RESEARCH NOTE HOW ANALYTICS MAKES MIDSIZE COMPANIES MORE PROFITABLE

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1 Document L100 RESEARCH NOTE HOW ANALYTICS MAKES MIDSIZE COMPANIES MORE PROFITABLE THE BOTTOM LINE While many large enterprises have adopted analytics technologies such as business intelligence (BI), performance management (PM), and predictive analytics, misperceptions about costs and risk have caused small and midsize companies to embrace these technologies far more slowly. Nucleus has examined hundreds of analytics deployments and found that when senior leaders of small and midsize companies invest in analytics tools, they improve operating results by enabling managers to make decisions that are more fact based and data driven. MYTHS ABOUT ANALYTICS Small and midsize companies stand to make significant gains by adopting analytics applications such as BI, PM, and predictive analytics. Unfortunately, too many companies fail to adopt due to misperceptions and doubts about these tools. The biggest concern is costs. With limited IT resources, many small and mid-size companies predict that even a modest analytics adoption is beyond their budgetary capacity and fail to even investigate their options. Complexity is another factor. Companies often fear that they ll be unable to integrate their various applications, data sources, and spreadsheets into a centrally managed analytics tool. Pessimism is a third problem. Many CEOs and CIOs of small and midsize companies feel that the collective wisdom and intuition of their employees is strong enough that datadriven analytics won t meaningfully improve day-to-day operations. THE TRUTH ABOUT ANALYTICS Nucleus Research has examined a significant number of IBM Business Analytics deployments at companies of various sizes. Analysts recently compared the deployments of IBM Business Analytics at large enterprises with those at small and midsize companies in order to identify the traits, benefits, and best practices that characterize analytics deployments at smaller companies. Nucleus found that the decision to adopt analytics can enable a company to thrive and be more profitable rather than merely survive and move from one operational or financial crisis to another. With the truth about analytics being far sunnier than most managers think, there are three facts about analytics that managers should keep in mind. Even small companies can afford analytics Nucleus regularly performs in-depth examinations of analytics deployments and analysts have found good news about costs. First, deployment costs, including software, hardware, personnel, consulting, and training are well within the budgets Corporate Headquarters Nucleus Research Inc. 100 State Street Boston, MA Phone: Nucleus Research Inc.

2 of even small companies. Nucleus has also found that ongoing support costs are typically minimal, with the average deployment requiring less than a third of one IT administrator s time for support. Costs are also low relative to benefits, leading to high returns on investment. Some of the factors making analytics affordable include: Lower per-seat costs. Applications such as IBM Cognos Express are based on tools designed for large enterprises, but have been streamlined to provide a narrower scope and packaged with lower per-seat costs to make these tools more affordable. Although these applications lack some of the breadth and complexity of tools for larger enterprises, they require less configuration and, in many cases, they can grow in complexity if the operating environment changes. Nucleus has found that the average deal size for IBM Cognos Express is between $25,000 and $30,000 and spoke with one IBM Cognos Express user who said, When we shopped around, we were surprised that IBM Cognos Express came in at well under $50K. Flexible scopes. In examining deployments, Nucleus has found two ways that vendors and users typically use to minimize software costs. First, not all analytics functionality needs to deployed at once. Small and midsize organizations can choose to adopt any combination of BI, PM, reporting, dashboards, or predictive analytics and keep the functionality as narrow as they want in order to lower the costs. Second, costs can be lowered by deploying in stages. Nucleus has analyzed many deployments which began as narrow proof-of-concept projects that were followed by a series of increasingly larger deployments. At one company examined by Nucleus, the deployment team leader said, We caused analytics to catch on like a fad. We helped our sales people make better decisions and when folks from the purchasing observed this, they wanted analytics too. By managing costs and publicizing deployment wins, analytics champions were able convince internal decision makers of the value of analytics, enabling them to broaden their deployments. User friendliness. In examining analytics applications and deployments, Nucleus has found vendors have minimized their customers training costs in several ways. First, the applications have become so intuitive that end users typically require little or no training in order to use the application. An analytics champion at a company examined by Nucleus stated that cost was not an obstacle to adoption and said, Once we deployed, people learned how to use the applications on their own, so we didn t need to spend any money on formal training. Second, by enabling end users to build their own dashboards and reports, vendors have minimized the amount of support required by IT departments. Financing. To make it easier to adopt analytics, vendors such as IBM offer financing of both software and services so that upfront costs are not a barrier to adoption. Complexity can be managed Vendors have also found ways to simplify analytics deployments by making it easy to integrate multiple data sources. For example, IBM Business Analytics includes functionality that uses metadata, which is data about data, to ensure that information from all data sources is properly interpreted and integrated into assets such as reports and dashboards. When analytics tools don t have built-in data integration tools, applications such as DataStage automate the extraction and integration of information from multiple data sources. In fact, Nucleus found that 2

3 data diversity is rarely an obstacle during deployments. At one company that earned a high ROI on its analytics investment, an IT director said, Data wasn t an issue. The consultants helped us build a data cube for each department on the deployment. Building each cube took less than a few weeks and our consulting bill was less than $30k. Nucleus has found that with so many cost-effective ways to manage data during analytics adoption, small and mid-size companies are often able to integrate data sources as diverse as ERP systems, contract-related databases, financial applications, and point-of-sales terminals. Analytics improves decision making Analytics can improve the decision making of even the smartest and best-trained workforces. Although organizations have made significant investments in recruiting and training the best employees, many continue to underperform because of poor decision making. At most companies, employees routinely make important decisions with bottom-line impact on the basis of intuition, gut feel, and organizational folklore. Nucleus has found that when employees instead make decisions based on facts and data from well-governed analytics applications, they are far more likely to make decisions that increase revenues, improve gross margins, and eliminate operating costs. CUSTOMER EXAMPLES Nucleus has examined numerous deployments of analytics at small and midsize companies. Following are a few examples of deployments which generated high returns on investment and were well within the financial limitations of a small company. Nucleus found these companies followed a number of best practices that companies can use to maximize the returns on their own investments in analytics. Blue Mountain Resorts Blue Mountain Resorts is a diversified year-round resort located in northern Ontario and the third busiest ski resort in Canada. The company adopted IBM Cognos TM1 in order to give its managers in a variety of product lines and business units more information for making operational decisions. After a rolling deployment that began with the company s lodging division, IBM Cognos TM1 was then adopted by the skiing, rental, golf, and conference room businesses. For all of these businesses, data cubes consisting of historical financial data, current weather conditions, bookings, and employee schedules were built so that line-of-business managers could use this data when making daily operational decisions. Using IBM Cognos TM1 to enable more data-based decision making by managers and employees led to improvements in three areas. First, managers were better able to rapidly redeploy workers across the organization s different operations, resulting in a reduction to annual labor costs of $2.5 million. Second, by using IBM Cognos TM1 to replace manual report-building processes, the company avoided hiring new report builders. Revenue was another area of benefit. Sales agents increased both sales volumes and margins by using historical bookings data and weather-related information to make better pricing decisions when booking sales. The take away Building databases and datacubes based on business requirements, rather than on existing on-premise data sources, can be a critical success factor for a deployment. Prior to the deployment, the project team spent time with business-unit managers 3

4 to identify the types of reports and decision they wanted to improve. In the lodging department, sales representatives wanted information that would enable them to make better bidding decisions based on current weather and seasonal booking patterns. Although the resort had historical booking and revenue data, it did not have weather-related information. By purchasing information from a national weather agency, the sales department was able to combine seemingly unrelated data sources, learn more about customer demand, and make better pricing decisions on the phone with customers. Concept One Concept One is a provider of licensed accessories for men, women, and children. With an ERP system that had limited reporting capabilities, the company deployed IBM Cognos Express in order to both eliminate manual report building processes completed by the company s IS director and provide better operational data to managers. The deployment team first built a Microsoft SQL Server database populated with transactional data from the company s ERP system. After the database was integrated with IBM Cognos Express, existing reports were then replicated in order to automate the reporting process. Reduced costs were the primary result of Concept One s analytics adoption. By performing cost and benefit analyses on all of the company s design projects, the company found opportunities to eliminate projects and reduce designer headcount. Sales managers were also able to reduce costs. By using IBM Cognos Express to perform queries on each of the company s licensing agreements, they were able to determine which should be renewed and which should expire by obtaining extremely granular cost and revenue data for each agreement. After several years of better contract management, the sales department significantly increased both the company s revenues and its gross margin. The takeaway Returns on investments in analytics can be increased by maximizing the availability of cost-related information. Although the project team was aware of managers business requirements for analytics during the deployment, the team could not anticipate all the analyses or decision making that would be made with IBM Cognos Express. In order to maximize the range of business decisions that could be improved with analytics, the team incorporated as many cost-related data sources as possible, including some data sets that had not been requested by the lines of business. The deployment team correctly anticipated that with an abundance of data, managers would entrepreneurially seek out and identify cost reduction opportunities, eventually reducing both operating costs and costs of goods sold. Cincinnati Zoo The Cincinnati Zoo is a nonprofit 100-acre facility serving an average of 1.2 million people annually. In late 2009, the organization adopted IBM Cognos BI in order to give managers the information they needed to improve visitation rates, increase onsite purchases, and reduce costs. The deployment team first created a data warehouse consisting of point-of-sales data, geographic data, membership lists, and inventory records. The team then built 25 operational reports and dashboards based on the organization s operational and financial objectives. 4

5 Adopting analytics led to more data-driven decision making by managers, higher revenues, and lower operating costs. Revenue increases were the result of improving managers abilities to examine the preferences and habits of visitors and members, leading to increases in membership volumes, ticket sales, and sales of food and merchandise. For example, after examining ice cream sales data, the organization determined peak sales times for this product and adjusted the opening hours of the ice cream kiosks, leading to higher revenues. By performing highly granular analyses of the organization s marketing efforts and results, managers also identified and eliminated ineffective initiatives and ad campaigns. The takeaway Focusing on customers is one way to earn high returns on an analytics deployment. One reason the Cincinnati Zoo s adoption of analytics was so successful is that it was focused on helping managers learn about the zoo s customers: who they were, where they lived, why they visited, and what they purchased. This was achieved in two ways. First, every available customer-related data source was integrated with IBM Cognos BI. Second, the Zoo created a new and extremely rich data source by using point-of-sales terminals to get the zip code of a visitor during every onsite transaction. By gathering so much data on its visitors, the organization was able to learn more about the effectiveness of various ad campaigns and promotions. It also identified neighborhoods which were underrepresented in the zoo s visitor base, enabling the zoo to target its campaigns more effectively. CONCLUSION Nucleus finds that leaders of small and midsize companies can improve their operating results by seeing beyond the myths about analytics and investing in tools such as BI, PM, and predictive analytics. By delivering reporting and analytical tools to their managers and employees, leaders who adopt analytics change their companies in three ways. First, they enable their employees to make more databased decisions and rely less on intuition and institutional folklore. Second, by improving employees decision making, they enable those employees to independently find ways to increase revenues and reduce costs. Third, by using automation to eliminate manual report building processes, companies that adopt analytics are able to improve productivity. 5