NRF2020 – Algorithmic Customer Experience
Share it on
During NRF2020 The Vision, We showcased how Algonomy (previously Manthan-RichRelevance) partners with retailers to deliver exceptional customer experiences across channels, in real-time and over the customer life-cycle via an enterprise-class AI solution portfolio that consists of Customer Data Platform(CDP), Hyper-personalization and Algorithmic Merchandising.
Watch this video, where our team walks you through our solution portfolio.
Bhavna Sachar – Sr Product Marketing Manager, Algonomy
Amit Rohtagi – VP Consulting, Algonomy
Olivier Barth – Principal Solutions Engineer, Algonomy
Ganesh Sastry – Sr Pre-sales manager-Merchandise Analytics, Algonomy
Hi, everyone, it’s that time of the year. Again, we are in New York City at NRF. Retails biggest show. And we are going to be talking about algorithmic customer experience, and how we are enabling that for retailers. Today, as a consumer, what comes to your mind when you think of great experiences, what comes to my mind is the likes of Amazon, Uber, Netflix, which gives you the greatest experience. And these are the companies that are doing that, because they have gone digital. They understand data, they understand technology, they do it with algorithms, if you break this down, what this means is that you need a combination of data. And then how do you make decisions with that data in real time, which was where the algorithms come in, retailers have been kind of lagging on this front. And that’s because you know, they are not born digital, they are not technology companies. Now, how do you get the same level of experience algorithms make available to retail, that’s why we are here. So you have the combination of the best in data. So you have a single source of truth, unified view of the customer, and then the best in technology in decisioning, and the best in delivery, so you can orchestrate these great experiences, no matter whether your customer is visiting you in the store, or on your website and E commerce or anywhere. So welcome to the world of algorithms. This is what algorithmic customer experience is about. It’s the end to end of bridging the experience gap for the customer, and also for you as a retailer maximizing their lifetime value.
Hi, I’m going to be talking about Manthan(now Algonomy) customer data platform. Manthan’s(now Algonomy) Customer Data Platform unifies customer data, surfaces, customer insights and activates customer data. We bring in customer data from all sources within your retail organizations, be it point of sale data, weight loyalty data, where your data from your digital systems, your websites, your mobile applications, or be it from your campaign management systems. We bring all this data together and create a unified profile of customer for you. We bring in a deep understanding of retail systems and data management capabilities that are uniquely designed to understand these retail data sets. We also package within itself, marketing orchestration capabilities, and real time personalization capabilities for you. Our marketing orchestration capabilities can help you create marketing journeys for your customers, which can help you handle scenarios of multiple campaigns where you want to reach out to them through a range of channels as their as they engage with you across these channels. Manthan’s(now Algonomy) customer data platform brings in a unique integrated an end to end platform, which not only can handle your data management capabilities, but also brings predictive capabilities within it. And an ability to deliver highly personalized experience your customers across a range of channels, be it emails, text messages, push notifications, or even delivering personalized experience on your website when our customers are browsing your website or making a purchase.
So today, I’m presenting you the hyper personalization. So we are living in exponential economy world nowadays. So people and customer are expecting to have a personalized experience to leave Memorable experiments to remember what they are doing. If you think about Uber, Spotify, etc, and Google everything is personalized. So this is what return users and consumer I expecting from retailer, so retailer have more and more data they have a lot of data nowadays and more and more data from their different touchpoints from the store from more products will display and to put to sell as well. So the goal and this is how the personalization is solving is too much and to liberate those data to provide really a personalized experience for the user. And this is what we are presenting here where we have a person here that is Jane do. She’s really high value customer she’s selling purchasing a lot of different let’s say product every week. She’s like, have an Alexander McQueen and other high hand brand affinities and then as soon as she will go to the website here. We are understanding who she is and kind of affinity she has to personalize the full experience. So what you see here because she already put chase a dress let’s say on the store. We are personalizing with this banner, something that is interesting for her as well as him. We know that she’s loyal. So we are pushing like the specific loyalty program for her, as well as the weather and all the product recommendation that is fitting her need today to inspire her to find what she’s looking for. So when Jane Doe is entering this website, we can leverage all those data to provide a unique experience for her. And this is what we are doing in real time, thanks to the AI and intelligence artificial engine that we have here. If you have a quick look here, let’s imagine that Jane is browsing now, the website and she’s looking for a dress for going to a party tonight. So she’s browsing the dress category here, you’ll see that there is more than 200 product to display. But thanks to the personalization, we are able then to understand better Jane and then to provide more relevant results at the beginning for her related to the brand she loves. And related to the certain kinds of items that she was browsing before. So let’s say that she’s interesting that that dress here that is really relevant for her because we know that she likes Alexander McQueen and she loves the red color. So we are personalizing of that page as well some similar items, as well as other cross sell items that may complete the look. And that is really matching the same the same color and the same kind of dress that she’s looking for. So Jane was able then to interact with this website. And as you have seen, she get like full personalized results from the browser search as well as product to recommend with this product here. So this is really a convenient experience for her really memorable and this is why she’s going to be loyal to this brand and this retailer because she will be able to have something unique browsing all those different touchpoints with hyper personalization.
Hey guys, this is the algorithmic merchandising product that we have in offering. Essentially help you to do the right buy decisions. So the demand led assortments you know, to do the right buys, and the in season inventory orchestration to improve the ROI on the inventory, and finally on to creating ways of memorable and winning consumer experiences, you know, to the consumers via the store personal so that’s the in store in instep decisioning app that we have. As we have given you the brief, we help you to do three essential things. So the demand lead assortments show how essentially on to bringing up the customer centric or localized, you know, assortment mixes that is pretty much relevant to the consumer that who walks into the store? Or who closes your website? How essentially, can you kind of stock the right amount of products in the right place in the right time doing your right, you know, by right decisions. So that’s the demand let the assortment planning piece. And the second important piece that we spoke about was on the in season, you know, inventory orchestration? So a lot of time, you know, the emphasis has to be like how agile, are you in terms of quickly reacting to the trends that happens within the season? How quickly can you identify what is potential out of stock? What is going to be the best seller? can you maximize, you know, the sale of the style that is actually you know, becoming a fast seller? Or if something is not selling well? Can you kind of do some profitable liquidation strategies all algorithmically. So, that’s the essential, you know, second part that we talk about on the in season merchandising. And this system also comes up with the precise recommendations on the markdown liquidation strategies, I mean, which products have to go for discount for how long and by how much? And what is the kind of impact that you would essentially have on to your, you know, bottom line and to the top line as well. While this is all you do, in terms of the doing the right buy decisions and orchestrating your inventory, we take the next level in terms of delivering the right set of experience or building experience to your consumers. You know, that’s what we do buy in store in decisioning you know, app that we have essentially for the store associates to help have enriching or more contextual conversations today the consumers, I mean, what is selling? Well, I mean, if a person has walked in, do I actually know about his preferences and talk something, you know, office preferences one second, you know, on the inventory, I mean, if something is doing really well in his stores, you know, can he actually initiate actions within the store to kind of, you know, replenish the products or, you know, get the right things, you know, which is very, very contextual to his particular store. So, that’s the kind of delivering the experience that we bring onto the table via the each store in decisioning piece that we have.