Campaign Optimization & Segmentation
The issues
| Why are our direct marketing campaigns so inefficient? If we continue to have to write to five hundred customers to get just ONE customer to buy an add-on product, it's simply not profitable. |
| Do we really have to offer all customers our loyalty discount in order to retain that 3 percent of customers who want to cancel? |
| Our field sales team treats all customers the same… I think though that we need to change our approach. But how should we distinguish between customers? And what should we do with these customer groups? And how can we then teach the field sales team this…? |
If these are questions you are asking yourself, contact us. We will quickly and effectively help you better understand your customers, segment them with regard to marketing and sales-related factors and optimize your campaigns and sales programs based on this understanding. We don't need to carry out any time-consuming or expensive 'diagnostic phases' to do this, nor do we need to spend a lot of expensive man days in your company; usually, all our analysts require is the data that you already have.
Project examples & Impact
Churn prevention: Our client, a major telecommunications provider, concludes annual contracts with its customers; its high profit margin justifies its investment in customer retention campaigns. Even before we started collaborating on our joint project, our client had been carrying out these kinds of campaigns. In accordance with the selection of customers by remaining contract term and simple socio-demographic criteria, the relevant customers were called and all offered a discount. That cost a great deal of money - and yet each year, around 5% of customers still cancelled their contracts at the end of their contract term.
We carried out a 360° investigation into the company's customers, i.e. in addition to customer master data; we also included in our analysis sales channels, billing data, any past technical problems and complaints, and even data about regional technical problems relating to the provision of services. Using a complex statistical method, we developed a model that improved the identification of potential churners by factor 8 compared to a random selection and that also provided the basis for tailored - and in most cases free - preventive measures. Within a period of four weeks, this model was developed and further optimized and could be used by our client to prevent cancellations effectively. Read the whole case study.
Pharmaceutical sales: A major pharmaceutical company offering prescription medicines in a wide range of indications had the same problem as its competitors: its field sales team was securing increasingly fewer meetings with doctors - and when they did secure meetings, were making only modest returns on the medicines discussed at these meetings.
In co-operation with a pharmaceutical consulting company, we developed a comprehensive model for the preference and behavior-based segmentation of practicing doctors on the basis of the market research data available at the client and, based on this data, came up with specific strategies for initiating meetings, created a list of selected medicines, and elaborated product folders. In addition, we identified clear parameters to also help the field sales team to classify future founders of practices on the basis of just a few questions. The sales and marketing management team was initially skeptical and tested our recommended segment strategy in the field. The strategy resulted in significant increase in prescriptions, compared with the control group. Contact us.
E-commerce: One of the largest B2B online traders wanted to reduce its marketing costs, address new customers in a more targeted manner from day one, and better exploit its cross-selling potential. We therefore developed a model to segment its customers based on behavior factors, using all of the data available. From the master data, we obtained 'static' information, such as sector, region, and company size. In addition to the number of orders and their volumes, the transaction data also provided us with 'dynamic' key figures, such as the length of inactive phases. Finally, we also included the order content in our analysis - categorizing this information into product groups made it easier to handle. As a result, we were able to divide our client's customer base into 12 clearly defined segments that instantaneously proved beneficial with regard to winning over new customers and increase the number of orders from existing ones. Learn more about our e-commerce solutions.