Getting started in Business Intelligence (BI) on a budget

This is a simple demonstration as to how you and your firm can get started turning the data that you already have into the information you desperately need using tools you already own. The task of turning data into information for decision-making is the essence of business intelligence (BI).

So, here we go.

Everybody has data

Everybody has data. Many companies are wallowing in data. What they are lacking is “information.”

Read my posts here and here for more about the differences between data, information and knowledge.

Quick! Take five or ten minutes to peruse the following table of data and write down everything that you see in these data to help make decisions about the firm’s future.

I will give you one hint: the column identified as ‘ARPAC’ is “Average Revenues per Active Customer.”

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Okay. Times up.

Hold on to your list.

Turning data into information—simply, easily, cheaply

In order to produce what follows, I used only Microsoft® Excel™ and its native ability to access databases to fetch and refresh data.

Here’s the first graph I produced:

This is nothing more than a simple bar graph of column “SOSales” (Sales Order Sales, as opposed to Invoiced Sales, for example) shown in the data above. I used Microsoft’s native capabilities to add a “trend line.”

By looking at this simple graph, several questions might come to mind that would bear further investigation:

1. Why have our monthly sales dropped from just over $8 million a month to an average of about $6 million per month over these 29 months?
2. Why or how were able to produce about $11 million in sales in July of 2008? What did we do differently? How can we build on what we learned in that experience?
3. Is my drop in sales related to lost customers?

The next graph that I produced looked like this:

This graph answered my question number three above—at least partially. Month-to-month our firm has stayed pretty steady in terms of the number of active customers served. The firm is hovering right in the 250-customers-per-month range.

On the one hand, that is good. It means the firm is steady in this regard, but it does provoke other questions that would need to be answered through further digging:

1. We are serving about 250 customer per month, but is the same 250 customers, or do I have high turnover rates for customers?
2. Are we constantly having to spend precious marketing resources to capture new customers, or do we have a high volume of repeat business?

But wait! If we are not loosing customers (at least in numbers), but our sales are falling off (in aggregate), what is that telling us?

The third graph I produced was “Average Sales per Active Customer” (month-to-month). This graph clearly shows that between January 2008 and May 2010, the firm’s average sale per active customer fell from about $32,000 per customer to under $25,000 per customer.

Here again, this graph immediately provides clues worthy of further, more detailed, investigation:

1. Are these different customers buying less product? Or, are we serving pretty much the same customers, but they are just buying less from us?
2. Either way, we should figure out why: Are they buying similar quantities, but our prices (and, perhaps, margins) have shrunk over this period? Or, are they buying smaller quantities of merchandise or services from us?
3. Either way, we should find out why: If they are buying smaller quantities, is some of that business going to our competitors?

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Next steps

As you can see, turning the data into information allows our mind to quickly digest it and move toward decision-making. In some cases—perhaps many cases, when you first start—the process will lead to further information gathering.

On the other hand, you will sometimes discover that tribal knowledge already present in your organization will help you take immediate steps to begin making more money tomorrow than you are making today. Frequently, those steps involve no investment at all. Sometimes all it take is understanding better what is happening. Other times, a simple policy change permits significant increases in Throughput and profits.

After all, isn’t that really what you want to do—not spending six-figures on a new business intelligence “solution”?

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Read more here about unlocking “tribal knowledge.”

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How I did it step-by-step

1. Identify the data
2. Build a SQL Server view or query
3. Connect Microsoft Excel to the data
4. Build the graphs

Total time: about 2 to 2.5 hours

Business Intelligence for the coming year

Recently I was asked by a business writer for my recommendations for “BI New Year’s Resolutions.” I doubt my response was what the writer had hoped for, since many business blogs and publications garner support from advertisers. And, when you are doing that for a living, you really want to write things that are supportive of the kinds of products your advertisers supply. These days, since business intelligence (BI) is all the rage, there are a lot of dollars being proffered for advertising of upscale business intelligence solutions.

For better or worse, I don’t have to worry about that. (Of course, my income is smaller as a result.) But, here’s what I wrote—along with some other advice to round it out.

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BI New Year’s Resolution

RESOVED – I will never, ever, ever again undertake a BI project just because someone in my organization thinks “it might pay-off.” Instead, I will faithfully resolve to calculate—in advance—the expected ROI (return on investment) for the project.

I have learned my lesson: BI is not like an engine oil additive: departments can’t just “pour it in and expect the company to run smoother, faster, longer and get higher mileage” through some mystical power brought to them by the BI fairy.

In calculating the ROI, I will also remember that “approximately right” is fart better than “precisely wrong,” so will not waste my firm’s precious resources trying to hone a number to perfection before taking action—especially in this tough economy.

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The second question to which this writer asked me to reply regard “top BI trends” for 2012. Once again, I’m pretty sure I let her down. Here’s what I wrote:

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BI Trends for 2012

In 2012, an increasing number of small-to-mid-sized firms will discover that, to get started in BI, they do not need to make six-figure investment. In fact, they may not even need to make a five-figure investment.

If they can unlock “tribal knowledge” and begin to understand what to measure in order to make a real difference in the Throughput of their system (i.e., the whole firm), chances are they can make use of tools they already have like Microsoft® Excel™ to capture data from their ERP system directly via ODBC (open database connectivity) or OLEDB (object linking and embedding for databases). This may lead to insights, and those insights may lead them to market segmentation or other innovative profit-improvers. They do not need expensive software to build a simple, yet valuable, dashboard so they can start making more money sooner—rather than later.

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In the next post, I will provide a concrete example of how simple BI can be done using tools your firm probably already owns.

(c)2008, 2009, 2010, 2011 Richard D. Cushing

Finding Common Ground Between the CFO and COO – Part 4

[Continued from Part 3]

The Banking Trade

Our next example of how businesses might leverage business intelligence (BI) to segment their markets and thus allow them to increase throughput in significant ways comes from the banking industry. In this case, a bank creates a data bridge between a legacy database and databases maintained by its departments. The new application gives branch managers and other users access to business intelligence to determine who their most profitable customers were and which customers might be above-average targets for cross-selling new products.

Implementing these new tools liberated the IT staff from the task of generating special analytical reports for the departments and gave department personnel relatively autonomous access to a far richer source of customer-related data.

However, the bank need not stop with “cross-selling.” Consider that if the bank has information on “the most profitable customers,” they could dig deeper to determine the geographic and demographic corollaries among their “most profitable customers.” Uncovering and analyzing these corollaries employed in conjunction with a simultaneous thrust to unlock what the bank’s employees know—that is, tribal knowledge—might help the bank develop carefully targeted irrefusable offers. Such offers would undoubtedly allow the bank to

• Sell more existing products and services to new customers
• Create new offers that will attract new customers from the “most profitable” demographic and geographic market segments
• Create new offers that may interest existing customers and make offers that may be even more profitable for the bank

 

Your Business

Regardless of your industry, it is highly likely that a joint effort made by the CFO and the COO to unlock and join two valuable sources of data will lead to many valuable ideas for increasing throughput. Those two sources of data are

• What is available through (formal or informal) business intelligence about your customers
  
  with
• What is available—but probably undocumented and poorly understood—in the minds of your managers and employees in the form of tribal knowledge.

For this reason, I strongly suggest that for most SMEs (small-to-mid-sized business enterprises) the very first place to look at rapid ROI from business intelligence is to be found in market segmentation.

Understanding Your Customers’ World

One of the errors made by CFOs and COOs in most organizations use a definition of “quality” that is totally objective. After all, how else could or should the firm measure it? Most use a definition along the lines of “without defect” or “within tolerances” or “meeting or exceeding specifications.”

Toyota, however—the firm that came from behind to become a dominating automobile and light-truck manufacturer throughout the world—has learned and predicates it operations on an entirely different definition of quality. Toyota’s measure of quality is:

Does the product make the customer’s experience and results better or not?

Toyota’s concept of quality originated from concepts introduced to Japan in the 1950s by W. Edwards Deming. It was Deming who said:

“Constantly improve the design of product and service. This obligation never ceases. The consumer is the most important part of the production line.”

As a result, Toyota’s measure of quality takes into account, not just what the customer buys, but also:

• Who buys the product: Because the who will lead to different expectations and different feelings about the experience and the results expectations.
• When the product is purchased: Because the circumstances leading to the purchase of the vehicle will also contribute significantly to defining the experience and the results expectations of the buyer.
• Why the product is selected: Because the why is another significant contributing factor to the buyer’s experience and to defining the buyer’s expected results.
• Where the product is purchased: Sometimes product purchases are driven by regional factors (e.g., climate, urban versus rural or back-woods). These factors will affect the buyer’s experience and results expectations.
• How the transaction is structured: The economic construct of the transaction may include multiple factors such as the duration of the warranty, the payment terms, the time of delivery or lead-time, and more. These factors also influence the buyer’s experience and the sense of results.

Segmenting the market requires the whole supply chain to understand the customer because, fact of the matter is, No one in the supply chain has made a sale until the end-user has made a purchase. This is why both the CFO and COO should seek first to understand their customers. Next they should seek to segment their market—because different customers buy under differing circumstances and for different reasons.

These actions should lead to a plan for the creation of irrefusable offers which should, in turn, lead to rapid ROI.

[To be continued…]

Avoiding a costly “metrics obsession”

This year, 2011, is the centennial anniversary of the publication of Frederick Winslow Taylor’s autograph work, The Principles of Scientific Management. According to Taylor, almost every challenge management faced could be solved through the application of science. This view has become the staple of business schools for the better part of the last century, as a result.

Most small businesses—which, by the way, constitute the majority of all businesses in the U.S.—found the application of “scientific management” to be unduly burdensome. Many entrepreneurs lacked the training in the application of statistics or the time and energy to conduct “time and motion” studies when they knew—by the proverbial “seat of their pants”—that they could make a profit if they took this action or that one.

By the middle of the 20th century, another great voice in “scientific management,” W. Edwards Deming, was beginning to clear the air on the subject, a bit. While Deming certainly believed in gathering data and analyzing statistics in order to improve operations, he was also unequivocal about the limitations of “metrics” in achieving business success.

It was Deming who pointed out, for example, that “The most important figures for management of any organization are unknown and unknowable.” (Emphasis added.)

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“The most important figures for management of any organization are unknown and unknowable.” – W. Edwards Deming

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However, in the 1980s, along came the introduction of the “Personal Computer” (PC) and a plethora of software that enabled small businesses to collect, analyze, store and recall hundreds of thousands or even millions of data points. With the growth of computing power and falling costs of computer hardware and software, the collection of volumes of business data was soon within the reach of even the smallest of small businesses.

Even before the dominance of the Internet as a means for sharing data and collaborating across huge distances, many small-to-mid-sized business executives and managers had become enamored with the ability of computers to store and retrieve data. Even if they were entirely unaware of the pronouncements of Frederick Winslow Taylor, these executive and managers came to believe something along the lines of: “If we can collect and access enough data about our operations, we will be able to manage flawlessly.” The obsession with metrics had, indeed, come of age.

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The mantra of the “Obsession with Metrics” crowd: “If we can collect and access enough data about our operations, we will be able to manage flawlessly.”

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Another all too frequently heard proverb from the metrics-obsessed crowd is this: “You can’t manage what you can’t measure.” This, of course, has a tincture of truth to it, but is misconceived. There are all manner of things in which management is involved in “managing” in some way or other that are not not subject to objective quantification.

Here is a (non-exhaustive) list for your consideration:

• Corporate culture
• Customer relationships (we even have software that is supposed to do this!)
• Employee relationships (we have both software—human resource management applications—and entire third-party firms that engage in this kind of “management”)
• Customer loyalty (some companies even have “teams” or “departments” engaged in “managing” this aspect)
• Creativity / innovation
• Leadership
• Ethics
• Supply chains (especially the ‘relationships’ that really make them work; not just the inventory ins-and-outs)

Now, let me very clear here: I do not oppose the application of sound scientific principles to business when the application of such principles is done in an environment where cause-and-effect can be reliably demonstrated.

The correct statement is this one: “If you cannot define the ‘process’ and the theory underlying the cause-and-effect relationships in the ‘process,’ then you cannot manage it.” More importantly, if your theory is wrong, you will not get the results you expect.

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“If you cannot define the ‘process’ and the theory underlying the cause-and-effect relationships within the ‘process,’ then you cannot manage it.”

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This clear and correct statement explains why some companies actually see significant improvements in their business results after implementing new supply chain “management” (SCM), customer relationship “management” (CRM) or human resource “management (HRM) applications” while the vast majority of companies see little or no improvement.

Understanding your existing business processes (hint: it is likely they are NOT what you think they are) and tying them to a theory that will help you understand the cause-and-effect within your processes is not as hard as it seems. Nevertheless, most businesses fail to do so simply because they don’t know they need to do so! They think they already understand them—but do not.

That’s why no matter how many “metrics” they throw at the problem and—sadly—no matter how much money they throw at “fixing” things, they typically see little or no improvement in the things that really matter—like making more money!

There is a better way!

Change comes through people - Part 3

We are continuing to revisit the Key Points raised in the Webinar on “Emotional Intelligence.”

Middle managers need to implement change while managing their employees’ emotions – anxiety, resistance and inappropriate behavior

Notice the tone of this statement and the mandate it sets for middle managers: middle managers need to implement change while managing their employees’ emotions. Here are a couple of questions I raised with the presenters during the Webinar:

1.       How much time, energy and money should a company spend in “emotion management”?

2.       At what point should executives stop and re-think their plans when their top-down actions raise anxiety and resistance from middle management and below to such a level that additional resources must be deployed just to “manage emotions”?

This statement amply demonstrates just how out-of-touch executives and managers in some organizations must be with the rest of their enterprise – i.e., middle management and below on the organization chart. Not only so, but it also shows that these same executives have no idea the damage that their distance from the organization’s “heartbeat” is likely to cause. And, I am not talking about damage to the “peons’ egos or sense of self-worth.” I mean sincerely the damage that is done to their enterprise in measurable terms with which CEOs and CFOs should be concerned – namely, lost Throughput and profits.

Interestingly, when I raised the question regarding when executives should stop and re-think their plans, the presenters of the Webinar pretty much told me that in the face of ego-driven ERP, once the enterprise gets far enough down the traditional ERP path to start raising anxiety and resistance from “the masses,” there is no turning back. It is the rare, rare exception that executive management would dare to stop and rethink the project underway.

This is a sad state of affairs and emphasizes just why traditional ERP – Everything Replacement Projects have such a lousy success rate when it comes to delivering return-on-investment.

Middle managers face the challenge of grasping a change they did not design and negotiating the details with others who are equally removed from strategic decision-making

This seems to get more ridiculous the further we drill-down on the details, doesn’t it? This statement reminds me of a combat situation. Here’s the scenario (in metaphor):

The generals (executive management) have come up with a battle plan – apparently with little or no consultation with the boots on the ground. The generals, in their wisdom, have passed this battle plan down from “on high” and told the lieutenants (middle management) in the field just how they are going to “take that hill.” The lieutenants – many of them being 90-day wonders, fresh out of college and with little practical experience – are now tasked with convincing chief master sergeants, master sergeants, gunnery sergeants and ground troops – some of whom have 15, 20 or even 30 years of “boots on the ground” experience that the plan promulgated by the generals is a good plan and ought to be carried out.

The problem is, the lieutenants do not necessarily understand all of the implications of the plan for those who must carry the weapons and bring the plan to success. They do not comprehend what it takes in day-in, day-out hand-to-hand combat to actually “take the hill.”

Nevertheless, it is the lieutenants’ job to “sell” the plan and “negotiate the details” with the seasoned front-line personnel who actually know the risks that they will be taking.

I think you get the picture. Is it any wonder that a firm operating in this way might experience some problems with their traditional ERP – Everything Replacement Project? Is it any wonder that fewer than half the firms undertaking efforts like this achieve any measurable net benefit from their traditional ERP effort?

No, of course there is no wonder.

Complexity rises for middle managers of change as work demands are modified and multiply, thus creating conflicts

This is a traditional ERP – Everything Replacement Project – after all. Executives have decided to tear the guts out of the entire organization and transplant new technological “guts” in the name of “improvement” that the executives themselves have likely not quantified. To say that “Hope is not a strategy” is an understatement at this point.

Executives have, more likely than not, promulgated a technology “strategy” based on little more than “hope” and the so-called “promises” of the vendors or resellers. Then, these same executives have largely left execution in the hands of lieutenants (see above) and third-parties (e.g., the vendors, resellers and consultants). Then, by some inexplicable stroke of magic executive management expects improved profits to be the result at the other end of this journey. Why else would you spend millions of dollars to disrupt virtually every operation in your enterprise virtually simultaneously and continuously for upwards of 18 months?

Of course “work demands are modified.” A “strategy” (falsely so-called) has been set forward and most of middle management have no idea what that strategy will actually mean for the “boots on the ground.” (See metaphor in previous section.) In this case, it is not “the enemy” forcing a change in plans. As Pogo once said, “We have met the enemy, and it is us.”

Lack of clarity renders new demands uncertain and frequently misunderstood; without clear understanding, managers can be seen as taking wrong actions or no action at all

Given our discussions up to this point, does anyone have an difficulty in understanding just why there might be “lack of clarity” in the traditional ERP – Everything Replacement Project?

Maybe it is because, while it is necessary to understand the operation of your organization as a whole – that is, as an integrated “system” – it is not possible to “focus” on the whole organization all at once.

If executives ask themselves the question, “What needs to change in order for our company to start making more money tomorrow than we are making today?” How likely is it that the answer that would come to mind would be, “Everything!”?

My guess is that the answer would never be everything! Typically, the number of things that need to change to begin making more money tomorrow is very small – say, fewer than a half-dozen. And, even if there are a half-dozen, they need not all be undertaken at once. Then, add to that, the fact that among those half-dozen things that need to change, it may probably be found that half or fewer of those things actually require a change in technologies to support the change.

Yet, purveyors of traditional ERP- Everything Replacement Projects will try to convince executives that what needs to change is everything – and then the company will make more money. Unfortunately, many executives are all too willing to believe this is actually “the solution” of which they have always dreamed. “Just pour it in and everything will run smoother, faster, longer and we’ll get better mileage, too.”

Summary

As W. Edwards Deming said, “You do not install knowledge,” and “Knowledge comes from theory.” These executives go far afield from actually helping their organizations improve because they lack a valid “theory” about how their “system” – their enterprise – actually functions in carrying out the customer-to-cash chain of inter-dependent functions and events.

Virtually all of what they need to know lies resident within their own organization in what I call “tribal knowledge.” But such executives as would seek to manage their employees’ emotions through “Emotional Intelligence” – read: manipulation – will never reap the benefit of unlocking the treasure of “tribal knowledge,” because, it seems, this knowledge comes from “the working class” and it is beneath their dignity to learn from “the man that runs the machine.”

It is the executives with such a mind that are the losers as a result. But their employees lose, too. Firms under such management will never be as profitable or durable as they otherwise might be. Employees cannot be paid as much, because profits are too low. Some employees will lose their jobs because the company cannot compete.

This is all too senseless.

©2010 Richard D. Cushing

Contact me!

Business Intelligence and “Tribal Knowledge” – Part 3

In Part 2 of this series, we ended by say just how valuable it is to begin unlocking the "tribal knowledge" that is undoubtedly resident in the minds of the people you have working in your business enterprise.

Taking another look a the client's situation I have been referencing in this series, the client has a complex sales cycle that involved multiple individuals and organizations in the processes of funding and purchase decision-making. To refresh our collective memories, here's a list of the participants we have previously identified:

• The school district, including administrators and, sometimes, board members
  
• The school(s) and the school(s)'s administrators
  
• Teacher(s)
  
• The school(s) and/or school district's IT department
  
• Government programs and associated bureaucrats
  
• Not-for-profit or other sponsors
  

Now, it seems clear, each of these participants that may be involved in the process of a single sale to a school or district will likely have somewhat different motivations for buying the products offered by my client. For example, what excites a teacher about using the product in his or her classroom will probably have an influence on the school and school district's administrators. But in order to get the administrators to look upon the purchase favorably will involve other satisfactions and assurances than those required by the teacher alone. The same may be said for all of the potential parties involved.

Asking the right question to unlock "tribal knowledge"

While looking at current sales accompanied by geographic, demographic or even salesperson correlations may be helpful in seeing some patterns that can be leveraged to increase Throughput, if an organization is going to come up with real breakthrough offers – so-called "mafia" offers, because they are offers that can't be refused – will probably require more than that. It requires unlocking tribal knowledge so that the firm's mark can be segmented and the "offers" can be ever more targeted and effective.

When many companies begin this process, they begin by asking the wrong question (in my opinion). They ask their sales and marketing team something along this line: "Why do our customers buy from us?"

Of course, this makes sense, doesn't it? This question correlates to the data the management team looked at in Part 2 of this series. They looked at differences between Category A sales and Category E sales and now they want to know why so many customers in Category A bought from us.

The right question to ask, however, revolves around what is keeping the company from making more money tomorrow than they are making today, and that question would be: "Why did so many potential customers in Category E not buy from us?" After all, it is the lack of sales that is keeping the organization from increasing Throughput; therefore, it is essential to find out what is keeping sales from happening. In theory, all of the prospects have already been exposed to the factors that caused those who already purchased to decide favorably.

Unlocking "tribal knowledge" to identify patterns and constraints

If my client's team were to begin by sitting down with their sales team, they might put forward a challenge something along these lines to them:

"I want each of you to list the 5 top things – from your experience – that keep you from selling more (fill in the blank)." (The blank might be a product, a product line, in specific geographic areas, or in specific demographic categories.)

[Note: Ideally, it would be good to correlate the results of this into a Current Reality Tree to further unlock potential root causes, as there likely are some that should be addressed. However, let us leave that aside right now and just consider the matter with regard to "business intelligence."]

Discussions evolving from the resulting list of sales inhibitors – along with some provocations to think below the surface – might result in some fascinating factors emerging. The results might lead to understandings similar to this hypothetical list:

• It seems like it is easier to sell Product A into school districts where the administrator is younger and, therefore, more likely to be attuned to technology in education.
  
• It seems like it is easier to make a strong and effective ally of a teacher with more than 5 years in service, but fewer than 15 years. (This might be because the less experienced ones don't have the confidence to bring new ideas to their administration and the ones with more than 15 years in-service are "stuck" in their old ways.)
  
• It seems like it is more difficult to sell Product C into inner-city school districts with high populations (fill in the blank with an ethnicity).
  
• We have not yet discovered how to interest upper-class suburban school districts in our Product Line B.
  

Now, from the tribal knowledge the management team has just begun to unlock, there should be a two-pronged approach to moving forward:

1. Statistical verification
   
2. Development of new "mafia" offer concepts
   

Which portion of this two-pronged effort should receive the major emphasis should be guided by another tribal knowledge factor – intuition – which is right far more often than it is wrong. If the team intuitively senses a strong, "YES! We've hit on something that rings true." Then, the emphasis should probably be given to the development of breakthrough thinking for new "mafia" offers to overcome the constraint and in Throughput.

On the other hand, if the team is more reserved about an emerging concept – if they believe it has some validity, but would feel more comfortable if it could be further corroborated, then the team should put the emphasis on statistical verification.

Statistical verification

The process of statistical verification gets us back to "business intelligence" in an information technology sense. However, it is likely that the appropriate demographic data is not presently available to the firm at this moment. For example, school or school district administrators' and teachers' years in service is probably not a data point currently being collected.

In this scenario, if I were on the management team at this client, I would strongly suggest that we take two or three years of sales history and take a survey. If the firm presently has excess capacity, then take some of the excess capacity resources and put them to the task of calling these customers to gather the demographic data in question: "Years in service."

[Note: If the firm is going to have this done, there might be other demographic data that has come to light and may be of value as well, such as the inner-city ethnic composition of the schools and school districts. And, by the way, while this survey is being undertaken, I would add another element: Gather email addresses and permission to correspond with these parties electronically with occasional messages "including helpful news about technology applications in education and other valuable education insights."]

Similarly, even without formal data accessibility or a lot of detailed research (although much would probably readily accessible via the Web), the firm's team could probably add reasonably accurate demographic data regarding "inner-city" versus "upper-class suburban" schools and school districts that could be used for further analysis. These data may be refined and made more accurate as time and data availability allow.

Once the demographic data is collected, it will be a relatively simple matter to see if there is a real statistical correlation matching the team's intuitive sense regarding years in service for administrators and teachers.

Development of new "mafia" offers

As we said above, a "mafia" offer is simply an offer constructed in such a way that it is simply too good to be refused. This means understanding the motivations of the market segment you are approaching and, as the organization grows in its application of this powerful blend of tribal knowledge and business intelligence, its ability to segment its market into smaller and smaller elements will grow. This will tend to increase the firm's ability to offer even more targeted "mafia" offers.

Going back to some of the examples mentioned above, consider the following:

• It seems like it is easier to make a strong and effective ally of a teacher with more than 5 years in service, but fewer than 15 years. (This might be because the less experienced ones don't have the confidence to bring new ideas to their administration and the ones with more than 15 years in-service are "stuck" in their old ways.)
  • Example question to ask: How can we develop a "mafia" offer that will convince teachers with less experience to become a stronger and more effective ally in bringing out products to their superiors?
    
  • "Mafia" offer concepts: This might involve a "hand-holding" offer with more direct involvement with the teacher in this process, or simply providing more effective "ammunition" so the teacher feels better equipped to address questions from his or her superiors in administration.
    
• 
  
  
  It seems like it is more difficult to sell Product C into inner-city school districts with high populations (fill in the blank with an ethnicity).
  • Example questions to ask: What can we learn about the specific culture (ethnicity) so that we can construct "mafia" offers that will overthrow the reticence exhibited by this culture in adopting our products for education?
    
• We have not yet discovered how to interest upper-class suburban school districts in our Product Line B.
  • Example questions to ask: What are the objections raised by those in upper-class suburban school districts when approached regarding Product Line B? How can we develop new "offers" that overthrow these objections?
    

Hopefully, if I have been clear, you are beginning to see how power the blending of tribal knowledge with computer-based, low-cost business intelligence could be to help your firm segment its market and create breakthrough "mafia" offers that should lead to increased Throughput.

Contact me rcushing@geewhiz2roi.com if you have questions or would like assistance in applying these techniques effectively in your organization.

©2010 Richard D. Cushing


 

Business Intelligence and “Tribal Knowledge” – Part 2

In Part 1 of this series, we were talking about a firm that had identified that the thing that was keeping them from increasing Throughput – from making more money tomorrow than they were making today – was understanding their customers and the other participants in the decision-making process better. I also pointed out that this firm already had some data available to them that could be used to begin the process of understanding their customers better. They had, of course, their historical sales data. But this firm also had available to them an independent database that contained some additional demographic data about their customers and prospects that could be correlated with their own sales data.

I closed Part 1 by suggesting that they could employ these available data to begin exploring relationships such as:

• Sales by salesperson
  
• Sales by salesperson by geography (e.g., city, state, region)
  
• Sales by salesperson by demography (e.g., size of school or school district)
  
• Sales by product line by geography
  
• Sales by product line by demography
  
• Sales by salesperson by product line
  
• Sales by salesperson by product line by geography
  
• Sales by salesperson by product line by demography
  

There are other data elements (dimensions) available to virtually every firm that we are not including here. For example, if you introduce the additional "time" dimension, it may be easy to spot trends over time – e.g., salespersons, regions, or demographic groups where sales are growing or decreasing over time.

This is an example of what business intelligence practitioners call "cubing the data." The data is summarized by various "dimension." In the example above, the sales data is being summarized and the "dimensions" are:

1. Salesperson
   
2. Geography
   1. City
      
   2. State
      
   3. Region
      
3. Demography
   1. Size of school (number of students)
      
   2. Size of school district (number of students)
      
   3. Teacher/student ratio
      

As I said, all of this can be done using low-cost tools available to almost every small-to-mid-sized business and already on the desktop of almost every computer. Microsoft Excel, especially Office 2007 and later versions, is capable of digesting a large set of data within its own operating context. However, if you or your firm has a Standard Query Language (SQL) server and these data reside in a relational database (such as Microsoft SQL Server, especially SQL Server 2005 and later), you have even more relatively low-cost tools to manipulate and digest even larger data sets. SQL Server 2005 and later is even capable of calculating and summarizing data cubes on the fly. These pre-digested data may then be presented to Excel as a presentation tool and user-interface.

Introducing "tribal knowledge"

So what is keeping companies from leveraging the data that they already have in order to use the insights discovered through such analyses? Generally, in small-to-mid-sized businesses I find the following factors are holding them back:

1. Uncertainties regarding the value – I have to put this one at the very top of the list for one simple reason: If executives and managers in the firms were convinced that discovering new factors about their marketplace – market segmentation – would help them make more money tomorrow than they are making today, they would find a way to get it done.
   
2. Uncertainties regarding the costs – Sadly, the business intelligence community itself has much to do with making small businesses wary of the costs moving into the realm of business intelligence. Many who make their money by selling and implementing business intelligence tools want you to believe that is not possible to make real gains and reap significant business benefits without investing in expensive business intelligence software and spending lots of time, energy and money to build expensive data warehouses and, perhaps, hundreds or even thousands of "cubes." This is simply not the case, but it is frequently the belief.
   
3. Uncertainties about how to get started – Again, in part to the pseudo-mystique surrounding the world of "business intelligence," many executives and managers do not feel that they "have what it takes" to get started benefiting from understanding their customers and marketplace better by leveraging the data they have been collecting in their ERP systems for years. There are simple ways to get started and one can always make the leap to more sophisticated business intelligence applications when conditions warrant.
   

But, wait!

So far in our discussions I have intentionally left a tacit implication on the table. That implication is the one that drives far too many executives and managers in companies of all sizes, and it is this: What is valuable and can be leveraged in "business intelligence" is found in our data systems and the data stored or collected.

This is very far from true!

Some of the most important contributions to making computer-based "business intelligence" valuable do not come from the data, nor from the software. These valuable contributions come from the people that have worked in your enterprise year after year. Your people know things about your customers, your prospects, your products, your industry and your marketplace. I call this kind of knowledge held within a business enterprise "tribal knowledge."

Now, tribal knowledge in every organization extends well beyond the examples I will suggest in this series, but I think you will begin to see just how adding tribal knowledge into the blend with the data you have available to you extends the power of business intelligence and may lead to truly valuable breakthrough thinking.

Suppose that in analyzing sales data currently available, they looked at the data summarized in a certain way and the graph looked like the following figure:

The questions that ought to be asked when looking at such a data summarization should be along these lines:

• Why are sales in category 'A' five times better than sales in category 'E'?
  
• What can we learn from what we do to get the results in category 'A' in order to apply it to the other categories?
  

Now, let me bring this down to more practical examples:

• Categories are product lines: What factors make Product Line A perform so well? Do we sell it differently than Product Line E? Do we promote it differently? Do we sell it to different kinds of customers? If so, what are the differences between the kinds of customers? How can we apply what we know about how we sell Product Line A to improve results for Product Line E?
  
• Categories are salespersons: What does 'A' do to get results that 'E' does not? Are these results simply differences by sales territory? Are there demographic differences in 'A's customer list from the customer lists of the other salespeople?
  

Naturally, this of questioning can go on and on, limited only by the management team's ability to think of the "right" questions to ask. Some of the questions can be answered using the data and re-summarizing it in a different way. For example, to answer the question, "Are these differences [between salesperson results] simply differences by sales territory?" it may be necessary to re-summarize the data by sales territory. However, if salespersons and sales territories are synchronous and exclusive, then one might need to compare similar but broader territorial results to see if a pattern exists. (For example, if the salesperson assigned to Washington State is Category A, then one might compare results for other West Coast states to see if they are similarly high even though different salespersons are assigned to these territories.)

The basic point, however, is that the people involved in your organization are carrying about with them "tribal knowledge" that can help you and your management team discover new ways to segment your market and increase Throughput.

[To be continued]

©2010 Richard D. Cushing