The second quarter of the
year typically proves to be one of the strongest. This time around not
only we were not
disappointed but we also started seeing some strong upside for the
the year. Both our enterprise SaaS and
adtech platform portfolio companies performed extremely well, and a few
from enterprise and adtech) are seeing strong enough demand that could
them to increase their 2H13 bookings and revenue targets. As public
SaaS companies are starting to
report quarterly results we are starting to see similar strong
some of them, whereas the majority are expected to at least meet analyst
expectations. The North American market fuels this growth,
whereas international markets, particularly Europe, remain a concern.
Also, we are seeing more activity in certain
industries such as retail, parts of manufacturing, and logistics, where
application budgets are holding steady and even increasing. In other
industries we continue to see
budgetary pressures as I had mentioned in last quarter’s commentary.
public enterprise SaaS companies we monitor, e.g., Netsuite, Workday,
Demandware, ServiceNow, Jive, Cornerstone OnDemand, have either started
announcing or are expected to announce strong 2Q13 results, that are at least
in-line with analyst expectations. In
fact Netsuite beat analyst expectations.
The public adtech companies had a rougher time during 2Q13. Tremor Video and Marin Software are two
adtech companies that went public during the quarter and the market didn’t
welcome them with open arms. They started
trading down soon after their IPO.
Similarly, Valueclick and Millennial Media continue to be scrutinized by
public markets. Two other private adtech
companies, Yume and Adap.tv, are expected to go public during this quarter, and
a few more will file to go public before the end of the year. I believe that the companies which have
either already filed to go public, or are planning to file, are of higher
quality than the ones that have already gone public. As a result, I wouldn’t be surprised if their
stocks perform better in the public markets than the current set of public
were two acquisitions worth mentioning: Salesforce’s acquisition of
and Adobe’s acquisition of Neolane. In
addition to the size of these transactions, it is interesting to note
allow both acquirers to strengthen their CMO suites. Another
interesting SaaS transaction was the
acquisition of CompuCom by TH Lee, a buyout firm. In the broader cloud
category I should also
mention IBM’s acquisition of SoftLayer.
Large private equity firms are increasing the pace of their investments
in SaaS and adtech companies, e.g., Insight Venture Partners’ investment
Brightedge. Finally, SAP acquired Hybris which derives a relatively
small percentage of its revenue from a SaaS application even though the
majority comes from on-premise software.
Positive aspects of our SaaS portfolio’s performance:
Strong license revenue growth of 10-30% QoQ
for the online advertising platform companies, and 15-20% of the remaining SaaS
companies. We are seeing an accelerating
trend by brands to enter into yearlong contracts with adtech platform
companies, in the process bypassing their ad agencies and foregoing
campaign-based contracts. I had first
reported this trend during last quarter’s commentary. This type of contracts provides more predictable
revenue ramp to adtech platform companies that the public markets appreciate.
The accelerating adoption of SaaS
applications continues. Our enterprise
SaaS portfolio companies are signing more enterprise customers on a quarterly
basis and they expand their footprint within each client enterprise.
Steady renewal rates (90%+) and improvement
on the churn we had seen in the social application companies.
Sales pipelines growing faster than in the
Large IT vendors continue to move
aggressively to partner with private SaaS companies as they are trying to
incorporate more cloud-based solutions in their portfolio.
Negative aspects of our SaaS portfolio performance:
The negative macro environment outside the US
particularly in Europe, and less so in Asia.
This is having more of an impact to our adtech platform companies.
Mobile SaaS solutions are attracting more
attention but not big dollars yet.
Talent acquisition, particularly for sales
and engineering remains difficult.
Under normal market conditions the second quarter tends to be better
than the first and this year there was no exception. However, 2Q13 gave us more indications that
this can end up being a strong year for our SaaS portfolio if the economy continues
to mend and remains in its current trajectory.
In my last blog I tried to define the concept of insight. In this post I discuss insight generation.
Insights are generated by systematically and exhaustively examining a)
the output of various analytic models (including predictive,
benchmarking, outlier-detection models, etc.) generated from a body of
data, and b) the content and structure of the models themselves. Insight generation is a process that takes place together with model generation, but is separate from the decisioning processduring which the generated models, as well as the insights and their associated action plans are applied on new data.
Insight generation depends on our ability to a) collect, organize and
retain data, b) generate a variety of analytic models from that data,
and c) analyze the generated models themselves. Therefore, in order to
generate insights, we must have the ability to generate models. And in
order to do that we must have data. Insights can be generated from
collected data, data derived from the collected data, as well as the
metadata of the collected data. This means that we need to be thinking
not only about the data collection, management and archiving processes,
but also about how to post-process the collected data; what attributes
to derive, what metadata to collect.
In some cases data is collected by conducting reproducible
experiments or simulations (synthetic data). In other cases there is
only one shot at collecting a particular data set. Regardless, insight
generation is highly dependent on how an environment is "instrumented."
For example, consumer marketers have gone from measuring a few
attributes per consumer, think of the early consumer panels run by
companies such as Nielsen, to measuring thousands of attributes,
including consumer web behavior, and most recently, consumer
interactions in social networks. The "right" instrumentation is not
always immediately obvious, i.e., it is not obvious which of the data
that can be captured needs to be captured.
Oftentimes, it may not even be immediately possible to capture
particular types of data. For example, it took some time between the
advent of the web and our ability to capture browsing activity through
cookies. But obviously, the better the instrumentation the better the
analytic models, and thus the higher the likelihood that insights can be
generated. Knowing how to instrument an environment and ultimately how
to use the instrumentation to measure and gather data can be thought of
as an experiment-design process and frequently requires domain
Insight generation also involves the ability to organize murky data,
which is typically the situation with environments involving big data,
and focus on the data that makes "sense," given a specific context and
state of domain knowledge. Focusing on specific data given a particular
data doesn't mean that the rest of the collected data is unimportant.
It's just that one cannot make sense of it at that point in time.
It is important to not only collect and organize data, but also to
properly archive it, since insight generation may only become possible
when a body of archived data is combined with a set of newly collected
data under a particular context. Or that the combination of archived
with new data may lead to additional insights to those
generated in the past. As the body of domain knowledge increases and
new data is collected it may be possible to extract new insights even
from data collected in the past. Consequently, having inexpensive and
scalable big data infrastructures enables this capability.
Insight generation is serendipitous in nature. For this reason,
insights are more likely to be generated from the examination of several
analytic models that have been created from the same body of data
because each model-creation approach considers different characteristics
of the data to identify relations. We maintain that model analysis,
and therefore insight generation, is facilitated when models can be
expressed declaratively. A good example, of the advocated approach is
used by IBM's Watson system. This system uses ensemble learning to
create many expert analytic models. Each created model provides a
different perspective on a specific topic. Watson ensemble learning
approach utilizes optimization, outlier identification and analysis,
benchmarking, etc. techniques in the process of trying to generate
While we are able to describe data collection and model creation in
quite detailed ways, and have been able to largely automate them, this
is still not the case with insight generation. This is in fact the most
compelling reason for offering insight as a service; because we have
not been able to broadly automate the generation of insights. What we
have characterized as insight today has to be generated manually by the
analysis of each analytic model derived from a body of data, even though
there there is academic research that is starting to point to
approaches for the automatic generation of insights. The analysis of
the derived analytic models will enable us to understand which of the
relations comprising a model are simply correlations supported by
the analyzed data set (but don't constitute insights because they don't
satisfy the other characteristics an insight must possess), and which
are actually meaningful, important and satisfy all the characteristics
we outlined before.
As I mentioned, in most cases today utilizing insights that are
generated manually by experts and offered in the form of a service may
be the only alternative organizations have to fully benefit from the big
data they collect. The best examples are companies like FICO, Exelate,
Opera Solutions, Gaininsight and a few others. However, there are
additional advantages to offering insights as a service:
Certain types of insights, e.g., benchmarking, can only offered as a
service because the provider needs to compare data from a variety of
organizations being benchmarked.
Offering insight as a service could lower the overall cost of
generating and reasoning over insights. This means that even
organizations that can generate insights on their own may ultimately
decide to outsource the insight generation and reasoning processes
because specialized organizations may be able to perform them more
efficiently and cost effectively.
Offering insight as a service enables organizations to benefit from
the expertise the insight generator develops by offering insights to
multiple organizations of the same type. For example, FICO has now
developed tremendous credit insight expertise which no single financial
services organization can replicate.
I wanted to close by making the following point: I have argued that
for an insight to be valid it must have an action associated with it.
This action is applied during a decisioning process. The
characteristics of a particular decisioning process will also need to be
taken into consideration during the insight/action-generation process
because the time (and maybe even other costs) allocated to apply a
particular action during the decisioning process is very important.
Watson's Jeopardy play provided a great illustration of this point, as
the system had a limited amount of time to come up with the correct
response to beat its opponents. Below I provide an initial, rudimentary
illustration of the time it needs to take to action specific actions in
are starting to make progress in understanding the difference between
patterns and correlations derived from a data set and insights. This is
becoming particularly important as we are dealing more frequently with
big data but also because we need to use insights to gain a competitive
advantage. Offering insight-generation manual services provides us with
some short term reprieve but ultimately we need to develop automated
systems because the data is getting bigger and our ability to act on it
is not improving proportionately.
A little over two years ago I wrote a series of blogs introducing
Insight-as-a-Service. My idea on how companies can provide insight as a
service started by observing my SaaS portfolio companies. In addition
to each customer's operational data used by their SaaS applications,
like all SaaS companies, these companies collect and store application
usage data. As a result, they have the capacity to benchmark the
performance of their customers and help them improve their corporate and
application performance. I had then determined that insight delivered
as a service can be applied not only for benchmarking but to other
analytic- and data-driven systems. Over the intervening time I came
across several companies that started developing products and services
that were building upon the idea of insight generation and providing
insight as a service. However, the more I thought about
insight-as-a-service, the more I came to understand that we didn't
really have a good enough understanding of what constitutes insight. In
today's environment where corporate marketing overhypes everything
associated with big data
and analytics, the word "insight" is being used very loosely, most of
the times in order to indicate any type of data analysis or prediction.
For this reason, I felt it was important to attempt defining the
concept of insight. Once we define it we can then determine if we can
deliver it as a service. During the past several months I have been
interacting with colleagues such as Nikos Anerousis of IBM, Bill Mark of
SRI, Ashok Srivastava of Verizon and Ben Lorica of O'Reilly in an
effort to try to define "insight."
An insight is the identification of cause and effect relations among elements of a data set that leads to the formation of an action plan which results
in an improvement as measured by a set of KPIs. Insights are
discovered by reasoning over the output of analytic models and
techniques. This output can take the form of predictions,
correlations, benchmarks, outlier identifications and optimizations.
The evaluation of a set of established relations to identify an insight, and the creation
of an action plan associated with a particular insight needs to be done
within a particular context and necessitates the use of domain
Most analytic model outputs do not provide insights. There are two
reasons for this. First, the models don't suggest a meaning for each of
their findings. Second, they don't put each finding in an actionable
context (even if the meaning were known). Finding a pattern doesn't
imply that you automatically find meaning and that you understand it.
It just implies that you are finding a correlation among a data set.
Moreover, finding causality alone is not necessary and sufficient for
generating an insight. One needs to be able to derive an action plan
that can successfully and effectively, i.e., with impact, be applied in a
particular context. This requirement implies that even knowing the
meaning of the finding doesn't tell me how to generalize it and use it
for something in the context I am trying to impact. That step requires
knowledge of my environment (business, social, education, etc.),
my strengths and weaknesses, other forces that may enhance or diminish
my efforts, etc.
An insight must be:
Stable. This means that an insight must not vary
depending on the relation-identification algorithm/model being used.
For example, if I use two different samples from the same data set to
create a predictive model employing the same model-creation method, then
the resulting models have to provide the identical result under the
same new data input.
Reproducible. This means regardless of how many
times a feed a particular data set through an insight-generation system,
the same insight will be produced.
Robust. This means that a certain amount of noise
in the input data will not diminish the quality of the insight. This is
particularly important requirement in big data environments.
Insight-generation systems must be able to organize noisy data and
focus on the data that makes "sense," based on a particular context.
Enduring. This means that the insight is valid for an amount of time that is related to the underlying data's "half life."
Because of the above requirements, insight-generation necessitates
the deeper analysis, including the causal analysis, of the underlying
relation-identification models, rather than just the testing of each
model's accuracy, as it is typically done in predictive analytics tasks.
Such causal analysis implies that when trying to generate insights it
is preferable to utilize machine learning techniques that describe
patterns declaratively, e.g., decision trees, rather than black box
approaches, e.g., neural nets and genetic algorithms. As a result of
this requirement, one may need to sacrifice prediction accuracy and
speed for expressiveness. Therefore, one needs to identify the domains
where insight-generation may be more important than predictive accuracy.
Moreover, because the models themselves need to anallyzed, simpler
models may be prefered to more complex ones.
Insight-generation is not a single shot process. Once an insight is
generated and the associated action plan is created, it is important to
apply the plan in the particular context and measure its impact. The
collected data must then be compared to the set of established KPIs in
order to determine whether the particular insight/action-plan pair led
to an improvement. Depending on this analysis, the system must then
decide whether to attempt improving the action plan, create a completely
new plan (assuming that alternatives can be found), or try to create a
brand new insight. This means that from a set of initial input data the
insight-generation system must seek to derive all possible predictions,
based on the set of available models.