Data Excellence

From BI Review Magazine | December 2006 Issue
By Barbara Lewis, Dan Otto
December 1, 2006

Data excellence is proving to be the critical key to marketing success. With the advent of desktop econometrics and market response modeling, companies with mega budgets can finally determine which marketing activities are effective and which are not. Econometrics examines the relationships over time between marketing mix variables that are controlled, and performance measures, such as sales or market share, that represent the outcomes of marketing plans. The relationship among all the variables is teased out, so that companies can determine the lift that each marketing activity can attain.

Furthermore, companies can optimize their budgets to determine the ideal overall dollars as well as line item amounts that will accomplish goals such as maximizing customer equity, generating new customers, gaining market share or improving cross selling. Financial services companies with mountains of data are leading the way in marketing resource management. However, it won't be long before all industries are using econometrics and optimization in their analyses.

The cornerstone of the analysis is data excellence - not just quality data, but the right data - captured correctly and measured appropriately. We have found that a number of the Fortune 500 companies we've worked with have many common data issues that inhibit accurate analysis. Here is a selection of recommendations based on the major problems that we've encountered.

1. Prioritize Corporate Goals: A company may have different strategic goals that are in conflict with each other. The goals of market share for the marketing department and profitability for the corporation oppose each other. The ramifications are broad and include, for example, that different departments are compensated for achieving conflicting goals. Case in point: A company may desire optimum budget allocation yet achievement may be difficult with contradicting goals of market share gains as opposed to profit increases. The data needs to reflect corporate goals from the top down.

2. Define Required Data: Oftentimes, the data that is captured does not measure the accomplishment of the strategic goals. For example, a company whose goal is market share does not capture or track any market share data on a consistent basis. They were hard-pressed to locate market share data and couldn't relate the rate of change in their share.

3. Gather Necessary Data Only: Reams of accumulated data may be irrelevant in measuring the success of the goals. One company captured over 50 different data sets, yet after defining strategic goals and identifying the metrics needed to track accomplishments, found that only 17 were necessary. Data should have a direct tie to measuring results of the goals.

4. Keep Data Consistent: Data inconsistency makes analysis difficult. Within the marketing department, different regions may be capturing different data in different formats. Corporate data protocol should be established on what data is captured, how often and the measurement.

• Standardize the database. In one company, the marketing data from various regions was gathered in Excel, Word and PowerPoint. Data should be captured in a database and those databases should be consistent across the organization.
• Standardize the frequency of data capture. In another marketing department, one region tracked data weekly, another region monthly and still another annually, making regional cross-sectional analysis impossible. Another company gathered weekly data and then aggregated it to monthly data every quarter. In econometric analysis the more data points there are, the more detailed the analysis. Fifty-two weekly data points are immensely more valuable than 12 monthly data points.
• Standardize the metrics. Tracking the number of event attendees versus the amount of money spent on the event is not a consistent metric for events. Metrics should be standardized, so the analysis is comparing apples to apples.

5. Develop a Corporate Wide Data Policy: This should include department guidelines and a data dictionary. All too often, we're given undefined data sets and managers can't explain definitions because the database developer has left the company. Institutional data memory should be documented. Explanations should include the rationale for capturing each data set, so the employees aren't operating in the dark. They know what and why they are tracking specific data.

6. Develop Data Responsibility and Transfer Procedures: If an employee is responsible for data gathering, tracking and monitoring, build it into the job description. Define the transfer protocol from one employee to the next. In a number of instances, missing data was blamed on the previous employee, long-gone from the company. When an employee leaves the company, the exit process should include review of the database for which the employee was responsible.

7. Establish Tracking Timelines: Expense tracking should be recorded when applied, not accrued or when the bill is paid. Part of econometric analysis is linking marketing spend to results, for example, the number of new customers acquired. If magazine advertising was purchased in January, but the ad didn't run until July, the expense should be noted in July, so it ties to the results.

8. Vary the Expenses: In econometrics, if the expense is exactly the same month after month with no variation, lift cannot be determined. Expense amounts need to change to ensure that there is variation in the data, so the analysis can identify the change in the relationship with other variables.

Although companies scrub their addressable data to ensure accuracy, most are not committed to data excellence. While data mining has permeated corporate America, data excellence is struggling and lagging behind. Data excellence can drive budget decisions for a competitive advantage. The winners will be companies with strategic goals aligned with data capture, comprehensive data protocol and standardized measurement, which will lead to superb analysis and optimization of the budget.

Gone are the days when CXOs couldn't figure out which of their marketing activities were working. Econometrics and optimization takes the guesswork out of the equation and data excellence drives the process.

Barbara Lewis and Dan Otto are principals at MarQuant Analytics, which helps companies define, capture, measure and analyze data to improve marketing productivity. They can be reached through their Web site at www.MarQuantAnalytics.com.