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.

1. How to price Enterprise Social Computing
offerings?
Value and utility pricing through Volume-
Increasing price structures
Julien Le Nestour
www.coreedges.com
February 09
jln@coreedges.com
@jnestour
CORE
EDGES
CORE
EDGES

2. A classic Volume-Discount pricing scheme is the most common structure used Current situation
for Enterprise Social Computing offerings
Average cost for the organization of a new
1
user active on the application
Dollar scale $
Price is usually capped
2
after a threshold
Average Cost
per user
N
0 10 50 200 1000 5000 10000 20000 40000 60000 100000 Users scale (here
in total number
Number of active users (absolute number)
of users)
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.
CORE
EDGES Link to accompanying post

3. Variations like flat pricing may occur, but most usually fall back to the same old Current situation
and classic Volume-Discount pricing scheme
But of course you negotiate when you’re big
2
and fall back to Volume-Discount
$
Average Cost
per user
Flat price per user announced as
1
a list price
Average Cost
per user
N
0 10 50 200 1000 5000 10000 20000 40000 60000 100000
Number of active users (absolute number)
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.
CORE
EDGES Link to accompanying post

4. Thanks to increasing returns dynamics, the average value per user increases in Current situation
scale for clients
Dollar scale: $ Marginal value for the organization of a new Average Value
user active on the application per user
value extracted
by the client
organization
N
0 10% 20% 30% 40% 50% 55% 60% 65% 70% 80% Users scale (here
in % of total user
Number of active users (% of total population)
population)
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.
Additional sources: Umair Haque, The Age of Plasticity Edge Competences and Network Economics 2.0
CORE
EDGES Link to accompanying post

5. !"#$%&'(')*+,$
The level of increasing returns scale effects depends on how well designed the Current situation
application is
-(.$/+012(,$0'$3&-4+
2.0 RETURNS TO SCALE
The returns to scale of web
Combinatorial (Haque)
and software applications
vary according to their
properties.
Increasing returns scale
Returns
Exponential (Reed) effects are now commonly
used by consumer and
corporate applications. The
type of returns achieved
(their slope) depends on the
Polynomial (Metcalfe)
properties of the
applications.
Scale
How shoulduse a simplified graphic version of the value curve, but vendors should strive to achieve the
We will 2.0 economies scale? Viral and network economies, because they
directly mediate users and/or peers, should realize polynomial-exponential returns
best scale effects possible within their offering.
to scale. Distributed economies, because they micromediate the recombination of
plastic microchunks, should realize exponential-combinatorial returns to scale.
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
Source: Umair Haque, The Age of Plasticity Edge Competences and Network Economics 2.0
CORE
EDGES Link to accompanying post

6. The size of the client’s organization impacts its value curve for absolute numbers, Current situation
not relative numbers
Dollar scale: $ Small co Mid co Big co Average Value
value extracted per user
by the client
organization
N
0 10 50 200 1000 5000 10000 20000 40000 60000 100000 Users scale (here
in total number
Number of active users (absolute number)
of users)
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).
CORE
EDGES Link to accompanying post

7. Value and cost are completely mismatched with a Volume-Discount pricing Rationale for
change
scheme while they should be as closely aligned as possible!
$ Average Value
Dollar scale:
per user
value
extracted by
the client
organization
Average Cost
per user
N
0 10% 20% 30% 40% 50% 55% 60% 65% 70% 80% Users scale (here in
Number of active users (% of total population) % 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.
CORE
EDGES Link to accompanying post

8. The incentives for large (hence risk averse) companies to try a disruptive Rationale for
change
technology are weak
$ Average Value
per user
1 2 3
Pilot population Deployment being done Full deployment
population
Pilot Cost per
user
N Average Cost
0 10% 20% 30% 40% 50% 55% 60% 65% 70% 80% per user
Number of active users (% of total 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 pilot
2) Small scale pilot to test and mitigate business, technical and user adoption risks
3) If pilot successful, expand to a production deployment
CORE
EDGES Link to accompanying post

9. A Volume-Discount pricing scheme increases the cost of transitioning from pilot Rationale for
change
to production for disruptive technologies
$ • Large scale deployment Average Value
per user
to reap scale economies
• Small scale deployment
for user adoption
• High total cost
• Low total cost • High ROI per user
because of Volume-
• Unsustainably low ROI
Discount pricing
per user due to Volume-
Discount pricing
• Project at risk because the
ramp-up period for user
• Project at risk if does not
adoption will be long,
scale quickly to lower cost
while the cost paid and ROI
per user and increase ROI Pilot Cost per
planned assume full
user
deployment
N Average Cost
0 10% 20% 30% 40% 50% 55% 60% 65% 70% 80% per user
Number of active users (% of total population)
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 core
2) (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.
CORE
EDGES Link to accompanying post

10. By switching the price to align with the value, the total revenue for a vendor Benefits
stays the same, even if reached at a different pace
1
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
Pricing out Forgiving value
the price (=cost) curve
3) If we switch the cost to align with the value, then the
Cost growth in revenue has a different pace, but the total
N
revenue stays the same
0 10% 20% 30% 40% 50% 55% 60% 65% 70% 80%
Number of active users (% of total population)
Cost
2 Value 3 Value
$ $
Potential revenue area Potential revenue area
with Volume-Discount with Volume-Increasing
Cost
N N
0 10% 20% 30% 40% 50% 55% 60% 65% 70% 80% 0 10% 20% 30% 40% 50% 55% 60% 65% 70% 80%
Number of active users (% of total population) Number of active users (% of total population)
CORE
EDGES Link to accompanying post

11. Vendors need to shift from few clients at full price (Volume-Discount pricing) to Benefits
lots of clients at progressively increasing prices (Volume-Increasing pricing)
1 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
Revenue partial but not discounted (can’t have both!).
scale
Total revenue
a a) a very small number of clients have done a full deployment, providing
by client large revenue streams
b) a small number of clients are piloting the application. The number is
small because of the planned difficulties to transition.
b c) clients expressing an interest, but not seeing an ROI with a large
c N enough probability, are staying on the sidelines, due to the costs and
0 100 200 300 400 500 600 700 800 900 ... uncertainty associated with a pilot
Number of clients
2 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
Revenue easy for them to justify the project by giving them a stable ROI per user
scale
throughout the deployment. Manage a portfolio of clients that are at
Total revenue varying stages of their pilots and deployment and increase revenue as
by client they scale up.
a b c a) a bigger number of clients are in full deployment, but at varying stages
of it, progressively deploying the application as their organization is
N getting used to it
0 100 200 300 400 500 600 700 800 900 ... b) a large number of clients are piloting the application, attracted by the
Number of clients very good cost/benefits/risks ratio
c) 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
CORE
EDGES Link to accompanying post

12. Utility pricing, ie pricing per active user, is necessary to allow a successful Pricing Metrics
deployment of a disruptive technology
Average Cost
$ per user
2 Price per user continuously Average Value
to avoid thresholds effects per user
1 Instead of charging just 3
different prices for 3 ranges
N
0 10% 20% 30% 40% 50% 55% 60% 65% 70% 80%
Number of active users (% of total population)
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.
CORE
EDGES Link to accompanying post

13. Note on pricing metrics: why active users count is generally more efficient Pricing Metrics
Active user pricing 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).
Activity pricing not Activity pricing aims at matching value and price exactly. It is very difficult to
efficient 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.
Activity pricing too Another reason why activity pricing is a second best to active users pricing is
uncertain for disruptive the difficulty to define targets for disruptive technologies. Search is known.
technologies 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.
CORE
EDGES Link to accompanying post

14. The cost/benefit ratio of Volume-Increasing Pricing for small companies is too Segmentation
low, Volume-Discount Pricing is adapted here
$ Big co The mechanisms of value are the same for
Average Value
Mid co per user small companies. Looking at the value in
Small co terms of the proportion of total employees
bring the same results.
N
0 10% 20% 30% 40% 50% 55% 60% 65% 70% 80%
Number of active users (% of total population)
Looked at it in terms of absolute users,
$ however, the cost/benefit of implementing
Small co Mid co Big co Average Value Volume-Increasing pricing is too low for
per user 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.
N
0 50 1000 10000 40000 100000
Number of active users (absolute number)
CORE
EDGES Link to accompanying post