AI or artificial intelligence is said to have great potential in companies. Compared to other departments in companies, sales has not yet been digitalized to any great extent. The question therefore arises: what is the potential of AI in sales?
There are basically two types of AI: Generative AI and Predictive AI. Generative AI is used to create new content: text, videos, music, etc. Predictive AI analyzes data and enables predictions to be made about future developments. Generative AI is receiving a lot of attention at the moment, but we see predictive AI as a major lever for sales. In addition to the different types of AI, it is important to differentiate between where it is used: Front-end and back-end. Why is this distinction so important? Because digitalization in sales has so far focused too much on the front end. Front-end refers to customer interactions. A classic example is the chat bot. Customers can make inquiries to the company and the chat bots, supported by AI, are getting better and better at answering the inquiries. Sounds very good, if only an average of 5% of customers in Switzerland didn’t use a chat bot. Not to mention the satisfaction with the tools. Back-end, on the other hand, focuses primarily on the data. This is where the greatest potential, but also the greatest challenge for companies to successfully use AI in sales lies. The current data quality in most companies is too low for the use of AI. The systems are outdated, often not yet in the cloud and unconnected. This means that the IT ecosystem is not yet AI-ready. The various use cases are presented below and an assessment is made based on our AI consulting.
How can AI be used in sales?
We are currently seeing that AI in sales is primarily focused on the front-end chat bot case. There are numerous providers who are trying to convince even in Swiss German. Drift is one example. In principle, there is nothing wrong with this type of AI application. However, it is important to critically examine whether this is currently the best way to get started with AI. This is because resources are tied up, many discussions are held on the basis of uncertainties and few customers will use this tool. We therefore recommend starting with AI in sales at the back end.
Use cases for AI in the back end are:
- Automation of routine tasks
- Process automation
- Data evaluation
- Sales Enablement
(1) Numerous providers advertise support for the automation of routine tasks, such as data entry, email communication and order management. AI already offers a certain potential for increasing efficiency here. It optimizes the wording of emails and enables them to be sent at the optimum time. Apollo is an example of this. A survey of 1,400 people revealed that this use case is seen as the most important. Such an application supports employees, but also has limited potential for increasing profits. In addition, it is important to ensure that the content still has a personal touch for customers. It is therefore surprising that this use case is rated so highly.
(2) Process automation is another field of application. AI can suggest improvements to existing processes. This assumes that the processes are digitally documented and the individual process steps are also checked using KPIs. In most cases, the chat bot is “only” aimed at improving customer interactions. There is potential for optimization in companies due to a lack of responsibilities and other reasons. This is where AI can help. The really deep integration of AI into sales processes requires (except in e-commerce) that every employee provides the company with very precise information about individual process steps. We see a major challenge here in sales employees carrying out this activity. The feeling of control quickly arises here. In principle, this is a possibility, but in our view not the starting point for AI in sales.
(3) Data analysis can be performed by AI. However, several points must be taken into account:
- The relevant systems are connected in the cloud
- Valuable settings data is also collected
- The sales department has included data collection as a bonus component in the remuneration.
- A separate data system is available
- A proprietary data AI is being introduced and constantly developed.
In our view, data analysis offers the greatest potential for AI in sales. However, as the starting position is usually not ideal, we recommend that companies concentrate all AI activities here now. Even if points 1 and 2 are easier and quicker to implement, data analysis is the key to future success. Data analysis using AI can support the following points:
- Lead scoring
- Sales Forecasting
- Churn prediction
- Cross/up-selling potential
Microsoft and Salesforce in particular are currently outdoing each other with the possibilities of their systems. It is important for companies to critically examine whether the dependency in connection with the quality of the systems and the costs is optimal. We recommend setting up your own data platform and establishing your own AI for data analysis that provides targeted support for individual sales targets. Lead scoring and forecasting are the simpler tasks. However, it should be noted that many customers, sales employees and orders are required to ensure that the calculations are not too error-prone. Churn prediction is much more complex to map via AI. The biggest challenge, but also the greatest potential, lies in cross/up-selling. We are seeing that the major B2B manufacturers in Switzerland have now established or are in the process of establishing their own developments in order to be more successful in sales. We can therefore only recommend that smaller companies follow this path and take data analysis into their own hands, despite the big promises of the many CRM and automation providers.
(4) Sales enablement is based on data analysis and combines forecasts with content suggestions. The applications are still very basic but can already provide support. One example is Gong or Exceed. It will certainly take some time to determine which implementation is recommended. Too much AI, especially in content creation, can also mean that customers have the feeling that they are interacting with a machine. Therefore, more experience is needed regarding the optimal connection between AI and sales staff. We currently recommend focusing on data analysis, as this means the most time for implementation and enables the greatest profit leverage.
Overall, AI is a great support for sales. The topic of cross-selling in particular, which has hardly been implemented in many companies to date, can be tackled systematically with AI, even for small companies. One challenge is the technical implementation. Are standard systems really as powerful as currently advertised on websites and how can the ROI of these systems be assessed? We see a great danger here that companies will become dependent for fear of being left behind.
