“Big data” has become the hot new reality star. It has broken past the business and marketing magazines into consumer and mainstream news. It makes and rides its own social media trends. It hovers between savior and scandal. Indeed, “big data” seems to have its own talent agent and publicity team — and they have a star on their hands.
Yes, big data is everywhere. It is constantly generated and stored in virtually every aspect of our lives. Whether it’s diagnosing an illness, educating our children, or riding in a car, big data is transforming our society by identifying patterns of behavior and making correlations with predictive assessments.
While it’s been around since the advent of retail, The Great Recession put big data in main stage spotlight. The downturn’s long-term effects put more pressure on retailers to make sharper business decisions, while battling for thinner customer wallets and more elusive loyalty. It has since reached obsession-level in marketing social media circles where articles and blog posts on the subject are heavily shared and debated.
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Amid all of this big data hype, an urban myth has emerged — it seems MORE data means more and, like that fable of the alligator prowling the sewers, the bigger we can make it, the more interesting it becomes.
The big data urban myth can be a distraction from the value of current data. Many retailers seeking more data are really missing the right training, analysis and interpretation of current data to put the puzzle pieces into place.
Here are five key tips that can change the way data is viewed and interpreted to fuel better decisions for data currently in place:
Make The Case For Improved Analysis
If retailers are going to win with data, they must first start by addressing the key value challenge – properly analysis, interpretation and adoption of the data in place. Customer insight has the commercial power to better drive business decisions, and focus on specific retail issues, but only when it is transformed into company-wide knowledge.
According to IDC, “less than 1% of useful data is analyzed,” while a Brick Meets Click Survey shows that 57% of retail business executives “lack the capability to implement insights” from data within their businesses, and 58% are not leveraging data “that’s already available.”
A McKinsey Global Institute study also predicts that by 2018, “the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills” and the “know-how to use the analysis of big data to make effective decisions.”
Commit To Look At Data Differently
Let’s face it — we get stuck in our ways with data. Customers and their behavior changes, the technology to collect data evolves, yet making adjustments to our analysis of data remains a challenge. Senior level buy-in is perhaps the biggest challenge most retailers face when attempting to revamp their approach to internal data analysis. It’s easy to get stuck in a process that allows direct comparison of previous reporting and to miss the inherent ability of data to mislead.
While unrealistic to expect a drastic change overnight, important business decisions are impacted every day until there is a commitment to make the change and a clear process to train the team.
The mandate to change must come from senior leaders. And it should be connected to a process to train the team. This is a meaningful change within the organization and it will require a process to manage the shift.
Look to demonstrate quick wins and value adds to encourage this new direction. Focus on the metrics that really count. Set goals such as timelines for specific decisions that will demonstrate the effectiveness of this new process.
Retailers must refocus to become better consumers of data, with a greater appreciation for quantitative analysis in order to elevate their businesses to the next level. Emerge from the crevices of compiling vast databases of information. After all, it’s not likely that many of these numbers will ever see the light of day.
View Data Through The Lens Of Your Most Valuable Customers
We all know customers are not the same. Retail managers should look to segment their most loyal customers and view data through that valuable lens, as they are the key drivers of retail performance.
We often see this change in view have significant impact on key business decisions such as which products to add and which ones to drop. Let’s consider the following as a sample scenario. “Suds and Bubbles” dishwashing liquid has moved down the shelves to the bottom, reflective of its sliding sales performance. It’s been around for years and has collected dust as a brand. From a sales data perspective, this is an easy decision when considering shelf space to clear for new products like that line of eco-friendly detergents. Yet, looking at who still buys “Suds and Bubbles” reveals purchases by a high percentage of loyal customers. And they buy it frequently — probably because competitors have already dropped it.
This data analysis indicates that “Suds and Bubbles” could be a key to your relationship with your most valuable customers. You offer something they like that they can’t get anywhere else. When they come to your store for this brand, they make large purchases of other items, rewarding you for keeping that brand on your shelves.
Dropping “Suds and Bubbles” could mean losing some of your most loyal customers – and their sales revenue.
Get Context
Proper data analysis should tie valuable information to customer behaviors – what do they love, what are they rejecting, what influences them to buy more, who are your most loyal? We all agree this counts, yet these insights and patterns are not always evident on spreadsheets. Retailers can get caught in the hype of big data and lose sight of important context, adding to the risk of making flawed decisions.
Most of this context is likely present in your existing data. Like a prism that reflects varying colors when held to the light at different angles, finding this context in current data requires that change in view. The right tools can help uncover how shoppers view a category and how store layout may have an impact.
Great data analysis tools are those that make your business decisions more accurate. These tools should essentially set retailers up for long term success, by empowering managers to independently analyze data and interpret customer insights on their own.
If the right data approach is implemented at the senior level, followed by a round of new tools and training, retailers will see improvements in decision making as well as more precisely tailored products or services that meet the needs of loyal customers.
Tackle The Myth
Big data has the potential to unlock significant value for retailers by not only making information transparent, but by creating faster and more accurate decision making and a better shopping experience for customers. It can even increase “operating margin by more than 60%,” if analyzed properly.
There will always be a new data source that promises to revolutionize business performance, yet driving higher ROI likely means mastering the interpretion of the information already in place.
As with any urban myth, getting to the truth requires pointing out the evident weakness in the story. Many retailers can look to the outcomes of recent data-driven decisions as motivation to tackle the myth of big data and — finally — get to the analysis that counts.
