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November 17, 2006

Cow Tipping

Have you seen the movie Cars? It is very entertaining and better than I expected, as I found myself in need to pass some time on a transcontinental flight recently. Without question my favorite part of the animated movie is the tractor tipping scene. Don’t worry – I won’t spoil it for you, but it does remind me of an important topic that I often equate to cow tipping.

So, before anyone gets out of joint, I need to go on record as stating, 1) I have not performed the cow tipping act; 2) I do not condone cow tipping. Also, I understand that no cows or tractors were injured filming the movie Cars. With that disclaimer out of the way, let me explain.

There is a problem for many managers with regard to their attitude toward managing the natural dynamic we commonly know as “resistance to change”. A change is to be introduced into the organization, such as new technology, a new work process, or a new work schedule and everyone knows that it will be met with resistance. A common approach to dealing with the resistance is to keep the change quiet, introduce it quickly and without any choice, and then expect people to comply once it is announced, with no potential for discussion or modification. This is the cow tipping approach to managing organizational change.

So, you have heard of cow tipping, but are not quite sure what it is. It goes something like this. Some teenagers are out driving the back roads of rural somewhere after dark, possibly with a six pack, and they pass a field with cows standing, motionless, asleep. Somebody in the car suggests a cow tipping excursion, and they stop on the side of the road, jump the fence, and run as fast as they can toward the cow. Upon impact, they push with all their might against the side of the cow, which is taken by surprise and topples over.

This is the same as making a change without employee involvement (they are metaphorically asleep) because if you do this while the cow is awake, by the way, the cow has an amazing ability of leaning toward the oncoming push. Think about the physics – 2000 pound cow leaning against two 160 pound teenagers. Cows don’t tip over when they are awake. And employees also often push back when they are given the option of taking on an organizational change when they are awake – and they too collectively weigh more than management.

A more effective change management approach is more like getting the cow to agree to recline – don’t ask me how because I’m not a farmer or rancher – but I know its possible and the metaphor does not have to work perfectly – give me some license here. When you tip over a 2000 pound cow it often gets hurt in the process and dairy cows might not produce milk normally as a result. The same things happen to poorly introduced organizational changes. Employees have bad reactions and productivity gets impacted negatively.

When it comes to introducing significant organizational change, I always opt for some form of involvement to reduce the inevitable resistance. Don’t tip over the cow in the middle of the night and take what appears to be the easy approach. Rather, find a means to get it into the desired position in a way that causes fewer problems.

Now, go check out that tractor tipping scene. I guarantee you will laugh.

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.

November 03, 2006

Marketing is from Venus, but who is from Uranus?

It still does not cease to amaze me - again I just encountered yet another organization where the sales and marketing functions work together just like a family, a very, very dysfunctional family. You have all seen this. And, it should not be a big surprise since there are a number of organizational dynamics that tend to pop up from company to company, no matter what the industry - employees never seem to be satisfied with organizational communication, management never believes they have enough reports, service delivery people don’t like to get dirty with the process of selling, and marketing and sales professionals fight like adolescent siblings. I guess I need to accept this as a law of business.

Is this an, I’m-from-Venus-and-you’re-from-Mars-thing? Well, I have been known to make the proclamation that sales professionals come from a different gene pool than the rest of us mortals – but that is meant as a compliment. I don’t think this is an issue of different personality types not being simpatico. I think this is an issue of lead management.

When I have seen sales and marketing functions work together effectively (like grown up siblings that respect each other), there are a few factors in play that could serve as a recipe for others to follow. First, there are clear expectations between the two groups regarding what each will do. Most of the time the expectations go something like this: marketing is responsible for getting good leads to sales, and sales is responsible for using the leads to close deals - simple, but effective. A second factor at work is that the leads that marketing sends over to sales are expected to be good, qualified leads, and when a lead does not work out, sales is expected to provide feedback back to marketing as to why. One third factor that I think is also critical is that the right metrics are put in place to measure the lead management process. Measuring the wrong thing tends to cause the wrong behavior.

Marketing is getting better at generating leads. Automated campaign management tools make it much easier to push out offers and capture prospects. The revolution of web 2.0 is also making lead generation within broader audiences more cost effective by tracking web site activity as indication of prospective product and service interest. This is fantastic stuff, but it has a negative side – the proverbial second side of the sword where you cut yourself as you are swinging away at your opponent. This negative consequence is the generation of potentially too many leads. Marketing should not just generate leads, but rather, it should generate good leads. This pesky qualifier, good, is a big deal with regard to this poor relationship between these two functions. However, it is something that can be fixed.

The key to generating good leads is qualification. This topic deserves a lot more space than I am going to give it here, but you can check out this good CRM blog site where there is some healthy dialogue taking place to get more insight. My experience leads me to believe that qualification of initial leads is best done in the most cost effective means possible. This is often best accomplished through a telemarketing function that utilizes less expensive resources than the typical field sales force. However, more and more qualification is being accomplished through the web, utilizing content and messaging to direct prospects through self-selection prior to actual human contact. Check out Accelerating IT Sales for more on this topic.

Don’t think I am going to lay the whole rap at the feet of those hard working marketers. This is a two character drama, and those sales reps also play a role in the biz dev soap opera. The scene goes something like this: sales person receives lead from marketing, calls the contact who has no interest in the product, then throws out the lead, and exits stage left muttering how worthless marketing is, never to jump on a marketing-generated lead again. Have you seen this one? If lead management is going to work, bad leads need to be handled correctly. The most critical thing that can happen with a bad lead is for marketing to learn why, which requires feedback. The feedback will improve the targeting and increase the rate of good lead generation. But this does take cooperation with the field sales folks, plus a reasonable process for sending the lead back without too much effort.

Finally, one more element can play an important role in making this all work, which is the measurement of the lead management pipeline. Pipeline is an interesting metaphor, but there is also the concept of the funnel, and I think they can cause some confusion. Measurement is key, but the wrong metrics can cause the wrong behavior. Marketing should not be measured on lead volume (or least not on volume alone).

Yes, we need to get prospects into the marketing funnel, but they should not be designated as leads until they are qualified enough to get into the sales pipeline (hence the distinction between the two terms). Marketing should be measured on how well leads perform once they get into the pipeline with metrics such as lead conversion into proposals or lead conversion into closed deals. Yes, I know that marketing does not have control of the pipeline, but this is how you measure the effectiveness of a lead. If you want to put a measurement in place that marketing has more control over you can use a metric such as number of meetings scheduled (if telemarketing performs this task). By measuring the right metric you will increase the likelihood that what marketing does will serve the lead management process correctly, making them invaluable to their brothers and sisters in the sales organization.

The reverse is true as well - monitoring what sales reps do with leads is also critical. Using a CRM system or SFA tool for tracking leads in the pipeline is an easy way to measure sales rep activity and monitor how they are treating leads that don’t move through to close. Improving sales behavior in a way that sends better lead information back to marketing also causes marketers to better respect their sales siblings.

Ultimately, we are all from the same planet, not Uranus, and there is no reason for these two critical functions to get along so poorly. Focusing correctly on lead management is the means for better family relations.