GenAI supported sales processes in German medium-sized companies

According to a McKinsey study on generative AI, four areas account for around 75 per cent of the economic potential of use cases for this technology: Customer Operations, Software Development, Research and Development, and Marketing and Sales. The analysis shows that the monetary potential is currently highest in sales.

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The use of GenAI in sales opens up a wide range of opportunities for SMEs that go beyond pure efficiency gains:

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1. Automation of repetitive tasks

Many sales tasks, such as lead qualification, responding to frequent customer inquiries, or preparing proposals, are time-consuming and repetitive. GenAI can automate these processes so that salespeople can spend more time on strategic activities. This increases productivity and reduces operating costs.

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2. Informed decisions through intelligent data analysis‍‍

GenAI processes large volumes of data and provides valuable insights. This enables sales reps to make data-driven decisions, such as which leads to prioritise or which customers require a personalised approach. This leads to more effective use of resources and increases close rates.

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3. Personalisation at the highest level‍‍

In an age of rising customer expectations, personalisation is key. GenAI analyses each customer's behaviour, history and preferences and generates tailored recommendations and offers. This strengthens customer loyalty and increases the likelihood of a successful sale.

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4. Fast response and constant availability‍‍

With GenAI-based solutions such as chatbots, sales can handle customer enquiries around the clock. This enables mid-sized businesses to keep pace with larger competitors and respond flexibly to customer needs, even outside of business hours.

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In summary, GenAI offers not only sales efficiency gains, but also improved customer relationships and a stronger competitive position. In an increasingly digital marketplace, GenAI is becoming a critical tool for sustainable success.

GenAI can provide valuable services in various areas of sales. Here are a few practical use cases:
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1. Automatically extract information from customer emails

Every day, sales staff receive numerous emails containing customer enquiries and information. GenAI can automatically extract relevant data, such as specific requests, buying interests or technical specifications, and present it in a structured format. This allows enquiries to be processed more quickly and accurately, without missing important details.

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2. Virtual sales assistant to analyse existing customers

A virtual sales assistant helps analyse existing customers by evaluating purchase history, ordering patterns and interactions. It can identify cross-selling and up-selling opportunities and create personalised offers that increase customer loyalty and sales.

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3. Automated Lead Qualification and Scoring

GenAI can score and prioritise leads based on criteria such as industry, company size or online behaviour. This allows sales to focus on the most promising contacts, increasing efficiency and success rates.

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4. Help with sales and marketing materials

GenAI can assist in the creation of presentations, proposals or marketing copy by generating content that is tailored to specific customer needs. This saves time and ensures that communication is always relevant and targeted.

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5. Intelligent chatbot customer interaction

GenAI-powered chatbots can answer customer questions in real time and handle simple service requests around the clock. This improves customer service and frees the sales team from routine tasks.

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6. Mapping customer case books to own product catalogues

GenAI can be used to automatically match customer requirements from specifications with your own product catalogue. This enables the rapid identification of suitable products and the creation of initial customised quotations, saving companies valuable time in the sales process and increasing the accuracy of quotations. (Click here for more information).

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These use cases show how GenAI can revolutionise sales by automating, personalising and optimising processes. It can help mid-market companies increase efficiency and gain a competitive edge.

Selecting the right GenAI use case requires a systematic approach:

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1. Define the focus area

Determine what area of sales you want to optimise. This could be a specific process, a product or a customer group. A clearly defined focus facilitates targeted analysis and implementation.

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2. Analyse current processes

Analyse the current state of the chosen area. Use methods such as value stream mapping to visualise and evaluate each step in the process:

• Time spent per step
• Cost of the processes
• Data availability and quality

Identify steps with high effort and sufficient data, as these are particularly suitable for GenAI.

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3. Brainstorming and market analysis

Organise workshops with different departments to identify potential GenAI solutions. Control the process:

• Internal resources: Can you develop your own solutions?
• External offers: Are there suitable solutions on the market?

Use creativity techniques to identify innovative approaches and evaluate their benefits and feasibility.

In this context, the departmental use case development programme can also be helpful, helping companies to identify AI solutions for specific departments and develop customised AI bots. It provides a comprehensive AI skills analysis and identification of relevant use cases. Further information can be found here <insert link>.

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4. Prioritisation and decision-making

Evaluate the identified use cases using an impact-effort matrix:

• Benefits (Impact): Monetary benefits, efficiency gains, customer satisfaction
• Effort: Implementation time, cost, resources required

Prioritise use cases with high benefits and low effort for effective implementation.

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This structured approach ensures that you select the GenAI use case that delivers the greatest value to your organisation.

Learn more about identifying GenAI use cases in your own organisation in our blog article.

Despite the many benefits of GenAI, there are also risks that need to be considered:

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Data protection and regulatory compliance‍‍

The processing of personal data is subject to strict legal requirements, such as the GDPR. Improper data handling can lead to legal consequences and loss of trust. Ensure that all GenAI applications are data protection compliant and have appropriate security measures in place. Learn more about AI compliance in our blog article.

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Bias and bad decisions

GenAI models can be biased if the training data is not representative. This can lead to unfair assessments or incorrect recommendations. Use diverse and high-quality data, and regularly check models for accuracy and fairness.

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Technology dependency and lack of expertise

Over-reliance on GenAI can be problematic, especially if there is a lack of in-house expertise. Invest in training and building expertise to use the technology effectively and minimise risk.

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Lack of visibility and loss of control

Complex GenAI models can act like a 'black box' whose decisions are not always transparent. This can affect trust. Rely on explainable AI models and perform regular validations to ensure transparency.

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Ethical aspects and customer satisfaction

Automated interactions could be perceived as impersonal by customers. Make sure you find the right balance between automation and personal contact so as not to compromise customer satisfaction.

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Effort for data maintenance and quality‍‍

The effectiveness of GenAI depends heavily on the quality of the data. Maintaining a high-quality database requires resources. Establish clear processes for data management and invest in appropriate systems.

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The use of GenAI in sales offers SMEs enormous opportunities, but also harbours risks that need to be managed. Targeted training and education are essential to sensitise your team to the use of GenAI and avoid potential pitfalls. Through professional training, you can ensure that your employees use the technology competently and responsibly.