**Steven Forth** is a Managing Partner at Ibbaka. See his Skill Profile on Ibbaka Talent.

As this strange year moves towards its end, many of us have begun work on our strategic plans for 2021. One question in the world of pricing experts is, what approaches to pricing will dominate in the future? To deepen our understanding of the issues at stake, we put a simple poll up on the LinkedIn Groups for the Professional Pricing Society and the Coalition for the Advancement of Pricing. LinkedIn limits one to four choices, so we chose the following:

Dynamic Pricing
Outcomes-Based Pricing
Machine-to-Machine Pricing
Value-based Pricing

There are some other choices we considered. Behavioral Pricing has been getting a lot of attention lately. In the discussion there is also a very interesting proposal for what one could call Society Based Pricing.

Before we look at the results and what they may mean, let’s unfold each of these approaches to pricing.

Value-Based Pricing

This is the approach introduced by Tom Nagle that is currently recognized as the best practice for differentiated offers (one cannot apply value-based pricing to commoditized offers). Traditionally, value-based pricing is contrasted with cost-plus pricing and with market pricing. In value-based pricing, the economic impact of the offer relative to the next best competitive alternative is estimated. In updated approaches, emotional and community value drivers are also considered.

Dynamic Pricing

This is the approach being promoted by many pricing software vendors. Data is collected and analyzed, possibly using deep learning or some other approach to AI, and a price is proposed for each opportunity based on an estimate for Willingness to Pay (WTP). This approach has been used for many years to price things like air travel and hotels and its use is spreading to many other sectors.

Machine-to-Machine Pricing

Dynamic pricing is when the vendor uses AI to set prices. But two can play this game. Advanced procurement platforms are also collecting market data and using AIs. When these two approaches collide one possible outcome is machine-to-machine pricing (M2M) where two AIs negotiate the price with each other. This approach is highly likely where there are commoditized cloud services and it may spread to other sectors where vendors can be switched quickly.

Outcomes-Based Pricing

In outcomes or results-based pricing, the price paid depends on the outcome delivered. The value metric (the unit of consumption by which the buyer gets value) and the pricing metric (the unit of consumption for which the customer pays) merge. Rather than paying the full price up-front, payments are tied to value delivered.

Behavioral Pricing

Behavioral economics has been a big deal since its proponents have begun winning Nobel prizes (Khanehan in 2002 and Thaler in 2017) and Kahneman’s Book Thinking Fast and Slow became a bestseller. Behavioral economics provides lots of hints on how to design and communicate pricing, from how to take advantage of framing and anchoring to the importance of loss aversion. A number of pricing consultancies have been advocating greater use of behavioral economics insights in pricing design.

As of Monday November 16, 2020, there were 206 responses across the two LinkedIn groups mentioned above. There is some double counting as a few people may have responded on both groups. The distribution was as follows:

42% Value-Based
31% Dynamic
18% Outcomes-Based
9% Machine-to-Machine

All of these approaches have a role. We can bring some structure to these results by organizing them in two dimensions.

Dynamic pricing and value-based pricing are approaches to price setting and are relevant when the vendor is a price setter (and not a price taker). In reality most B2B sales are negotiated.

There are some who will tell you that dynamic pricing is value-based pricing because dynamic pricing estimates willingness to pay (WTP) and that WTP is based on value. This is wrong. WTP as estimated by pricing software is an attempt to infer price acceptance based on past data. Value-based pricing is an attempt to understand and then communicate value in order to frame price. Dynamic pricing is purely transactional and does not foster long-term relationships or partnership between buyer and seller.

In the pricing process, buyer and seller must eventually agree to a price. There is some alignment being created. This is made explicit in Machine-to-Machine (M2M) and outcomes-based pricing. In both cases there is a series of negotiations between two systems. In M2M pricing, these negotiations are done by the machines on either side, this already happens in many financial trades with all sorts of interesting emergent phenomena. See the Staff Report on Algorithmic Trading in U.S. Capital Markets issued August 5 , 2020 by the SEC.

In outcomes-based pricing this is more complex. Most outcomes have multiple, in some cases unpredictable causes, and getting to a shared definition of outcomes and causes is currently difficult in many cases. This can impose very large transaction costs as the two parties try to negotiate something neither can fully understand. There are some hopeful signs. The move to evidence and outcomes based practices in healthcare has led to the development of many new techniques that can be applied in other disciplines. This field is generally referred to as HEOR for Health Economics and Outcomes Research. The theoretical and computational understanding of causality has been greatly enhanced by Judea Pearl and his colleagues. At Ibbaka we think these trends will make outcomes-based pricing the dominant approach in healthcare and related applications within the next decade. It will become the general approach for any large negotiated sale within twenty years. That may seem like a long time, but smart companies are designing the data collection and analytic capabilities they will need for outcomes-based pricing today.

Another way to think about these four approaches to pricing is by using Mark Turner’s approach to blending and conceptual integration.

M2M Pricing is a blend of dynamic pricing and dynamic procurement. It is what happens when two different ways to use artificial intelligence collide in the market.

Ibbaka Value & Pricing Blog - M2M Pricing

Outcomes-based pricing is also a conceptual blend. In this case, the analysis from value-based pricing (Economic Value Estimation being one example) is combined with evidence-based management (beginning with the healthcare industry) to help buyers and sellers negotiate a price based on the actual outcomes.

At some point in the future (and as science fiction writer William Gibson says ‘the future is here, it is just not evenly distributed’) M2M and outcomes-based pricing are likely to come together to create a new model, in which the outcomes are predicted and tracked using AIs.

Is that the whole story? No. There is a fascinating comment by Eldo Kuriakose on the Coalition for the Advancement of Pricing Discussion on where pricing as a profession needs to go.

Since this is a question about the future with an indeterminate horizon, I'd like to add another option that is more aspirational. Pricing can and should be a path to sustainability vs. a spiral to zero-sum competition. Maybe "outcome based" is the closest among the options - but the term itself is too broad. Pricing could discover the balance between value destruction and exploitation of the ecosystem. Pricing can be the means to achieving fair trade, sustainable communities and even ecological balance. While the "how" of all this is quite murky now, we need to be thinking of a future state where the "how" is not obvious. There is ample untapped potential in game theory, scenario analysis, and ecology theory that can be brought to bear.

We are already seeing this with some of our customers at Ibbaka. Karen Chiang has been doing a lot of research into Community Value Drivers. An understanding of this class of value driver is becoming more and more important in market segmentation work, and at Ibbaka we build value-based pricing models on the foundation of a value-based market segmentation.