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Why vendors and clients should develop and agree on reverse pricing schemes for all the “enterprise 2.0” (meaningless but broad buzzword intended)?Increasing pricing structure that increase the price as the number of active users increases are far more efficient than current degressive pricing structure, that disconnect completely value and cost for clients.This explains largely why, even as large enterprises are expressing interest, the market for this type of applications is not growing nearly as quickly as needed and often anticipated. This would also help the puzzled vendors who wonder why, since their application add so much value (they are right), only few large enterprises are actually willing to buy them at what seems a reasonable pricing (they are wrong).We explore here how vendors and their clients can create mutual value by agreeing on increasing pricing schemes.
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COREEDGES
How to price Enterprise Social Computing offerings?
Value and utility pricing through Volume-Increasing price structures
Julien Le [email protected]@jnestour
February 09
COREEDGES
COREEDGES Link to accompanying post
Current situation
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A classic Volume-Discount pricing scheme is the most common structure used for Enterprise Social Computing offerings
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Dollar scale
Average cost for the organization of a new user active on the application
Enterprise Social Computing application vendors have generally adopted a classic Volume-Discount pricing scheme: the price per user is decreasing as you buy access for more employees.
Average Cost per user
Price is usually capped after a threshold
Users scale (here in total number
of users)Number of active users (absolute number)
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Variations like flat pricing may occur, but most usually fall back to the same old and classic Volume-Discount pricing scheme
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Flat price per user announced as a list price
Some vendors choose to display a flat price per user per period as a list price. But of course, it’s nothing more than classic Volume-Discount pricing after a — usually low — threshold. The same can be said for thresholds in number of users (pay this for up to 10 users, than you pay this for up to 100, etc.). The main effect of these variations is to disconnect the marginal and average cost per user. The trend for the latter remains the same however.
Average Cost per user
Number of active users (absolute number)
Average Cost per user
But of course you negotiate when you’re big and fall back to Volume-Discount
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Current situation
Link to accompanying post
COREEDGES
Current situationThanks to increasing returns dynamics, the average value per user increases in scale for clients
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Number of active users (% of total population)
Average Value per user
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Dollar scale: value extracted
by the client organization
Users scale (here in % of total user
population)
Marginal value for the organization of a new user active on the application
All offerings falling in the Enterprise Social Computing domain have some degrees of increasing returns dynamics: as more employees start using the application, the value they gain by using it increases. This can be anything from positive network effect for basic applications to more complex scale effects for elaborated offerings.To quote Umair Haque: “their marginal productivity increases in number of connected users”.
Since the individual productivity of each individual starting to use the application increases with scale, the marginal and average value of a new active user at the organization level is cumulatively even more exponential.
Current situation
Additional sources: Umair Haque, The Age of Plasticity Edge Competences and Network Economics 2.0
Link to accompanying post
COREEDGES
Current situation
Source: Umair Haque, The Age of Plasticity Edge Competences and Network Economics 2.0
The level of increasing returns scale effects depends on how well designed the application is
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How should 2.0 economies scale? Viral and network economies, because they directly mediate users and/or peers, should realize polynomial-exponential returns to scale. Distributed economies, because they micromediate the recombination of plastic microchunks, should realize exponential-combinatorial returns to scale.
2.0 RETURNS TO SCALE
Scale
Retu
rn
s
Combinatorial (Haque)
Exponential (Reed)
Polynomial (Metcalfe)
We will use a simplified graphic version of the value curve, but vendors should strive to achieve the best scale effects possible within their offering.
Refer to Umair Haque’s excellent work (figure extracted from his presentation: The Age of Plasticity Edge Competences and Network Economics 2.0) for a starting point:URL: http://www.bubblegeneration.com/resources/edgecompetences.ppt
The returns to scale of web and software applications vary according to their properties.
Increasing returns scale effects are now commonly used by consumer and corporate applications. The type of returns achieved (their slope) depends on the properties of the applications.
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COREEDGES
Current situation
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The size of the client’s organization impacts its value curve for absolute numbers, not relative numbers
Average Value per user$
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Dollar scale: value extracted
by the client organization
Small co
Of course, the size of the client’s organization impacts the form of its value curve. The larger a company is, the more extended its value curve will be. Note that when the scale used is the percentage of users within the total employee population, then size is not a factor and there is only one curve (see slide 4).
Number of active users (absolute number)
Mid co Big co
Users scale (here in total number
of users)
Link to accompanying post
COREEDGES
Value and cost are completely mismatched with a Volume-Discount pricing scheme while they should be as closely aligned as possible!
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Number of active users (% of total population)
Average Value per user
$
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Dollar scale: value
extracted by the client
organization
Users scale (here in % of total user
population)
The price paid per user is decreasing as clients add users whereas the value extracted from each user increases with each new one brought on board. The mismatch is striking and has several consequences.
Average Cost per user
Rationale for change
Link to accompanying post
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The incentives for large (hence risk averse) companies to try a disruptive technology are weak
Number of active users (% of total population)
Average Value per user
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Pilot population
Large companies will aim at a corporate-wide deployment, the one maximizing value. But they will approach it in a phased way:1) First contact and negotiation of the long-term pricing for the full deployment as well as punctual pricing for the pilot2) Small scale pilot to test and mitigate business, technical and user adoption risks3) If pilot successful, expand to a production deployment
Average Cost per user
Full deployment population
Pilot Cost per user
Deployment being done
1 2 3
Rationale for change
Link to accompanying post
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A Volume-Discount pricing scheme increases the cost of transitioning from pilot to production for disruptive technologies
Number of active users (% of total population)
Average Value per user
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After the pilot, 2 main strategies to deploy globally:1) (on the left) Start with a small group of users, usually early adopters and for whom the business value is clear, then expand from this core2) (on the right) Deploy globally as quickly as possible
A Volume-Discount pricing scheme makes it very difficult to justify either the total cost or the ROI per project. The more disruptive the technology, the more difficult to demonstrate its benefits, the more such a scheme makes it more difficult to deploy.This helps explain he difficulty to get pilots for vendors and the risk averse nature of clients.
Average Cost per user
Pilot Cost per user
• Large scale deployment to reap scale economies• High total cost• High ROI per user because of Volume-Discount pricing• Project at risk because the ramp-up period for user adoption will be long, while the cost paid and ROI planned assume full deployment
• Small scale deployment for user adoption• Low total cost• Unsustainably low ROI per user due to Volume-Discount pricing• Project at risk if does not scale quickly to lower cost per user and increase ROI
Rationale for change
Link to accompanying post
COREEDGES
Benefits
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By switching the price to align with the value, the total revenue for a vendor stays the same, even if reached at a different pace
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Number of active users (% of total population)
Value 1) With Volume-Discount pricing, vendors are pricing out at small scale, while forgiving most of the value at large scale
2) The total revenue with Volume-Discount pricing follows the price (=cost) curve
3) If we switch the cost to align with the value, then the growth in revenue has a different pace, but the total revenue stays the same
Cost
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Number of active users (% of total population)
Value
Cost
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Pricing out Forgiving value
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Number of active users (% of total population)
ValueCost
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Potential revenue area with Volume-Discount
Potential revenue area with Volume-Increasing
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2 3
Link to accompanying post
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Benefits
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Vendors need to shift from few clients at full price (Volume-Discount pricing) to lots of clients at progressively increasing prices (Volume-Increasing pricing)
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Volume-Discount pricing Strategy: Expect large revenue streams from a few clients, don’t go if cannot get a full revenue stream right-away. If client wants to deploy progressively, make it pay a discounted full price or partial but not discounted (can’t have both!).
a) a very small number of clients have done a full deployment, providing large revenue streamsb) a small number of clients are piloting the application. The number is small because of the planned difficulties to transition.c) clients expressing an interest, but not seeing an ROI with a large enough probability, are staying on the sidelines, due to the costs and uncertainty associated with a pilot
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Total revenue by client
a
bc
Revenue scale
Number of clients
Volume-Increasing pricing Strategy: Expect clients to start small-scope pilots to mitigate potential risks and demonstrate the value, then move on to a phased deployment when the value has been demonstrated. Make it easy for them to justify the project by giving them a stable ROI per user throughout the deployment. Manage a portfolio of clients that are at varying stages of their pilots and deployment and increase revenue as they scale up.
a) a bigger number of clients are in full deployment, but at varying stages of it, progressively deploying the application as their organization is getting used to itb) a large number of clients are piloting the application, attracted by the very good cost/benefits/risks ratioc) clients expressing an interest experiment with the basic versions of the application, or for very large prospects, kick-start an experiment/pilot with the vendor’s help
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Revenue scale
Total revenue by client
a b c
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Link to accompanying post
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Pricing Metrics
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Utility pricing, ie pricing per active user, is necessary to allow a successful deployment of a disruptive technology
Number of active users (% of total population)
Average Value per user
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When deploying a disruptive technology like enterprise social networking, it is important for the client to make it available to all its employees: which groups of employees will recognize its value first is unknown, and you may not target the correct group if you do a target deployment.
If charging with threshold effects ($x for 100 users, than $y for 1000 users), the vendor makes artificial and unnecessary disconnects between cost and value.
If charging registered users, the vendor does not charge for value but for its perceived potential to deliver value, which can be badly wrong.
Average Cost per user
Instead of charging just 3 different prices for 3 ranges
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Price per user continuously to avoid thresholds effects
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Link to accompanying post
COREEDGES
Pricing MetricsNote on pricing metrics: why active users count is generally more efficient
Active users activity is often the best proxy for value. It should be automatically tracked within the application and at a high enough frequency (ie monthly or quarterly, not just annually).
Another reason why activity pricing is a second best to active users pricing is the difficulty to define targets for disruptive technologies. Search is known. Take the applications delivering Twitter-like capabilities to the enterprise. The best would be to price by usage, that is, by message. But how do you define the “normal” usage to set your prices ? No one knows. Price it per active users however, and you do capture the value recognized by the employees, since they will connect only if they find value in its use.
Activity pricing aims at matching value and price exactly. It is very difficult to define activity metrics that match value exactly however, and generally the disconnect is too large to be used efficiently.Example: enterprise search appliances pricing per document indexed fall in this trap obviously. Most companies have poor archiving practices, keeping obsolete documents on the network. Charging to index those documents (that can represent a large portion of the total documents) simply increase cost without increasing value.
Active user pricing
Activity pricing not efficient
Activity pricing too uncertain for disruptive technologies
Link to accompanying post
COREEDGES
SegmentationThe cost/benefit ratio of Volume-Increasing Pricing for small companies is too low, Volume-Discount Pricing is adapted here
0 10% 20% 30% 40% 50% 55% 60% 65% 70% 80%Number of active users (% of total population)
Average Value per user
$
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$
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Average Value per user
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Small co
Number of active users (absolute number)
Mid co Big co
The mechanisms of value are the same for small companies. Looking at the value in terms of the proportion of total employees bring the same results.
Small co
Mid coBig co
Looked at it in terms of absolute users, however, the cost/benefit of implementing Volume-Increasing pricing is too low for vendors.
For small and sometimes medium companies, the best strategy is to keep Volume-Discount or flat pricing. A threshold then needs to defined by the vendor to determine when switching from Volume-Discount pricing to increasing. This needs to be based on the total number of employees in the client’s organization.
Link to accompanying post
