Artificial intelligence for even smarter CRM
AI technologies and systems have long arrived in CRM and help optimize customer relationships. The wealth of data from CRM provides a perfect breeding ground for AI and thus the basis for new intelligent tools. And the potential is far from exhausted. We support you with suitable CRM solutions that reflect the latest state of development. These include AI-based tools that can use your data and self-learning algorithms to recognize patterns, suggest solutions, and make predictions. These software tools can be used to address snd marketing issues. Once set up, these "assistants" are tireless helpers and reliably deliver valuable information and recommendations in response to questions such as:
- Which leads should I focus on?
- Which offers are most likely to become orders?
- What kind of items does the customer find interesting?
- Which customer should I call next?
- What topics is the customer particularly interested in?
In many companies, these everyday questions are often analyzed in a cumbersome and past-related manner or simply answered on the basis of gut feeling. Even though the latter is often correct, it is significantly more reliable in combination with an AI. The intuition of the employee supplemented by intelligent tools are good prerequisites for developing correct forecasts and conclusions for the decision.
To help you make faster and better decisions, we put "artificial assistants" at your side.
Use Cases
The areas of application of our AI solutions basically include the expansion of customer relationships the further development of the product range and the development of markets. The following could be concrete application areas in your company:
Lead Generation
AI helps figure out which leads are worth focusing on and assists with recommendations semantic methods.
Forecasting
The forecasting model is used to identify customers who are likely to place orders.
Customer Lifecycle
Identifying expensive customers: Where will the most money be lost? Who is worth retaining?
Churn Management
Receive timely warnings about which customers are at risk of churn.
Up & Cross Selling
Classics like "other customers have bought" to recommendations of what works well for specific customers.
Product Bundles
Which promotions have proven successful and what is the optimal composition of promotional products?
The technology behind it all
Standardized software tools are used in all projects. The principle is mostly the same: The systems we use are well-known solutions supplemented by self-developed components, so that a customized solution is used based on the business case.
The following graphic visualizes some of the AI tools we use and the different data sources from which they draw and process information, as well as the outputs they can produce.
Example: Closing Forecast
The central question in the customer's sales department was "Which opportunities are likely to become orders?" Extensive reports were created with a lot of effort to answer this question. The basis for this: 50,000 sales opportunities, thousands of activities, and at least as many offers. We identified the criteria that are relevant for closing orders. We looked at these along customer journeys and analyzed where orders were actually concluded.
From the findings, we developed the algorithm. This was applied several times and refined based on the results. We had an absolute bull's eye! The forecasts came true just as the machine calculated. We were able to predict which sales opportunities would become orders!
The customer journey directly before closing is often decisive. How and with what intensity did the sales representative interact with the customer? And if we understand successful journeys better, this also provides useful information for the behavior of the sales staff.
Example: Sales Forecast
Sales are often subject to seasonal influences. This is true for almost every industry, but especially so for fountain manufacturers. In summer, and especially when the temperature is above 30 degrees, it's boom time. These "natural" peculiarities are well known. In the past, various planning procedures have been established and built up that take these obvious influences into account. Special situations such as a global pandemic, on the other hand, override any planning. In our model, we have not only predicted seasonality, but also taken the pandemic times into account by means of suitable functions.
Trends are often not precisely recognizable in everyday sales due to the multitude of influences. With data analysis to determine trends, sales and corporate management receive important impulses for expected sales or capacity requirements for people or machines. These charts show an example of how a trend is broken down into a linear increase, the seasonal overlay. If necessary, however, as in the last chart, special effects - e.g. Christmas business under special conditions - can be modeled and the expected figures for the following year can be "predicted" and compared with sales experience.
Example: Recommendation of Sales Items
For many field services, the order that counts when visiting the customer. It is not enough to look at the order list of the past. It's about new products, special offers, but also about products that the customer doesn't order, but which are "doing well" with other comparable customers. The art is to analyze this skillfully and to offer it in the conversation. Then it is not simply about more sales, more quantity, more items, but about growth - making the customer more successful as well. In this project we have analyzed all classification aspects to article, customer, order and the orders to it. From the patterns we can deduce what the employee should suggest in the customer visit - that which promises success.
It is difficult for a salesperson to keep track of the complete order backlog. Not for an AI model. Just as with the recommendations from Amazon or Netflix, the salesperson can also be supported. What else have similar customers bought in my customer base that I haven't even addressed with my customer yet? Standard recommender can help quickly, generate offer suggestions and the results can be entered into the CRM as sales activities.
Our approach
At the beginning of a project, we focus on three aspects: The creation of a clear target picture, the securing of the data basis as well as the current status of the CRM. Together with you, we try to find answers to the following questions:
1. The Aim
- What questions should the AI answer?
- What does the customer journey look like?
- Does an AI solution make sense in this case?
- Who should the AI assistant support?
2. Data
- Is there enough data available?
- What is the quality of the data?
- Which data is relevant?
- How is data collected - is there a need for optimization here?
3. CRM
- Is the CRM up to the necessary standard?
- Which AI support does your current CRM offer?
- Which additional AI modules make sense?
Once this checklist has been worked through, development can usually get underway. Using an example for the recommendation system.
Building a wizard requires data knowledge and the practitioner. And, of course, the appropriate tool. That's all it takes. Our 3-step approach has proven itself.
1. Data Analysis for a Recommendation System
- Understand and review the classification system for customers, items and other data types as appropriate, including data quality.
- Effects of the structures and mechanisms.
- Review the system and provide appropriate data exports for the selected data.
- Elaboration of the objective function: which parameters should be optimized in interaction.
2. Algorithm
- Implementation of the objective function and verification of the results on relevant examples with AI components.
- Iterations for optimization
3. Drafting a design for Internal Marketing
- Procedure for getting to know and participating in the optimization of the assistant
- Presentation of results for the sales department about the analyzed data as well as the planned changes for the sales process integrated in CRM
We support you in how to integrate the predictions of the model into the workflow of the employees. Typically, the process involves constant adjustment of the task or model to optimize the flow.