Guest Post: The Digital Transformation of Retail

By ShiSh Shridhar, WW Director of Business Intelligence – Retail Sector, Microsoft

You go shopping; let’s say it’s a national hardware store because you have a painting project you’ve decided to tackle this weekend. You have done your research online, chosen the paint and now you are at the store to pick up your supplies and get started. But, when you reach the section with the paintbrushes, you realize you’re not exactly sure what you need. You stand there for a moment trying to figure it out, and then you start looking around, hoping a sales associate will appear. And one does! She’s smiling, and she’s an expert in paints and yes, she can direct you to the brush you need — and reminds you to pick up some blue tape.

Shish 1

Shopping miracle?

No, shopping future. This kind of positive customer experience is one of the many ways that artificial intelligence (AI), sophisticated data gathering, and the cloud are being used to empower employees, bring consumers into stores, and shorten the path to purchase. These advancements help brick and mortar retailers compete with online retailers in today’s world. It’s the digital transformation of retail, and it’s happening now in ways big and small.

AI + Data = Retail Revolution

This transformation is driven by data, which today can come from any number of sources. In this example (a product called Floor Sense from Microsoft partner company Mindtree), the data is collected from security cameras already in place throughout the store. The cameras capture footage of how people move through space, where they stop and what they do as they shop. The video feed is then analyzed using AI that has been trained to understand how a customer acts when he or she needs help. When that behavior is recognized, a sales associate with the right expertise is sent to talk to the customer and help the customer make a decision.

But a store’s proprietary data is only the tip of the iceberg. Today, there are millions of data points that are either publicly available or easy to purchase from companies like Experian and Acxiom. Retailers can combine that demographic data with their existing CRM data to model behavior and build micro-segmentations of their customer base. Insights from that narrow analysis allow retailers to personalize, predict and incentivize in ways that are far more accurate than ever before.

Putting data insights into the hands of employees

Already, that kind of analysis has helped make online shopping more productive with relevant, timely offers. The next step for retailers is to learn how to make data-driven insights useful to store employees, as in the hardware store example, so they can enhance the customer’s in-store experience. The data could come from a customer’s interactions with the retailer’s app, chat bots, social media, in-store beacons or Wi-Fi, all of which, when compiled, allows a retailer to make extremely accurate inferences about a given customer’s behavior.

Managed well, those insights help a store employee serve a customer better. Managed poorly, personalized targeting in-store has the potential to spook customers. To handle it well, retailers must do two things: First, any in-store tracking should be done through a consumer opt-in, with transparency about how the retailer will use the information. Second, the customer deserves a good value exchange; it must be clear to her how she is benefitting from sharing her information with the retailer, and how her information contributes to delivering her a frictionless shopping experience.

Using a customer’s digital exhaust to everyone’s benefit

As consumers explore purchasing options and develop their preferences using search tools, social media, apps, and in-store visits with a device in hand they leave behind a digital exhaust. Today, advances in AI, data aggregation, and the cloud allow retailers to collect that digital exhaust to generate a style profile of prospective customers, which can then be used to introduce those customers to other products they might like. In this so-called phygital world — where the physical and digital overlap — retailers can combine data from multiple places to make inferences that will help them sharpen their marketing approach. The techniques are at hand — now, it’s up to creative retailers to find innovative ways to use those insights to inspire their customers, and shorten the path to purchase.

This article was originally posted on Independent Retailer.

Definitions for “Big Data” – A Starting Point

Big Data

Written by Rob Lawrence, eSage Group’s Strategic Relationship Manager

Will someone please tell us all, once and for all, just what in tarnation is Big Data? What is it? Where is it? Who’s doing what with it? And why are they doing that? In one blog article I can maybe just scratch the surface of those questions. I might even provide some level of understanding for those curious marketers, bewildered and attempting to make heads or tails of the concept of Big Data. I could certainly dive deeper than even that because I’ve spent some time with this, and done homework, and lived Big Data. But this is a blog article, not a dissertation, so I’ll keep it at a 10,000 foot view of the ever elusive, yet intriguing, Big Data!

If you are one of the rare data scientists that have graduated recently from one of few schools offering Big Data degrees, which makes you an expert in this field, please feel free to stop reading here, or continue on to better understand what the rest of us are, well, trying to grasp when it comes to Big Data. For the rest of us, here is my take on the whole Big Data craze:

Big Data is simply all the data available. That means, in realistic terms, all of the data one can gather about a subject from all the places data resides: data sitting in some long forgotten enterprise software program in the basement of a large corporation, data from social media websites, website traffic data (click-through’s and pathing and such), text from blogs, even data from a sensor on a rocket ship or bridge in Brooklyn (not sure if they’re using sensor data on the Brooklyn Bridge, but they could be). Sources of data are vast, and growing. It’s cheaper to store data than ever before, and we now have the computing capability to sift through it, so now there is lots more data being collected, “Big” amounts of Data are being stored and analyzed. There is a lot you can do with all this Big Data, but this is where it gets dicey. You can collect all kinds of data with one subject, question or problem in mind, but end up realizing (through analysis) more important information about a totally different subject, question or problem. That’s why Big Data is so confusing to lots of folks just getting their hands dirty with it, and apparently also why it is so valuable to Marketers, Engineers, CEO’s, The FBI, Data Geeks, and anyone else interested in edging out the competition. Let’s explore some basics:

Wikipedia says: “Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process the data within a tolerable elapsed time. Big data sizes are a constantly moving target, as of 2012 ranging from a few dozen terabytes to many petabytes of data in a single data set. With this difficulty, new platforms of “big data” tools are being developed to handle various aspects of large quantities of data.”

The Big Data Institute says: “Big Data is a term applied to voluminous data objects that are variety in nature – structured, unstructured or a semi-structured, including sources internal or external to an organization, and generated at a high degree of velocity with an uncertainty pattern, that does not fit neatly into traditional, structured, relational data stores and requires strong sophisticated information ecosystem with high performance computing platform and analytical capabilities to capture, process, transform, discover and derive business insights and value within a reasonable elapsed time.”

So, we’ve only scratched the surface of truly understanding what Big Data is here in this blog, and really the multitude of possibilities Big Data represents has only begun to unfold to those of us using it to better understand whatever it is we’re collecting data about. I hope at a minimum by reading this you have gained a better understanding of what “Big Data” is, but moreover, a curiosity to learn more and perhaps even apply it to something you are working on. These are exciting times whether you are using data for marketing or designing a new rocket ship to explore Mars. Big things are coming, and it’s all due to Big Data!

Here are some great articles I’ve recently enjoyed regarding Big Data:

Saffron is more than just a spice!

panoramaLast night was the 8th eSage Group co-sponsored Seattle Scalability MeetUp hosted at WhitePages.com. There were about 130 people in attendance to hear about HBase and Saffron. Very cool stuff!! Here is the SlideShare.

Summary:

Nick Dimiduk from Hortonworks, the father of HBase, gave us a sneak peek at what’s in store for the developer using HBase as a backing datastore for web apps. He reviewed the standard HBase client API before going into a framework architecture that makes HBase development more like other frameworks designed for developer productivity. He then went over fundamentals like rowkey design and column family considerations and also dug into how to tap coprocessors to add functionality to apps that otherwise might normally be overlooked.

Nick’s Bio: Nick Dimiduk is an engineer and hacker with a respect for customer-driven products. He started using HBase before it was a thing, and co-wrote HBase in Action to share that experience. He studied Computer Science & Engineering at The Ohio State University, specifically programming languages, and artificial intelligence.

Paul Hofmann from Saffron gave a talk titled “Sense Making And Prediction Like The Human Brain.” It was an amazing presentation on machine learning and predictive analytics. Cool stuff!!

Abstract of Paul’s talk: There is growing interest in automating cognitive thinking, but can machines think like humans? Associative memories learn by example like humans. We present the world’s fastest triple store -SaffronMemory Base- for just in time machine learning. Saffron Memory Base uncovers connections, counts and context in the raw data. It builds out of the box a semantic graph from hybrid data sources. Saffronstores the graph and its statistics in matrices that can be queried in real time even for Big Data. Connecting the DotsWe demonstrate the power of entity rank for real time search by the example of the London Bomber and Twitter sentiment analysis. Illuminating the Dots We show the power of Saffron’s model free approach for pattern recognition and prediction on a couple of real world examples like Boeing’s use case of predictive maintenance for aircraft and risk prediction at The Bill and Melinda Gates Foundation.

Pauls Bio: Dr. Paul Hofmann is an expert in AI, computer simulations and graphics. He is CTO of Saffron Technology, a Big Data predictive analytics firm named top 5 coolest vendors in Enterprise Information Management by Gartner. Before joining Saffron, Paul was VP of Research at SAP Labs in Silicon Valley. He has authored two books and numerous publications. Paul received his Ph.D. in Physics at the Darmstadt University of Technology.

Make sure to put April 24th for the next Scalability MeetUp at RedFin.