Retailers’ Successes and Struggles with Big Data in 2014

“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. Our experience suggests that for retail and CPG companies, the upside is at least as great, if not greater.”

Peter Breuer, director of McKinsey & Co.’s retail practice in Germany

With November half over and 2015 starting to peek at us over the horizon, we decided it was time to take a look at a few examples of what retailers have been using big data for in 2014. Here are three examples of use cases for Big Data in retail that have emerged in the last year, followed by a few InfoTrellis predictions about what will happen next in the new year when it comes to the evolution of how companies are implementing their Big Data strategies.

Macy’s

Personalized Marketing

 The main goal for Macy’s CEO, Terry Lundgren, is to offer more localized, personalized and smarter retail customer experience across all channels. They use Big Data among others to create customer-centric assortments. They analyse a large amount of different data points, such as out-of-stock rates, price promotions, sell-through rates etc. and combine these with SKU data from a product at a certain location and time as well as customer data in order to optimize their local assortments to the individual customer segments in those locations.

In addition to that, Macy’s gathers, and of course analyses, a vast amount of customer data ranging from visit frequencies and sales to style preferences and online & offline personal motivations. They use this data to create a personalized customer experience including customized incentives at checkouts. Even more, they are now capable of sending hyper-targeted direct mailings to their customers, including 500,000 unique versions of a single mailing. The results are compelling; Macy’s e-commerce division alone has witnessed a growth of over 10% and an overall annual revenue growth of 4% with the use of Big Data Analytics.

(Macy’s Is Changing The Shopping Experience With Big Data Analytics)

Personalizing the user experience is a ubiquitous use case for big data, so it’s exciting to see a retailer actually implementing the technology to accomplish and prove out the value of this marketing strategy. Four percent growth is nothing to scoff at; this number represents millions of dollars on pure profit they didn’t have before. For companies that still believe they can accurately segment their hundreds of thousands of customers with fewer than a hundred profile archetypes, this is an undeniable piece of proof that they may need to consider getting on the bandwagon if they don’t want those millions of dollars to be coming out of their share of the customer’s spending habits.

What’s more, this is a front-and-center application of big data that is highly visible to the shopper. Whether or not they can articulate the difference in quality of experience they get from a retailer that uses it and a retailer that doesn’t, it’s a difference they can intuitively feel and will definitely react to by rewarding one store with loyalty over the others.

Once companies start pulling social data and combining it with internal customer data, their targeting and micro-segmentation capabilities will enable even more uniquely tailored marketing and customer experiences. So long as companies remember that the purpose of this data-collection is to minimize friction and irrelevant messaging for their customers and never to manipulate them or milk them for money like a mindless herd, the consumer stands only to benefit from the evolution of this practice.

For this reason, my prediction is that this will be a big differentiator in the coming years as the companies that experiment with it first (i.e. the early adopters) get better and better, making the gap increasingly noticeable to the end consumer. There will be a scramble by the companies that lagged behind to try to catch up, and this will represent a big shift for the retail industry’s established best practices in much the same way the idea of the loyalty program and the digital storefront did.

LUSH

Supply Chain Efficiencies

 LUSH Fresh Handmade Cosmetics used big data technology in 2014 to drive in-store profitability and deliver savings of over £1 million in stock loss. Working with many datasets – retail data within EPOS systems, supply chain and stock management, payroll and timesheet systems for staff management – LUSH sought and implemented a technology platform that could be used by employees at every level throughout the business to provide access to relevant sales, stock, store and staff information to improve performance.

The BI tool is deployed across the entire LUSH organisation. The retail accounts team uses their new platform to dig into the numbers behind the sales in each shop, to keep an eye on ledgers, petty cash and every other aspect of running the business financially.

The technology is used in all the stores, so all the shop managers and employees on the shopfloor have access to updates every hour. The big data technology is also used in LUSH’s manufacturing department for stock management, allowing the team to facilitate orders between the factory and the shops and can keep an eye on stock position around the country. The gathering and analysis of big data has also helped LUSH to get a view of its sales and stock which has led to improvements in forecasting and sourcing of key ingredients from suppliers.

(LUSH Fresh Handmade Cosmetics saves £1 million with user-driven BI)

Big Data doesn’t just have to mean customer-obsessed data like social media or purchase data, and LUSH has demonstrated excellently how better managing and understanding the “big data” generated by business processes can lead to the increased efficiencies that save companies real money. The above quote doesn’t go into a great amount of detail about this, but LUSH is also apparently giving their in-store staff the power to make decisions about the physical customer experience around displays and promotions using insights gained from this data.

The big advantage here is the immediacy of the data and the accessibility of it across the organization. Giving employees in the store both the ability and the direction needed to make swift data-driven changes means a real-time responsiveness that is key to taking advantage of interesting correlations like how shopping behavior changes during certain weather or changing a marketing strategy on the fly when regional tweaks have the ability to boost sales.

The more data they have and the closer they get to real-time, automated analysis, the better companies like LUSH get at managing their backend operations. This isn’t something that the customer will see as clearly as a customized marketing experience, but when a store always magically has the kinds of things they’re hoping to buy and then a few other things they didn’t realize they wanted until they laid eyes on them, repeat visits are likely just for the convenience of the inventory. Customers don’t much care about whether a store loses money on excess, unwanted inventory, but they’re unknowingly benefiting just as much as the company is when predictive analytics can prevent that unfortunate occurrence.

Going forward, these kinds of clever algorithms stand to get better and better as companies pull in data from outside of their company, using social media to understand demand and competition as well as more granular breakdowns by region and even individual store location. I don’t anticipate the rush to get in on this use of Big Data technology will quite as dramatic as need to invest in its marketing applications over the next few years, but it’s a tangible, valuable use-case that will apply to the more pragmatic of executives. We will likely see slow but steady growth in the number of companies that decide to implement Big Data technology with the goal of improving their BI behind the scenes.

Starbucks

Maximized Profitability of Store Locations

Understanding the pools of information pouring into the databases of Starbucks has become a major focus at the international brew chain, even though its high-profile CEO doesn’t much care for it. “Howard [Schultz] doesn’t care about data. He has absolutely no head for data,” said Joe LaCugna, director of analytics and business intelligence at Starbucks during a session at the Big Data Retail Forum in Chicago.

A full quarter of Starbucks transactions are made via its popular loyalty cards, and that results in “huge amounts” of data, Mr. LaCugna said, but company isn’t sure what to do with it all yet. The same goes for social media data, he said. Starbucks has a team who analyzes social data, but, “We haven’t figured out what exactly to do with it yet,” he said. It’s a common refrain among brands, and many of the speakers and attendees here at the conference.

(At Starbucks, Data Pours In. But What to Do With It? – Published in 2013)

In 2007 and 2008, Starbucks’ CEO Howard Schultz was forced to come out of retirement to close hundreds of stores, and rethink the company’s strategic growth plan. This time around, Starbucks took a more disciplined, data-driven approach to store openings and used mapping software to easily analyze massive amounts of data about planned store openings.

The software analyzed location-based data and demographics to determine the best place to open Starbucks stores without hurting sales at other Starbucks locations. The software is also helping to determine where the next 1,500-plus stores should be placed not only to help the company expand, but drive revenue for new store developments.

(How Big Data Helps Chains Like Starbucks Pick Store Locations — An (Unsung) Key To Retail Success – Published in 2014)

Starbucks is an interesting case because it’s collecting all the right data to be implementing something like Macy’s is doing, but doesn’t seem particularly motivated to try it just yet. Indeed, a year ago they admitted they didn’t have a clear strategy for using the data generated by their loyalty program.

This year, they’ve put forward a clear case that benefits them in an area that is very important to the franchise; store location. For retailers, this has always been an absolutely essential part of decision-making when it comes to growing their profits and customer base year over year. It makes sense that Starbucks, having lost so much money with bad calls on store locations in the past, would choose to use their data for determining where they can gain the most long-term growth and profit when planning new locations.

This is a great example of uses for Big Data that aren’t as intuitive as customer experience related ones. It speaks to the kinds of use cases that speak to CEOs like Schultz – nothing fancy or weird, just better quality and higher quantifies of information being used to answer a question they would have been asking anyways as part of their overall strategy. It goes to show how powerful Big Data can be when you use it to approach an established goal from a new direction.

It does, however, raise a few eyebrows. Starbucks is considered an industry leader in many regards, but the reluctance to use their glut of data for more than just location planning could be a serious miscalculation by the chain. My prediction is that one of their competitors will figure it out sooner than they do and offer a data-driven, social-media-integrated loyalty program that tailors its messaging and rewards at a clearly higher level of sophistication, and when this happens Starbucks will either swiftly release an imitation or else find themselves with a significantly reduced grip on their core market.

Still, that’s not to say that it’s a bad idea to be using their data in the way they are now, and I expect other retailers will adopt the methodology in 2015 – many of them already are.

What to Expect in 2015

We found that 62 percent of retailers report that the use of information (including big data) and analytics is creating a competitive advantage for their organizations, compared with 63 percent of cross-industry respondents.

To compete in a consumer-empowered economy, it is increasingly clear that retailers must leverage their information assets to gain a comprehensive understanding of markets, customers, products, distribution locations, competitors, employees and more. In this industry deep dive, we examine industry-specific challenges, as well as provide our top-level recommendations for retail organizations.

IBM

The number of retailers initiating Big Data projects in 2014 jumped sharply from the number in 2013. The adoption rates are increasing steadily and the industry is soon to reach a tipping point and the arms-race will begin.

Although there has been plenty of hype around Big Data this year, the explosion of in-earnest technology implementations has yet to begin. 2015 looks like it will either be the beginning of this tipping point or will be the year where one or two huge successes will hit the news and initiate the rush to follow suit.

Core to these major successes will be the ability to source both internal and external data for these analytics. The next-best-offer that can build on data about you from your Twitter and Facebook profile, sharpening its understanding of your wants and needs, will have a clear advantage over algorithms that only use past purchase data. The loyalty program that understands what kind of incentives motivate you using the same kind of connected information across multiple platforms will similarly benefit.

We expect to see early adopters achieve great success with leveraging the in depth understanding gained from social media – to target their customers or loyalty program members as individuals. We also expect that the results (and market share gain) of those early adopters will kickstart similar projects within a larger number of retailers.

We also anticipate an increasing number of implementations of new micro-segmentation models. Retailers using classic segmentation approaches (for example, one classic model uses just 66 segments and only updates their characteristics once per year) will start considering big data enabled dynamic segmentation models with larger number of segments, where the segments are updated potentially weekly – to better reflect the dynamic nature of today competitive environment

All in all, 2015 looks like it will be a very exciting time for retailers.

allsight, Big Data, Big Data Analytics, bigdata, Customer ConnectId, Data Lake for Retail, Retail

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