Developing a Successful Analytics Strategy

Carrie is the Practice Area Lead for Information Management at Slalom Consulting. With nearly 20 years of experience delivering business intelligence solutions, Carrie now specializes in Advanced Analytics. Utilizing quantitative approaches to uncover actionable insights based on consumer behaviors, attitudes, perceptions and attribute, Carrie has serve as a trusted advisor to help clients assess their current analytic and data infrastructure challenges and then implement the architecture, processes, and organizational requirements needed to address those business challenges.

Carrie is the Practice Area Lead for Information Management at Slalom Consulting, specializing in Advanced Analytics. With nearly 20 years of experience delivering BI solutions, Carrie helps clients leverage quantitative and qualitative results to meaningfully drive their businesses.

As a major part of our work within the Advanced Analytics Practice at Slalom Consulting, we guide our clients toward the right balance of activities, information, and insights that allows the experience and intuition of the decision-makers to be guided by appropriate information and quantitative analysis. This balance sits at the intersection of finance, operations, sales, and marketing, and encompasses the broad field of advanced analytics, which is currently getting an immense amount of attention in the business press. With this new awareness comes much hype and confusion about how to effectively execute an analytics strategy. From our work with hundreds of clients across many industries, we have found six essential considerations for developing a successful analytics strategy.

Establish Common Definitions to Deliver Consistent Information

While the finance team specializes in categorizing and defining information consistently, marketing (and to a lesser extent, operations), often uses less robust definitions. Metrics such as “awareness” for advertising or “footfall” for store operations are not necessarily defined or captured in the same way in each instance. Bringing together a business’s core groups to adopt a common set of metrics, agree upon how to govern the collection, and determine how it is used will help ensure smoother cross-team communication.

Business activities around promotion, marketing, competitive impact, and operational events are not necessarily captured in the same fashion as general ledgers or sales transactions. However, to have a consistent view of internal and external information, these data points must be captured with a degree of care similar to that used with key enterprise assets.

Apply Business Knowledge to the Process

Insights are most valuable when they leverage the experience of the team along with a broader set of trusted internal and external findings. The most successful teams will include representation from finance, operations, sales, and marketing, all communicating in a common language and with shared understanding. Knowing how data is collected and having specific business rules in place for analyzing that data is critical to understanding statistical insights.

Advanced analytics are only useful for making solid business decisions when all three legs of the stool are in place: data (internal and external), business and domain knowledge, and statistical models.

Select and Capture the Appropriate Metrics

In today’s global enterprise, there is no shortage of financial, operational, sales, and marketing metrics. However, we see wide variation in the discipline and use of this metrics. A study by Currim and Mintz at the University of California Irvine shows that an appropriate use of combined financial, operational, and marketing metrics can be a significant predictor of business performance.

The study indicates that higher-performing companies use a two-to-one balance of financial to marketing metrics, whereas lower-performing companies overweigh their financial metrics. Interestingly, there is a wide variance in the use of metrics by type of marketing activities. Most firms, regardless of their performance quartile, measure pricing or promotion performance. However, companies in the lower ranges of performance typically don’t measure social media or public relations, while top-quartile performers measured at least four key metrics in those areas

Keep It Simple and Experiment Often

When trying to be more analytical, companies often fall into the trap of adding unnecessary complexity. This is often driven by the natural risk avoidance inherent in large organizations, coupled with a reliance on quantitative analysts tasked with finding one “right” answer. As with most things in life, business decisions can seldom be delivered by one complex model that returns the perfect response. The most successful decision-making models often operate with the smallest number of factors possible to arrive at a reasonable answer.

Promote a Culture of Sharing and Storytelling

Insights are not very useful unless shared with the entire decision-making team. It’s natural to sometimes hold on to knowledge that may give one a perceived internal advantage, but this short-term thinking does not allow an organization to grow analytically. Encourage sharing within your organization by using stories. Create a story on the front end to generate hypotheses, and on the back end to help craft the final message to your audience. In the middle, use research methods, data, and statistics to quantify, evaluate, and modify the thinking.

Rewarding team members who not only create insights by leveraging new data sources or models, but also then sharing those insights with others in the organization will help foster teamwork and morale. Team thinking is essential to not only capitalize on the collective wisdom of the group, but also to allow your best analysts to move on to new areas of exploration.

Take a Measured Approach

The evolution of the decision-making process must be undertaken at an appropriate pace. Trying to displace too many decisions with quantitative or scientific models too quickly will invariably backfire. Organizations must have the time to allow analysts and managers to learn from the newly available information and to supplement their current processes before undertaking new ones.

In working with our clients, we help them keep these considerations in mind as they craft their analytics strategy. Striking the right balance here results in a strategy that maximizes the organization’s return on their analytics investment.

We would like to hear your thoughts regarding this approach to building an analytics strategy.  What do you think is the most important consideration?

Carrie Steyer is a member of Slalom’s Information Management Thought Leadership Committee. For more information, email the team at

About carriesteyer
Carrie Steyer is a Practice Lead in Slalom Consulting’s Chicago office, focusing on Advanced Analytics and Information Management.

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