Ready to Buy: the In-Market Consumer 1 Ready to Buy: the In-Market Consumer 2Ready to Buy: The In-Market Consumer Akamai’s Predictive AnalyticsThere are approximately 140 million online shoppers in the United States Of this Of 140 million online shoppers in In order to pinpoint in-market consumers, Akamai’s predictive analytics consist of twogroup, the target market for any given product generally falls somewhere in the range the US, an in-market audience for an parts — model training and model scoring.of tens of millions of shoppers. This is the audience online advertisers typically try to advertiser’s products, at any one time,reach to create awareness and consideration for their brand and/or product offering. ranges from tens of thousands to Model training consists of building a predictive model based on a historical data setTargeting for these objectives often uses a combination of display advertising tactics hundreds of thousands of shoppers. of user shopping behaviors. Each model should be customized to the specific transac-such as user demographic data, website contextual information, and traditional tions an advertiser wishes to drive.behavioral targeting. By analyzing the historical data, these models learn — across thousands of differentBut while the target market is important, there are many advertisers who are specifi- shopping behavioral variables — how consumers act when they are nearing the targetcally focused on trying to reach the subset of their target market that is in-market for transaction. The models determine which variables are most important to the particularwhat they sell right now. The in-market group is the tens of thousands to hundreds transaction in question, and how to combine these variables into a scoring calculationof thousands of shoppers within the target market who are ready to make a purchase that achieves the best predictor of behavior. Every model is unique.in the very near future and require a different set of data-driven targeting tactics. What should be noted is that, typically, no single variable ends up being weighted more than about one-tenth of the overall score — meaning there is no single dataShopping Data Drives Understanding of point that predicts shopping behavior with consistency. Generally, the best predictors No single variable accounts for more than roughly 10% of a model’s score include a long tail of variables, reflecting the inherent complexity of modeling humanIn-Market Consumers behavior. So, for example, knowing that someone put a surfboard in their shopping – as such, there is no single data cart in the last 24 hours is not enough to accurately predict that they are likely to pur- point that can consistently predictThe world of direct marketing has long proven that the best predictor of future shop- shopping behavior. chase that surfboard. On the other hand, knowing that they had also recently bookedping behavior is past shopping behavior. It is upon this premise that Akamai has built a a trip to Hawaii and purchased a wetsuit substantially increases the likelihood of a near-large online shopping data co-op which is made up of anonymous shopping behavioral term surfboard purchase.data from across over 500 consumer shopping websites from virtually every shoppingcategory. With over 6 billion shopping events seen across nearly every online U.S. 100%consumer, Akamai is in a unique position to understand and identify the in-marketaudience segment. In-Market 12.7% 7.6% Target 6.9% 6.5% Percent Contribution to Model Market 5.8% 4.7% Every U.S. 4.3% Online Shopper 3.5% 3.2% 2.8% 2.6% 1.4% 1.2% 1.1% 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Variables Contributing to Model For optimal results, models should be retrained every few weeks, as shopping behaviors change over time in response to seasonal and holiday cycles as well as market trends.Figure 1: Understanding how an in-market audience differs from a target market helps Because consumer behavior differs over time based on external factors such as season-advertisers create the right advertising approach for both. ality, economic conditions and new product offerings, each model must be continu- ously retrained over time. Model scoring is the process of giving each user (that is, each anonymous cookie) a score that reflects the probability that the user is in-market for the given product. The predictive engine does this by looking at the user’s most recent behaviors and comput- ing a score based on the variables indicated by the model. Scoring should be done continuously to incorporate users’ most recent actions. This is critical, as consumers continually move in and out of market, often within a span of just a few days.
Ready to Buy: the In-Market Consumer 3 Ready to Buy: the In-Market Consumer 4The Dynamics of the In-Market Consumer About Akamai ADS forIn-MARkET COnSUMERS ARE SIx TIMES MORE lIkEly TO MAkE A PURCHASE. Predictive SegmentsThere are roughly 30 million in-market consumers at any given time, although this ADS for predictive segments, part of the Akamai ADS Solution line, provides multi-number spikes considerably higher during the Q4 holiday season. As expected, these channel retailers and product manufacturers with a way to drive incremental salesconsumers display a much higher frequency of online shopping activity than normal. to their websites by accurately targeting buyers who are in-market for their goods.When a shopper is in-market, he or she: Akamai makes predictive segments available to online advertisers on its owned and operated acerno ad network. Once our models identify a consumer as being in-market, we observe that he or she: ADS uses predictive modeling based on data from its unique online shopping data The Akamai Shopping Data • Performs 5 tiMes MORE SHOPPInG EVEnTS, cooperative — which includes more than 500 online retailers and provides $13.5 billion Co-op includes more than 500 • InItIates 8 tiMes MORE SHOPPInG CARTS, AnD worth of quarterly, anonymous consumer shopping transactions. This unique data set online retailers and provides makes it possible for Akamai ADS to understand the distinctive behavioral patterns $13.5B worth of quarterly • Is 6 tiMes MORE lIkEly TO MAkE A PURCHASE THAn WHEn nOT In-MARkET. shoppers display that lead up to making a purchase — and thus identify in-market consumer shopping transactions consumers with accuracy.This high purchase intent is a powerful force for a company that has the ability to iden- Its unique predictive engine, combined with the acerno ad network that reachestify and leverage it. But timing is critical, as the in-market consumer is a moving target. 100% of US online shoppers, enables ADS to offer efficient, transaction-generatingEvery three weeks, roughly 80% of the group of in-market shoppers turns over, with advertising at scale, along with pay-for-performance (CPA-based) pricing that reducesin-market duration varying directly with price point. The lower the product price point, risk and helps advertisers increase sales at their own ROI goals.the less time the average shopper stays in-market.The fact that in-market status is so transient creates a serious challenge for advertisers Every three weeks, roughly 80% ofwho do not have the ability to identify this group of high-value consumers. These shop- the group of in-market shopperspers are completing transactions — potentially with competitors who may be able to turns over, with in-market durationrecognize their in-market status. varying directly with price point.The ability to identify in-market consumers enables advertisers to run campaigns ofscale without worrying about whether they have defined the right audience segmentsor whether they are focused on the right seasonal schedule — the in-market consumertranscends these artificial boundaries. Instead, by using predictive analytics to find thehidden patterns in their buyers’ behaviors, marketers can simply focus on those mostprimed to buy their products — using a well-timed advertisement or promotion to driveconsumers to take the desired action.The Akamai ADS Privacy StatementAkamai takes privacy considerations and best practices seriously. ADS for predictivesegments builds on Akamai’s unique and trusted position in the online industry. Akamaialready powers the online efforts of many of the Internet’s largest publishers and adnetworks, as well as hundreds of leading consumer brands. It is this relationship wehave with our many customers that enables us to provide them with better insightabout the behaviors of their online visitors.Akamai is a member of the national Advertising Initiative (nAI), a cooperative of onlinemarketing and analytics companies committed to establishing responsible business anddata management practices and standards. It creates, and relies on, effective industryself-regulation and sensible protections for online consumers.Akamai’s guiding principles with respect to privacy include: • ProtectIng clIent confIdentIalIty and end-user PrIvacy If you are interested in speaking with an Akamai representative to learn • sIng only anonymous and non-Personally u more about ADS for predictive segments, and how we can help you reach IDEnTIFIABlE InFORMATIOn your in-market audience to drive sales, please contact us at 1.877.4AkAMAI • commIttIng to consumer choIce and notIce (1.877.425.2624)