Whether as voice assistants such as Siri and Alexa, translation tools such as DeepL or navigation systems such as Google Maps - AI applications are now used in almost all areas of life. And new fascinating examples of these applications reach us almost every day.
These developments also have an impact on the way companies interact with their customers. Why? Because they enable a highly personalized and efficient customer journey, for example by analysing customer data, in the form of chatbots or through automated processes. But how can companies enter the world of artificial intelligence? And how can individual areas of the company, from marketing to sales and service, be optimized through its use?
Artificial intelligence or learning algorithm?
In order to understand the diverse application possibilities of AI in companies, a central question arises first: What is actually behind the term artificial intelligence? As the term intelligence is not clearly defined and is also associated with a human characteristic, we prefer the term "learning algorithm". The learning algorithm differs from terms such as "big data" or "data warehouse" in that it continues to develop based on the data it processes and does not just answer data queries.
Artificial intelligence therefore offers a wide range of applications and great potential for optimizing business processes. But how can artificial intelligence or the way it works help to inspire customers?
AI at the customer interface
Here at the Steinbeis Sales and Marketing Institute (Steinbeis VMI), we investigated the possible use of AI or learning algorithms along the customer interface for a medium-sized manufacturing company. To do this, we first looked at which companies are already using such methods today and, in particular, where they make sense. The investigation revealed that similar companies are already using learning algorithms in the following areas, among others:
Dynamic customer segmentation according to various criteria, such as purchase frequency, turnover, revenue, length of use, satisfaction level, products purchased, etc. The system provides new suggestions based on recognized patterns- Individual product suggestions for existing customers based on the above samples
- Product selection for marketing campaigns, proposal for campaign and target group based on historical sales data, among other things
- Generation of product texts for various applications (online, campaign, sales communication, etc.) Tools with AI modules act like an assistant that independently makes suggestions. These can then be sorted, supplemented if necessary and approved by employees.
- Supporting sales communication with individual e-mails based on existing communication with the customer
- Use of chatbots for customer advice and customer service (self-service). There is a three-way split here between classic algorithms, learning algorithms and human expertise.
- Determination of sales probabilities
- Pricing - calculation of prices based on sales data, regional data, sales employees, customer segments, etc.
From the status quo to the intelligent use of AI: what is important?
There is a wide range of possible applications for AI solutions along the customer journey. But what should companies look out for before introducing "intelligent" systems? The answer to this question depends on various factors. However, the following aspects should always be taken into account, regardless of the area of use and application scenario:
1. Data protection without compromise
Learning algorithms rely on large amounts of data in order to provide support with suitable suggestions. Companies must ensure that data is collected in accordance with legal data protection regulations: What legal requirements regarding data protection must be observed? Has an opt-in been obtained from the customer for the personalized data? How does the company retain sovereignty over the data used, that is, how is control over the collection, storage, use and processing of the data guaranteed? The use of AI and compliance with data protection regulations need not be a contradiction in terms; when used correctly, AI can also be used in compliance with data protection regulations in everyday working life.
2. Focus on employees
The introduction of learning algorithms is a complex process that requires a holistic approach and does not just concern technical aspects. As always with the digitalization of processes, this is also a "change" process in which employees must be involved. Early information, support during the introduction and transparency in the deployment scenario are important components for successful implementation.
3. Data quality determines results
The basis for any use of artificial intelligence is the necessary data. Without it, even the best algorithms cannot deliver results. It is therefore worth checking the data situation in a company as a first step. Learning algorithms can only be used where data is available in sufficient quantity and quality.
If, for example, inventory or sales forecasts for products are to be improved with the help of learning algorithms, data is needed that is also available in context. In other words, sales data alone is not enough; it must be placed in the context of procurement data, business and economic data, sector-specific economic data and, if necessary, demographic or climate data, to name just a few.
4. Step by step to the goal
When using AI, it is important to get to grips with the technology and identify useful areas of application in order to simplify or optimize processes.
We recommend starting as small and as limited as possible. The use of bots, for example, has proven to be particularly suitable here, both for product advice and after sales service, as mature systems are now available. You can start on a small, manageable scale to gain experience that can be used in further development.
5. Intelligent assistants in CRM and ERP
Another decisive factor that should be taken into account when using AI in companies is (existing) ERP and CRM solutions. The reason: many manufacturers are already integrating learning tools or AI assistants that can be used with little effort. For example, with little additional investment, they can be used to achieve dynamic customer segmentation or better predict purchasing decisions.
Where is artificial intelligence heading?
The use of AI in companies offers both opportunities and challenges as well as seemingly unlimited potential for optimizing processes along the customer journey. Currently, these tools can help to strengthen customer relationships, increase customer satisfaction, boost productivity in the long term and, in particular, automate routine tasks.
In the future, intelligent, autonomous systems will act even faster and more precisely in many areas than we can with our current tools. As a result, virtual assistants will provide an ever better information base for making optimal decisions. In some areas, such as stock exchanges, this is already happening today.
This also means that the combination of human expertise and artificial intelligence is becoming increasingly important. In future, AI applications will provide even more support in solving complex problems, implementing processes more efficiently and gaining new insights. It is crucial that these systems only act in a supportive capacity and that the decisions continue to be made by people.
About Markus Deutsch
Markus Deutsch, Dipl. Math., is Senior Partner of the Steinbeis Sales and Marketing Institute. He has been active for many years in both industry and as a sales and marketing consultant. Additionally, he was actively involved in the founding and market entry of many new companies in various sectors.
The Steinbeis Sales and Marketing Institute (VMI) offers implementation-oriented support for entrepreneurs and companies, especially SMEs, in sales and marketing through management consulting, business coaching, market research, workshops, seminars and training.
Steinbeis VMI employees combine experience with innovative strength. Diverse in their skills, but with a strong connection: practitioners, implementers, doers.
We attach great importance to this, because this was our founding myth and is still what defines us today. We combine consulting expertise for SMEs with first-hand management and business experience and combine this with science.
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