis

Segment Profile

  • Total Customers in Segment: 8,000

  • Average Order Value (AOV): $350

  • Location: Primarily Northern Region of the United States

Current Behavior

  • Segment members are currently browsing winter coats.

  • Popular Products: “Luxury Wool Blend Winter Coat” and “Puffer Jacket with Detachable Hood.”

Upsell Opportunity Overview

Winter Coat Enthusiasts represent a valuable segment with a high propensity to make purchases. Capitalizing on their interest in winter coats presents an opportunity to increase AOV and maximize revenue.

Segment Insights

  • Average Time Spent Browsing: 15 minutes

  • Page Views Per Session: 10 pages

  • Browsing Devices: 65% on mobile, 35% on desktop

  • Preferred Brands: 50% prefer premium brands, 50% prefer value-for-money options

Upsell Recommendations

Bundle Deal Offer bundle deals:

“Winter Essentials Combo” includes

  • Winter Coat: “Luxury Wool Blend Winter Coat”

  • Shawl: “Silk Shawl in Matching Color”

  • Woolen Gloves: “Handcrafted Woolen Gloves”

Based on preferred brands and browsing history
  • Value and convenience for the customer

  • Opportunity for higher-margin sales

Loyalty Promo

Offer loyalty program benefits:

  • Exclusive 20% discount for loyalty program members

  • Double loyalty points for purchasing winter coats

Based on loyalty program, status, and browsing history
  • Incentivize immediate purchase

  • Reinforce customer loyalty and engagement

Size & Fit Help

Provide size and fit assistance:

  • Virtual fitting room for trying different sizes

  • Size guide tailored to mobile and desktop users

Based on browsing devices
  • Reduce returns due to sizing issues

  • Enhance shopping experience and satisfaction

Urgency Alert

Create a sense of urgency:

  • “Limited Stock Alert!” for high-demand coat sizes

Based on inventory levels and location preferences
  • Encourage prompt purchase

  • Prevent missed sales opportunities

Expected Impact

By implementing these upsell recommendations tailored to the market can expect to see an increase in AOV, higher customer engagement, and improved customer satisfaction within the Winter Coat Enthusiasts segment.


Capturing the attention of Winter Coat Enthusiasts in the market with personalized upsell strategies can result in substantial revenue growth and stronger customer loyalty. By tailoring these recommendations to individual preferences, devices used for browsing, and inventory availability, the company can optimize its upsell efforts and create a win-win scenario for both the customers and the business.

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