The future of the point of sale:
5 trends that are redefining the POS in Latin America
How technology and data are changing the way we manage each point of sale.
A product may appear in stock in the system and be missing from the shelf. A promotion may be approved, but displayed with the wrong price. These are familiar situations for any sales or trade marketing team. The difference is that today there are more signals to detect them and less margin for reacting late.
Furthermore, in Latin America, large chains with daily data coexist with distributors, discount formats, and thousands of traditional stores. Managing that diversity requires understanding what happens in each store and deciding where to act first.
From general reports to relevant alerts
Sell-out, inventory, purchase orders, assortment, promotions, visits: teams receive more information than they can manually review.
That is why it is gaining ground management by exception. The systems highlight changes that deserve attention, such as an atypical drop, several days without a sale, or inventory that isn't moving. A good alert also provides context: a day without sales can indicate a stockout, but also low demand, a closed store, or an error in the source.
Illustrative example of the structure of a useful alert: the fact, its potential impact, possible causes, and the person responsible. The data is for reference only and does not correspond to an actual operation.
Everything for everyone
- Extensive dashboards that need to be interpreted
- The same view for each role
- The finding depends on who is looking
- The action falls outside the system
Only what requires a decision
- Notable exceptions above the rest
- Context explaining the sign
- Order by potential impact
- One person in charge and one status per case
The dashboard begins to function as a work tray: it shows what happened, how much could be at risk, and who can intervene.
Perfect Store adapts to the potential of each store
Applying the same checklist to all points of sale can lead to wrong conclusions. A convenience store, a wholesaler, and a regional supermarket fulfill different functions for the category. The expected assortment and the value of a display are not the same either.
Perfect Store is evolving towards models segmented by channel, format, commercial potential, and buying behavior. This makes it possible to evaluate each location with more realistic criteria and identify cases where the standard has become outdated.
Convenience store
Immediate replacement purchase, short ticket, and high frequency.
- Expected assortment
- Short, high-turnover core
- Critical variable
- Availability of the essential
- Monitoring
- Frequent and very limited
Wholesale
It supplies other businesses: volume, large formats, and price.
- Expected assortment
- Volume formats and packs
- Critical variable
- Inventory and supply continuity
- Monitoring
- Focused on stockouts and price
Regional supermarket
Planned purchase, broad categories and display space.
- Expected assortment
- Broad portfolio by segment
- Critical variable
- Shelf share and additional display
- Monitoring
- With a focus on promotional execution
Three example archetypes. The exact criteria depend on each company's category, market, and business strategy.
AI begins recommending the next action
At the POS, artificial intelligence is useful when it reduces the volume of decisions a person must review. Models can detect unusual patterns, estimate the risk of stockouts, and rank opportunities based on their potential impact.
The most interesting progress is in the concrete recommendations: which store to review, on which SKU, for what reason, and with what priority.
Proximity format
High turnover in the area
Atypical sales pattern
Sorted by impact
Signs that explain the recommendation
- Consecutive days without a sales record
- Theoretical inventory that does not move
- Deviation against the store's own historical data
- Comparable store sales
Illustrative example of the format of an actionable recommendation; it does not represent data from an actual transaction.
These results also require human judgment. A lost sale estimate helps compare opportunities, even though it is unlikely to represent an exact accounting figure.
McKinsey points out that, within CPG, analytics and traditional AI maintain a greater operational potential than generative AI.
In execution, this directs attention toward forecasting, anomaly detection, and prioritization. Source: McKinsey, Fortune or fiction? The real value of a digital and AI transformation in CPG (2024).
Photographs become verifiable data
Computer vision allows extracting information from in-store photographs: product presence, number of facings, shelf share, location, or promotional compliance.
This is how you can review a volume of images that is difficult to process manually and focus attention on significant breaches or doubtful cases.
Illustrative diagram of what a shelf photo can turn into data: automatic recognition of clear cases and routing of exceptions.
The quality of the capture remains decisive
An incomplete angle, reflections, low light, or an outdated packaging can alter the result. The most reliable implementations automate clear cases and leave exceptions for human review. Before scaling, it is advisable to test recognition by category and channel.
The collaboration incorporates action tracking
Many POS problems affect manufacturers, retailers, and distributors alike. Inventory may be logged and remain in the warehouse. A promotion may be active in the commercial plan and poorly implemented in the store. Detecting the difference is of little use if no one knows who is supposed to resolve it.
Leading CPG companies in Latin America place greater weight on collaboration with retailers and distributors, the use of data to manage the sales force, and direct access to point-of-sale information.
Source: McKinsey, Leaders in Latin American CPG: A commercial excellence evolution (2025).
The complete cycle of an opportunity
What was detected
The signal, with its context and its potential impact.
SystemWho takes the action
A clear leader between manufacturer, retailer, or distributor.
Assigned person in chargeWhen was it resolved
The action executed in-store, with date and status.
Field teamWhat happened next
The observed result after the intervention.
MonitoringCollaboration improves when it includes the status of each opportunity. To sustain that cycle, what is needed is common definitions of product, store, inventory and availability.
The standards of GS1 They help maintain consistent identifiers between systems.
A faster and more specific execution
The future of the POS is being built around shorter decision cycles: detecting a signal, assessing its impact, assigning an action, and checking the result.
Detect
A signal that goes off the pattern.
Evaluate
How much could be at stake.
Assign
An action with a person in charge.
Check
What happened after acting.
Technology can accelerate that journey. The value is seen in the operation: less time reviewing information, better prioritized visits, and opportunities addressed while they can still be recovered.
From POS data to measurable actions
At Teamcore, we help consumer packaged goods companies connect point-of-sale data, detect opportunities, and activate measurable actions for their teams.
Learn how Teamcore works