Take a moment and view the excellent blog post by our very own Steve Massi featured on the IBM Big Data Hub. Steve takes an in-depth look at Enterprise Wide Integration and why it’s an essential component of any Big Data initiative.
What can sports learn from one of the largest retailers in the country? We believe the current J.C. Penney challenges can be quite instructive.
Let’s start with initiating a major change in go-to-market strategy. We’re not talking about a shift here, but a major abandonment of their current strategy. First was a change in pricing strategy, wanting to completely eliminate the traditional retailer “high-low” approach by offering Everyday Low Prices (EDLP). Second was the planned re-invention of the in-store experience by creating a local main street, where small store-within-a-stores would be designed to offer a more intimate customer experience.
Of these two components, the one that has created the most customer upheaval has been the pricing change. J.C. Penney’s current problems are nicely summarized in this recent Forbes article, J.C. Penney’s Comeback Is A Farce So Far, And It Could Get Worse. So where has J.C. Penney failed?
One only need look at supermarket retailing to understand that a solo or unsupported EDLP pricing strategy does not work. Even WalMart uses circular ads to drive traffic through additional price promotions. Retailers like Ahold’s Giant (Carlisle) have enhanced their EDLP programs by adopting a dual pricing strategy. This is a combination of EDLP pricing, plus aggressive price and bundles promotions. Through data analysis and strict category management, they have been able to stabilize overall pricing and still drive foot traffic by exciting shoppers. By creating a stable of “bundled” promotions, which offers shoppers better value than buying those same items individually, Giant has benefited by generating more revenue per shopping trip, and more margin dollars.
Giant and WalMart have tapped into a key psychological sales tactic called “framing”. This is where some initial price is set as a benchmark, and then various pricing tactics are used to show value against the benchmark.
So what can Sports franchises learn from this? Our takeaway is that sales and margin dollar success lies in a flexible, complimentary pricing strategy. One that combines price discounting/promotions and bundled packages. Sports has historically used price discounting alone to move inventory. They have missed opportunities to provide greater value for the fan while driving higher revenue per customer and margin dollars through creative bundling.
IMS’s own STADIS© Advanced Promotions and Data Integration Platform provides the technology foundation to create unique, exciting and relevant promotions for fans. Create bundles that focus on item-level, progressive, time or location based parameters. Engage with fans in real-time based on their in-event purchase behavior.
Contact us to find out how STADIS© Advanced Promotions can help you.
Another great week during IBMbigdata’s #cxo chat. This week’s topic brought up discussion around utilizing the customer’s digital footprint to enhance the customer’s experience. Much discussion centered around integrating data and using all data sources to develop a complete view of the customer. As expected, discussion moved to the customer’s offline experience and how to:
- Integrate offline and online data sources
- Doing so from a customer-centric point of view
Much data discussion is centered on the big data areas of unstructured data and social data. It seems that the integration of transactional data from a customer-centric POV is taken for granted. From our observations, that assumption is a big oversight.
IMS believes that from a transactional data perspective, you have product-centric data and fan-centric data. Aligned much like most corporate business and support lines, the vast majority of transactional data resides in separate, product-centered data bases. Disparate POS systems, e-commerce systems, catalog systems, even ticketing systems contain their own data. The world is viewed from outside-into the customer. We think that data tracking and capture should be from the customer-out. If we really want to improve the customer’s experience, we need data that’s customer-centric.
So offline, how do we generate customer-centric data? First, the customer must have some type of trackable, scanable identifier that’s unique to them, and use it throughout their shopping/purchase journey. These issues present 2 very different challenges. Getting every customer to have a unique identifier, while a large task, has been happening for years. Just check your key-chain and see how many retailer key fobs you have on it. The bigger challenge is getting the customer to use this ID every time they engage with you. This engagement starts the customer-centric data engine. The more engagement that’s ID’d, the more customer data that’s generated. The big opportunity is mobile. As smartphone penetration and usage continue to grow, and as mobile continues to integrate into the offline shopping experience, the more fan-centered that data will become.
Loyalty programs have attempted to create a reason for customers to ID themselves, but generally the typical loyalty reward program/system has been too little, too late. No one wants to have to spend a thousand dollars to qualify for a free bobble head. A more relevant platform like STADIS©, that can deliver diverse, immediate as well as progressive rewards for customers to ID themselves, and generates real time customer-centric data, must become central to your promotion and engagement activities.
Let us know what you think!