Preview: Technical guide #5

Optimization & Reporting: How a sales system learns and reports day by day

Detailed technical information about training models on private data (fine-tuning), automated A/B Testing, and real-time reporting of user behavior.

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Guide #1 AI in Sales was launched on 21st Jan 2026. Read it here

The next guide will be published starting on March 25th 2026.
You will receive the PDF file 48 hours before the official release.

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Why sales agility is mandatory

2.5x

Scaling speed

Companies with adaptive AI scale implementations much faster than competitors.

Source: BCG ➛

+12%

Growth via A/B

Revenue per online search increases through a culture of continuous A/B experiments.

Source: HBR ➛

Daily

Re-training

Google Cloud AI systems (recommendations, chatbots) learn continuously from clicks and quotes.

Source: Google ➛

+20%

Conversion rate, real client

ecommerce portal uplift after the technology upgrade.

OPTI case study ➛

The technical problem: Static rules vs. Dynamic reality

Most managers analyze sales based on monthly reports and set precise rules in the ERP system, the eCommerce site structure, or the CRM. Between monthly reports, the market changes, sudden challenges or opportunities appear, and new rules launched without testing might be abandoned when new data shows up.

Technology enables a feedback loop through database analysis, real-time user behavior, demand forecasting, fine-tuning, and A/B Testing experiments. Here’s how it works:

Scenario: In inflationary conditions, consumers search for cheaper products.
Static system: Keeps promoting the premium range (old rule set by margin).
Result: Quote completion rate drops.

The AI can learn dynamically from what customers searched for, what they chose, and what they ignored, protecting the company. Classic software stays the same until the next paid update or one built in-house.

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The solution: Feedback Loop & A/B Testing

Iterative improvement cycle:

Collecting signals: The system monitors every action: view, selection, order, contact, chatbot dialogue.
Re-training (Learning): AI models self-adjust. Either automatically (standard in Google technologies like Vertex AI Search for Commerce and Recommendations AI) or through a controlled process (fine-tuning).
Autonomous optimization: If Brand Y becomes popular, it will be promoted in search. Recommendations (complementary, bought together, upsell) recalibrate.
Experimentation (A/B Testing): Changing business rules (e.g., search priorities) is tested on a limited sample before release.

Case study

Situation: Inflation. Customers reject the premium range. Sales drop.

Management hypothesis: "We need to bring in other premium suppliers." (Risk of dead stock).

Testing: An A/B test is run: Group A sees the new suppliers, Group B sees the old catalog.

Test result: Group A converts worse than Group B, so no order is placed.

AI self-adjustment: Meanwhile, the model prioritizes more-viewed products (mid-range), orders increase.

AI forecasting: Following the order increase, the management approves the automated proposal to replenish mid-range products.

Conclusion: Sales recover thanks to the right mix, and the company avoids a bad investment.

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The PDF guide ”Continuous optimization & reporting” will contain the full technical explanations.

Table of contents:

Machine learning, fine-tuning, and demand forecasting concepts.
Reporting & observability. Looker Studio dashboards.
Explicit / implicit feedback. What clicks say vs. ratings in eCommerce.
Optimization techniques. User feedback in search and chatbots in Google Cloud.
A/B Testing methodology. Statistically valid experiments.
Analysis and tracking. Visualizing the evolution of recommendation algorithms and Google Analytics.