Quantis Next Best Actions — Data-Driven Sales Recommendation System
A salesperson starts the day with a list of several dozen clients. They don’t know where to begin. They check purchase history, review notes, ask colleagues — and waste time before even picking up the phone. Meanwhile, somewhere on that list is a client who has just stopped ordering. And another who is ready to buy something new — but no one has offered it to them.
Next Best Actions is a module of the Quantis system that solves this problem. Every day, automatically, for each salesperson individually — it generates a personalized action queue. Who needs contact. What to offer. Why right now.
Who is Next Best Actions for?
Next Best Actions was designed for two roles — and each sees the same system through a different lens.
For the Salesperson
A salesperson doesn’t analyze data. A salesperson talks to clients. NBA shortens the time between waking up and the first valuable conversation. Instead of reviewing purchase history and guessing who to call today — they open the dashboard and see a ready-made list. With justification for every item.
For the Owner and Sales Manager
NBA is a management tool. It answers questions that previously required meetings, reports, or instinct: which client is slipping away, where are the untapped opportunities, and whether salespeople are working on the right priorities.
The Problem Next Best Actions Solves
Sales opportunities disappear in silence
A client stops ordering. They don’t resign dramatically — they simply start buying less. More slowly. Less frequently. And before anyone notices, it’s too late for a simple conversation. You have to save the relationship instead of growing it.
At the same time — there are clients in the database who buy only part of what they could. Similar companies in the same industry regularly buy a given category. This client doesn’t. No one had time to check it.
Prioritization based on intuition, not data
In most sales teams, the order of actions comes from who called last, who is an “important client” in the salesperson’s mind, or who has a payment deadline. This is not a bad starting point. But it is not optimization.
A system that processes purchase history, sales dynamics, and the behavior of similar clients — sees things a person won’t have time to check across several dozen active accounts.
Knowledge exists, but is inaccessible
Data about which client is slipping away, what similar companies buy, which products fit a given customer — all of this lies in the database. Quantis calculates it. NBA turns these calculations into concrete, actionable items for a person who doesn’t have time for analysis.
How Next Best Actions Works — System Logic
NBA is not a simple rule like “if a client hasn’t bought for 30 days, call.” It is a three-layer analytical engine that combines three independent perspectives into one ranked list.
Layer One — Churn Risk Detection
The system continuously tracks every client’s behavior in every product group. When a client starts buying less than their historical pattern — the system registers a risk signal. It doesn’t wait until purchases drop to zero. It reacts to early deviation.
This is the most important signal in the system. Protecting existing revenue always has higher priority than building new revenue.
Layer Two — Cross-Sell Opportunity Identification
The system compares each client with a group of similar companies in the database. It looks for product categories that those companies buy regularly — but this client does not. These are portfolio gaps: areas where cooperation could be broader, but isn’t yet.
Opportunities are identified at the product group level — not individual SKU. This allows the salesperson to talk about need, not a specific catalog item.
Layer Three — Specific Product Recommendation
For each identified group opportunity, the system selects specific products. It filters out those the client already buys. Among the rest, it chooses those with the highest fit score — taking into account product popularity among similar clients and the strength of the group opportunity.
The salesperson receives not only “suggest something from category X” — but “specifically suggest this product, because similar clients buy it regularly and this client doesn’t know it yet.”
Combining into an Action Queue
All three layers are combined into one action table — sorted by priority. Protecting an existing client always comes first. Then expanding cooperation. Finally, a specific product proposal.
Each client receives only as many recommendations as needed to keep the list actionable — not overwhelming.
Next Best Actions Dashboard — What the Salesperson Sees
The NBA dashboard has two views. One tells what to offer. The other tells who to start with. Together they replace an hour of morning data review.
Product Recommendation List
For each client — suggested product, its fit score, target category, market popularity, and recommendation confidence level. The salesperson sees not only what to offer, but also how strong the signal behind the suggestion is.
Sales Action Queue
A ranked list — client, what to do, priority, goal, and source of recommendation. The salesperson starts from the top of the list. They don’t have to decide who to call first — the system has already done it.
Three types of actions on the list:
- Recover the client — the client used to buy regularly, now they are stopping. A signal to protect the relationship before revenue visibly declines on the invoice.
- Expand cooperation — the client doesn’t buy a category that similar companies buy. An opportunity to broaden the portfolio without starting a new relationship.
- Suggest a product — a specific SKU recommendation. A ready starting point for a sales conversation.
Filtering and Context
The dashboard can be filtered by salesperson, region, client, or action type. Each recommendation shows where it comes from — the salesperson understands the logic behind the suggestion before making the call.
What Next Best Actions Does Not Do
This is an important boundary of the system — and it’s worth stating it clearly.
NBA does not send messages to clients. It does not generate offers without the salesperson’s knowledge. It does not make sales decisions. It does not replace the relationship with the client.
The system provides analysis and suggestions. The decision — what to say, how to conduct the conversation, whether to call today or tomorrow — belongs to the person who knows the client and understands the context.
NBA eliminates guesswork. It does not eliminate the salesperson.
Integration with the Quantis Ecosystem
Next Best Actions does not work in isolation. It is one of the modules of the central Quantis system — and it uses the data that Quantis calculates for the entire company.
Churn risk data comes from ERP transactional history analysis. Cross-sell opportunities are built on ML segmentation of the entire customer base. Product evaluations are based on demand forecasts generated by XGBoost and Facebook Prophet models.
The salesperson receives a recommendation — but behind it stands the entire computational apparatus of the system. Not a simple filter. Not a rule based on the date of the last order. Behavioral analysis of the entire customer portfolio.
→ Read about the Quantis system architecture
→ Quantis Logistics — order and inventory management
FAQ
Does Next Best Actions require a separate implementation?
NBA is a module of the Quantis ecosystem — it works on data that Quantis already calculates. Implementing the NBA module does not require building analytical infrastructure from scratch, provided the company is already using the Quantis system.
Does the system take the client’s industry specifics into account?
Yes. Client comparisons are based on segmentation — the system groups companies by purchasing behavior, not just by PKD code or size. Recommendations are built on market similarity, not only categorical.
What if the salesperson disagrees with a recommendation?
A recommendation is a suggestion, not an order. The salesperson knows the relationship context that the system does not — and their judgment is always superior. The system does not evaluate whether the salesperson followed through. It provides information. What they do with it is up to the human.
Does NBA support multiple salespeople at the same time?
Yes. Each salesperson receives their own personalized queue — based on their client portfolio. The manager sees everything across the team — they can check individual salespeople’s queues and see where the biggest opportunities or risks are in the team.
How to measure the effectiveness of recommendations?
Quantis tracks the history of recommendations and their effectiveness over time. Which types of actions bring results, which products actually lead to sales after recommendation, which clients were saved from churn. This data allows continuous improvement of the system’s logic with every new cycle.
Does the system work if the company has few clients in the database?
The more data, the more accurate the recommendations — this is true for any ML system. With smaller customer bases, the system switches to more conservative comparisons and a higher confidence threshold. There are fewer recommendations, but they are more precise.
What does cooperation with Origami Effect look like after system implementation?
The project does not end when the code is launched. Every solution at Origami Effect is created with the assumption that we build only tools we ourselves would like to use every day — without compromises and without mass templates. Every system is treated with craftsman-like attention to detail, like our own venture that must work exceptionally well and genuinely build company value.
Experience gained from working on the investment fund side has shaped the principle that a strong business needs constant access to the best, clean data. That is why after implementation, Origami Effect remains a long-term technical partner. The system is continuously monitored, optimized, and developed together with the company. Quantis is not a project that gets closed and put on a shelf — it is a living tool that grows and adapts to the changing physics of the market.
Do you need someone who instantly understands the problem — and knows exactly what to do with it?
Most companies have data. What’s missing is the idea of what to do with it — and someone who will actually execute it. Origami Effect delivers both.
