All posts tagged: Business Intelligence

The following are some examples of how predictive analytics can be applied in financial services, retail and manufacturing.  This list is not comprehensive, but it provides some interesting applications.
In the financial services the cost of making the right decisions provides marginal benefits, while making the wrong decisions can have significant costs.  Most applications of predictive analytics in the financial services industry help companies avoid making the wrong decisions. For example, credit card companies are able to determine who is most likely to default on their credit cards in the next 6 months by applying predictive analytics to customers purchases and demographics.
In retail understanding customers is essential to success.  Retailers have provided customized shopping experiences by using predictive analytics to understand the drivers of profitability, loyalty and activity for each customer segment and develop specific campaigns for each segment.  This has allowed retailers to wow customers with personalized services while scaling and keep prices competitive.  Predictive analytics have helped both offline and online retailers determine which products to carry, optimize marketing plans, and develop promotional and loyalty programs.  Imagine only offering effective loyalty promotions to profitable customers at risk of leaving, while avoiding offering discounts to unprofitable or already loyal customers.  Another example is knowing what a customer is most likely to purchase next, so that your staff or website can make informed recommendations.
Manufacturing is about knowing what, how, and how much to produce.  Predictive analytics have helped manufactures manage their supply chain and production schedules by accurately forecasting demand, and have helped manufactures produce goods in the most effective way possible by predicting failure of equipment, monitoring workers, and identifying ways to eliminate inefficiencies.  For example, CAN has helped companies with tens of thousands of sales each month forecast sales within a 10 to 50 units, so that they can optimize production schedules and supply chains.  Also, imagine being able to understand why different managers have different levels of employee turnover, employee injuries, and equipment failure.
Beyond specific applications, predictive analytics has the unique ability to help companies become less wrong, scale decisions and systematized learning.
Less Wrong: The basic idea of Less Wrong is that in business, and almost anything in life, you can never be perfectly right, but you can be less wrong and by striving to continually become less wrong you get closer and closer to being right.  By using predictive analytics you will not get the perfect answer, but you can determine what is happening, what most likely happen and most is most likely the right thing to do.  For example it might be really nice to know exactly what commodities prices will be in a month, unfortunately this is not possible.  However, using predictive analytics you might be able to predictive with 70% accuracy which direction a commodity price will trend and if this is better than before predictive analytics then it most likely will be worth the investment.  Another example would be picking which commodity would most likely be the best investment in the next 6 months.  The reason that predictive analytics can’t produce exact results is because it is not a simple science, for more read my post on Simple vs. Complex Science.
Scale Decisions: Predictive analytics has the unique potential to allow executives to scale their decision making as organizations and decisions become increasingly more complex with ever thinner margins for error.  Predictive analytics can be used to create a model of the business based on the organization’s data and executives’ theories.  For example an experienced sales manager will be able to determine which sales leads will most likely respond, purchase and be profitable customers, however he or she does not have time to review every lead for a 200 person sales team.  Also, while a sales manager knows what make a lead worth pursuing, he or she most likely finds it difficult to communicate the rules and criteria to their team.  Using predictive analytics a sales manager can develop a model to score incoming sales leads.  This model can be coded directly into the company’s customer relationship management (CRM) system so that leads are scored as soon as they are entered into the system.
Systematized Learning: In the future, profits will be directly related to your company’s rate of metabolizing new knowledge, as opposed to renting out existing knowledge.  Predictive analytics can increase your company’s ability to metabolize new knowledge by continually studying the data produced by your company, customers, non-customers and competitors to find important patterns that will impact your business.  For example, predictive analytics can help executives identify when customers stop responding to a certain campaign and why.
While every company can benefit from becoming analytical, like any other tool, predictive analytics can not fix anything.  However, it is most certainly the next step in the evolution of business intelligence.  If properly applied, predictive analytics has the potential to help businesses work smart. Read a related post on when to and not to apply predictive analytics.

We love predictive analytics and if we are not careful, it is easy to start reducing everything into predictive models. I have even caught Tadd standing at the window collecting primary data on the smoking habits of the people in our building. To make sure that CAN and predictive analytics experience continued success, we have developed a guide for when to apply predictive analytics.

First, predictive analytics should not be applied if:

The cost of being wrong is low.

You should not apply predictive analytics if reducing uncertainty does not provide enough value. Predictive models should only be applied in situations with a high cost and/or probability of being wrong and where predictive analytics can provide information to reduce uncertainty. To determine if predictive analytics is worth applying to a decision you need to calculate the expected value of information. In the book How to Measure Anything, Hubbard provides the following formula, expected value of information is equal to the difference between the expected opportunity loss before and after information. The expected opportunity loss is equal to the chance of being wrong multiplied by the cost of being wrong. (more…)

This post is part of a series of interviews with experts in business intelligence, sales management, marketing, customer retention, management and strategic planning.  Everyday, the CAN team interacts clients, mentors, and friends who are leaders in their fields, and we started this series to share their expertise.
Corporate business intelligence has hit a roadblock, according to Cameron Ludwig, the Director of Analytics at BlueCross BlueShield of Nebraska. “As a discipline, we have been more enamored about what we can do, and not what we should do”.  Business intelligence of tomorrow needs to put less focus on technical capabilities, and instead, emphasize designing solutions that focus on answering essential business questions.  This need for a shift in focus is due to the exponential increases in data availability and the increasing reliance of executives on data in their decision making.  For example, in a recent study by McKinsey there is a projected 40% growth rate in the amount of new data generated per year, with many companies having hundreds of terabytes of data (link).  As a discipline, business intelligence  has matured to the point where we need to move beyond collecting and displaying of data.  It is time to shift to the next level.
“Now the knowledge is taking the place of capital as the driving force in organizations worldwide, it is all too easy to confuse data with knowledge and information technology with information.”- Peter Drucker, 2005
In order for BI to make the transition from what is technically possible, what we can do, toward what is valued by business, what we should do, requires a shift in focus for the emerging field of data science.  Although I am hesitant to say that data scientists should study business at the exclusion of technology, this shift requires that data scientists become students of business as opposed to technology.  That is, their greatest value comes from studying technology to the point of knowing what is possible and how to apply technology to meet the needs of their end users.  For example when a contractor builds a house, he doesn’t study the hammer, he studies architectural plans and creates a finished product from raw materials.  The same goes for data scientists; they should focus on understanding the problems that need to be solved, then spend time studying how to use raw materials (data) to create a valuable finished product.
Keeping with the need for a shift in the field of business intelligence from technology to application, the valuable finished product is not a dashboard displaying metrics, but rather actionable intelligence focused on answering the business questions of the end user.  This renewed focus of business intelligence requires that BI only provides decision makers with what is essential to answer their questions.  All the slick user-interfaces, gauges and dials of flashy dashboards will never provide as much value as the algorithm behind an executive report that integrates ten different historical and environmental variables to advise which projects to bid on, including anticipated profit margins.
Tremendous value exists in the proper application of data science, but the maximum value comes from a deep understanding of the needs and objectives of the end user.  Ensuring that the end product fits the end user requires the right feedback, and at least as much criticism as creation.  When self- and peer-reviewing their work, Cameron recommends that data scientists should be required to justify the existence of each sentence, idea, graph, and model.  This requires each BI report to be designed with simplicity in mind, but also maximizes value to the end user and builds trust in BI by focusing only on that which contributes to solving the problem at hand.  If an artifact, tool or feature cannot be defended, it is most likely of little value and should be eliminated.  In order for business intelligence to contribute maximum value to the organization, every element of business intelligence must justify its existence.
“Capital importance of criticism in the work of creation itself.  Probably, indeed, the larger part of the labour of an author (programmer) in composing his work is critical labour; the labour of sifting, combining, constructing, expunging, correcting, testing: this frightful toil is as much critical as creative”- T.S. Eliot