Only 16% of 372 key account managers say they can focus mainly on strategic activities. Most spend the majority of their time on operational coordination, reporting, and internal alignment – according to a Gartner survey.
Still, Key Account Management remains highly manual and relationship-driven for many companies.
Strategic partnerships are expected, but daily routines often prevent managers from focusing on them.
Solutions like TPM and technologies like AI help your competitors close this gap every day.
In this article, we will look at the actual routine modern key account managers deal with every day.
We will focus on practical, accessible uses of AI that can support these tasks. Not theoretical AI strategies or future concepts, but specific applications that companies can implement today.
Before going further, it helps to look at a simple definition of Key Account Management.
What Is Key Account Management (KAM)?
In 2026, we still see two types of Key Account Management specialists (KAMs): those equipped with modern digital tools and applications, and those that still rely mostly on manual coordination, spreadsheets, and email threads.
The difference between them lies not in the role’s goal. The Key Account Management definition remains the same in both cases, and we will look at it next.
Key Account Management Definition
Key Account Management (KAM) is a strategic approach manufacturer use to manage their most valuable customers. These key accounts often bring significant revenue or play an important role in the business.
Instead of treating these clients like regular accounts, brands assign dedicated managers and develop tailored plans for each one.
The aim is to build long-term partnerships, support customer retention, and better understand the client’s business.
In simple terms, Key Account Management meaning refers to focusing on a small group of high-value customers.
Why KAM Is a Strategic B2B Approach
In FMCG/CPG consumer goods markets, key accounts are typically large retail chains, major distributors, and, increasingly, eСommerce platforms.
For brands, it usually means dealing with a small number of partners that generate the biggest share of total sales.
These partners control large product volumes, shelf space, and market visibility.
Moreover, they compete with brands by selling their own private-label products. According to Circana, private labels account for 42% of value sales across the EU6 (€317 billion). Their share in supermarkets has reached 44% (€175 billion).
Supermarkets are the most influential channel for both private labels and branded products. Therefore, large retail chains have strong control over shelf space, promotions, and category decisions.
Given that the chains are simultaneously partners and competitors, relationships with them require careful management.
For brands in B2B that operate in such an environment, Key Account Management helps:
- coordinate pricing and promotion strategies
- align category and product plans with retailers
- manage long-term partnerships with the accounts that drive most of their sales
Key Account Management vs Traditional Sales
To understand what is key account management in sales, it helps to compare it with traditional sales models.
In many fast-growing regions, such as the Asia-Pacific, a large share of FMCG sales still comes from small independent stores and traditional retail channels.
However, even in these markets, Key Account Management plays a significant role. Large retail chains, regional distributors, and fast-growing e-commerce platforms still control significant market share.
Key Aspects of Effective Key Account Management
What is effective Key Account Management? It depends on several key principles and best practices that shape how companies work with their most important customers. These principles form the foundation of what is key account management strategy in practice.
| Aspect | What it means in practice |
| Strategic Focus | Companies prioritize partners that support long-term category growth and brand strategy, not just the ones generating the biggest orders. |
| Customer Centricity & Relationship Building | KAMs work to understand each retailer’s business, performance, and priorities to build stronger partnerships and more balanced negotiations. |
| Revenue Growth Management | Growth comes from optimizing pricing, promotions, assortment, and pack formats rather than simply selling more units. |
| Internal Alignment | Sales, marketing, finance, and product teams coordinate around the same account strategy and shared data. |
| Data-Driven Decision Making | Decisions about promotions, pricing, and investments rely on structured data rather than assumptions or past experience. |
Strategic Focus
A strategic partner is not always the biggest customer.
One of our clients, a global pet food brand, sells through many channels, from national retail chains to small independent pet shops. However, one of the most important channels for the company is specialized pet retail chains.
These partners are valuable not only because of sales volume. They are also more willing to align their strategy with the brand, supporting:
- new product launches
- category development
- in-store promotions
At one point, the client discovered, based on data from the company’s SFA system, SalesWorks, that KAMs were spending about 75% of their time manually taking orders in these stores. This left little time for relationship building or product promotion.
As a solution, the company automated ordering through an SSBS B2B eCommerce platform, where AI in B2B eCommerce can further support ordering, recommendations, and retailer engagement.
Because these retailers were already engaged in long-term cooperation with the brand, they were willing to move the ordering process online.
The result is:
- less time of KAMs spent on operational tasks
- more time for partnership development
- stronger focus on category growth
Customer Centricity and Relationship Building
Large retailers hold significant negotiating power. They compete with other chains and often push brands for deeper discounts or stronger promotions.
At the same time, relationships with retailers are not equal across brands. Category leaders or brands that drive store traffic usually have stronger negotiating positions, while smaller brands may face greater pressure on pricing and promotional terms.
Because of this, key account managers must clearly understand how their products perform in each retailer. They need answers to questions like:
- Which promotions actually increased sales?
- Which SKUs drive category growth?
- When do deeper discounts stop delivering value?
Without reliable information, negotiations often become reactive. Retailers request better conditions, and brands have limited evidence to respond.
To build mutual understanding, companies increasingly rely on systems such as SFA, TPM/TPO, CRM, and analytics tools that track sales performance, promotion results, and trade investments across accounts.
These technologies make data easier to access and interpret, and we will look at them in more detail later in the article.
Revenue Growth Through Upselling and Cross-Selling
Growth rarely comes from simply selling more products to the same retailer. It usually depends on a combination of pricing decisions, promotion mechanics, assortment strategy, and pack formats.
This is what the Revenue Growth Management (RGM) framework focuses on in marketing and commercial strategy.
Because many of these factors influence one another, companies usually rely on several digital tools to see in real time which actions lead to which changes.
Internal Alignment Across Sales, Marketing, and Product Teams
Managing key accounts rarely involves only the sales team.
Decisions about pricing, promotions, product launches, and trade investments usually require input from several departments:
- sales
- marketing
- finance
- product teams
When these teams work with different data sets, problems quickly arise.
For example:
- marketing plans promotions without full visibility into retailer agreements
- finance tracks trade spending separately
- sales negotiates with retailers using different numbers
This slows decision-making and often creates internal conflicts.
To avoid this, many FMCG companies introduce shared platforms that allow all teams to see the same account data, promotion plans, and investments.
Systems such as SFA or TPM/TPO platforms often become the foundation for this shared view.
For one of our clients, the shared system to improve cross-department collaboration became our TPM/TPO platform, PromoTool.
Case in point
For one of our clients, a leading poultry producer and exporter, this shared system became our Trade Promotion Management Software, PromoTool.
Most trade promotion management platforms focus solely on promos, leaving other trade investments outside the system. These are often tracked separately in spreadsheets or financial tools.
In this case, PromoTool was implemented to systemize the company’s entire trade spend, not just promotional activities. This allowed sales, marketing, and finance teams to work with the same data on contracts, investments, and promotion performance.
As a result, the company gained full visibility into its commercial conditions across key accounts.
Within one year, operational efficiency increased fivefold, and the company unlocked hundreds of millions of euros in additional value through better control and alignment of trade investments.
Data-Driven Decision Making
Imagine two key account managers preparing for a meeting with the same retailer.
One relies on scattered spreadsheets, delayed reports, and personal experience. When the retailer asks for deeper discounts or additional promotions, the manager can only estimate what might work.
The other comes with structured data from sales, promotion performance, and trade investments. They can clearly show which promotions increased category sales, which price levels worked best, and where additional discounts would not bring real growth.
Both managers share the same goal: strengthening the partnership and growing sales. But the one supported by reliable data can make stronger arguments and plan decisions with much more confidence.
What Does a Key Account Manager Do?
The routine of modern key account managers can look very different depending on the digital maturity of their company.
Let’s compare two typical scenarios.
| Key Account Manager Routine: Two Scenarios | ||
| Task | Manual Routine | Data-Supported Routine |
| Account preparation | Gathers data from emails, spreadsheets, and teams | Reviews dashboards with sales and promotion data |
| Promotion analysis | Estimates results based on past experience | Uses structured data to evaluate promotion performance |
| Retail meetings | Negotiates mainly on price and discounts | Discusses category growth opportunities |
| Internal coordination | Aligns information through long email chains | Teams work from shared systems and reports |
| Time allocation | Most time spent collecting information | More time spent on strategy and relationships |
Core Responsibilities of a Key Account Manager
At the core, the role is the same in both cases.
A key account manager is responsible for managing relationships with a company’s most important customers (in large organizations, the position may be called a global key account manager). Their job includes developing account plans, maintaining regular contact with clients, identifying growth opportunities, and coordinating internal teams that support the account management process.
They also track account performance and ensure the company delivers the level of service expected by strategic accounts, helping maintain customer satisfaction.
But the way this work is done can differ greatly.
KAM without structured tools:
- prepares account reviews manually
- collects sales data from multiple teams
- tracks promotions in spreadsheets
- spends hours aligning numbers internally
A large part of the routine is spent gathering information before decisions can even be made.
KAM with integrated systems (TPM, SFA, CRM):
- opens a dashboard with sales and promotion data
- reviews account performance in real time
- quickly identifies risks or growth opportunities
- spends more time planning a strategy with the retailer
Instead of assembling data, the manager can focus on acting on it.
Managing Relationships with Strategic Clients
Maintaining strong relationships with decision makers in retail organizations remains one of the most important parts of the role.
This includes regular meetings, business reviews, and discussions about future plans.
But here again, the routine can look different.
Without structured data:
The conversation often revolves around price negotiations or promotion requests, with limited evidence about what works.
With structured data:
The KAM can bring clear insights:
- which promotions increased category sales
- which SKUs are growing fastest
- where additional shelf space could generate value
This shifts discussions from price pressure to joint growth opportunities.
Coordinating Internal Teams to Deliver a Unified Strategy
Key account managers also act as the main link between the client and internal teams.
They work with sales, marketing, product, and finance teams to align pricing, promotions, and product launches for the account.
Again, the routine can differ.
Without shared systems:
- teams work with different data
- updates happen through long email chains
- decisions take longer to coordinate
With shared platforms (SFA or TPM/TPO):
- all teams see the same numbers
- promotion plans and investments are visible
- decisions are made faster and with fewer conflicts
In this setup, the KAM becomes less of a data messenger between departments and more of a strategic partner for the retailer.
Top Technologies to Simplify Key Account Management Routine
Modern key account managers spend a large part of their time working with data, documents, and retailer requests. The right technologies help reduce routine work and make decisions easier.
Here are some tools and features that simplify Key Account Management teams’ daily routine.
SFA or CRM (or Both)
| System | What it helps KAMs do |
| SFA (Sales Force Automation) | Manage store visits, track execution, collect field data, and monitor sales activity across accounts. Often acts as the operational hub connected with DMS, ERP, TPM, and Image Recognition tools. |
| CRM (Customer Relationship Management) | Store customer information, track retailer communication, manage meetings, and maintain a structured view of the relationship with key accounts. |
Key Account Management teams use different systems depending on how their organization works.
Some companies rely mainly on SFA, others use CRM, and some use both if different departments need different capabilities. Modern platforms can also be customized and combined to better align with company processes.
In FMCG, SFA often becomes the core system.
This is because it is built around field execution and daily sales operations.
Typical SFA tasks include:
- tracking store visits
- managing orders and product availability
- collecting field data
- monitoring sales activity across accounts
Because of this operational focus, SFA often becomes the integration hub.
It easily connects with tools such as DMS, Image Recognition, or ERP.
CRM plays a different role.
In CRM systems, teams can:
- store customer information
- track communication history
- organize meetings and account activities
They also integrate well with marketing, email, and customer support tools.
However, CRM platforms usually struggle with:
- real-time store execution
- offline field visits
- shelf-level data
- route planning
Because of this, in FMCG companies, CRM usually complements SFA rather than replaces it.
TPM/TPO Platforms
TPM/TPO systems help KAMs manage retailer agreements, promotion planning, and trade spending in one place.
Take our PromoTool as an example. For a key account manager, the system means having:
- a complete profile of each retailer with contract terms and commercial conditions
- tools to plan promotion budgets and track trade investments
- alerts when planned activities risk exceeding the allocated budget
- the ability to create and compare promotion scenarios, run trade promotion forecasting, choose the most profitable option, and many other possibilities.
These systems can also be integrated with Image Recognition technology. For example, if a retailer was paid to place a product near the checkout, Image Recognition can confirm whether the placement actually happened. If the agreed execution is missing, the system helps prevent unnecessary payments.
This gives Key Account Management teams better control over promotion execution and trade spending.
Examples of AI Agents Inside a TPM/TPO Solution
| Role of AI agents in PromoTool | What the agents do |
| Assistant | Helps users navigate the system, explains processes, and performs tasks such as creating promotions or retrieving reports through a chat interface. |
| Advisor | Analyzes historical promotion data, summarizes performance, visualizes results, and provides recommendations based on patterns detected by ML models. |
| Expert | Calculates baselines, forecasts promotion results, and runs scenario simulations to recommend the best retailer, timing, and discount level. |
In our TPM/TPO solution, we gathered three approaches to AI in trade promotion – classic ML for calculations, generative AI for conversations, and trending (for a reason) agentic AI for automation.
Agentic AI plays the leading role and performs three main roles:
- assistant
- advisor
- expert
Let’s look at these roles in more detail.
Agentic AI as an Assistant
Instead of navigating multiple menus or reading long manuals, KAMs can interact with the system in a conversational manner.
For example:
- “How do I adjust promotion parameters for this retailer?”
- “Where can I find last quarter’s promotion results?”
- “How do I create a multi-retailer promotion?”
The chatbot explains the process step by step and guides users through the system.
The agentic part becomes especially useful when users want to perform an action.
Instead of only explaining, the system can execute tasks.
For example:
A KAM can say:
“Create a promotion for Retailer A from June 1 to June 14 with a 20% discount.”
The AI agent then:
- opens the promotion planning module
- fills in the required fields
- suggests products
- prepares the promotion for review
Instructions can also come from voice input, not only text.
Agentic AI as an Advisor
In this case, AI supports trade promotion analysis and decision-making.
The system can analyze historical promotion data and answer questions like:
- “Which retailer gives us the highest promotion ROI?”
- “Which discount level usually works best for this product?”
- “Why did sales drop during the last promotion?”
For example, the agent might analyze several years of promotion data and respond:
“Promotions with a 15-20% discount in Retailer B typically generate 18% higher uplift than promotions with a 10% discount.”
The AI can also combine internal data with external market information. Meaning, the agent can gather information from outside of the system.
For instance, it might detect that a competitor has started a promotion in the same category, or that the category’s price level has dropped.
This allows KAMs to see the broader market context, not just internal performance.
Agentic AI as an Expert
Some brands admit that they still guess their baselines.
The problem is simple: truly clean periods without promotions or external effects are rare, so estimating normal sales levels is difficult.
With AI, the system can automatically calculate baselines.
For example, the solution can analyze:
- historical sales
- seasonality
- previous promotions
- retailer-specific behavior
Based on this data, the system can estimate:
- baseline sales for current promotions
- expected results for future promotions
The important part is that the results are explained in simple language, not hidden inside complex dashboards.
Agentic AI also enables scenario simulations.
For example, the system can test various promotion strategies with:
- different discount levels
- different retailers
- different promotion timing
AI might suggest something like: “Running a 15% promotion with Retailer C in week 42 is likely to generate 12% higher uplift than running the same promotion in week 38.”
Conclusion
Key Account Management has always been designed as a strategic function focused on long-term partnerships and growth. However, in many companies, the daily routine of key account managers is still dominated by operational tasks and manual coordination.
Modern digital tools such as SFA, CRM, and Trade Promotion Management platforms, together with AI technologies, help automate routine tasks and structure account data. This gives Key Account Management teams the time and insights they need to focus on strategy, relationships, and long-term value creation with their most important customers.
