A term often used by the CPQ vendors is guided selling. But what does guided selling really mean, and how does it work in practice?
Guided Selling Historically
To be able to describe guided selling, we should go back to the experienced traditional sales rep communicating with a customer. Historically it is believed that good sales people are good at relation building, but modern sales training focus on helping the customer fulfill their need or solve a problem. How can a sales person do that? They need to understand the customer, so they ask questions about the customers needs. For example:
- How many km do you drive every year with your vehicle?
- What is the bottleneck of your current process?
- What are the site conditions of where the product is to be placed?
- What is your priority, investment cost or total cost of ownership?
Instead of asking the traditional questions:
- Which product do you want to buy?
- Which options do you need?
With the information from the answers above the sales rep puts together a competitive offer focusing on the need of the customer. This is historically done manually based on the knowledge of the experienced sales rep. For example the sales rep knows that product A is very competitive when driving over 100 000 km every year, and option B is optimal when the customer is focusing on low cost of ownership.
Guided selling in CPQ
Guided selling implemented in CPQ is very much like an experienced sales rep, the tools asks questions about the need of the customer. But instead of forcing the sales rep to understand which product and options match the customer needs, the tool is able automatically to put together an optimal solution based on the needs of the customer.
For the tool to be able to do so, it needs information about how a customer need may be fulfilled by a product. Classically this is done by matching specific components in the solution to design criteria. For example if we have a number of components that are affected by the site temperature:
Component A: 0 °C to -20 °C
Component B: -21 °C to -30 °C
Component C: -31 °C to -35 °C
Component D: -36 °C to -40 °C
However, this requires each component in the solution to be hard-coded to match specific design criteria, like temperature in this example. And as a component is removed or added, all the criteria needs to be updated.
A state-of-the-art guided selling configurator use the design criteria of the components in combination with the optimization criteria and let the configurator decide on the best solution. So if we take the same components, but instead look at minimum design temperature in addition to price which is our optimization criteria:
Component A: -20 °C $100
Component B: -30 °C $ 140
Component C: -35 °C $ 135
Component D: -40 °C $ 190
Image a scenario where the requirement i -28 °C, which component should the configurator select? The design criteria is fulfilled by components B, C and D. The optimization criteria finds that component C is the cheapest.
It should be noted that this scenario becomes quite a bit more complex as a product can consist of thousands of components with rules and relations between them, but the methods are the same. However, the optimization criteria needs to look at the selection of not just one but all components to find the optimal price of the complete product taking into consideration all rules. This adds adds additional requirements on the configurator.
By definition guided selling is the process that helps potential buyers of products to choose the product best fulfilling their needs and hopefully guides the buyer to actually purchase the product. To be able to implement this, a configurator must be able to work with design criteria in combination with optimization.