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Jump-Starting Prosperity in 2013
While the 80/20 rule is widely known among business analysts and managers many companies do not even try exploit its power to increase profitability. A valuable application of the Pareto rule in business refers to a distribution ratio between profits (reward) on the one hand and products, markets, sales territories or customers (effort) on the other.
Drilling below the surface of the 80/20 distribution one often finds that the top 20% of the products contributes 80% of the profit, the next 40% generates 20% and the bottom 40% usually has no contribution to profitability.
Also, typically only 5% of the customers generates close to 50% of the company’s profits while the bottom 50% customer group contributes about 5% to the profit pool. As a result the top 5% group is 100 times more profitable than the customers in the bottom 50%.
The application of Pareto Strategy to streamline business profitability has three main goals:
a) Reduce the cost and expense that supports the bottom 50% in order to reduce the number of unprofitable customers and products or move them into profitability.
b) Use business analytics to uncover what makes the top 20% of products or customers 16 times more profitable than the rest by understanding what is that they have in common. The objective here is to replicate their successes by creating more products, or acquiring more customers that share their characteristics.
c) Repeat this process to optimize the business profitability across different dimensions like brands, products, SKUs, technologies; customer parents and their branches; sales territories, distributors, dealers, agents; market segments and business units.
To guide the process Simafore and Strat-Wise recently launched an online, interactive learning module geared to helping business analysts understand and apply the Pareto Principle. The one hour hands-on module aims to improve the profitability of different business units with easy to understand material that includes practical examples and exercises targeted to help business analysts, product managers and executives looking to streamline different areas of the company.
Certainly an initiative to consider as part of your business New Year resolutions.
Exploiting The Pareto Rule to Optimize Business Results
Ann Arbor, MI – SimaFore and Strat-Wise Consulting announce the launch of an online, interactive learning module geared to helping business analysts understand and apply the Pareto Rule or 80/20 Principle.
This hands-on module aims to improve the profitability of different business units. The material is easy to understand and includes practical examples and exercises targeted to help business analysts, product managers and executives looking to streamline different areas of the business.
The one hour learning module describes the effective use of the Pareto Principle to segment and optimize customer and product portfolios as well as inventory.
The techniques shown apply to most aspects of business optimization: from rationalizing under-performing products to firing those chronically unprofitable customers that drain the company’s resources.
Participants also learn to uncover the common factors of highly profitable customers and products with the objective of replicating their successes.
The module is hosted on SimaFore’s analytics learning platform. http://bit.ly/TI2ss8
Employees, Productivity and Pareto Thinking
I've personally utilized the 80/20 principle to optimize business results in different areas of multiple companies. It never ceases to amaze me to find Pareto’s Rule conspicuous presence across different areas of the company - where 80% of the reward is invariably caused by about 20% of the effort.
Typical examples of this are:
Customers: 80% of revenue is generated by 20% of customers.
Marketing: 80% of market share t is held by 20% of competitors.
Cost Management: 80% of cost is originated by 20% of raw
materials.
Product Management: 80% of profit is generated by 20% of brands.
Quality: 80% of customer complaints are caused by 20% of product defects.
Inventory Management:
80% of stock is held by
20% of SKUs.
Sales Management: 80% of revenue is produced by 20% of
sales-reps.
Product Development: 80% of future profits will come from only 20%
of current projects in the R&D portfolio.
Decision Making: 80% of results are generated by 20% of
actions.
Time Management: 20% of tasks use-up 80% of the available
time.
An inherent characteristic of Pareto’s Rule is its uneven relationship between effort and reward. Looking deeper inside the data we can typically find that:
This means that the top
five percent of customers, products or business units generates close to 50% of
the profits or revenue. Conversely, we
can see how 50% of a business’ time and effort is practically wasted to produce
only 5% of the profit or results.
Interestingly, customers
or products in the top 5% group are 100 times more profitable than those in the
bottom 50% group.
Estimating Employee Productivity
Based on what we already
know about how the profitability distribution of different business dimensions
responds to the Pareto Rule, we could safely infer that the following statement
is also correct - even if not easy to measure:
HR:
20% of employees produce 80% of the company’s value.
In this case, if we could
accurately measure the Variable Contribution Margin - as this represents the fresh money from the outside - generated
by each employee, the profitability distribution would very likely fit the
Pareto rule where:
1) The
top 5% of employees that produces 50% of the contribution margin are Critical
Employees.
2) The
next 15% that produces 30% of the margin are the company’s Key Employees.
3) The
following 30% that produce 15% of the contribution margin are Average
Employees.
4) And
the bottom 50% of employees that only produces 5% of the margin represents the Laggards.
For this theoretical exercise we will consider a mid size company with 100 employees that generates annually $30 million in variable contribution margin and spends $15 million in employee compensation - consisting of salary and benefits.
At this point if we bring
employee compensation cost to the equation we should be able to calculate the annual
return on compensation (ROC) for each
group of employees:
Looking at the table it’s easy to see that in a successful company like this, every dollar paid to employees (in salaries and benefits) overall produces $2 of Contribution Margin. However the productivity of each of the four groups of employees is very different as measured by the return on their compensation (ROC):
Critical employees are strategic players that produce 20 dollars of Contribution Margin (CM) per each dollar paid to them in compensation. This is a selected group of individuals that greatly contributes to the success of the company across different areas. Typically they have a superb understanding of customer needs, how to translate them into successful products or services and they are very effective working in cross-functional teams to make this happen. Needless to say companies should identify these employees, do their best to reward and retain them and figure out how to attract more candidates that fit their profile.
Key employees produce 4 dollars of CM per each dollar paid to them in compensation. These are highly productive individuals that always go the extra mile in search of greater operational efficiencies and cost reductions. The company should also strive to keep them and recruit more employees with their initiative and motivation.
Average employees produce 1 dollar of CM per each dollar paid to them in compensation. This exercise suggests that about 30% of employees are likely to be average performers whose contribution breaks even with what they’re paid. These are the “that’s not my job” folks that only do what they are told or what’s on their job description. Companies should identify individuals in this group and either train or motivate them to transform them into critical or key employees or find them an area where they can perform at higher level.
Laggard employees produce 20 cents of CM per each dollar paid to them in compensation. Probably close to 50% of employees are in this “cash draining” group that only returns 20% on what is spent on them. According to the Pareto distribution, within this group some employees contribute more than others. Laggards are typically the “I just work here” folks. Management should identify them, have a discussion with each one of them and decide whether training or motivating them is a viable option. Another alternative is to move laggards into jobs where they can perform at key employee level - even if that eventually means moving to another company.
Looking at the table it’s interesting to observe that critical employees are 5 times more productive than key employees, 20 times more productive than average employees and 100 times more productive than laggards.
The key question remains: how
to approach a practical measurement of CM productivity per employee?
Please feel free to share your experience, ideas or disagreements.
Is Your Business Analytics Driving Profitability?
By Bill Cabiro
It should be no surprise that data-driven decision making is more effective than relying on managers’ intuition. Research clearly shows that analytics oriented organizations largely outperform their peers.
Making sense of the data becomes an increasingly challenging task as corporate data continues to grow at a 40% yearly rate as a result of the rapidly declining cost of computer memory.(1)
An IBM-CFO study shows that analytics-driven organizations had 33% more revenue growth, 12 times the earnings (before interest, tax, depreciation, amortization) and 32 percent more return on capital invested.(2)
This is where Business Analytics (BA) becomes handy providing data management, integration, multi-dimensional analysis, visual discovery, data mining and statistical methods to improve productivity in nearly every business function.
Business Analytics is an all-encompassing name for a new discipline resulting from the integration of Business Intelligence and Predictive Analytics. BA not only includes the classic Business Intelligence functions of reporting what happened, drill-down to how it happened, find out the cause of why it happened and alert management as soon key metrics move in the wrong direction.
In addition, BA includes a highly sophisticated subject called Predictive Analytics that can be defined as Advanced Computational Statistics. It uses large cleansed historical data sets to look forward. Specifically, Predictive Analytics finds patterns in the data to forecast what has a high probability to happen in the future.
BA applications include operational intelligence, strategic and competitive analytics, customer acquisition and retention, risk management, fraud detection and demand driven forecasting among others.
According to Tom Davenport, “Companies that invest heavily in advanced analytical capabilities outperform the S&P 500 on average by 64%”.(3)
Having a clear view of the profitable customers, products, regions and market segments is fundamental to understand the causes and expand upon the successes. Equally important is to find those customers, brands, markets, segments and competitors responsible for draining cash and quickly stop the bleeding.
Business Analytics is a fundamental discipline to find the root cause of issues and probable outcomes. It allows to take prompt corrective actions to satisfy customers’ needs faster and better than the competition providing the company a competitive advantage.
In their book, “Competing on Analytics – The Science of Winning”, Thomas Davenport and Jeanne Harris define decision support, business intelligence, data mining and predictive analytics and put all these concepts well in perspective.
The book shows the five stages of analytical competency and gives plenty of examples of companies that are successful in both the internal and external implementation.
Stage 1: Analytically Impaired – Lack of analytical skill or executive interest.
Stage 2: Localized Analytics – Uncoordinated activities or silos.
Stage 3: Analytical Aspirations – Good intentions with slow progress.
Stage 4: Analytical Companies – Widely use analytics internally.
Stage 5: Analytical Competitors – Use analytics as a competitive advantage.
Up until now, BA has been the exclusive domain of large companies that were able to afford the investment. Companies like Bank of America, Progressive Insurance, Amazon, Walmart, Capital One, Netflix and Google were pioneers in this area.
Fortunately, this situation has changed. Today, there are many analytic applications, either on premise or in the cloud, that are powerful, user friendly and very cost effective; making them ideal to bring mid size and smaller companies to the forefront of the 21st century technology to become true analytic competitors.
The Business Analytics Maturity Grader is an independent attempt developed by Strat-Wise and SimaFore to assess the analytical capability of small organizations.
Please feel free to take the short test to see where your small company stands in the analytical landscape. After the test you may want to download our Suggested Action Plan Report to help your company find the next step in the Profitable Business Analytics Journey.
References:
1) Aberdeen Group
2) IBM Whitepaper - Business intelligence for business users: Insight when and where you need it
3) Harvard business Review: Analytics and the Bottom Line – September 23, 2010
4) Competing on Analytics – The Science of Winning, Thomas Davenport and Jeanne Harris
5) SimaFore Article
Angel Customers & Demon Customers - Can You Tell Them Apart?
In their book, Larry Selden & Geoffrey Colvin uncover the trillion Dollar opportunity corporations are missing by not being able to clearly identify unprofitable customers.
Even in the most successful companies, while some customers can be very profitable others have a profound negative impact on the bottom line.
In a business that has more than twenty customers, the Pareto Principle will be evident.
Over a century ago, while studying the wealth distribution in European societies, Vilfredo Pareto discovered the 80/20 Rule by which 20% of the population accounted for 80% of Income.
The 80/20 Principle still has wide application in economics, market analysis and business strategy, where 20% of the effort delivers 80% of the results.
If a business is large enough to be significant, its sales, cost and profit data usually respond to a non-linear statistical distribution that contains the 80/20 Rule.
This means that the top 20% of customers generates close to 80% of profit while the bottom 80% of customers g
enerates only 20% of the profit.
Looking inside the famous 80/20 principle one usually finds that the top five percent of the customers generates close to 50% of the profits while the bottom 50% of the customers generates only 5% of the total profit.
Note that the top 5% of customers are 100 times more profitable than the bottom 50% as (50/5)/(5/50) = 100.
The story gets worse as the bottom 40% usually generates no profit at all. Within this group some customers are slightly profitable, some are profit neutral and some are true cash drainers.
This analysis is fundamental to understand who the strategic customers are, how they are different from key customers and standard accounts; and finally identify those in the cash drainer group to either make them profitable or yield them to the competition. This exercise usually frees valuable resources to be re-deployed to support strategic customers and develop additional profitable accounts.
The 80/20 Strategy goes beyond customer analysis and is based on two steps:
1) Segmentation of clients, products and markets to differentiate the vital few (stars) from the trivial many (dogs), in order to plan the re-deployment of resources given to each segment strategically, with the objective to reducing cost and freeing time for new business generation.
2) Identification of those areas that fit the profile of the stars (top 20% customers, products or segments that generate 80% of profits). The objective is to understand the causes in order to replicate their profitability.
I've successfully used the 80/20 Analysis and Strategy for many years to increase profitability, by increasing revenue and reducing cost, in the following areas:
a) Product portfolio
b) Customer portfolio,
c) Market segment strategy,
d) Sales force, and distribution channel productivity
e) New product innovation metrics.
While the Pareto Rule is widely know, many companies do not fully exploit its potential to increase profitability. They have a hard time identifying which customers belong to each group.
Ideally, companies need to know at any time of the month, quarter or year exactly which customers, products or segments fall in each category, and understand the reasons why, in order to drive the profitability distribution towards a higher, more balanced and stable bottom line.
Knowing which 80% of the company's activities yield only 20% of the profits brings the opportunity to optimize by reducing resources given to low profit activities and deploying them to replicate the success of the top performing ones.
The 80/20 Principle is a powerful strategic tool to differentiate the vital few (stars) from the trivial many (dogs), in order to plan the allocation of resources given to each segment strategically, in order to achieve profitable growth.
A Strategic deployment of Business Analytics provides the right answers instantly by allowing to visualize the 80/20 map for each customer, product, sales rep, business unit or market segment in any combination.
You can also learn more here:
References:
1) Richard Koch’s “The 80/20 Principle” is an excellent book that clearly describes not only the Pareto Rule, but how to implement it to maximize profitability with minimal effort, both in the company as well as in personal life.
2) “Angel Customer & Demon Customers” by Larry Selden & Geoffrey Colvin offers great examples of the impact of customer profitability on value creation & destruction.
What Is Business Intelligence 3.0?
Interactive Visual Analytics represents what Gartner Research calls the consumerization of Business Intelligence. It’s a good example of a disruptive innovation or, as Qliktech’s Donald Farmer calls it, a market changer. According to Tableau Software “visual analysis is not a graphical depiction of data. Virtually any software application can produce a chart, gauge or dashboard. Visual analytics offers something much more profound. Visual analytics is the process of analytical reasoning facilitated by interactive visual interfaces”
This new generation of front end BI tools is so intuitive to the user that little or no training is necessary. This is a great feature since, according to Gartner, BI users do not want to read manuals. They demand intuitive BI interfaces, in line with the internet experience they are accustomed to, like Google searches or iPhone apps.
The current low BI utilization rate of about 5% does not provide many companies an acceptable return on their Business Intelligence investment. The new user-friendly/ intuitive BI tools will help companies exploit the treasure of business trends and patterns hidden in their oceans of data by increasing the number of employees that participate in the data discovery process. The technology enables casual users to transition into power-users and power-users into app. developers in a matter of weeks.
Depending on the vendor, the new software class is known by different names: Data Discovery, Advanced Visualization, Visual Analytics, Business Discovery, Self Serve Business Intelligence or what Forrester Research’s James Kobielus calls BI 3.0.
Advanced data visualization is based on the fact that 70% of the human sensory receptors are dedicated to vision while the other four senses share the remaining 30%. In addition, our brains are much more effective recognizing shapes trends and patterns than analyzing spreadsheets or tables full of numbers. Visual data analysis principles are based on the work of Edward Tufte and Stephen Few.
There are close to a dozen applications that fall into this category from companies around the globe. The leading ones are growing their acceptance at a very fast rate. Five of them have made it to Gartner Research's 2011 Business Intelligence Magic Quadrant Report and I think they’re going to stay there for the foreseeable future.
Gartner points out an interesting fact: business users are purchasing these applications either with or without IT consent. Similarly, the recent global survey “2011 Wisdom of the Crowds” by Dresner Advisory Services reveals that 50% of self serve BI purchasing decisions take place in the business areas and not in IT.
Self-serve BI does not mean “IT Free” as a strong IT-Business partnership is always helpful to ensure data quality through proper governance and ensure the proverbial single version of the truth. Self serve refers to the user’s ability to perform data exploration and discovery without having to request IT to create specific data marts, build OLAP cubes or predefined reports, as this would delay the analysis process.
In addition to the typical functionality of multidimensional analysis (drill-down, drill-through, roll-up, sort, group, filter and calculation) some tools offer “what-if” scenario analysis, data animation and mobile capability.
A great feature of this new generation of BI software is its data blending functionality. The applications allow connecting simultaneously to disparate types of data bases or tables, whether in a data warehouse, data-marts, spreadsheets, text files, Access tables and, in some cases OLAP cubes and even data in websites.
The results are instant since the process takes place efficiently in-memory. Additionally the visual interface, when used proficiently, permits to digest huge amounts of information and visualize trends and patterns in seconds. This process enables what many call “analysis at the speed of thought”. Meaning that the answers to business questions can be found fast enough not interrupting the “train of thought” that leads to the next layer of questions, seeking to find the root cause of issues or opportunities.
Visual Analytics packages are not replacing traditional BI in large organizations but complementing them. They provide fast analytical capabilities to more people that need to gain a competitive edge in the current fast changing market dynamics.
For small and medium size companies that haven’t yet invested in BI, self-serve/ Visual Data Discovery can be a fast and cost effective solution.
This new generation of BI software takes analytics to a whole new level. That's the reason for the triple digit growth rate some of these tools have experienced during last year.
It only takes a few minutes to download the free versions most vendors offer for testing purposes. This is something I’ve done and changed my views about the future of BI. Now I say “visualizing is believing”.
The video illustrates visualization techniques that make 120,000 data-points tell a compelling story that anybody can easily understand.
If you have used or tested Visual Analytics, please share your opinion - If not, are you planning to test any of the products? - Why?
References:
Gartrtner - Consumerization of BI
Panorama Software - BI 3.0
Gartner BI MQ 2011
Wikipedia - Edward Tufte
Stephen Few - Perceptual Edge
TDWI Best Practices Report – Third Quarter 2011 – Self Serve BI
Tableau Software
Qliktech
Dresner's 2011 Wisdom of the Crowds BI Market Study
Can Small Companies Justify Investing in Business Analytics?
by Bill CabiroEven in the case of extremely small businesses, a strategic deployment of a Business Intelligence solution can have major impact on the growth and profitability of the company.
Having a clear view of the profitable customers, products, regions and market segments is fundamental to understand the causes and expand upon the successes.
Equally important is to find those customers, brands, markets, segments and competitors responsible for draining cash and quickly stop the bleeding.
- Should we treat all customers equally? (If you say yes, I’d suggest reading “Angel Customers & Demon Customers”)
In a company that has more than thirty customers, the Pareto Principle will be evident. Looking inside the famous 80/20 rule one can find that five percent of the customers generate close to 50% of the profits while the bottom 50% of the customers generate only 5% of the total profit.
Many companies have a hard time identifying which customers belong to each group. A strategic deployment of Business Analytics provides the answers instantly.
Even when sales revenue is growing, management should be able to ask key questions and find the answers right away, so the train of thought isn’t interrupted:
- Is revenue growing profitably? Where? How and Why?
- Are we paying sales reps commissions for bringing in unprofitable tonnage?
- Can we quickly tell whether the growth trend is just over last month, last quarter, same quarter last year or year to date?
- How about the profit growth of the last 52 weeks compared to the previous 52 weeks? Is it really growing?
- Is our growth accelerating or decelerating? How much?
- How is our profit growth versus budget or business plan? Where is it failing to meet objectives? Why?
- Which competitors are threatening our business?
- In what regions and market segments can we maximize our growth?
A BI solution is fundamental to find answers to the seven layers of WHY's in order to get to the root cause of issues. Being able to understand and correct these issues faster than the competition provides the company a competitive advantage regardless of how small the business is.
In the past deploying a BI solution was not affordable by small companies, not only due to licensing cost but also because the internal and external resources needed for set-up and maintenance.
This situation has changed though. Today, there are many new analytics applications, either on premise or in the cloud, that are powerful, user friendly and very cost effective; making them ideal to bring small companies to the forefront of the 21st century technology to become true analytic competitors.
References:
1) Angel Customer & Demon Customers by Larry Selden & Geoffrey Colvin
2) The 80/20 Principle by Richard Koch
3) Competing on Analytics y by Thomas H. Davenport, Jeanne G. Harris
4) Strategic Knowledge IQ Test
When Data Destroys Value
By Bill Cabiro
A survey by Gartner Research found that poor data quality costs companies an average $8 million per year. In a different study published by The Data Warehousing Institute more than 50% of companies experience customer dissatisfaction and cost problems due to poor data.
According to Gartner, about 80% of Business Intelligence implementations fail; while an Accenture survey of managers in Fortune 500 Companies found that 59% cannot find valuable information they need to do their jobs; 42% accidentally use the wrong information about once a week and 53% believe the information they receive is not valuable to them.
It’s pretty obvious that the main reason for the failure to meet Business Intelligence expectations is probably data quality; reinforcing the proverbial GIGO principle.
This is only getting worse considering that Aberdeen Group estimates the amount of corporate data companies need to digest is growing at 41% annual rate, coming from an average 15 unique data sources.
It’s clear that today’s Business Intelligence & Analytics software capability is light-years ahead of the semantic quality and structure of the data. After years and millions of dollars spent in BI deployment, folks in areas like Marketing, Sales or Strategic Management feel like they are drowning in an ocean of data and yet thirsty for the Strategic Knowledge they need to grow the business.
It’s common to see state of the art Business Intelligence applications that cannot deliver strategic direction or analysis until armies of analysts download the data into spreadsheets, manipulate, clean, fix and structure the data manually, for hours or days at a time.
Many folks recognize the transactional data has plenty of errors, at least at the customer, product line, brand, market and segment level, but they never get fixed and, even worse, they populate the BI systems.
This is a classic case of cultural miscommunication. Marketing and Sales people think this is an IT problem and expect the data warehouse analysts to fix it, while IT thinks it’s a business problem and expects Sales or Marketing to take action. The result is that data seldom gets corrected, people give-up, and running raw or bad data through sophisticated BI systems becomes the norm.
After a while, Sales, Marketing and Management don’t trust the data, can’t see the value of BI, quit using the system and continue making decisions based on intuition.
When Analysts or Power users need a quick answer about market share, profitability or growth of a particular segment, product line, customer or competitor it takes days to go through the analyst’s manual process. This consists of running the right queries, exporting them to Microsoft Excel, manually cleansing data errors, adding look-ups from external data sources, and finally creating pivot tables to find the right answers.
Even worse, they have to go through the entire process over and over again every time they need a progress update, either the following week or at month-end, quarter-end or year-end for each one of the business units and markets they serve.
This not only is an inefficient process but fixing data based on analysts’ personal assumptions leads to undesirable multiple silos and different versions of the truth.
As a result, it’s common for people to spend a large portion of team meetings arguing whose data is correct instead of focusing on the critical issues. The numbers generated by Finance do not agree with the analysis performed by Marketing or the explanations provided by Sales. They need a single version of the truth, but different folks run different queries, made different data cleansing assumptions and customized their spreadsheets based on different metrics.
A Simple Solution that Works:
While IT may own the physical aspects of the data (storage, security, format, connectivity, etc.), the Business (e.g. Marketing & Sales) must own its strategic meaning and be committed to auditing and maintaining its high quality.
Proficient analysts who spend most of their time cleansing the data constantly after downloading it to spreadsheets, every time they need a report, need to change and become Data Stewards that cleanse and structure the transaction data using external market intelligence BEFORE it enters the Data Warehouse or BI systems. They need to maintain, update, format and upload their data corrections on a weekly or monthly basis.
This small process change requires a committed partnership between IT and the Business but truly makes a difference because it breaks the Business Intelligence vs. Data Quality vicious cycle.
When the data makes sense to BI users they start to recognize their business models, develop trust in the data and become better users. They’ll soon find additional data issues and take the initiative to get them promptly corrected. In a few weeks there will be no more serious data issues as this process becomes a virtuous cycle where the BI utilization rate grows giving the company an analytical competitive edge.
References:
1) Organizations Perceive Significant Cost Impact From Data Quality Issues - Gartner Research: August 2009 - http://bit.ly/j86yDv / Summary: http://bit.ly/kIAM3O
2) Taking Data Quality to the Enterprise through Data Governance: TDWI 2006 Best Practices Report - The Data Warehousing Institute: May 2006 - http://bit.ly/kGza8G
3) Managers Say the Majority of Information Obtained for Their Work Is Useless, Accenture Survey Finds - Accenture: January 2007 - http://bit.ly/l9v7BA
4) Gartner Research: http://tinyurl.com/3gy2aty
5) Additional Resources - http://wp.me/p1D4Fz-p
Maximizing Your Business Intelligence’s ROI
By Bill Cabiro
To increase the return of a given BI investment requires maximizing the extra profit the company makes a result of the BI deployment.
In his Theory of Constraints, Dr. Goldratt states very clearly that in a for profit company, the main goal is to achieve sustainable profitability. In other words, it’s to “make money now and in the future”.
In order to increase profitability companies can only do three things:
1. Decrease Operating Expenses.
2. Decrease Investment / Inventory.
3. Increase Sales Revenue (by increasing sales volume and/or selling price of the products or services marketed).
Note that a company can only decrease operating expense or inventory up to a certain point beyond which servicing customers would not be possible, resulting in loss of sales.
On the other hand, the potential to increase Sales Revenue has no limit, as long as the company offers products or services that create value for its customers faster or better than the competition. This is exactly what Apple Computers has done with its creative new products (iPod, iTunes, iPhone, iPad).
Therefore the most effective way to maximize profitability and ROI is by increasing Sales Revenue.
Business Intelligence solutions should be deployed to support all three activities. Unfortunately, most BI implementations aim just at controlling operating expenses and inventory as they populate sophisticated BI software with just internal raw data from the ERP. The problem is that while this supports tactical functions, it doesn’t provide the immediate strategic analysis and direction necessary to grow Sales Revenue.
Supporting the increase in revenue is more difficult because the necessary external market and competitor data is not readily available in a compatible format and therefore it’s normally left out of BI objects or data marts.
When companies integrate both sources of data, the external market intelligence provides structure to the internal transaction data.
Current BI interactive analytic applications are useful to drill down, filter, and drill through in order to find the root causes of both problems and opportunities very fast.
Data integration and interactive software help Marketing and Sales do their job of growing the business.
When BI is implemented to support commercial strategy the company gets a competitive edge because decisions will now be based on a 360 degree view of the market-business-profit reality, right from the BI software; while the competition would still be relying on internal raw data and managers’ intuition.
I’ve seen this strong competitive advantage translate into increased market share, revenue and profit; fully supporting the main goal of the company.
This is the most effective way to increase the return on the BI investment, normally so difficult to justify when BI supports just back office functions and not strategic and profitable growth.
Are you a Business Intelligence Avoider?
By Bill CabiroFrom six different studies we conclude that approximately only 5% of employees use BI tools to perform Analytics effectively*.
I think this is in part related to the fact that except for folks with backgrounds in science, engineering, economics or finance; most people across the company do not feel too comfortable around numbers, math or logic. I've seen this during many years of training corporate employees on the strategic use of Business Intelligence and Analytics.
This is how the numbers work: close to 50% of employees have access to BI tools, 20% of them actually use them, and about half of the users (5%) are in the analytical / power user categories.
If you were to plot a histogram showing only those 20% actual BI users in any organization, it would probably approach a normal distribution (bell shape curve) consisting of the following categories, where close to two thirds of the users would fall in buckets 3 and 4.
1) Non Users: Run canned reports once a quarter or less frequently. These are people who are either math averse; do not like computers, or executives that have their secretaries run and print just static reports for them.
2) Infrequent Report Users: Run canned reports about once a month.
3) Frequent Report Users: Run canned reports on a weekly or daily basis.
4) Analytic Users: Modify static reports and cube views by grouping, sorting, formatting as well as changing some dimensions and measures. These folks save their new customized reports for future use.
5) Power BI Users: Create new ad-hoc reports from scratch applying multiple dimensions, measures and filters. These users travel OLAP cubes from corner to corner using drill down, drill through, and most available custom features in search of the root causes of both: problems and opportunities.
6) Expert Analysts: Search and provide new data bases, join disparate data, design and build customized cubes and pivot tables, perform statistical, financial or marketing analyses and usually export results to MS Excel to complete the last analytical mile.
Buckets 4, 5 and 6 represent the 5% of employees that use BI tools to perform analytics effectively. But when it comes to Advanced Analytics (statistics, predictive modeling, data mining, etc.), according to Gartner, the users are 10% of that number or a staggering 0.5%.
To become a true analytic competitor, the company has to change the culture so everybody, not just the experts, thinks and acts based on facts and understands the drivers that support strategy and sustainable profitability.
While this isn’t easy, it’s possible. I’ve seen it quite a few times. It requires long term commitment from top management, adequate training, data structured to be business-intuitive and the software configured in an extremely user-friendly manner so all types of users can perform analytics with no help from IT, analysts or even spreadsheets.
In my experience, the organization improves its financial results through this implementation as people gradually advance to the next level, leaving buckets 1, 2 and 3 virtually empty.
Which buckets is your organization using?
* References:
- SAP White Paper: “20 percent of workers who could use BI tools take advantage of them, according to a 2007 Baseline Magazine article”
- Forrester Research estimates the fraction of total employees that use BI applications between 5 and 9%. http://blogs.forrester.com/james_kobielus/10-03-23-number_people_using_advanced_analytics
- Tableau White Paper: “A recent survey called “The BI Report” reports that just 8% of potential BI users actually use their BI applications. In other words, 92% of users do not use the BI tools designed for them”.
- According to the Business Application Research Council’s (BARC) ninth edition of their BI (business intelligence) study only 10% of enterprises have “deployed BI to a majority of their users.” http://www.bi-survey.com/faq.html
- http://www.datamartist.com/business-intelligence-adoption-low-and-falling
- Gartner estimates the users of Advanced Analytics at 10% of the number of BI users.
Business Intelligence & Analytics Trends for 2011
By Bill Cabiro
As the global market for Business Intelligence and Analytics continues to experience double digit growth, BI vendors from around the globe will offer creative and lower cost solutions to gain share in the mid and small size company segments that are turning to BI for the first time.
Cloud computing vendors will expand their offerings to include ETL, Data warehouse, reporting, OLAP, advanced visualization and dashboards in one package for the early adopters that moved passed the security concerns.
Software companies will follow the IBM-Cognos lead integrating advanced statistical packages and predictive analytics modeling into their current BI solutions.
More vendors will offer better mobile BI, analytics and advanced visualization applications.
More companies will realize that they can use BI/ Analytics beyond the back office tactical day-to-day operation. They will start to exploit the power of BI software to perform strategic and competitive analysis on the fly to understand market trends better than the competition and increase market share, revenue and profit.
More companies will realize that the semantic quality of their data is constraining the Business Analytics results. They will start to cleanse, organize and structure the data to contain more strategic meaning and provide direction to grow the business.
Some companies will gradually realize they have built unnecessary complexities in the configuration of analytic, BI tools and data marts. While this isn’t an impediment to IT or analyst experts, it causes casual business users to underutilize or not utilize at all the systems. This will gradually change towards a more business intuitive, self serve “analytics for the masses” model.
Can Marketing Love Business Intelligence? ...Really?
by Bill Cabiro
Why is that after the successful deployment of state of the art ERP and Business Intelligence software folks in strategic areas of the company still struggle to make sense of the data?
The reason is pretty simple. Strategic Marketing is the headlights of the business; their job is to predict the future and exploit that prediction in a manner that increases market share, revenue and profitability. Their focus is mostly outside and in the future while ERP data is all about inside and in the past.
When companies populate their BI systems solely with internal data from the ERP, this only supports the back-office or day-to-day operation but it does not help folks in strategic functions. Marketing, sales or line of business management need a much wider perspective to impact the bottom line.
Marketing needs a 360 degree view of the market-business-profit reality. This includes both internal and external sources of data usually not found in the transaction system. They need to access external market intelligence to compare it with company performance metrics. This can be market and customer segmentation, updated customer and competitor merger and acquisition status, competitive opportunities, business and products under threat, market size, forecasts, etc.
In my experience, using Business Intelligence software to integrate internal and external data has always a positive change in the company’s culture, where instant access to strategic knowledge helps folks in charge of strategy to accurately answer complex business questions within minutes.
Marketing becomes an avid Business Intelligence user when the configuration of both data and BI software provides the key measures and dimensions, in a user-intuitive way, to monitor business direction against the company’s strategy. They can quickly find the root cause of problems and opportunities, with no intervention of IT or business analysts, and take immediate action faster than their competition.
A well organized strategic BI implementation transforms Marketing from Art to Science while unfolding an analytical competitive advantage that translates in bottom line impact. I found that Marketing deems BI indispensable when its deployment passes the Strategic IQ test.
Does Your BI deployment pass the Strategic IQ Test?
I. Select ten questions you find most valuable to have answered in your organization.
II. Ask these questions independently to six people (marketing, sales, financial analyst, IT, accounting or business management) requesting confidentiality and a prompt response.
III. Keep track of the time it takes to obtain each answer.
IV. Compare the answers for quality and consistency between respondents.
Note that answering most of these questions should take a few minutes and the answers need to be consistent across the different respondents.
Sample Questions
1) Where are we more profitable: strategic customers, key customers, distributors, agents or dealers? Why? Is it due to pricing, volume or product mix?
2) What is our current market share in a particular business unit in Europe?
3) Are our differentiated brands more profitable than the commodity ones? Why?
4) Are our new products more profitable than the old ones? How much? Why?
5) Who are the top 5% of my customers that generate 50% of profits? Why?
6) Who are the customers and what are the products that drain our cash? Why?
7) How much of which product is Sales-rep X supposed to sell to each of his customers in September of next year? What will his profit margin be?
8) Why is profitability by customer / region / country / business / market segment or sales territory significantly down this month?
- Is this because we lost volume?
- Is it product mix?
- Too many price exceptions?
- Has raw material cost increased?
- Has manufacturing cost increased?
- Have freight / distribution expenses gone up?
- Combination of which factors?
9) Show me a comparative Profit & Loss (P&L) statement
- By region / country / state
- By market segment / sub-segment
- By sales representative / distributor / dealer
- By brand / SKU
- By product technology
- By customer location
- Differentiated products versus commodity products at our distributors
10) Who do our distributors, agents and dealers sell to?
- In what markets / segments / sub segments?
- What is their mark-up? / Profitability?
- What is their new versus old product ratio?
11) What does our opportunity pipeline look like?
12) Which pieces of business are under competitive threat? (By product, competitor and customer location, in units and dollars.)
13) What is each of our competitors’ current market share by segment?
Scores
- If you got less than 6 answers, they took longer than one day, or the answers are completely different; your BI deployment maybe supporting the back-office operation but it’s unlikely to drive profitable growth since most strategic decisions are based on raw data and intuition.
- If you got more than 5 answers within one hour with 50% of the analysts in agreement, you are in the right track but need to refine the process further to compete on analytics.
- If you got more than 8 answers within one hour and both analysts and casual BI users from different areas agree; your BI deployment has become a competitive advantage that allows the organization to perform strategic and competitive analysis on-the-fly while ensuring a single version of the truth.
Spreadsheets and BI - Allies or Enemies?
With well over 500 million users around the globe, it’s no wonder that spreadsheets are the de-facto Business Intelligence/ Analytics application.They are easy to use, allowing users to collect data, structure it and perform complex analysis and calculations quickly, whether it’s for accounting, finance, manufacturing, product management, engineering, sales, supply chain, customer service, forecasting or other areas requiring data analysis.
Usually, IT folks are uncomfortable that the myriad of personal spreadsheets across the company are nearly impossible to audit, running the risk of propagating errors that could potentially drive critical decisions the wrong way.
To their credit, it’s disturbing to see how human error impacts the accuracy of spreadsheets. A University of Hawaii study , estimates that between 20% and 40% of all spreadsheets contain errors and that 5% of all calculated cell formulas are wrong.
There is also the dangerous possibility of complex spreadsheets used with fraudulent intent or casually compromising the security of corporate data downloaded to spreadsheets and sent outside the company.
While some IT organizations have attempted to ban the use of spreadsheets others, based on their versatility, have adopted their use and integration into the BI environment.
Having worked on both sides of the fence (IT and Marketing) I’d like to share my experience on how to minimize this common issue.
The Problem
Many companies have no choice but to use spreadsheets because their BI solution does not provide the strategic answers Marketing, Sales and Management need to grow the business.
These companies typically populate their BI with internal raw data from the ERP. This is only half of the story. People in strategic areas need other original sources of data not found in the transaction system.
They need to access external market intelligence to compare it with company performance metrics. This can be market and customer segmentation, updated customer and competitor merger and acquisition status, competitive opportunities, business and products under threat, market size, etc. Unfortunately, this information usually only resides in market intelligence spreadsheets developed by Marketing, Sales or Finance.
Every time someone needs a quick answer about market share, profitability or growth of a particular market segment, product line or customer, it takes weeks to go through the process of running the right BI queries, exporting them to Microsoft Excel, manually cleansing data errors, adding look-ups from the market intelligence sources, and finally creating pivot tables to find the right answers.
Even worse, folks have to go through the entire process over and over again every time they need a progress update, either the following week or at month-end, quarter-end or year-end for each one of the business units and markets they serve. I can’t help but ask, is this good use of marketing and sales people’s time? Shouldn’t they be investing that time doing market research or in front of customers finding opportunities to grow the business?
One Possible Solution
Minimize this lengthy manual spreadsheet process by having a cross functional team including IT, Marketing, Finance, Strategic Planning and other interested areas share their needs and discuss their processes with the objective to add the market intelligence into the data warehouse.
They need to agree to have only one or two proficient analysts become data stewards to maintain, update, format and upload the market intelligence on a weekly or monthly basis. Today there are a number of commercial applications that simplify these tasks by allowing auditing, loading and integrating spreadsheet data into the data warehouse.
Once the market intelligence is integrated and the BI tool configured in a manner intuitive to the users, it will provide the strategic answers the commercial folks need to grow the business. The teams will finally share a single version of the truth right from the Business Intelligence application, since manipulation of data in spreadsheets will, for the most part, no longer be necessary.
Configuring DW and BI software in this way allows folks to perform strategic and competitive analysis on-the-fly. This gives the company a competitive edge that results in positive bottom line impact.
Why Marketing Finds Business Intelligence Useless
by Bill Cabiro
During my years of industrial marketing and sales, I learned that there is a big difference between corporate information and Strategic Knowledge.
Corporate information usually consists of internal raw data while Strategic Knowledge is the business wisdom necessary to achieve profitable growth consistently.
Although internal data is necessary to manage logistic or tactical areas like, supply chain, manufacturing, accounts receivable, transportation, accounts payable or customer service; it’s far from enough to perform marketing and sales management jobs effectively, namely increasing market share, revenue and ultimately net profit.
The reason is simple, internal data is a by-product of the order entry system (ERP), designed to ship orders on time; not to build a strategic data base for marketing use.
Unfortunately, most Business Intelligence implementations populate sophisticated BI software with just internal raw data from the ERP. This is only half of the story because ERP data does not usually contain competitive or market intelligence.
This is the reason why BI usually does not provide the immediate strategic analysis and direction necessary to grow the businesses, leaving Marketing and Sales teams struggling to find the information they need to increase profitability.
Typically, after Sales or Marketing run BI reports several times and they keep seeing the wrong structure or hierarchy in their customers, markets and territories, they stop using BI and continue making decisions the old fashion way: based on intuition.
Can Companies Really Compete on Analytics?
Absolutely, Business Intelligence is much more than software. Business Intelligence should be a reliable, analytical process that transforms raw data into relevant, accurate and useable strategic knowledge.
Strategic Knowledge is the result of the seamless integration of internal transaction data with external market intelligence. This includes market & customer segmentation, channel structure, budget and sales projections, market share, growth, business opportunities, competitive analysis, profitability metrics, product innovation metrics and the ability to run instant P&L’s on any combination of these dimensions for any time period in the past, present or future (like budgets or business plans).
The key is to utilize the powerful business intelligence software available today to perform this integration and automate the process.
In my experience, the result of this full integration is always a change in the company’s culture, where instant access to strategic knowledge means that anyone who needs to know (normally marketing, sales, finance, R&D or management) can accurately answer extremely complex business questions within one minute. They can find the root cause of problems, right from the BI software, with no manual intervention of IT or business analysts, and take immediate action.
Being able to react to changes in the market-business-profit reality, faster and better than the competition, provides the company with a competitive advantage that invariably translates into increased market share, revenue and profit.
This is the most effective way to increase the return on the BI investment, normally so difficult to justify when BI supports just back office functions and not the strategic and profitable growth of the company.
The 5 Elements of a Successful Business Intelligence Implementation
by Bill Cabiro
Having seen BI implementations ranging from the very good to the very bad, I think a BI solution needs to be:
1) User friendly, self-help oriented and designed for the users and not for IT experts, because constantly needing an IT person to run SQL, MDX or complex reports is a slow and unproductive process for everyone.
Having representatives of all business areas attend the vendor presentations and participate in the BI selection process is fundamental to develop commitment and support. Of course, IT leads and facilitates the process.
In order to succeed, a BI project needs to be an equal partnership between IT and all the business sectors that will use the solution. A clear understanding and agreement on the definitions of user needs, metrics and dimensions is the base of a system that people will like and use.
2) The solution needs to accommodate a flexible design of the meta-data to be "business intuitive" because you do not want business folks to rely on analysts as intermediaries to translate BI reports into useful information through extensive and time consuming manipulation of data exported to spreadsheets.
3) The BI tool needs to be fast and interactive (like OLAP cubes) to be able to drill down, drill through and filter, so everyone can uncover the root cause of problems in a very few minutes.
4) The ability to connect multiple data sources is fundamental to enrich the scope of the decisions. Merging internal transaction data with external market intelligence will ensure a 360 view of the market-business-profit reality, instantly, right from the BI software. This gives the company a competitive edge, because the competition is still likely to rely on internal raw data from transaction driven static reports and managers’ intuition.
5) Top management needs to fully commit to the project, and once implemented should demand that the organization continues to use it, strengthening the data-driven culture, so BI does not become the flavor of the month.
Finally, while cost may be important to some folks, Return on the BI Investment is the ultimate measure of success. If the selection and configuration of the BI solution allows the company to beat the competition, increase market share, revenue and profit everybody wins.
What do you think?