Posts Tagged ‘Analytics’

There are three relatively new technologies converging to make Cloud 2.  All three technologies have been available for many years, though in less robust forms and with less powerful integration.  The convergence is driven by their ubiquity, low cost and ease of use.  They are social media, mobility and analytics.  Together these technologies offer a future that is vastly different from conventional enterprise information processing served from a traditional data center or from a data center somewhere else on the Internet that made up the backbone of Cloud 1.  Read the full story here.


A lot of information is coming together this quarter that begins to put new spin on Social CRM.  While we’ve all been busy getting networked in our personal lives and professionally, a huge mountain of data has been accumulating that will make our work in social technology more valuable.

Last week Harvard Business Review released a report sponsored by SAS Institute which shows that while many enterprises are well on their way in adopting various social technologies for business use, the number that also are deploying analytics lags.  I know of at least two other reports that will contribute similar information when they arrive on the scene too.

This disparity between data accumulation and data analytis is temporary because as an organization accumulates customer data without basic analytics most of the data is useless.  If you want to know who your best customers are, it’s relatively easy to get a report that says who bought the most in the shortest period of time.  But with analytics you can also delve into the data to ask questions of the why and why not types and there life gets interesting.

Asking why can often uncover alternatives, things that were or were not done and to examine the root causes.  In finding those causes you can uncover new opportunities, revenue that is there for the taking because you know where and how to look.

Last week in Las Vegas I listened to many smart people from big companies discussing how they used SAS Analytics to gauge customer sentiment, run marketing campaigns and manage the conversations they have with customers.  I learned about millions of found dollars brought to the bottom line because analytics were able to make sense of the data thrown off by each customer transaction.

Now, granted, in a billion dollar company a few million bucks may not seem significant but it’s the easiest money you can make.  There’s nothing to invent, market or sell to get the revenue, it simply comes from doing a job better.  Also, if you happen to be lucky enough to own the P&L for a department using analytics, your growth goal in a challenging economy might look a lot easier to attain with analytics.

Consider the above as playing offense, analytics help with defense too.  According to the Harvard study, most companies don’t know what their customers are saying about them or where (Facebook, Twitter, blogs etc.) they are saying it.  Even my crude research a few weeks ago into using search engines to discover how many customers dislike their vendors, indicates a certain lack of intelligence about the outside world.  If hundreds of thousands of my customers were angry enough to write blogs about my company, I would want to know who they were, but most vendors aren’t at the level of having the appropriate tools yet.

Using analytics to digest customer sentiment and make the data actionable is another way that a company, through reputation management, can potentially earn more on the work it does thus taking some pressure off growth objectives.

So for these and other reasons, social media is building the case for a virtuous relationship between analytics and the data that social media generates.  As a result I see plenty of reasons that analytics will continue to shed its outdated reputation as a technology that is only used by an elite few in an organization.  The big data sets involved also make a strong case for web based analytics processing to help defray the hardware costs, at least for some vendors.

Embedding analytics in the applications and processes—especially those governed by social media—that deal with customers and capture their data will become more important over time.  That’s why it is inescapable to me that analytics will become the secret sauce of a well-run social media or social CRM implementation.  Isn’t there an old adage that says it’s not the data it’s what you do with it?  There should be.


I was a guest in the audience yesterday when Cloud9 Analytics came to Boston to meet with customers and talk about the releases that will be part of their offerings later this year.  The presentation lasted a bit over an hour and included presentation of new sales management data by Jim Dickey of CSO Insights and a customer testimonial from Brainshark, EVP, Dave Fitzgerald.

Cloud9 CEO Swayne Hill spoke about the future releases and current status of the company.  The company must be doing a few things right because Hill said they have more than 90 customers now and a 50% revenue increase quarter over quarter.  That’s good news given that it’s so expensive to launch a SaaS company and capital is not exactly overflowing.

Last year was the worst year for VC investments since 1997, if you want to know.  And the industry actually raised less money than it invested and I don’t know how long it’s been since that happened.  Last year was also the year a competitor, LucidEra — another SaaS sales analytics startup — went to the boneyard.  So, long story short, Cloud9’s advances in such a market speak volumes.

We also know that if last year had been terrible it would have been an improvement in most companies.  Jim Dickey, whose company performs an enormous survey of sales and sales management professionals each year was there to talk about his most recent survey and analysis, which is due out shortly.  Without giving away all of Jim’s IP (which I can’t do simply because it is so voluminous) some numbers that blew me away:  Last year the win rate on forecasted deals was 44 percent.  Forty-four percent makes picking red or black look like genius work.  Forty-four percent makes a mockery of the whole forecasting process.  It means you’re better off not forecasting.

But there is more.  In the same year, in the face of an economic tsunami, 86% of the companies studied raised sales quotas.  That’s right, they raised their expectations in the face of overwhelming odds against.  I’m sorry but Tennyson is screaming in my ear about the Light Brigade,

Theirs not to make reply,

Theirs not to reason why,

Theirs but to do & die,

Into the valley of Death

Rode the six hundred.

Eighty-six percent is just about everybody.  Now I can understand if the rise in quota had something to do with layoffs and consolidation of territories but you can’t have it both ways.  If you jettison the underperformers in the face of the tsunami, you can’t simply put their quotas on the backs of others.  If you have a realistic expectation that the quota can be attained, why get rid of some staff to begin with?

But I digress.  Dickey’s big point, which I think is very good, is that too often management flips coins when it comes to forecasting and you can’t completely blame them.  The sheer number of deals in a pipeline and forecast make it impossible to know much about any of them.  That’s why Cloud9 Analytics makes so much sense.

The Cloud9 approach is to manage the exceptions.  If nothing changes in a deal then it is assumed to be on track.  When something does change notifications go out to relevant parties like managers and others who subscribe to a forecast’s or even a deal’s feed.  The whole subscription and feed idea is very Sales 2.0-ish and a good thing to have.

But what Hill spoke of and Dickey gave numerical support for, is the next piece in a sales analytics maturity model that I see evolving.  Hill’s contention is that we already use performance management tools in the back office for things like manufacturing.  For instance, we don’t use spreadsheets to monitor quality or relationships with vendors in the supply chain but too often we do the equivalent in the front office.

Hill’s goal is to make sales performance management as rigorous as other performance management and his road map for additions and enhancements to the Cloud9 SaaS service point in that direction.  All this reminds me of Davenport and Harris’s very good book, “Competing on Analytics” which discusses an organization’s need for an analytics maturity model ranging from tactical to strategic use of analytics to improve performance.  Cloud9 appears to be on an interesting track to help customers do this and their further announcements for this year will be interesting to dissect.

I owe you an analysis of Dave Fitzgerald’s testimonial about how Brainshark is using Cloud9 as well as a broader constellation of tools but that will have to wait.

SAS introduced its Social Media analytics product today and, given that SAS is SAS, it will serve to give some new legitimacy to the field.  Social Media analytics has been around for a long time, probably as long as the Web minus a day or two.  But that doesn’t mean that SAS can’t add to the conversation in some important dimensions.

For instance, two of the major constraints on analytics have always been CPU throughput and memory.  The more CPU cycles you can throw at the problem of finding a needle in a proverbial haystack the faster you can find that needle.  But CPU only works on the data in memory and if you need more data from disk each seek slows you down.  So SAS has done a lot to provide the highest performance possible by throwing CPU and memory at the problem.

Last night at the opening keynote, Dr. Jim Goodnight, showed what this can mean.  He took an analysis process from a bank that had taken eighteen hours to load and reduced it to a couple of minutes by putting the whole shebang in memory and dividing the task among just over a thousand processors with multiple cores.  The importance here should be obvious, that massive amounts of data that a large enterprise might typically generate or have generated about it can now be analyzed in time to make the result relevant for ongoing business processes.

The simple performance demonstration preceded today’s announcement of SAS’s social media analytics for a good reason.  If you add together high performance and the very large datasets you arrive at a useful solution for corporate marketers trying to make sense of the twitter-facebook-blog-and-other-social-media data streams that are a fact of life today.  As the demo at the press conference made clear, it’s nice to know that sentiment may be up or down but it’s even better if you can analyze it by source, season and other parameters.  The result may enable a marketer to pinpoint a particular article or post and determine the most effective course of action.

In the right hands, Social Media analytics can also help you understand the unintentional information that’s given off by your competitors whenever they enter the social web or when the market reacts to something they do.

SAS is offering its analytics as an on-demand package but it’s not simple or something that you buy this morning and start using this afternoon.  The company has a well thought out process for on-boarding customers and sticking with them over time to mentor, coach and occasionally perform consulting projects.  This all seems very reasonable.

Several smaller Web marketing and marketing analytics companies have taken the same approach lately of providing product and ongoing service and it’s a business model that I think will become more common over time.  We’ve spent decades trying to make analytics simpler to use and the results have been good.  But the reality is that it will always be something close to rocket science to understand and use tools that predict what people might do with a given amount of information.  Also, the marketplace is changing.  In a zero-sum marketplace, like the one we’ll be in for a while, it is shrewd for a company to cross sell service rather than another product.  Selling service further cements the bond and enables further discovery leading to more cross and upselling.

So the net of the SAS announcement.  Interesting that SAS has put a stake in the ground in social media analytics, their enterprise customers will appreciate it and I think it’s important for the continued growth of the company to offer a service like this.

Lesson learned from LucidEra

Posted: June 29, 2009 in CRM
Tags: , , , ,

LucidEra’s unfortunate announcement that it was suspending operations hits the SaaS industry hard.  The on-demand sales analytics company had a good record of providing valuable solutions for sales organizations and managers interested in better understanding their pipelines and deriving meaning from their SFA data.  But ceasing operations highlights one of the chief risks inherent in adopting a SaaS solution namely, that an application can become unavailable.

Fortunately, the people at LucidEra are classy people and they have spent the last week or two trying to ensure that their customers could migrate their data to another on-demand provider.  They called it an “orderly transition.”  That’s about the best you can expect and if every SaaS provider that goes out of business in the future did that we would have the rough equivalent of the scenario when a conventional vendor goes belly-up or stops supporting a version.

The conventional software model leaves you with the software that will run as is regardless of whether its vendor is solvent.  Support and upgrades are a separate issue and they do not materially differ in either case.

So to the voices that have said LucidEra’s demise is proof of why conventional software is better, I say not so fast.  Throughout the SaaS era — roughly ten years — the SaaS industry has had to cope with numerous similar situations where there were no precedents.  Up-time, security and disaster recovery all had to be re-thought for SaaS and vendors invariably found solutions.  LucidEra is providing another example of a SaaS provider figuring out a solution to a tough problem and I think they should be applauded.

Nonetheless, the SaaS industry should not act like this kind of thing could not happen again and it would be wise to consider contingencies for a SaaS company going down in the future.  It may be wise for customers to consider requiring that SaaS providers carry insurance against failure or, more precisely, insurance to cover the contingencies associated with shutdown.

Surely some big insurance company would be happy to underwrite the risk in the same way that multiple insurers already provide business insurance against all sorts of calamities including errors and omissions.  Such insurance might not have been feasible ten, five or even three years ago.  But the popularity of SaaS and the business advantages it offers clients says that the risk pool is reaching critical mass if it has not surpassed that threshold already.

Let’s say insurance against a SaaS company’s demise costs a dollar per month per seat declining for really big implementations or customer bases.  Who couldn’t or wouldn’t afford that, especially in this market?  A dollar isn’t much but given the market I can’t see how it wouldn’t be a profitable business.

I don’t expect that any SaaS company will rush out and advocate for insurance and, as is often the case, demand for such insurance will have to come from the customer.  But all of the pieces seem to be in place and, like mirrored data centers and SLAs (service level agreements) I expect transition insurance in the event a SaaS company goes out of business will become standard fare in this still evolving industry.