The key to retail today is customer understanding —where each customer stands on his or her personal shopping journey, whether in-store or out. Retailers must “know” each shopper as never before. And they must offer the kinds of contextual, personally relevant experiences that will optimize their merchandise mix, create faster inventory turns, and drive greater customer engagement.
After all, the typical customer today is mobile, connected, and has heightened expectations. Many are accustomed to a deeper level of real-time interaction from innovative online retailers than from traditional brick-and-mortar stores.
Yet, as a recent Cisco study revealed, offline retailers – or retailers that combine on and offline capabilities – have their own unique advantages – if they step up to the opportunities of the Internet of Everything (IoE) economy. By blending the benefits of the physical store — such as the ability to touch, compare, and try on products — with the benefits of the virtual world, retailers can create a new value proposition that can’t be matched by their online-only competitors. In the process, they not only drive their own industry’s disruption but challenge for market leadership.
To do so requires them to have a core capability in analytics that will support the kinds of IoE-enabled experiences that customers will come to expect.
Online retailers already gain key insights from the rich data created by the “clickstream” as consumers browse, research, and purchase products online. Online retailers then use this data on likes, dislikes, and interests to gain a better understanding of each shopper’s journey. Offline retailers are bringing this clickstream into the physical world and gaining real-time insight into each shopping journey. In effect, clicks are meeting bricks.
But key to capturing unrealized value is “going to the edge.” Much of the IoE value for retail will be generated by innovations that depend on real-time analysis of data that is captured at the “edge” of the business — from sensors, RFID tags, IP cameras, access points, beacons, mobile devices, and even weight and motion sensors and ambient condition sensors for moisture and weather. The challenge is to turn this mountain of data at the outer boundaries of the retail network into relevant, real-time insights as close to the customer’s buying decision as possible
With a traditional model, it might take days or weeks to transmit all the data back to a central data repository, sort out what is relevant, and extract key insights. By that time, of course, the customer has left the store and the opportunity to close the sale is gone.
By analyzing data at the edge of the network, where it is generated, retailers can sort information in real time to address fleeting opportunities. They can use real-time insights to make contextual offers, reduce checkout times, ensure items are in stock, and optimize store operations. As our study confirms, consumers increasingly value and expect this level of personal shopping relevance.
With in-store sensors, wireless networks, and the right analytics solutions in place, retailers can gain a wealth of customer insight as they drive ever-greater efficiency, shopping engagement, and satisfaction. With a clear window into each shopper’s journey, the retailer can respond with agility to any situation, addressing customer needs in real time.
After all, the shopper in Aisle C who needs an automobile part for a fast fix requires a different experience than the shopper in the electronics department who is taking her time comparing prices in anticipation of a future TV purchase.
By leveraging analytics and building dynamic understanding of each shopper, a retailer can adapt to each customer’s needs in real time — and drive new levels of loyalty and engagement.
What do you think the future of shopping will look like based on the exploding amount of personal retail data? How do you envision your buying experience changing for the better through the use of real-time information available through retail analytics? I’d like to hear your thoughts on how analytics will redefine the meaning of a shopping trip as we know it.
Nice informations! because its time i learning cisco. thanks!
A customer in the interior of a apparel show room can be shown a pop up or some interactive means to let him/her know that it is raining and rain boots are need of the hour.
To someone who isn’t expecting to see targeted ads and other interactive features, it can have a “creepy” factor. I think it’ll take some time for the general public to build understanding that this is the marketing strategy of the 21st century.
I am strongly believe that Analytics have changed my most of the online content and similar to my clients portals too.
Some great information that will come in handy. Thank you
Great overview – thanks for sharing
My comments are only for apparel part of retail.
I am one of those people who is averse to shopping especially in US. As someone who grew up in India, getting a dress tailored was affordable and easy. When I came to US I realized that this was a luxury that I could not afford. My experience also differed when I went to a retail store. Back home, there were store associates who were able to guide and help me choose a style/color that I wanted. Here, such an experience is only available at a high end store. When I go to a store here, a dress that looks good on the shelf might not suit me based on my body structure and so I would spend cycle trying to find something that suits me. I think the designers here create dress for certain generic body types so an individual experience is not possible.
I have seen videos of the future where a customer can come in and choose to overlay different clothing and see how that looks but I think that it works for people who look good in anything. I would like a similar experience but with a more realistic approach that is catered towards me. One of the options that I would like to have is the store is a device that captures my body image in 3D as non-intrusively as possible. Based on that, it will show me a more realistic image of what I will look in a particular sized clothing. For ex, the clothing that looked fabulous on the shelf would not look good on me since it is too tight around my waist. Or the size x by a particular brand might not compare to the same size of another brand. It could analyze the body image and along with automated good design principles can guide me to cloths that will accentuate my positives and hide my flaws. It could also suggest accessories that would go well with the clothing. Also the data from these will help designers provide clothing that cater to different body types. With the advances in automation this can also finally help me to get me a personalized cloth that is affordable.
I think the main hurdles are non-intrusive rendering of body image (ex: True&Co), privacy and data security. I do not want to be a product (whatsapp Vs Facebook). The analytics part of it can be done relatively easily if we have the data. If these issues are removed it will be a win-win situation for the retailer and the consumer.
Thanks for the great info. I am going to join in Cisco next month first week. So excited 🙂
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