Crisis management is generally a costly business. Switching gears away from your forward-thinking strategy and pulling in resource to deal with issues not on the radar can really stymie growth and efficiency.

Especially in the IT management space.

In the past, the role of the IT manager was largely reactive: as soon as a problem occurs, they would have to jump in and manage the crisis. This was, and continues to be, a costly exercise for IT departments - often costing organizations millions of dollars annually.

Investment in predictive analytics has the potential to drastically reduce the surprises faced by IT management. In a recent article in Enterprise Networking Planet, Drew Robb shows how predictive analytics can be used to monitor networks across enterprises and mine behavioral patterns to get out in front of potential issues like usage spikes and plan for them before they occur. As IT moves towards virtualization and cloud models which allow for flexibility in terms of resource allocation, predictive analytics really comes into its own as a tool to help manage these spaces. For instance, with a cloud-based installation, resources can be deployed or changed in minutes, rather than weeks. If you have multiple users and applications on the installation, predictive analytics can be used to determine where resources should be apportioned prior to any impact on service levels.

Maintenance isn't only the area where predictive analytics play a role.

Steven Sams, IBM’s vice president of Global Site and Facilities Services points out that by 2012 global data storage capacity will need to be 6.5 times what it is today (fueled largely by internet cloud-based services). He recently explained to Forbes' Quentin Hardy how predictive analytics can be used by data center managers to plan for this growth:

"Tech planners need the same kind of big pattern-finding software more commonly used by designers, chief executives, and finance types. Among the new analytic offerings from IBM are cash flow-based scenario software, for figuring out whether to build, consolidate, or do nothing"

Obviously these decisions can have serious implications on business operations and costs. Sams highlights a Chinese bank that has managed to go from 38 to 2 data centers with a cost saving of $180 million a year using this technology. To better serve this market, IBM has launched a predictive analytics tool for use by the Global Business Services division on data center engagements.

As we move into 2011 and beyond, predictive analytics can play a major role in the way IT departments manage data centers and their operations. Given what's at at stake, expect to see a lot more interest in this area.

There's a growing battle in the location-based services business between Foursquare and Facebook. Foursquare, with its past emphasis on gaming and status building (who wants to be the mayor of the local laundromat?) is now focusing on a more functional aspect: helping people decide where they should go next. According to a report in Brandweek (backed up by this article on a recent job ad), Foursquare sees offering recommendations as its chance to avoid being squeezed out of existence by Facebook, who, with over 500 million users, is the ostensible gorilla in the room.

How does it plan to do this? Brandweek suggests it will adopt predictive services which are common on sites like Amazon and Netflix:

"Those services crunch behavior data—what movies you watch and books you read—to suggest new products. Foursquare wants to do the same, only with recommendations of real-world activities."

For instance, let's say you are a sushi freak living in Chicago who's been active on Foursquare for the last year. You've been using Foursquare to capture badges for most of the top local Japanese eateries. Foursquare can see your penchant for fine sushi in the windy city and look across its network for others in your area who share the same passion. It realizes that there is a new joint downtown and can suggest you check this out.

How does this crunching work? The data is mined along a process which runs something like this for each individual visitor:

• What are the past actions you have recorded
• What patterns can be determined from your actions
• Who else in the network is like you
• Where are the gaps between your actions and their actions?
• Offer as predictions these actions that people like you have performed

Note, this obviates the need for a user to fill in a vast registration form listing all their likes and interests. The system can figure this out by looking at past behavior.

In terms of making predictions, systems need to be smart enough to factor in elements that can cause shifts in our patterns of behavior:

• Seasonality (no taste for raw fish when snowing)
• Change in tastes (eg. pregnancy pushes sushi off the menu)
• Removing system bias (eg. not only favoring well-established popular places, but allowing new entrants a chance to prove themselves)

Whether Foursquare makes a concerted move in this direction remains to be seen, but as web and mobile applications creep further into every aspect of our existence (with their inherent ability to track behavior), expect to see an increasing use of business intelligence and predictive analytics to create smarter systems offering us more relevant information.

Integrating search engine marketing (SEM) and search engine optimization (SEO) projects and teams is a best practice that can deliver a powerful Virtuous Cycle.  Built on the foundation of an analytics platform such as Coremetrics Continuous Optimization Platform, an integrated approach to SEO and SEO can significantly improve the ROI from your web presence.

Multiple surveys and studies have indicated that SEO projects consistently provide extremely attractive returns on investment.  Yet eCommerce and online marketing teams frequently struggle to quantify SEO ROI: both prior to the project as part of an internal budgeting process, and after the project to evaluate its success.  Using a recent case study of a global powersports company, we will demonstrate how Coremetrics Digital Agency worked with the client to optimize their lead generation engine by integrating Search Engine Marketing with Coremetrics' Web Analytics. Building on this SEM experience we then targeted keyword phrases with the potential for the highest, measurable SEO ROI.

We will show the virtuous circle at play between SEO and SEM:

For instance, you can see significant improvements to your SEM campaigns by applying lessons learnt from analyzing your SEO efforts (such as which keywords drive most interactions).

Attend this upcoming seminar with Coremetrics' John Zoglin, Senior Director, Search Marketing Services to learn more.

Date: Wednesday, December 15, 2010
Time: 1:00 PM EST | 10:00 AM PST 
Register now!

More about Coremetrics

Early birds catch the blogworms. Or so suggests research by blogging metrics maniac Dan Zarrella. You have the best chance of getting eyeballs to your posts if you get that content out before 10am US Eastern time. In a recent webinar hosted by Hubspot, Dan unleashed a torrent of findings from his surveys and research of over 170,000 blog posts.

This fine infographic does a great job of summing up general reading/feedback trends seen across the blogs studied:

Whether it’s views, links or comments, most activity happens early in the day. Saturday is a big day for commenting. Which could well be related to this activity on social networks:

Retweeting follows a similar path. It looks like most people read content early in the day, with little variance across the week. As we get nearer the weekend, people start getting social: whether that be retweeting on Twitter or sharing on Facebook (and getting around to commenting).

Judging by the success of this webinar, interest in blogging definitely isn’t on the wane, which makes me somewhat skeptical about a recent study suggesting that although corporate blogging isn’t exactly dead, it’s reached saturation point.

There was no evidence of this during Dan’s study of blogging, which had the Twittersphere ablaze for the full hour of the presentation. You’ll see there was particular interest in the tie-up between blogging and other social media: in particular those duelling siblings Twitter and Facebook. And that’s where blogging can really come into its own: as the content destination for inbound marketing tactics across Facebook and Twitter.

To my mind the Dan’s research also highlights a key difference between search- and social media marketing. For search marketing, attracting those indefatigable search bots that trawl the web for new content is a time-independent task. Just make sure you get content out in short order to win favor from the recency filter was the long and short of what I was told not so long ago by search experts here at IBM. The time of day really has little importance: algorithms aren’t more likely to read posts in the mornings.  Whereas this research from Dan bears a closer resemblance to the findings you might see around email marketing which is often deemed to be time-sensitive. Readership is near-synchronous and content is highly perishable. And if you are blogging outside the time-zone of your key audience, beware. Your content could well end up overlooked. As you may have noticed, I’m taking Dan’s messages to heart and working on getting this content out in a timely fashion. Right, now time for breakfast!

For further details on this study, check out the aforementioned post by Dan or listen to the On Demand recording of Dan Zarrella: Science of blogging

In the current edition of Analytics, a cross-brand team from IBM (Irv Lustig, Brenda Dietrich, Christer Johnson and Christopher Dziekan) explain IBM's view of the structured data analytics landscape.

Key to this model are three categories of structured data analysis:

1. Descriptive Analytics: A set of technologies and processes that use data to understand and analyze business performance
2. Predictive Analytics: The extensive use of data and mathematical techniques to uncover explanatory and predictive models of business performance representing the inherit relationship between data inputs and outputs/outcomes.
3. Prescriptive Analytics: A set of mathematical techniques that computationally determine a set of high-value alternative actions or decisions given a complex set of objectives, requirements, and constraints, with the goal of improving business performance.

As the authors explain, this model can help businesses make better decisions, rather than just simply automate standardized processes.

Let’s use the example of a fictional global shoe manufacturer we’ll call ‘Footloose’ to see how each category could be used to increase business performance.

Descriptive analytics

These are your flexible dashboards that let you focus in on key areas of the business. For Footloose, this could be all the standard operations dashboards eg. like the one showing monthly shoe sales by region. Footloose should be able to see how actual sales fared against the forecast. Where there are deviations (say the sales of sandals in Spain has gone through the roof), they can use descriptive analytics to drill-down into the data. They may see that the growth is coming from the Madrid and possibly related to a major marketing push during a hot spell in that region.

IBM Cognos solutions offers this kind of descriptive analytics (including business intelligence) that can be implemented to measure and explore how a company is performing.

Predictive analytics

Here we use data from the past to make predictions about the future. For Footloose, this could include combining seasonal sales variations for a sports shoe with the longer term uptrend they have been seeing for the last few years. Footloose can also use predictive analytics to improve their web presence: they can launch a recommendation engine to suggest what a visitor might want to view next based on what they (and people like them) have looked at in the past (like the book suggestion service Amazon offers).

IBM SPSS offers a set of predictive analytic tools which allow business users to employ predictive insights at the point where decisions are being made.

Prescriptive analytics

How can we achieve the best outcome, whilst addressing any uncertainty in the data? Prescriptive analytics can help us answer this question. Let’s say Footloose has made its prediction about what shoe sales are likely to be over the coming year. Now they just need to figure out how to respond to those predictions. Sales of sandals are expected to remain high in Spain so they need to increase their distribution channel there. How should they achieve this? Increase the fleet of vehicles or buid more (costly) distribution centers.

Footloose can plug the data into an optimization model (costs of building a new plant, buying new trucks, gas) to calculate what would be the most efficient supply chain to deliver the extra required capacity.

IBM ILOG Optimization has technologies specialized for these kind of calculations where there are large data sets with potential uncertainty.

I’ve used this example to present a simplified view of IBM’s approach to structured data analysis and how IBM technologies can be used in tandem to improve business performance. A key advantage of these technologies is that their utility stretches across various industries and applications.

For a fuller explanation of this field, I’d definitely recommend reading the full article in Analytics Magazine. 

In a recent interview with David Meerman Scott of Web Ink Now, Ben Edwards, VP Digital Strategy and Development here at IBM talked of the move from outbound to inbound marketing.

See an excerpt of the interview:

What exactly does this mean? If you aren’t aware of the term ‘inbound marketing’, HubSpot has an excellent definition. Essentially, rather than pumping a message out through broadcast channels like billboard advertising, inbound marketing is more concerned with finding people that are researching your products or industry and engaging with them at that point.

This has a particularly strong fit with online marketing, whether that be a traditional channel like search or an emerging discipline like social media. On that note, Ben points out there are over 400K employees at IBM: 200K have profiles on Facebook and roughly the same number have a presence on LinkedIn. Add to that 30K declared IBMers on Twitter and you’re looking at a lot of connections! The communication through these channels is more about engaging in conversation. It’s more about helping those prospects that might be interested in your products and services speaking with employees who have expertise in that area.

For instance, if an IT architect from the retail sector is looking into a business process management (BPM) solution, she can join an IBM BPM group on LinkedIn and ask questions of IBM experts before synching up with the regular sales process.

To make this a reality, we’re seeing more integration between the IBM website and IBM social networks. Take a look at this section on the newly revamped Software Overview page:

There’s a virtuous circle at play here. Giving prominence to social media on the corporate website helps drive up community involvement. As these communities grow, whether they be on Twitter, Facebook or on IBM’s own domain, they will channel more visitors back to the IBM site. All without spamming email inboxes or cluttering freeways with billboards:

On the subject of advertising, IBM has been experimenting with a new generation of online ads that moves away from the traditional broadcast model and lets the viewer interact and provide feedback through the interface. Here’s an example on Slashdot:

The inbound marketing model serves as a good framework to look at the future of marketing where the communications are conversational, relevant and requested, rather than authoritative, broadcast and pressured. Social media usage at the business level shows no sign of abating, and it’s encouraging to see major corporations like IBM embracing this change at the highest level.

Decision management expert and consultant James Taylor was 'cornered' recently at the IBM IOD conference and asked to explain the present and future of business analytics. An eloquent speaker and veteran in this field, James does a great job of highlighting the current growth and energy in this space, some of the confusion this has engendered, and the questions you should be asking yourself in determining whether analytics are right for you:

He also highlights three critical issues on which I'll share my viewpoint:

Where can analytics be employed?

Decisions take place across the organization: from the CEO deciding who to appoint as the new sales director, down to the customer service rep asking if you want to take out a maintenance plan when you buy a new computer. At not all points does it make sense to employ analytics to inform the decision process. If you have highly automated business processes in your organization, then company-wide business analytics may make sense. Alternatively, it may just make sense to use analytics to sharpen up one department such as the marketing operation.

There is a political dimension to this which also has to be considered. It could be that the marketing department has a tight agency relationship who strongly pitch an analytics solution highly tailored for the marketing operation. Whilst this may be able to drive up efficiencies in marketing, it won't help the support decision process (or possibly cross-sell or up-sell opportunities).

On the other hand, it could be that the IT department, in the interests of cost-cutting, prefer to go with a centralized solution with a narrower maintenance footprint. 

These considerations (which tend to be aggravated in larger organizations) need to be taken into account in addition to the theoretical/modeling questions:

"What are the decisions that drive my business? how do I apply analytics to make a better decision or drive my metrics in the direction I want?"

Figure out how to align Business, IT and Analytics

In the past it was tough enough to engage business and IT departments (which can be heavily siloed and have the kind of relationship you see between Siamese Fighting Fish). But now you’re throwing an analytics team into the tank.

Although I'd suggest that this analytics team can provide the glue that holds together those creative types in marketing and the IT logicians. It's not unusual to find the analytics practitioners sitting somewhere in a department such as the corporate office. Whilst they may have strong knowledge of the tools, they are also plugged into the business imperative. As long as the importance of their role is realized and they are given due authority, they may well be able to spearhead the implementation of a business analytics solution and its systematic application.

Begin with the decision in mind

Why? So you don't end up drowning in data that will do nothing to drive your business. James points out that you need to understand what is a good and bad decision (for instance be clear on what a positive or negative outcome looks like).

Just like the scientist needs to understand there is inherent bias in the questions she asks, so you should realize that the decisions you choose to focus on can have a profound effect on your business. For instance just applying analytics to short term decision making (such as maximizing quarterly sales) could pull you out of synch with any strategic objectives and hurt you in future years. If you go overboard using predictive analytics to decide what to offer individual customers next on your website based on their past behavior, you may end up looking like a creepy stalker. Keep an eye out for symptoms of unintended consequences!

James is one of the most prominent/prolific bloggers in the decision management space and can be found at JT on EDM and ebizQ.

In the video James references these IBM technologies: Cognos business intelligence, predictive modeling from SPSS, and business rules and optimization solutions from ILOG.