David Selinger, CEO of RichRelevancesays that social shopping accounts for less than 1% of the total online shopping sessions but hey, that’s still money in the bank, right? So, his company put together this lovely, holiday-themed infographic that shows how the top social shopping channels stack up.
The first thing you’ll notice when you review the panels is the inclusion of a site we don’t often talk about – Polyvore. This social site asks user to curate sets of products from a variety of online retailers. The example you see on the right is someone’s idea of a cool living room. Click on the objects and you get a detail page including the price and where to buy it. One more click and you’re on the site that sells it.
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* Face scans, heat sensors, phone signals used to track shopper
* Smart data allows stores to copy e-commerce personalisation
* Store wi-fi can track shopper within three metres
* U.S. data firms agree privacy code of conduct
* Mobile ad spending seen tripling to $39 bln in 2018
You may not be using your phone, but it is giving out a unique signal that the retailer may be monitoring. A face scanner may check your age and gender while sensors pick up your body heat to help locate popular parts of the store.
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Personalisation drives increased spending and loyalty for the European customer, new IDC research reveal.
As soon as a shopper lands at BuildABear.com, the site’s recommendation engine technology has a sense of who the shopper is. It knows how she landed on the site, what device she’s viewing the site on, where she’s located, whether she’s visited the site before and, if she has, what she has looked at and bought. With each click through the site, the engine presents her with suggestions based on its insights into the shopper and what similar consumers typically buy, says Bryan Sawyer, the retailer’s e-commerce director.
While the retailer had used a product recommendation engine for years, it was only in June, when it began working with RichRelevance Inc.‘s recommendation technology to dig deeper into individual shopper’s characteristics that the retailer began seeing significant results from its suggestions, Sawyer says. Since then Build-A-Bear Workshop Inc. has posted strong “double-digit” sales growth online thanks largely to BuildABear.com doing a better job upselling and cross-selling merchandise, he says. And since June the retailer’s web site has outperformed its stores in terms of the ratio of shoes sold per stuffed animal sold, a key metric the retailer regularly tracks.
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Twenty-one years ago, a year before the first web browser appeared, Walmart’s Teradata data warehouse exceeded a terabyte of data and kicked off a revolution in supply-chain analytics. Today Hadoop is doing the same for demand-chain analytics. The question is, will we just add more zeros to our storage capacity this time or will we learn from our data warehouse infrastructure mistakes?These mistakes include:
- data silos,
- organizational silos, and
- confusing velocity with response time
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Chicago—Marketing creatives and their data scientist colleagues had a meeting of the minds today at the Direct Marketing Association’s annual conference and expo here. A panel representing the two groups discussed the tension that exists between them and agreed that coordinating their talents would pay off in better-performing campaigns.
“There is a tension between the “Mad Men’ and the “Math Men’; both camps have a passion about the customer but have different approaches,” said Doug Bryan, principal sales engineer at marketing personalization company RichRelevance Inc.
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Throughout Monday’s plenary sessions, a key message from panelists was the need for directors to blend quantitative—harder—data with qualitative—softer—complements. For example, a focus on shareholder return but with a stakeholder view, the intersection of situational awareness and the ability to use intuition, or the need to harness qualitative data with application of context. In an interview with Jeffrey M. Cunningham, managing director and senior advisor of the National Association of Corporate Directors, RichRelevance Co-founder and CEO David Selinger shared how directors can bring big data into the boardroom.
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Can big data help other retailers keep up with Amazon’s online success in areas like product recommendation? David Selinger, one of the company’s former software managers from the recommendation team and a small team of his old colleagues, are trying to do just that with RichRelevance.
Plenty of retailers consider Amazon as the “common enemy” and serial entrepreneur David Selinger, co-founder and chief executive officer of RichRelevance, can certainly understand that. As a former software manager in customer behavior research at Amazon from 2003 to late 2004, Selinger has been in the belly of the e-commerce giant beast.
Selinger worked on the product recommendation technology that online shoppers have come to know well—the “recommended for you” suggestions that appear like the digital shopping buddy you didn’t know you have but who is always at the ready tempting you with ideas on what you should buy next.
“[At Amazon’s recommendation team, we asked ourselves] how do we take this data and make a little bit more money, how do we apply it in this channel differently and it was really neat,” said Selinger who I met in New York Tuesday before his speaking engagement at a DataLove event. “Now it is being called ‘big data’ in the marketing and media world, but at the time we were doing this stuff, it was just kind of putting one foot in front of the other.”
RichRelevance, the San Francisco-based company Selinger launched in 2006, has especially benefited from the Amazon “big data” experience that Selinger and the ex-colleagues bring to their websites, as he says a growing number of retailers are finally beginning to see Amazon, which has been making moves into new areas such as fashion apparel, for the competitor it has always been as it amasses sales in category after endless category.
“After I left Amazon, I realized how far behind all the other retailers were,”Selinger said. “So I started RichRelevance to arm other retailers with the prediction that data would become a battlefield on which retail was going to get fought.”
Other retailers, he says, were woefully underarmed in terms of not just core technical and data knowledge but also with the business acumen to be able to turn the data into something actionable. Initially his company started out with the recommender systems which Amazon is famous for that suggest that if you like one product, you might like more.
Through a cloud-based service, RichRelevance now delivers 140 “predictive models,” which can tell retailers things such as how much a consumer is going to spend next, what brands they want and which product categories they want, giving them a better sense of who their customers are and what they will spring for so that they can market to them accordingly.
RichRelevance isn’t the only company offering recommendation technology. Our colleagues over at the San Francisco Business Times, who named Selinger to the 40 Under 40 list, have reported that its California rivals include Cupertino-based Baynote, Redwood City-based MyBuys, San Mateo-based Aggregate Knowledge Inc. and in Cambridge, Massachusetts, there’s ChoiceStream.
That said, RichRelevance has a pretty enviable list of clients including six of the top 10 U.S. online retailers. Its list includes Walmart, Target, Office Depot, and Costco as well as specialty retailers such as Neiman Marcus, Saks Fifth Avenue and Barneys. What they want? The same type of data about their customers that Amazon has been mining for a decade, so that they too can provide online recommendations to customers.
As a result of the data, RichRelevance clients have seen sales increases of anywhere between 3 percent and 15 percent, depending on the type of retailer and other factors. Of course, each retailer wants something a bit different and it appears differently on their websites.
The retailers want to compete with Amazon and get the same type of conversion from browser to selling results, but not look the same.
“You have to get them all to this baseline and that’s how you kind of catch up with Amazon if you will,” Selinger says. “But then you have to let them customize.”
There’s a few blunders that retailers make when it comes to data, he says. One is buying tools to collect it themselves without really knowing how they can use it. The other is thinking that data can achieve things that, perhaps it can and perhaps it can’t.
The key blunder as he says it is “overthinking the problems and over-specifying the solutions when the process here is much like any other technology exploration process.” Instead, he says to think like a big data person: “Don’t overspecify (but ask a question like) ‘I’d like to within the next three months be able to better communicate with my top customers. End.”
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