Leveraging Integrated Data for a More Personalized Retail Experience

Imagine this scenario. You are strolling downtown or in a mall and, as you approach one of your favorite clothing stores, a text message appears offering you “an extra 25% off purchases until 2:00 pm only.” And although you were just window-shopping, the deal is too good to pass up. Upon entering the store, a welcome message and store map appear on your phone. The map directs you to the area of the store where your size and styles are in stock. You find a sweater you like. Scanning it with the app to see the sale price, you are offered the option to buy in store or take advantage of free shipping. Not wanting to carry a bag around for the rest of the afternoon, you choose free shipping and add several more items to your order.

Though this scenario may have sounded futuristic 10 years ago, technology advances have made this our new reality. Still, putting all these pieces together remains a challenge for retailers. The good news is, that by taking advantage of integrated data, the possibilities are endless when it comes to creating the ultimate retail experience. And creating these types of individualized, immediate and intelligent interactions is easier than ever before.

What do we mean by “integrated data?”
Now more than ever we see the benefits of an integrated data approach to business processes and problems. Integrated data is the next step to big data, addressing both the type of data and the siloed limitations of big data.

Simply put, integrated data is the analysis of data from multiple systems, providing actionable intelligence to improve operations and processes. Data sources can be structured (finance, procurement, POS) or unstructured (forms, video, data feeds, websites), and can include input from multiple internal and external systems. The objective of integrated data systems is to harness the power of artificial intelligence and data analytics to produce improvements in operational efficiency.

Applying integrated data to the retail experience
The data powerhouse that enables this type of experience is varied, nuanced and enormous. There is real-time foot traffic outside the store, a heat map of customer history (based on app integration), real-time store inventory, and intelligence around trends and merchandise for other potential customers with a similar purchase history. The agility of this data can affect buyer behavior in real time to drive sales. The greatest challenge for retailers is taking that data out of silos and harnessing it for maximum benefit.

Think about today’s typical e-commerce experience, in which you are constantly being served up purchase options for items you have either purchased in the past, researched or that are similar to items or categories of items you have been known to consume. That is Machine Learning and AI at work and it is 100% data-driven. For brick and mortar retailers, even though most have an online retail experience as well, the data has not been so easy to wrangle – but it is there. And for every new piece of the data puzzle a retailer can collect and apply, the retailer shopping experience can be made more individualized, immediate and intelligent.

If we think about access to customer data in levels, here are some examples of how a retailer can make that data and intelligence actionable. These levels are arbitrary, but serve to paint a picture.

Level 1: Basic Awareness
Mr. Customer wears a size 32x32 pants; Medium shirt and a size 10 shoe. He can only buy a particular subset of store inventory, so for example, any app-based or push notifications would know enough not to send him special offers for a surplus inventory of size XL coats.

Level 2: Learned Buying Patterns
Mr. Customer purchased four pairs of slim fit chinos from the store in July 2017, three merino wool crewnecks in the past 18 months and various other items. His buying pattern indicates he should be in the market for more sweaters so he gets a push notification on his phone that sweaters are on sale this week.

Level 3+: Predictive Intelligence
Mr. Customer, with a deep purchase history, has completed a detailed online profile. His purchase history and preferences indicate that he is the ideal target customer for a new, upscale store being opened by the retailer just 15 minutes from where he lives. With this intelligence, the retailer sends him special offers and the opportunity to participate in the store’s grand opening event.

Also, based on Mr. Customer’s profile, the retailer knows he has a wife and three children. The retailer has several brands and is able to target specific offers according to the family’s buying history and preferences for gift suggestions, “back to school” items or seasonal purchases, such as a new pair of soccer cleats for his daughter.

Conclusion
Integrated data is not simply about mixing data sources. It is about using data to drive desired business outcomes. In retail effectively using data creates the ability to drive sales and increase basket by visit. It also allows retailers to understand and optimize the interplay between the brick and mortar and digital shopping experiences. With that deeper knowledge and understanding, retailers will be better positioned to create modern, fulfilling, individualized buying journeys for every customer they serve.

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