Join us on November 9 to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers at the Low-Code/No-Code Summit. Everybody is rushing to deploy machine learning (ML) into their marketing processes in the hopes that it will bring unprecedented power to outperform the competition. Marketing, after all, relies heavily on data and communications, and it evolves so quickly that many programs are stale by the time they are ready for deployment. ML increases both the speed and flexibility of many marketing processes, but it is not a one-size-fits-all solution. Some functions benefit mightily from a good dose of ML; others only marginally.

Artificial Intelligence-powered product recommendations and a more scientific approach to data has seen IKEA lift average order value (AOV) by 2% worldwide. Here Albert Bertlisson, head of engineering at Edge at IKEA Retail (Ingka Group) explains how the company did it. "At IKEA we have multiple places in our customer journey in various channels where different kinds of personalisation can deliver a superior customer experience," he says. "After a while in the broader'recommendations' team there was a decision to split the team to have one sub-team focused on product recommendations. The pandemic altered customer behaviour and needs as well. At that inflection point we decided to change our way of working and dive head-first into a more scientific approach to handle the operational complexities of delivering high quality product recommendations at scale. We deemed this necessary to improve our level of personalization and to have a holistic understanding of our customers."

Algolia, the leading API Platform for Dynamic Experiences, today announced the general availability of Algolia Recommend, a high-performing, Artificial Intelligence (AI)-optimized API that accelerates the creation and implementation of product recommendations across digital touchpoints. "We've had several hundred companies use Algolia Recommend during our two-month beta period. Some companies, such as The Vegan Kind, went into production with Algolia Recommend within days," said Julien Lemoine, CTO, Algolia. "The beta program has been a success, both in terms of the number of beta users and their successful use of the new API to easily'switch on' the new recommendation capabilities very quickly within their own environments." For The Vegan Kind, a provider of subscription boxes of vegan goodies, it is all about helping customers make the right choices by providing them with great alternatives and suggestions as they browse.

With multiple facets to optimise for higher conversions, ecommerce brands often treat on-site search as an afterthought. By failing to address the search experience, you are losing customers, especially when you consider that shoppers who use the on-site search bar reportedly have a higher conversion rate and average order value. Machine learning may be hyped technology but it's already making a major difference to the on-site product search experience. Here are three examples demonstrating how progressive ecommerce brands are succeeding with it. Commonly misspelt words are a lost revenue opportunity for ecommerce companies.

Since 1975, Woodhouse Clothing has been paving the way for modern menswear. Now a premium online retailer, its unique portfolio of over 50 brands includes BOSS, Armani and CP Company, alongside new and niche brands, such as McQ, Albam and Woolrich. Forty years on, Woodhouse still embodies its original values, housing only the best brands on the global market, with the aim of bringing that personal touch back to the world of retail. Nosto is the fastest growing ecommerce personalisation and retail AI platform in the world. Supporting more than 2,500 retail brands in more than 100 countries, Nosto has fuelled over 25 billion personalised experiences and delivered a gross merchandise volume (GMV) of more than $10 billion through its customer stores. Nosto's end-to-end personalisation platform provides deep customer insights that drive real-time personalized shopping experiences across any touch point and any device. Through its patented technology, proprietary machine learning algorithms, vast network of ecommerce data and dedicated customer success team, Nosto supports its retailers from its offices in New York, Los Angeles, London, Berlin, Paris, Stockholm and Helsinki. More information can be found at https://www.nosto.com/.

Lounge apparel retailer FlyPolar uses a new service from vendor Sentient Technologies to test many elements quickly across multiple pages and is on track to double sales this month. Artificial intelligence can lead to real sales increases--and fast ones, too. Just ask loungewear retailer ShopFlyPolar.com. The web only merchant had hit a sales downturn, generating about 277,000 in web sales in February 2015 to about 50,000 in July 2016 says, says Shawn Broadus founder and CEO of FlyPolar. "We hit a slump," Broadus say.

Lounge apparel retailer FlyPolar uses a new service from vendor Sentient Technologies to test many elements quickly across multiple pages and is on track to double sales this month. Artificial intelligence can lead to real sales increases--and fast ones, too. Just ask loungewear retailer ShopFlyPolar.com. The web only merchant had hit a sales downturn, generating about 277,000 in web sales in February 2015 to about 50,000 in July 2016 says, says Shawn Broadus founder and CEO of FlyPolar. "We hit a slump," Broadus say.

At this time, all participants are in a listen-only mode. A question-and-answer session will follow the formal presentation. I would now like to turn the conference over to your host, Jandy Tomy, Vice President of Finance and Investor Relations. With me today on the call are Robert Apatoff, President and Chief Executive Officer; and Becky Sheehan, Executive Vice President and Chief Financial Officer. Before we begin, please remember that, during the course of this call, management may make forward-looking statements within the meaning of the Federal Securities Laws that address the Company's expected future business, financial performance, and financial condition. These forward-looking statements involve risks and uncertainties that could cause actual results to be materially different than those expressed in our forward-looking statements. In addition to the Company's reports filed with the Securities and Exchange Commission, please refer to the text in the Company's press release issued today for a discussion of the risks and uncertainties associated with such forward-looking statements. Also, please note that, on today's call, management will refer to certain non-GAAP financial measures, including adjusted EBITDA, adjusted net income, and free cash flow. The Company believes these non-GAAP financial measures provide useful information for investors. Please refer to today's press release for definitions and calculations of these non-GAAP performance measures, as well as reconciliations of the non-GAAP performance measures to the Company's GAAP financial results. Now, I'd like to turn the call over to Robert Apatoff, President and Chief Executive Officer. Good afternoon, everyone, and thank you for joining us today. I will provide a brief overview of our business highlights, integration efforts, and strategic and operating initiatives. Following my comments, our CFO Becky Sheehan will review our financial results and outlook for 2016 in more detail. Finally, I will provide a few closing remarks, and then we'll open up the call to take your questions.