There are a lot of uncontrollable factors that can affect your business. Economic, technological, competitive, regulatory, political issues and even succession, can all have an impact on your business at the most inconvenient of times. But planning in advance can be the key to maintaining your business through these shocks.
“If you plan in advance, even for a negative event, you can oftentimes increase your options in the event of a shock,” says Jim Lane, director of Redbank Advisors with GBQ Partners LLC. “If you know a drop in demand would force you to reduce your head count, planning in advance and restructuring work assignments can help you do that without endangering quality or sacrificing customer satisfaction. You’ll be in a much stronger position than you would be had you not thought it through in advance.”
Smart Business spoke with Lane about the importance of data mining to help prepare for these uncontrollable factors.
What is data mining, and why is it important?
Data mining is taking a look at the inventory of information that you normally generate as part of your ongoing business, and peeling it apart for insight into your business.
The biggest reason to do it is because we tend to operate on automatic pilot. As you’re driving to work everyday, you don’t take into account everything you pass. The first few times you saw everything and were making careful decisions about where to turn, or anticipated it. Now, you go there on automatic pilot.
The same is true in your management approach. You get in a groove for managing your business in a particular way and don’t look at the scenery anymore. The scenery in business is constantly changing. Unlike your route to work, which only changes when they change the road, the business environment is changing all the time. Competitors are coming in and changing it. Today, the economy and even political change are factors that people are not used to taking into account when changing and improving the business. Data mining gets you in touch with what’s going on right now and what the trends have been, so you can identify what’s been changing.
How can you start data mining in your business?
That depends on how sophisticated your company is, in terms of infrastructure. If you have sophisticated systems, often they come with reporting and data mining tools that you can use. If your business is smaller, you may need to export the information from your business systems into Microsoft Excel or another analytic tool in order to do trending and spot extremes.
A good place to start is with your order records. Make a line for every sale you’ve made in the past 24 months, including the sale price, cost, customer and the market segment that customer is in. By doing that, you can begin to spot trends on pricing. Is erosion occurring in your pricing? You can look at your margins and determine how you are doing on an order-by-order basis. You can compare sectors, divisions or plant performances against one another. That’s an easy way to get into it.
How often should you complete data mining and how can this be used in business planning?
You should do it on an ongoing basis. It’s the kind of thing you want to take into account in your daily operations. It’s looking at what’s happening in the business, what the trends are, how you are doing against your plan, and performance monitoring on a monthly or periodic basis. You also want to analyze trends and performance on an annual basis, prior to strategic planning. This allows you to determine if your strategy needs to shift, or if you’re on track. If you do need to change your performance, analytics help you see where there are opportunities for performance improvement in the business and what those improvements might be.
What challenges and risks are associated with data mining?
The challenge is having the data in a form that’s usable. In spite of the fact that we all think of information systems as being a done deal, in many businesses, they are still not all that useful. Getting information that is decision-ready can take some massaging and normalizing.
The risks occur in not analyzing your data and taking a look at what’s happening in your business. The risks of doing this are relatively few. You need to be conscious of the amount of data you’re looking at so you have a statistically valid sample. Once you get beyond that, the benefits of looking at the data and doing the analytics far outweigh any risks of making a wrong decision based on it.
What are the benefits associated with data mining?
When you’re in touch with your business, and the performance of your various products and services, as well as customers, you can begin to make decisions about where to focus the energy of the business. If you find a product line that’s more profitable than others, you might want to put more emphasis on selling that. If you find a group of customers that are not profitable to serve, you can focus your efforts on other customers and potentially raise prices on the customers who are not profitable to serve. More sophisticated analyses enable leadership to play ‘what if.’ This is incredibly valuable in shockproofing a business. It puts control into leadership’s hands to determine the direction of the business and the response it will take to those external factors.
Jim Lane is the director of Redbank Advisors with GBQ Partners LLC. Reach him at (614) 947-5257 or email@example.com
After battling a lackluster economy for years, most executives are out of ideas for increasing revenue and lowering operating costs. But the smart execs are reviewing history to predict which customers will buy more or splurge on high-end products and enticing them with strategic advertisements. And some are analyzing data to hone inventory purchases or decide how to optimally deploy resources.
The savvy executives don’t have a crystal ball, but they have invested in software and staff to conduct data mining and predictive analytics. Research from Accenture confirms that high-performance businesses are five times more likely to use analytics strategically when compared with low performers.
“Business leaders can avoid mistakes and predict future demand for products and services by utilizing data mining and predictive analytics,” says Dr. Zinovy Radovilsky, professor of management for the College of Business and Economics at California State University, East Bay. “Unfortunately, most don’t know where to start, so they continue to make decisions based on managerial opinion instead of facts.”
Smart Business spoke with Radovilsky about the opportunities to control costs and increase revenue through mining and predictive analytics.
What are analytics and data mining?
Analytics is a diverse field of statistical, qualitative methods and models used for predicting future business trends and customer behavior, and savvy executives are using the practice to make opportunistic business decisions. The process starts when professionals extract or mine data so it can be analyzed and used to identify relationships between the predicted parameters and other factors. The analysis phase is called descriptive analytics, which helps organizations discover what happened in the past, why it happened and how these events impacted the business. Predictive analytics uses the results of both data mining and descriptive analytics to make predictions and optimize business decisions.
How can mining and analysis turn data into dollars?
Analytics may highlight ways to increase customer retention or cross-sell certain additional products and services. At the same time, predictive analytics can reduce operating costs by predicting demand so companies can better forecast inventory or reduce wasted resources. The need for predictive analytics is spreading across various industries and business functions, like marketing, finance, operations, supply chain and human resources.
Predictive analytics and data mining help executives forecast future demand by analyzing customer behavior and profitability by market segments, so they can boost revenue and profit margins by selling additional high-value products and services to certain customers. For example, 1-800-FLOWERS.com attracted 20 million new customers and increased repeat business 10 percent by employing a real-time decision manager that uses predictive analytics applications, business logic and historical purchasing data to motivate customers by offering flower arrangements that appeal to their personal preferences.
How can predictive analytics and data mining reduce operating costs?
These examples illustrate how data mining and analytics can reduce operating costs.
- Predicting future demand helps operations and supply chain managers develop accurate inventory forecasts, purchase the right amount of supplies and eliminate unnecessary waste.
- Predictive analytics can substantially improve allocation of critical resources including equipment, labor and material. For example, after developing and implementing an optimization model to allocate small boat resources, the U.S. Coast Guard reduced its small boat fleet by some 20 percent and overall fleet operating cost by around 5 percent.
- Response modeling allows companies to identify repeat customers from the outset of the relationship and reduce the cost of mailing or calling by targeting only those who are likely to respond. The bottom line is that companies can cut marketing costs by targeting fewer customers while getting the same response.
How does a lack of data analysis or an inability to forecast future events restrict a company’s success?
Companies have accumulated a substantial amount of quantitative business data about their products and services, customers and suppliers. But, their ability to create a competitive advantage by utilizing the data and employing appropriate quantitative models varies significantly. In fact, two-thirds of U.S. companies surveyed by Accenture acknowledged that they need to improve their analytical capabilities. Some organizations still rely on executive opinion instead of using data and analysis to predict the future and make prudent business decisions.
What should executives consider before embarking on a data-mining mission or investing in software or experienced personnel?
Implementation of data mining and predictive analytics can be very time consuming and requires changing the existing decision-making processes and culture, hiring analytical staff and making investments in computer technology. Executives should consider several important things before implementing and managing predictive analytics projects.
First, clearly formulate strategic goals of using predictive analytics and data mining, and identify where the tools will likely make a difference. Then, prioritize the goals by their business impact and ease of implementation and utilization. Finally, identify prospective return on investments before purchasing software, hiring staff and training current managers to use analytics.
Dr. Zinovy Radovilsky is a professor of management for the College of Business and Economics at California State University, East Bay. Reach him at (510) 885-3302 or firstname.lastname@example.org.