Data & modelling challenges in the B2B space and their solutions
Reluctance to adopt ML-based pricing originates from two main sources i.e. data related challenges and the complexity surrounding the pricing as a business process (See Figure: 2). In this section, we have tried to address some of these.
- ML models need huge volume of data and in B2B, the volume of data is less to build an effective model.
Solution - This stems from the common perception is that ML models need a large data set. While it is true that some of the more sophisticated models like deep learning cannot be trained without large volumes of data, there are multiple algorithms in ML e.g. Decision Tree or Generalized Linear Models (GLM) that do not really need huge data sets. In fact, the first few areas of applications of statistical modelling were clinical trial and agriculture where data volume is even less compared to some of the B2B organizations.
- High cost of an error: A typical B2B deal size runs into millions of dollars and losing a deal can have a significant impact on the company’s revenue. Therefore, B2B deal pricing is a much riskier endeavor to be left to algorithms.
Solution - We advocate ML models to be a supplement to human decisioning and not a substitution of the same. The human amendments to the model’s recommendation should be sent as a feedback to the model. It should be captured as an insight that can be leveraged in subsequent pricing decisions.
- Data quality issues: Poor data quality is a reality and good models cannot be built on these data.
Solution - Over the years, most organizations have made significant investments in their applications like ERP, CRM as well as enterprise data warehouse. Additional checks like multi-level reconciliations across systems, investing in a master data management systems will just not help pricing but the overall organization. Ultimately a good model needs good data. It is strongly recommended to treat data enrichment as a continuous process to reap benefits from any analytics initiative.
- Sparse data: Some stakeholders in B2B organizations feel that most of the deals are unique and an ML model can hardly learn from the past data during model training.
Solution - Techniques like Bayesian Hierarchical Models or Decisions Trees can be leveraged to model such scenarios. Let’s say, you are selling a product in a territory and you don’t have any past history of selling the same product in the same territory, hierarchical models intelligently roll up the data to the next level in hierarchy where you have available historical data and generalize those insights.
- Complexity in B2B sales process: B2B buying decisions are complex and often the price may not just be a function of quantity but the terms and conditions of contract as well. This makes the calculation of price elasticity extremely difficult.
Solution - The focus of the modelling should be to compute the Bid Price vs. Win Probability and not estimation of price elasticity i.e. demand as a function of price. The outcome in a B2B sales cycle consists of multiple phases but the advantage here is the seller has the option to revise the quote according to the response from the buyer. The different stages in the B2B sales life cycle can be modelled as state transitions that can be factored as an input to the model so that accurate prices can be determined earlier in the flow. One advantage while modelling pricing in B2B is the buyers and sellers in a B2B environment are expected to behave rationally compared to their consumer counterparts, and models need not really factor in the behavioral pricing that are frequent in the consumer space.