Akamai Helps Retailers Reach In-Market Consumers and Drive Transactions with Ease and Efficiency

* Using predictive modeling based on shopping data, Akamai helps its customers reach in-market online consumers with display advertising in real time and at scale
* Akamai eliminates need to implement tracking pixels, enabling retailers to quickly launch campaigns with limited effort on the part of IT departments


CAMBRIDGE, MA . - Akamai Technologies, Inc. (NASDAQ: AKAM), the leader in powering video, advertising, dynamic transactions, and enterprise applications online, today announced the availability of ADS predictive segments, part of Akamai’s Advertising Decision Solutions (ADS) line, a unique solution that provides multi-channel retailers, product manufacturers, travel, and telecommunication websites with a way to drive incremental transactions online.

ADS predictive segments are based on sophisticated predictive analytics and modeling of data from the industry’s only online consumer shopping data cooperative. Retailers can access the power of the Akamai data cooperative by running online ad campaigns on acerno, the Akamai add networkTM, in order to reach in-market online shoppers with display advertising in real time and at scale. The benefit for retailers is a quantifiable improvement in reaching the right consumers and driving transactions. For an Internet Retailer 100 company, for example, Akamai’s predictive segments solution was able to drive a 2.5 percent incremental increase in online sales on average per month.

In addition, by eliminating the requirement to implement tracking pixels, ADS predictive segments enable retailers to launch campaigns quickly, with limited effort required from their IT departments. Faster ’time-to-live’ means retailers don’t have to miss time-sensitive campaign opportunities. Plus, the more effective system enables larger and higher performing campaigns.

“How a consumer shops is the best predictor of what they are in-market for right now. This has been proven true for many years in the offline world, and is now proven true online as well,” said Mike Afergan, Akamai’s CTO and senior vice president of Advertising Decision Solutions. “Akamai leverages comprehensive and anonymous shopping data in order to help our clients advertise their products on the acerno add networkTM to consumers who are in-market for what they sell. As a result, Akamai can drive significant volume for our customers, in some cases over 10 percent of online sales, in effect becoming a new sales channel. Now, with the introduction of ADS predictive segments, available with or without pixels, we are creating an even faster way for retailers to drive incremental sales. And, because everything is priced on a cost-per-action (CPA) basis, advertisers only pay when a transaction occurs, which ensures that the program’s economics are in line with their financial objectives.”

Akamai’s unique shopping data cooperative
The Akamai cooperative provides a unique solution to the complex problem of reaching in-market, online consumers. Hundreds of multi-channel retail, travel, telecommunications and product manufacturer websites across nearly every product category contribute fully anonymous data about how consumers shop on their sites to the Akamai data cooperative. This cooperative contains data from approximately 140 million online shoppers in the United States and represents an estimated over $15 Billion worth of consumer spending for the coming fourth quarter of this year. The predictive analytics utilized by Akamai help identify which users are currently in-market for an advertiser’s products online.

In addition, the cooperative’s value stems from the fact that its data and predictive models are continuously updated every day. Akamai data shows that there are 30 million shoppers typically in-market at any given time. This data also reveals that every three weeks, roughly 80 percent, or 24 million new users, will come into market, replacing consumers that have exited the market. This real-time insight is critical to optimizing online marketing. Akamai continuously analyzes thousands of shopping behavioral variables in order to understand when data across a variety of product categories becomes relevant. As an example, shopping data around varied items such as clothing, footwear, or even music can help locate in-market buyers of auto parts and accessories.

As one of the earliest members of the Akamai data cooperative, JC Whitney, a leader in the automotive aftermarket providing customers with the widest selection of parts and accessories, is leveraging ADS predictive segments, without pixels, to more effectively and quickly predict online buying patterns.

“Akamai ADS predictive segments is an extremely valuable solution for us to help drive transactions,” said Geoffrey Robertson, General Manager & Vice President, eCommerce at JC Whitney. “It is beneficial for us on a number of fronts. First, Akamai is helping us find customers that have never visited our site, by looking for patterns of customers that may have a high interest in our product - customers that we deem to be in-market. Using their predictive modeling, they are able to display tailored ads that pique consumer interest and drive consumers to our site. And, since it is a ’pay for performance’ structure, we are only paying for the customers that come to our site and make a purchase. Second, by providing a pixel-free technical solution, we expect to achieve great efficiency to launching advertising campaigns. Akamai creates a very powerful way for finding people, and doing it in an economical fashion.”

Akamai’s ADS predictive segments build on the company’s unique and trusted position in the online industry. Akamai is a member of the National Advertising Initiative (NAI), a cooperative of online marketing and analytics companies committed to establishing responsible business and data management practices and standards. As a result, Akamai is committed to consumer choice and notice, and protects the privacy of shoppers by using only anonymous and non-personally identifiable information.