Business Process Overview
- Capture Photo In-Store: Document current store conditions for analysis.
- Image Recognition Detects SKUs: Automatically identify products on display.
- Customer Visit History Analyzed: Review past customer interactions for insights.
- Customer Category Movement Analyzed: Examine customer buying patterns by category.
- KPI Performance Analyzed: Evaluate key performance indicators to inform strategy.
Aforza Vertical AI (Ava) enables a sales team to maximize the effectiveness of their in-store visits. By utilizing advanced image recognition technology and analyzing customer behavior, this process provides field users with actionable insights tailored to improve their interactions with customers.
Key aspects of this process include capturing in-store photos, analyzing product positioning and customer visit history, and assessing performance metrics. The insights derived from this analysis enable sales representatives to identify opportunities for improvement and enhance overall performance. This systematic approach not only helps maintain optimal product placement but also fosters customer-centric engagement, ensuring that field users can make informed decisions to drive sales success.
1. Capture Photo In-Store
This step involves documenting the current state of the store.
- Take high-quality photos of product displays and layouts.
- Ensure all relevant angles and sections are covered.
- Use a mobile device or camera for efficient capture.
- Prepare photos for subsequent analysis.
- Upload images to the system for processing.
2. Image Recognition Detects SKUs
This step focuses on identifying products using image recognition technology.
- Process uploaded images to detect SKU details.
- Match detected SKUs with the product database.
- Capture data on product positioning and visibility.
- Identify missing or misplaced products for corrective action.
- Provide a summary report of detected SKUs.
3. Customer Visit History Analyzed (Automated)
This step reviews historical data on customer interactions.
- Access customer visit logs to evaluate past engagements.
- Analyze patterns in visit frequency and duration.
- Identify successful strategies used in previous visits.
- Gather insights on customer preferences and feedback.
- Use this information to tailor future visits.
4. Customer Category Movement Analyzed
This step examines buying patterns within customer categories.
- Assess sales data by customer category to identify trends.
- Analyze which categories show growth or decline.
- Determine the impact of external factors on purchasing behavior.
- Identify opportunities for cross-selling based on category movements.
- Leverage insights to enhance promotional strategies.
5. KPI Performance Analyzed
This step evaluates key performance indicators related to store visits.
- Gather data on sales, customer engagement, and conversion rates.
- Compare current performance against set KPIs.
- Identify areas needing improvement or adjustment.
- Provide actionable insights based on performance analysis.
- Set new benchmarks for future visits based on findings.