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Understanding the Analytic Challenge

Transactional data can give you the power to understand your customer, increase product profitability, and boost store performance. However problems with the quality, timeliness, and sheer quantity of data can quickly stall even the most promising analytic applications.

Analysts are challenged with reviewing and analyzing years of transactional data in a short amount of time, which can be a daunting task. Even if you manage to cut through the clutter and bring together a clean and useful data set, you must then analyze the results and find insights that have real impact on your bottom line. What insights support decisive actions and generate positive return? Which outliers or exceptions are important? Which variances are meaningful and actionable? The answers can provide real value, but are far from easy to come by.

Defining Analytic Maturity

Advanced analytics is one of the few ways to bring order to, and generate value from, large amounts of transactional data. In the past, companies could only consume organizational data through standard, rigid reports or cumbersome ad-hoc queries. Today, companies have greater access to this data utilizing advanced technology and science to examine data in extensive detail. Further along the analytic maturity curve, data mining and modeling yield more complex insight. However, optimization is the only tool that lets you take full advantage of both analytics and science to determine the optimal solution to a business problem. For example, using customer demand to drive optimization you can determine the optimal products to promote for the largest return on your investment. As the only true forward-looking and selecting analytic approach, optimization offers the best opportunity to maximize your competitive advantage.  

 Figure 1

Figure 1. The Analytic Maturity Model

Assessing Analytic Prowess

How analytically mature is your organization? First, assess your situation: What types of analysis do you perform today? Do you use Excel exclusively? Can you pull data from BI systems and create reports? Do you create ad hoc reports from multiple data sources by date, store, or product levels? You may need to improve accessibility to new tools, data sources, and different groups within your organization.  

What skill sets are available from the current personnel? Organizational change management will play an important role in assessing your analysts. You may need to acquire or develop personnel with new skill sets and/or the motivation to learn advanced analytics. A pricing analyst who has used the same tools for 20 years may find it hard to adapt to a sophisticated optimization model, or they may relish the challenge.

Identifying Where to Apply Analytics

Next, you’ll need to define the business problem(s) you want to solve. If the business problem is large, consider breaking the problem into smaller, more manageable pieces that can be completed and absorbed incrementally. Assess each issue by the level of benefit gained and level of difficulty to complete. It is important to consider both the hard benefits, such as financial value, and the soft benefits, such as eliminating manual processes, to save overall time and money. Be sure to consider competitive advantages, such as the ability to manage competitive price changes on a daily, weekly, or bi-weekly basis.

Then look at data availability and cleanliness. Depending on the business problem, you may need very granular data and may need to change data structures or acquire data that is not currently available.  Do business users trust the data as accurate? Such issues complicate analysis and diminish ROI. Finally, look at who will use the data; analysis that can make an impact beyond the analyst group has the greatest value. Rank these factors for each business issue, then choose accordingly.

Figure 2.  An Example of an Analytics Assessment Matrix

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* “Ability to do internally” value of 1 to 3 assumes that a 3rd party will assist, and subsequent answers will assume this is the case.

** Rank is determined by averaging the rates for each problem

Do not fall into the trap of creating sophisticated analytics for the sake of analytics alone. Powerful knowledge retains its power only if all involved clearly understand the business goals of each analytic initiative. Let those goals drive the end result.

Factoring for Success

The success of any analytic program can be predicted by several factors: first, a strong partnership with a third party can help you grow the analytic maturity of your organization and build strength in the areas of need. This is especially important if you lack in-house expertise, or if your analyst team is mired in outdated practices. Second, a strong enterprise data warehouse or business intelligence system from which to pull data will allow for easy access to critical information. Third, easy-to-use tools – such as visually appealing dashboards and customizable scorecards – improve your likelihood of success. SAP Performance and Insight Optimization offers these tools and more, helping retailers of all sizes get the most out of the insights they generate, and put it to use to improve the top line.    

Realizing the Full Potential of Analytics

Retailers have a unique opportunity to feel the pulse of their customers and understand micro and macro effects of economic, demographic, cultural, competitive, and product shifts and evolutions. Most businesses have only scratched the surface of this knowledge. Analytics allow motivated retailers to set themselves apart – to understand these changes and to translate that understanding into insightful knowledge of customers and their shopping behavior, to use that insight to improve service, introduce new products, and increase their top line.  

For more information on Performance Insight and Optimization Services from SAP, please visit www.sap.com/services/pio.

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