Starbucks is a perfect example of what retailers and consumer packaged goods (CPG) companies are grappling with when it comes to analyzing the data deluging their networks from inside and outside the corporate walls.
As much as a quarter of the data the coffee brewer is dealing with comes from its loyalty cards, but the firm doesn’t quite know how to use the data yet, says Joe LaCugna, director of analytics and business intelligence at Starbucks.
In addition, while the company has a team that analyzes social media, it hasn’t yet figured out exactly what to do with that information, either, he notes.
Still, the company is making strides in its overall data analysis strategy; it has reduced the number of reports it offers to managers about store operations from 300 to 11 key performance indicators, LaCugna adds.
Many retailers and CPG firms are in the same boat as Starbucks, either drowning in a deluge of data without being quite sure how to gain insight from it or skeptical about the much-hyped potential of data analysis.
However, big data and advanced analytics are among the most important battlefields for retail and CPG companies today, according to a blog post by Peter Breuer, director of McKinsey & Co.’s retail practice in Germany.
“Recent research by McKinsey and the Massachusetts Institute of Technology shows that companies that inject big data and analytics into their operations outperform their peers by 5% in productivity and 6% in profitability,” Breuer notes. “Our experience suggests that for retail and CPG companies, the upside is at least as great, if not greater.”
Here are some of the potential ways companies can achieve these gains:
- Data analysis lets them understand a customer’s behavior throughout each step of a shopping journey. This allows them to bolster up-selling and cross-selling activities.
- Companies can “listen” to how customers talk about their products via social media, including the features they like and if they would purchase the same products again.
- Data analysis allows companies to better track customer responses to promotions and media campaigns so they can make decisions on refining those campaigns for the highest success rate.
“But big data initiatives shouldn’t be fishing expeditions,” Breuer says. “We recommend instead a ‘decision-back’ approach, which begins with the company answering two related questions: which decisions do we want to improve? What data and analyses will help us improve those decisions?”
After putting a plan to manage data in place, companies must hire and develop the talent to translate relevant insights from the data, according to the post.
“But it’s important to strike the right balance between analytical expertise and commercial sense. Retailers are of course commercially driven organizations,” Breuer notes. “The key is to recruit top-notch analytical talent without subverting the company’s commercial DNA. Analytics should be an enabler for the commercial functions, not an end in itself.”
People with both analytical skills and good business judgement can translate the results of data analysis initiatives to stakeholders in a way they can understand. They can also defend the answers from data analysis initiatives if they are unexpected.
“Companies must make sure the new analyses and insights are embedded seamlessly into managers’ and frontline employees’ day-to-day decision processes,” Breuer suggests. “Leaders should prepare the organization for fundamental mind-set changes: people must be willing to reinterpret results, admit mistakes, and correct course if the data bring to light suboptimal decisions made in the past.”
Breuer offers the following advice for data analysis success:
Get quick wins. Chose an area or two where an investment in analytics can prove the business case quickly.
Garner a senior level executive who can spend 5% of her time on data analysis. “Senior sponsors must be involved right from the start and make sure the big data experiment follows a decision-back structure,” Breuer says. “They then need to participate in validating results and use their authority to rewrite the relevant business processes.”
Look outside. Companies should involve outside experts for help with their biggest analytical challenges. “Consumer companies can also take inspiration from big data success stories, not just in the consumer sector but in analogous industries; some groundbreaking initiatives in health care, government, and financial services, for instance, offer useful lessons for retail and consumer-goods players worldwide.”
