As online advertising becomes integrated with online purchasing, the value of placing an advertisement in the right context may rise. If the placement of an ad results in the immediate purchase of the product, the advertiser is very likely to offer a higher price to the publisher. DSP and DMP success depends directly on their ability to track and match consumers based on the consumers’ perceived information need and their ability to find advertising opportunities related closely to an online sale of associated goods or services. If the consumer has already purchased a product and is no longer shopping, it is a poor investment in advertising.

Let me cite a specific example to illustrate the point. Financial services and telcos are heavy spenders in online advertising. As I studied their spending, I found that in investing their advertising dollars, they are seeking specific audiences. Advertising agencies employ DSPs to participate in the bidding process for an exchange or Ad Network to place the advertisement. However, more than half of these advertisements are placed on the browser screens of customers who have already purchased the advertised product and are unlikely buyers. In many cases, the customer may be using the product, for example, the broadband connection, from a telco to view the advertisement (for a broadband connection from their current supplier). If this situation reminds you of all the mail you received from your telco offering you a bundled product of wireless, wireline, and cable, while you were already using a bundled product, you are not alone. In addition, the advertised product may not be available in your neighborhood.

So, how do we insert the knowledge of customers’ existing products, their communication needs, and the product availability in their area? A telco can build a telco profile that represents their customers and their needs. They can supply this information to a DSP like Turn, which uses this information, along with online bidding rules, to identify customers with specific needs and bid up or down a telco advertisement based on the telco profile. In many situations, a marketer may have additional campaigns, such as upsell of options available to those customers.

If marketers can establish a personalized advertising connection with customers, how about taking the next step in personalization? A marketer can establish a dialogue with the customer and use advertising to make information available as it takes the buyer through the stages of buying. Online advertising can be highly personalized, as a marketer can target a personalized campaign to a specific subscriber. Let me use a scenario to show how advertising in online advertising can be personalized to the context and state of selling.

Linda is currently shopping for a smartphone. She uses a search engine to look for Android phones and finds the website for a wireless service provider that is offering Android phones. Linda is price conscious and lives in an urban area with spotty wireless coverage. While she uses

the website to browse for phones, she does not pay attention to coverage and price plans. As the wireless site reports Linda’s use of the web page, she is flagged as a potential phone buyer. At the same time, the next advertisement from the wireless service provider emphasizes wireless coverage and price plans—two important aspects that will move the wireless service provider closer to her buying consideration. As the wireless service provider provides an indication to their DSP, they may provide a higher bid for coverage and price plan campaigns, refining the messaging to Linda and making it relevant. The campaign may differ across subscribers, reflecting their buying criteria. In addition, the DSP would use additional criteria to decide how to bid for an advertisement targeted to Linda. If Linda were offered the price plan advertisement five times and she did not click it, the DSP might stop bidding that specific advertisement.12

How do we get this orchestration to work? A marketer would provide a list of target customers and the advertising campaigns to its DSP. The DSP would prioritize its bidding for advertising using the targeted customer list from the marketer, combining it with the DMP information collected regarding past advertisements to a specific customer. A sophisticated set of bidding rules would watch over the bidding process to avoid saturation and other real-time-bidding considerations. Once the customer sees the advertisement and clicks on it to purchase the product, the marketer would update the target list to exclude the customer, thus removing the candidacy of a customer from receiving future advertisements for a product he/she has already purchased.

For this orchestration to work, the customer IDs have to match. This is easier said than done. The marketer must organize its customer profile to identify an individual based on his/her browser IP address. Once the proper ID has been associated with a customer, the target list can be consumed and used for bidding by the DSP.

If we now move from advertising to promotion, through an active discount coupon, privacy rules may need to be enforced. Most marketers would only place a coupon once the consumer has agreed to receive context-based coupons. Let me take you through an example of how a marketer would engage a customer in a targeted coupon program and how the targeting would be done.

Cuppa Heaven is a new barista chain offering coffee in its retail outlets. Cuppa Heaven is interested in operating coffee stores next to movie theaters in the mall. In its research, it has found that moviegoers are likely to bring a latte to watch a movie. Cuppa Heaven would like to cater to this growing segment of the market. To identify the target population, Cuppa Heaven connects with OfferTel, seeking mobility patterns for OfferTel subscribers. Using the raw location information from its network data, OfferTel provides a report to Cuppa Heaven highlighting the percentage of subscribers from each community surrounding a mall, who regularly go to that mall at least once a week to watch movies. Cuppa Heaven decides to target the top 25percent of the communities, sending a letter in the mail to each resident in those communities asking if they would be interested in downloading a free movie guide app. The app provides the resident with a schedule of movies at the movie theater and also lets him/her purchase movie tickets, watch movie trailers, and get promotions. Cuppa Heaven also places a poster at the movie theater offering the app to download. Based on the download statistics, Cuppa Heaven decides that placing the poster at the movie theater is the best way to promote its app.

When the app is downloaded, it seeks the customer s permission to connect to his/her Facebook and Twitter accounts in exchange for a Cuppa Heaven promotional coffee. It also offers a coupon for a bundled coffee and a movie ticket package for two. By analyzing the tweets, Cuppa Heaven decides to offer an endorsement program to have the customers place “like” on its Facebookpage. All of these campaigns are analyzed for their effectiveness and fine-tuned.13

The above scenario is effectively utilizing an orchestration framework to analyze data, target customers, offer campaigns, compare responses, and make adjustments. The initial data from OfferTel is big data covering the entire population. The location analytics is conducted at an aggregate level, so the analysis results can be sold to Cuppa Heaven without any loss of consumer privacy. The offers to customers are based on an opt-in process, which explicitly seeks permission before making location-specific offers. The campaign’s yield can be analyzed. For example, Cuppa Heaven may send different bundles to different customer sets and compare the results to seek the best bundle. This is a very closed-loop process.

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