The groundswell of interest around the potential for analytics to drive growth and profitability has been supported by numerous success stories and research.
However, generating returns from analytics is often heavily dependent on the span of analytics across an enterprise, according to a recent post in Harvard Business Review by Brian McCarthy, managing director of information and analytics strategy for Accenture Analytics.
Those companies that aggressively adopt analytics across the entire organization are achieving business outcomes including customer retention, product availability, and risk mitigation, McCarthy points out.
“When a company expands its analytics purview from functional to horizontal, it opens the door to greater opportunities and successes,” he noted. “While removing silos and taking a teaming approach to analytics is part of an internal virtuous cycle, another cycle is also created — the attained results are experienced by the customers and will keep them coming back for more.”
Accenture suggests companies can take a series of steps to drive an enterprise-wide focus on analytics, including:
Identify the business goal and metrics for an analytics initiative.
“For a high-performing retailer, we found that customer retention, product availability, labor scheduling, product assortment, and employee engagement were all leading indicators to driving growth and profitability for the company,” McCarthy notes. “Selecting the right critical metrics is a cornerstone of success as it brings focus and clarity on what matters most to the business.”
Bernard Marr, a big data and analytics consultant, suggests in a recent article that companies can start with business questions such as:
- Which customers are most likely to buy my product?
- Which customers are likely to leave?
- Which employees are likely to leave?
“But before you start collecting and analyzing data, it’s important that you know what questions you want to answer,” Marr advises. “Without clearly defined questions they are likely to get lost in the fog of irrelevant background noise, generated in a world where everything is digitized and datafied.”
Bring together a cross-functional team.
Create a group of data scientists, business analysts, and domain experts for sales, marketing, finance and research and development to find solutions that can be deployed across the business to solve problems that span multiple functions.
Develop capability to analyze root causes.
“The retailer example mentioned above used root cause analysis to answer the question around what factors contributed to an unsuccessful marketing promotion,” according to Accenture’s McCarthy. “They tested hypotheses by asking questions such as: were results poor because of the marketing message, pricing and bundling, product availability, labor awareness of the promotion or did a competitor have an attention-grabbing marketing campaign happening at the same time?”
Make data-driven decisions collaboratively.
By working with the cross-functional analytics teams, functional managers can make decision based on the insight from the data analysis.
“It’s important to note that once data-driven decisions are made and actions are set in motion, companies should track their progress against the metrics that were established at the start of their analytics journey,” McCarthy concludes. “If goals are not being realized, they should repeat the process to understand the root causes of an issue that will help them achieve their business goals.”