How AI optimizes sales routes: from data to field action

Artificial intelligence has burst into the lives of mobile sales teams: data analysis, meeting preparation, smart selling, intelligent recommendations, automation, and… organization and sales route optimization.

The concrete use cases of AI in sales are numerous, which is why we are going to focus, for the duration of this article, on a specific topic: how artificial intelligence can help you and save you time on optimizing your sales routes, going far beyond simple logistical organization?

Find the answer in this article.

What does AI bring to sales route optimization?

When you optimize a sales route, you simultaneously answer the following questions: who to visit? When to visit them? How often? In what order? Keeping which objectives in mind? With what field constraints?

For a few points of sale, a human can answer these questions. But to organize routes every week, across dozens or even hundreds of points of sale, this is not possible. That is when technology takes over, but sales route planning and optimization tools start from a key assumption: the list of locations to visit is already defined.

This is where AI truly changes the game: artificial intelligence comes into play before, during, and after route optimization. While a traditional optimization tool works on a given list, the AI integrated into a route optimization tool builds and prioritizes the list. Artificial intelligence analyzes the entire store network, cross-referencing it with sales history (sell-in and sell-out), recent trends, weak signals, field data, and the commercial context (promotions, launches, etc.).

How AI concretely optimizes sales routes

AI transforms data into field priorities

When your sales representatives plan their routes, they must select the points of sale they will visit. Let's forget about routes based on habit and take the example of an account manager who selects stores based on objectives or data.

Either they select them manually, which can take time and lead to errors. In this case, we advise you to have them read this ebook on which stores to prioritize according to the month's objectives.

Or, you help them properly plan their sales routes by creating dynamic views. Since it is rare to work on your entire store network at once, dynamic views allow you to filter your points of sale based on specific criteria (store status, overdue visits, visit frequencies, numerical distribution, revenue, etc.), finding them more easily and quickly. They can be customized for each of your sales reps and are, of course, scalable over time. Results update dynamically as you apply or modify filters.

From these views, your sales reps can organize their routes, choosing points of sale based on specific goals and needs. Artificial intelligence can do this work for you. It analyzes data from your CRM and other sources (if connected to your CRM), such as checkout data, to 1. identify points of sale with high potential or those losing momentum, 2. detect weak signals (drop in revenue, product out of stock, low numerical distribution, etc.), and 3. classify points of sale by priority level (urgent, strategic, in jeopardy...). AI tells you concretely where to go and why.

But beware: just like a human, AI needs data, and clean data at that. If the collected data is incorrect or skewed, what the AI proposes will be too.

AI offers smart routes

If you are equipped with a route optimizer, it will help you find the best possible itinerary based on the geography of your points of sale. It optimizes driving time but does not necessarily take into account the business value and store relationships.

Artificial intelligence aggregates all the criteria that matter to you:

• The location of points of sale

• The potential of points of sale

• The ideal visit frequency

• Current commercial objectives

• The sales rep's personal constraints

• Constraints of the store and department manager

This allows it to build a more balanced and profitable route. If requested, it can also design a route that includes overnight stays.

To better understand how this works:

AI continuously adapts based on results

Today, if an appointment is canceled, your sales rep goes into their mobile app and deletes the appointment. Automatically, their calendar adjusts to reorganize the day's visits accordingly.

However, and this is normal, your account manager cannot monitor every point of sale that undergoes a risky change during their route. Artificial intelligence can. A change in objective, an unexpected competitor promotion popping up, a logistical issue with a flagship product... AI can take all these last-minute changes into account to adjust in real time or reorder priorities for the coming weeks.

AI allows for a better distribution of efforts among sales reps

Sales territory alignment is a real challenge. Properly dividing your territories requires local knowledge, understanding your teams, and patience. But in a profession with high turnover, it is not always easy to know your sales reps and their capacity well. As a result, some areas may be oversaturated and others overlooked. Some reps end up overwhelmed while others are underutilized.

AI can assist you in this task by suggesting a redistribution of certain points of sale and zones, based on availability, performance, and/or visit frequency. It helps you rebalance the workload among your team members.

Concretely, how does AI work to optimize sales routes?

As you read this article, you might think that artificial intelligence makes many things possible, but how does it actually work? The goal is to show you how data is used, how priorities are set, and how routes are generated and then adjusted.

Step 1: Collection of sales data

As mentioned earlier, AI needs reliable data to work. This is where your sales reps play a key role and remains indispensable.

In order to recommend the best possible routes, it will rely on all the data present in your CRM, such as:

• Your customer database: stores, contacts, locations, statuses, orders, sell-in...

• Your prospect database: stores, contacts, locations, statuses...

• Visit objectives

• Past visits: frequency, visit duration, purpose of the visit...

• Your contacts' constraints: store opening hours, preferences, working days...

• Data gathered by your sales reps during their visits, via in-store audits: product presence, display presence, store importance, type of store...

• Analyzed audit data: numerical distribution, evolution of numerical distribution, share of shelf space, revenue...

• Visit reports

• Commercial constraints: maximum visits per day, field days, assigned zones...

• External data linked to your CRM: ERP, sell-out...

Step 2: Analysis of sales data

The raw work of data collection and integration is done. Artificial intelligence can now cross-reference data and analyze it to identify priorities and urgencies. To do this, it will score your points of sale by calculating a priority score. Here is a non-exhaustive example:

• Drop in revenue = +15 points

• Not visited for 30 days = +10

• Active promo to pitch = +20

• Low numerical distribution in a key category = +15

It will then detect anomalies, alerts, or opportunities:

• Visit missed twice

• No order placed despite product promotion sold in

• Rising sell-out

• Etc.

Step 3: Selecting stores to visit

Based on the analysis, the AI will create a prioritized list of stores to visit for each sales representative. This list is often too long for the account manager to visit everything in one week. Therefore, it will select only the stores that are worth visiting.

Here is how artificial intelligence works:

• It filters stores based on the priority score

• It applies pre-defined minimum frequency rules per store type. For example, hypermarkets are visited every month, convenience stores every two months...

• It excludes those that have just been visited or do not represent an active challenge.

Step 4: Automatic route generation

This is the final step, the one visible to you and your teams.

Once the calculation is done (which takes less than a minute), it will generate an optimized route plan, taking into account travel time, itinerary, breaks, business rules, and personal constraints (remote work days, working hours, assigned zone, mode of transport...).

Once the route is validated, it appears automatically in your sales reps' calendars. They just need to open their CRM app on the day of the visits to get on their way.

Step 5: Learning and adjustment

Fieldwork is a continuous loop. The sales rep will complete their route and collect new data through their CRM app. All this data goes back into the CRM so that the AI can use it and re-evaluate its priorities accordingly.

Thus, it will:

• Analyze the quality of the visits

• Re-evaluate the points of sale

• Propose adjustments for subsequent routes

Here is a simplified diagram of how AI optimizes your sales routes:

1. Data collection (CRM, store audits, sales) ↓ 2. AI analysis and scoring (priorities, alerts, opportunities) ↓ 3. Weekly field target selection ↓ 4. Automatic route planning (geography + strategy) ↓ 5. Field execution (mobile CRM) ↓ 6. Continuous re-evaluation (AI loop)

What are the conditions for AI to truly optimize routes?

Reliable, up-to-date, and actionable data

For artificial intelligence to bring real added value to the optimization of your sales routes, it is essential that your CRM is populated with reliable, up-to-date, and actionable data. AI relies entirely on the quality of the data it analyzes. If the information is incomplete, obsolete, or inconsistent, the recommendations produced will be as well.

Concretely, this implies:

• properly filled-out customer and store data,

• regular and structured visit reports,

• sales indicators tracked over time,

• basic discipline in data entry on the field.

Clearly defined business rules

AI needs explicit prioritization rules to make correct trade-offs. Artificial intelligence needs human intervention to function properly. You must indicate which client types are a priority, what minimum frequency of visits to comply with, what signals trigger an urgent visit, or which objectives prevail depending on the period. Without basic configuration (or a good prompt), it won't understand what you want.

Real adoption by the teams

AI only optimizes what is actually used.

On the sales side, teams must follow the suggested routes, document completed visits, and understand the reason why behind the recommendations. AI is only effective if it is perceived as an aid, not as a constraint.

On the management side, the sales director must analyze the results (at least initially) and adjust rules and priorities over time to properly direct the AI in its operations.

How Sidely's AI automates route optimization

At Sidely, we offer two ways to automate your sales routes and therefore save time.

Either you trust the AI, which takes care of organizing your sales route. For this, you enter the number of days you are available during the week, and Sidely selects the stores and offers you an optimized itinerary.

Or, you choose your stores. In this case, Sidely automatically optimizes your route and journeys, taking into account overnight stays (if necessary), day-by-day division, your contacts' time constraints, and the order of visits.

If you are interested, contact us!