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The adoption of machine learning started in the consumer space almost a decade ago, but B2B organizations have been rather slow in embracing machine learning algorithms for their pricing. However, the scene is changing.
Trends driving AI/ML adoption in B2B Pricing
B2B companies are realizing the potential of machine learning (ML) in making right business decisions including pricing. There are several trends driving adoption of ML in B2B Pricing (See Figure 1).
Figure 1: Trends driving AI/ML adoption in B2B Pricing
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.
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.
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.
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.
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.
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.
Figure 2: Data & modelling challenges in B2B pricing
ML framework for B2B pricing recommendation
Pricing can have several objectives beyond winning the bid. One of the most important being profit maximization. In addition, based on the strategy, an organization would like to maximize the revenue through cross-sell or up-sell. Elsewhere, increasing the customer trust to maintain long-term relationship with the customer may be the priority (See Figure 3).
Figure 3: Objectives of pricing
Figure 4: B2B Pricing Modelling framework
Figure 5: Bid Response vs. Price Ratio. Bid Response is depicted by 1(Win) or 0(Loss).
Price ratio in Figure 5 indicates the net price the customer will pay to the actual listed price. A price ratio of 0.2 indicates 80% discount i.e. customer pays only 20% of the listed price. Bid response of 1 indicates the win while bid response of 0 indicates a loss.
It is suggested to use probabilistic algorithms e.g. logit, power etc. while estimating the bid response function. Unlike algorithms that give a binary outcome, probabilistic models produce the probability of winning the deal at each price point. Also, they are more interpretable and the influence of price and other variables can be illustrated to the field sales force. Complex black box models which produce a binary outcome of win and loss (not probability) and not interpretable should be avoided.
Modelling process should follow the model development lifecycle comprising of exploratory data analysis, model training and performance evaluation in a holdout data. The performance of various algorithms across a set of parameters like accuracy, precision, recall, AUC, ROC etc. should be compared to choose the best performing model.
Once the Bid Response function is estimated i.e. the relative importance of the variables on the deal outcome (win/loss) is determined, it can be used to simulate the probability of win at different price points (See Figure 6).
Figure 6: Win Probability vs. Price Ratio at different price points
Bid response functions should be estimated for each pricing segment separately if estimating individual functions gives performance advantages compared to a single model across pricing segments. However, due consideration should be given to the availability of data in a particular pricing segment. If the number of records are significantly less for a given pricing segment to effectively train/test the model, a single model across pricing tiers may prove to be a better option.
Figure 7: Profitability % vs. price ratio to depict the profitability % at different price points.
Once the probability of win and profitability, both are determined, the profit can be estimated at different price points.
Estimated Profit = Probability of winning the deal * profit
The price point that generates the highest estimated profit will be recommended to the field sales persons.
Bringing together people, process and technology
In order to reap the benefits from the implementation of an ML-driven pricing program, organizations should ensure they have the people, process and technology policies aligned to:
People
Process
Technology
If you are starting your ML-enabled pricing journey, creating the pricing analytics platform is the first and the most important step. The pricing analytics platform will have the following four components.
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