Business Intelligence

April 02, 2010

Early Warning

I uncovered my boat this week, took off the winter blanket and brought my means of summer therapy out of winter hibernation. Hope springs eternal for me when I perform this annual ritual over the course of the first nice weather weekends of the vernal season. In addition to that first-of-the-year admiration of one of my great sources of enjoyment, I also do a quick overview of how the many different elements of the watercraft faired during its long winter nap. First is a quick glance at the winter grime followed by an examination of the different chafing spots from the cover. Then there is the look into the galley and head to see how things fared in the cabin. My biggest hope is that a raccoon family has not made a home in my vessel.

Eventually I’ll check out all the electronics and mechanics and make sure that everything is in working order before we drop in the ocean. This beginning of the season ritual also brings along memories from the past summer. Barbeques on the beach, watching fireworks afloat, outrunning thunderstorms – these are all the things that make the boating life so much fun. As I was looking over the helm I glanced at the depth gauge and was reminded of one particular trip coming back from a mid-summers dinner celebrating my daughter’s 18th birthday.

There was no moon that night so the trip back to port, following a long stretch of bird sanctuary, was a dark ride with no beach-side houses or businesses to light our way. Eventually this dune-scaped island turns a corner and our harbor appears out of nowhere. However, this presents a challenge as a constantly shifting set of sand bars protects the entrance to the harbor. At night it can be quite a challenge entering the harbor safely, especially with no lunar or artificial lighting. This is where the depth gauge becomes so handy.

When you keep your eye on the up and down movement of the numbers you get a great sense of what is below you, and, if you keep the speed in check, you can tell in advance if you are about to run up onto some undesirable sand. The depth gauge serves as an early warning system – a set of eyes on the bottom helping to keep you clear of an unpleasant journey’s end. On a boat the helm is like the dashboard of a car, and my number one dial on that dashboard is the one that tells me how much water I have below my propellers.

Hydro Therapy

There is a lot of talk about dashboards in the CRM industry these days and, in theory, they should perform the same function as the one on your car or on my boat – help to make sure we steer in a safe and correct direction. But, not all dashboards are created equally. The problem with most of the dashboards I see is that they don’t necessarily measure stuff that helps you steer – they mostly measure stuff about where you have been instead of where you are going. Just like my depth gauge, the most important dial on a CRM dashboard is the one that provides information to keep you on the right direction – especially a direction that keeps you out of trouble. This would be the early warning system.

When it comes to sales, I think it is great to know how many deals you close and what your close rate is. But if you want to steer the boat safely you also want to know what is in the pipeline. If you track the number of deals at early stages, monitoring the conversion rate, you will know if you will make the number at the end of the quarter, before you get to the end of the quarter. If you don’t have enough deals at stage 2 to fill the rest of the pipe, this is your early warning indicator. It means you need to pull levers that drive more early stage deals. This drives up your chance of being successful when the quarter end measures are made.

Marketing has early warning indicators too. For example, you don’t want to finish a campaign before you learn if it is generating leads or not. You need to measure lift early to know if this is a campaign to keep running. More importantly, we want to know if the leads being generated are moving through the pipe effectively. Good dashboards need to look at the relationship between campaigns and opportunity conversion to ensure that we don’t invest in campaigns that don’t pay off, just like we don’t want to run the boat up on a sandbar.

The services function needs its own early warning system. The last thing we want to find out is a poor customer satisfaction rating due to customers being dissatisfied. That may sound crazy, but there are indicators that will predict the possibility of poor satisfaction before it has to happen. Let’s monitor those and take action before we see a drop in satisfaction ratings.

Are you looking at the right dials on the dashboard? Do you even have the right dials on the dashboard? I had a boat without a depth gauge once. It was risky business. A good CRM dashboard needs to have the right stuff in place to make sure you aren’t driving by looking through the rearview mirror.

Stay off of those sandbars!

May 02, 2008

Decisions, Decisions

Sometimes when I am working with a client there comes a point in time when the organization gets stuck on a decision. This is one of the hazards of the consulting industry. The lack of progress can cause delay and it can be much worse if you are not there to help facilitate the process.

How do you get past a tough decision? I suggest that you follow a process that covers the following elements.

1. structure the process with criteria
2. include those who must be involved
3. have the information available
4. set a deadline and stick to it

Ultimately, I think the most important tool for get through the decision making process is to understand the criteria that ultimately separate a good decision from one that is less desirable. Starting this off with a look at the idea of using criteria, you might want to consider two types:
- Attributes
- Success Factors

Attributes are those aspects of the decision alternative that represent the basic appeal or the liability. For example, when deciding between two software packages, it is normal to compare what each package can or cannot do. One package may come out of the box with a more tailored look and feel for your industry, while the other package has more built-in features for customer service. Making a decision based on attributes is basically a beauty contest and relies on tabulating a catalog of pros & cons to determine which comes out looking best.

Success factors represent the ability for decision alternatives to satisfy the requirements of the business if chosen. In this case, the decision process examines what the decision alternative will produce as a result of being selected. For example, when choosing between structural options for a telesales function there could be a decision point for going with a product versus account / territory approach. So, the question is what are the important success factors? Product focus allows for deeper understanding of the product line and perhaps a greater ability to close the sale. An account or territory focus allows for better account relationship development and increases the reach rate for each dial. Which is best depends on the product you sell – a need for product knowledge or a need for reaching the buyer. Make the decision based on satisfying the need.

Which Way

Decision making based on success factors is more complicated than simply rting attributes. It takes more understanding of the ramifications of the different elements of decision alternatives. But, this is not to say that attributes are to be ignored. You have the ability to consider both. When creating a hybrid model, I do encourage a bit of weighting to be included. Attributes may not be as critical to satisfying business requirements as are success factors. Sometimes it can be helpful to give a hi/med/lo type of rating to the different criteria being used.

What about the other elements of the decision process listed above? Well, don’t go through all the work to uncover the decision criteria and then leave out a critical stakeholder. This will just serve to slow down the implementation of the decision. Have all the key people involved even if it adds a bit of time up front. It will pay off.

Information for decision making is frustrating when it is unavailable. Much of the time you learn that there is information you need at the time of making the decision, but you don’t have it. So, anticipate both – try to be proactive in gathering your information requirements, and also build your process so that you have time in between steps to augment the data needed to feel secure in your decisions.

Finally, if there is one tool that helps the decision process most efficiently it is a good deadline. Organic deadlines are best and are comprised of natural events that cause a decision to happen – board meetings, budget calendars, annual sales meetings. However, when these are not convenient, then just create one. Putting a psychological stake in the ground does work, especially if stick to your self-imposed deadlines. If you have a poor track record of this kind of thing, then look for a reason that is ancillary, but meaningful – before summer vacations impact scheduling, prior to the end of a quarter selling rush, before the end-of-year holiday down-time. Just pick a sensible deadline and justify it. Of course, you also have to stick to it.

April 25, 2008

Watch Out Below

We were on a nature paddle. The manatees were pretty much gone for the summer, but we still had hopes for spotting big game. Reports of wild boar and abundant gators had us on the lookout, multiple cameras at the waiting. A sunny spring afternoon should have provided plenty of opportunity to at least see the large reptiles out getting a tan. However, the herons and egrets were about our only company. On our way back, having given up on shooting anything noteworthy I practically smashed the poor fellow in the snout. He dove under my boat with such a fury I thought for an instant that I was going over. Admittedly, I was caught by surprise.

The problem was that I was not only looking in the wrong place, with my eyes peeled on the St. John’s River bank, I was also looking for the wrong size object. The floating stick that essentially escaped my attention was a 13 foot long suitcase in waiting.

Naturally the first thing that came to my mind was how much that alligator reminded me of customer data. Yes, you are correct, I am pretty much crazy. I like to get up close and personal with prehistoric fauna sporting serious dental capabilities. On top of that, these close encounters with the toothy kind have the propensity to get me thinking about CRM. Well, there is a connection here, just give me some latitude to put it together.

Most of my clients have more value stored in their customer data than they are seeing. What is on the surface is only a fraction of what is truly there. What is below the surface is also a much more realistic representative of the whole. Just like a gator sticking up a couple of eyes and nostrils, the data poking through the surface within your CRM program is small compared to what you really have on your hands.


Now, it is possible to take this metaphor down a strange path, such as the comparison with gator wrestling and how hard it can be to deal with customer data. While this is a reasonable analogy, and customer data can be a real beast, my preference here is to just focus on all that unseen and untapped intelligence. One of the challenges is that the data below the surface can be a little hard to see when the water gets murky, habitat especially favored by alligators.

Ultimately, the knowledge within customer data is better seen when separated. Looking at the numbers in too large of an aggregation is what causes the loss of clarity. For example, I encountered a situation where a client was finding their inbound service calls had increased in length, leading to a higher cost per service incident. Well meaning call center supervisors in some of the service units worked hard to get their reps to reduce call time. On the surface everything looked good – those units had lower costs and as a result that would foster better margin for the business. But a closer look at the customer data showed that retention was higher with customers who were serviced at greater length. Higher retention led to higher customer annuity value and better margin. Looking below the surface proved that what was visible at first glance was inaccurate.

I have seen similar situations with marketing campaigns. A new program gets launched but the numbers indicate that there was no net effect as a result of the offer. On the whole the business unit revenues were not impacted. But, with a deeper examination of the activity in the field, it was determined that some customers bought considerably more as a result of the campaign while some bought less. Looking at differences with the sales activities of the two sets of customers this client learned that certain sales activities encouraged the customers to wait, while others encouraged the customers to buy. Had the sales force all followed the same approach with following up the campaign it would have been successful overall. Separating the customer data by sales activity was the critical analysis.

Looking below the surface is really what business intelligence is all about. Measuring customer activity at a level of detail that enables better clarity also enables better decision making. This ensures that sales and service activity are honed for maximum performance. This is far superior as compared to getting surprised by what is below the surface.

September 07, 2007

The Big Match-up

Back in the 90’s a lot of my clients were into customer segmentation, particularly the consumer banking folks. Thanks to analytics software becoming more available to more business, segmentation is all the rage again.

Today, the big deal is focusing on an optimized match between customer segment and coverage channel. Do you want to send one of your sales reps on a 300 mile trip to get a customer order that comes up a few dollars short of covering the cost of the travel, or would that customer complete the same order with a less costly telesales rep? If that were a smaller account, would it be more efficiently served through a distributor? Once that customer becomes a regular buyer, can you keep them regular with e-mail offers directing them to an e-commerce site? Would an e-mail offer be the best way to soften up a new prospect prior to a visit from a field rep?

Breaking down your customer base into segments based on criteria for differentiation is the best way to answer the questions above. Building a channel strategy for best coverage of each segment is the new CRM Holy Grail, and for most companies the use of analytics is the path there.

Scary Monsters

However, there is a big caveat to all this. The use of analytics software to create segments and model the best channel for coverage requires a couple of key components of a CRM program to be in place. First, CRM needs to be integrated with order data, usually residing in the back-end. And, it also will likely require sales call activity history – across all channels.

When these two critical factors are not in place, most Business Intelligence programs never get off the ground. Integration with order data can usually be accomplished, although it may require some effort to get the data in order. Getting sales activity data in the mix can be a taller challenge. This requires the cooperation of the different sales teams, introducing those nasty variables of user adoption and data quality.

I am always amazed how these two factors never go away. So, the moral of the story is, yes, it is possible to get to the end state of having an effective channel / segment match. But the path goes through the mundane land of getting sales people to enter data correctly into your CRM system. The new match-up requires an old match-up. Good luck.

June 22, 2007

Analytics for the Masses

There is some pretty useful point-of-view provided by McKinsey in a late May posting on the Computerworld site. The article explains three sources of common data that can lead toward insight regarding levers for improving sales effectiveness. The research is pretty sound and the best news is that the data sources are common for any company with a large sales force or set of channel partners.

The problem is that if they had not performed the research to point out these best practices for utilizing analysis to uncover better sales actions, you might not know it. However, you may still have somebody crunching a lot of numbers anyway. This is the problem with the new BI. Analytics tools are getting easier to come by and landing on more and more laptops across the enterprise. Crunching numbers has gotten easy, but the trick to crunching effectively is asking the right questions.

No Limit

You may have people generating reports based on sophisticated calculations that lead to erroneous conclusions. This happened to me early in my career when I was running a training function for management development. We were using SAS to generate reports for managers based on 360 degree competency surveys. Unfortunately, due to an error in the report set up, the tool spit out random numbers. Because a large number of people were participating in the overall data collection and reporting process it was caught before doing too much damage. However, when individuals are given really powerful analytical tools and are not correctly schooled in their use, the same random results can lead to dicey decision making.

So, the salient term in that last sentence is “correctly schooled”. If you are going down the BI path, which is a great idea due to the rewards at the end of the path, take the correct measures to ensure that your ROI is as great as the software vendors promote. Even a little education will go a long way.

January 12, 2007

Year of the Intelligent Organization

Way back in the last century, circa 1990, a student of management science published a book, The Fifth Discipline, which introduced the concept of Organizational Learning to the business world. Peter Senge, the author of this best seller, took the business world by storm. I personally found the premise of this book very compelling – the enterprise can develop knowledge, much as a human does, and this organizational learning process can be managed intentionally for success.

15 years ago this was a big deal. Everybody seemed to have the book on a shelf at the office and there was huge follow up buzz including seminars and copycat publications. However, I am willing to bet that only a handful of professionals have put significant thought into the concept of organizational learning since that time. It is time to change that and 2007 could be the year.

Technology has caught up with the concept of organizational learning. CRM came first and provided the tool for capturing customer interactions – the basic building blocks of knowledge. Next, Web 2.0 provided a revolution in thinking about the notion of collaboration. All of a sudden, knowledge management became possible and the software vendors have been developing ever more usable solutions for harvesting knowledge. Place on top of this SOA technology and the integration of different sources of knowledge has become both more manageable and affordable. Finally, mix in a healthy dose of BI software and business processes and all of a sudden we move from organizational learning theory to organizational intelligence in action.

Old Cracks
007 should emerge as the year of the Intelligent Organization. Businesses now have the ability to put organizational learning into practice, building on their CRM systems, leveraging the new propensity for collaboration, and gaining the benefits of BI capabilities. If you have not finished your business plans for the New Year, think seriously about putting actions in place to raise your organizational IQ.

November 10, 2006

BI or Analytics?

Just the other day I was in a pretty hot sales meeting with a client where one of my colleagues said, in responding to a client question, something like, “BI, analytics, whatever, it’s the same thing.” I have to confess, that statement really bugged me. There is a substantial difference between the two, but you might be asking, “what difference could it possibly make?” Well, I think it does matter, but let me explain.

There is no question that in the world of IT solutions and CRM technology, analytics is a very popular topic. Analyst research for the last couple of years has put this technology at the top of the wish list for the average IT spending budget. More recently the term Business Intelligence (BI) has become more commonly presented and I have started to notice that the two terms are now becoming interchangeable, which can lead to confusion. Perhaps it is useful to offer some definitions.

BI as a term is doubly confusing because it really has two meanings. The first version of the definition refers to the general ability to use information to make informed, and therefore, better decisions. The second definition refers to a complex technical capability combining multiple sources of financial and operational data with sophisticated multivariate analytical software managed by comprehensive data collection and decision making processes. The latter definition is a subset of the former, but it represents the former on mega-steroids.

Analytics on the other hand is the software package that is utilized within the typical BI architecture. You can’t really do the latter version of the BI definition without the analytics software (unless you have about 10,000 mathematicians crunching numbers all day long). So far you are probably thinking, what is the big deal? Well, there is another confusing element in this mix, which is reporting. Companies use reporting tools to assemble and present the information provided by either analytics software or more complex BI systems. But some business decision making utilizes simple reporting software without the use of analytics or BI, and this is often incorrectly referred to as either analytics or BI. Now I bet you’re thinking that this is just a bunch of semantics and who really cares. You should care.

When you decide that your enterprise needs better decision support you can go down one of these paths, which are really on a continuum of sophistication. You can improve your reporting on the simple end of the continuum. These tools assemble business figures using simple calculations to make it easier to see a number of things in one place. Assembling revenue by territory for each fiscal quarter is a great example. Good reporting packages make it easy to break this information into revenue by sales rep, revenue by product, and highlight product lines with the best margin.

What happens if you want to know what led to some reps being more successful than others or why some products sold better in some zip codes? Enter analytics. This software helps to compare multiple variables, over time, to better establish the relationship between changes in the numbers (getting to the all important cause and effect). For example, can we determine what sales activity, when performed with the customer, increases the likelihood of getting the sale? Or, do we know what telesales message was most effective with a specific campaign to produce the best leads? Analytics software helps to determine that.

We cross the line into business intelligence when we combine sales, marketing, order, and financial data, with analytics software, and work to get the correct data flowing through each system with data entry best practices & policy, metrics established for constant monitoring, and a data architecture that allows all enterprise data to be logically compared. This type of program requires resolving critical issues such as customer data hierarchy, integration of external data sources, and often the challenge of cross-functional policy compromise.

But, oh what you can do with a real BI program. Why do you have better margins for one product line in one geography but not the other? Why are you making more money for products with further ship-to locations? Which customer segment is really the most profitable with the right warranty offer? What is causing the cost of sale for services to be higher this fiscal year versus the last two? These are questions that you get answered with BI. Plus, you can come up with answers to questions you have not even thought of yet.

So, what’s the problem? For starters, sales people are pushing reporting solutions and calling it BI. Not to mention that the cost of a reporting package versus the cost of getting a BI program running are similar to the differences between a Piper Cub and the Space Shuttle. Mixing them up is misleading because it drives expectations to be set incorrectly. Installing an analytics package gives you number crunching horsepower, but it might not give you any intelligence. It is important to know what you are getting into and that starts with knowing the difference between what your options are. Choose wisely.