white paper programmatic targeting · 2019. 10. 10. · programmatic targeting, for three types of...

Programmatic Targeting

WHITE PAPER

PreciseTarget.com I [email protected]

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The retail battleground has shifted to a battle of data superiority. Amazon has catalyzed the data science era of retail, as evidenced by 35% of their revenue coming from digital recommendations. Delivering the right offer, at the right time, drives conversion.

In earlier white papers, we discussed an essential first step for retailers: overcoming the sparse data challenge. Nearly every retailer faces the same problem, insufficient data to compute meaningful recommendations. The largest apparel retailer in America averages fewer than two items per customer, per year. Only Amazon has overcome the sparse data problem, with the Prime program greatly boosting their data store. It is estimated that the 100 million Prime customers purchase more than 60 items per year.

PreciseTarget provides a service enabling retailers to leverage an engine with over five billion taste events, to deliver personalized and targeted experiences to customers. This white paper discusses flexing your new, post-sparse data marketing muscles in the increasingly digital retail economy.

We’ll focus on acquiring new customers, expanding your relationship with existing customers, and reactivating inactive customers across your multiple sales channels. This paper focuses on a programmatic approach to driving higher conversion by personalizing the experience for individual customers.

Introduction

The Business Problem

American consumers have learned to expect personalized online experiences. Spotify personalizes your music, Netflix tailors your movie

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and television selections, Facebook personalizes your news feed, and virtually every online advertisement is personally targeted at you. Can you imagine going back to selecting your own music, song by song? Retailers have learned that while targeted advertising works, it’s also expensive and fiercely competitive. If you’re wondering why your Facebook news feed isn’t filled with apparel advertisements, it’s due to cost. Even with the help of Facebook’s ad technology, conversions cost retailers $15 to $35. Retargeted ads are the exception, illustrating the importance of targeting customers. By definition, a retargeted ad knows the right person to target.

There are three fundamental drivers of conversion, whether the consumer visits your store, or visits your e-commerce site:

1. Offer Relevance: Can you personalize the experience, and deliver theright product set to the customer? This means the right category and theright brand, at the right price point.

2. Intent: Is the consumer shopping for the product you propose? Youmight have the ‘perfect’ winter coat for the consumer, which won’t matter if the consumer bought a new winter coat last week. Retargeted ads perform well because the consumer has recently demonstrated intent.

3. Time Urgency: It’s human nature to only make decisions when required. We’re natural procrastinators, and if there’s no cost to waiting until tomorrow, we’ll wait. Consumers balance two fears, the fear of making a bad purchase versus missing out on a good deal. One way consumers mitigate this risk is by delaying the decision. We see this in shopping cart abandonment rates, which run from 75 to 95% depending on the retailer and category. Time urgency can be catalyzed by intense need, like needing a tie for an important meeting, or a deal that expires tonight at midnight.

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Can an Algorithm Act as a Digital Sales Clerk?If you were suddenly blessed with the perfect targeting engine, you’d like to think of it as the perfect all-knowing sales clerk. When the customer walks into your store, or visits your website, the perfect targeting engine would know the customer, including her favorite brands, categories, colors, fabrics, and price points. The engine would be an expert on your catalog, knowing the attributes of every product, able to expertly pick out the right products for the customer. Your engine would know the customer’s favorite categories, and would know what he or she buys from you, and what they buy elsewhere. Your engine would also make a note of when your customer has become inactive, and have an intuitive sense of the right offer to spur the customer to become active again.

Retailers universally suffer from sparse customer data, typically averagingfewer than two sold items per customer, per year. With this sparse dataset, it’s impossible to create an effective personalization engine. It shouldbe noted that no major retailer has implemented a recommendation engine for its email marketing. Why? The retailer’s data is too sparse. Imagine the challenge of recommending the right products for a consumer when the only knowledge you have is a green sweater purchased last winter, and a pair of shoes purchased in the spring. PreciseTarget tackled this problem by combining the data of over 200 major retailers, with the goal of driving higher conversion across channel is.

In simple terms, a conversion happens when the consumer sees the rightproduct, the need is high, and there’s a compelling reason to make thedecision now.

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Leveraging Your Data, without PII The PreciseTarget Taste platform is designed to provide greater insight about the tastes of consumers. The company’s research indicates that consumers are more responsive to shopping experiences that reflect their taste, rather than offers or advertisements for specific products. The platform uses aggregated data from a broad array of partners, including major retailers, publishers, and syndicated data sellers. The Taste Platform is based on probabilistic modeling constructs. All retail data provided to the platform must be transformed to a Taste Event before submission to the platform. The transformation process is often conducted by a trusted intermediary, like Equifax or TransUnion, resulting in the Taste Platform learning the consumer had a taste event. A taste event can come in the form of a product purchase, a product click, or other forms of consumer interactions. Taste event data uses an aggregated set of product attributes which teaches the platform. It does not include the product brand name. The Taste Platform uses a synthetically created identifier for each consumer, with no linkage to the actual consumer identity. Unlike email encryption, there is no possibility to reverse the synthetic identity. PreciseTarget has partnered with trusted intermediaries, like Equifax, that are the only parties capable of matching the synthetic ID to a real-world consumer. A customer, whether a retailer, bank, or publisher, wishing to augment their customer data must work through one of these partners as the trusted match intermediary. PreciseTarget stages its data, organized by Synthetic ID, in a secure staging center at the partner company. A customer wishing to purchase data would receive the data by doing business directly with the partner, who acts as the trusted matching intermediary.

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Programmatic Targeting, for Three Types of CustomersNew Acquisition: Your goal is to target consumers who are a good fit for your catalog and its underlying products, brands, and categories. You have never done business with the customer, but they closely match the attributes of your ideal customer. In this context, we are creating clones of your best customers, and determining the people who are similar from our nearly 400 million identities. Acquisition campaigns are designed to help you economically expand your customer base by targeting the right people, using a pay-for-success model.

Relationship Expansion: Most retailers have a very narrow relationship with the customer. He or she may occasionally buy a pair of shoes, but spends the bulk of his or her dollars at other retailers. The average consumer purchases 45 apparel items per year, although the typical apparel retailer sells one to two items per customer, per year. Expansion campaigns are designed to help you win a greater share of the customer’s wallet by pro-grammatically offering the highest-scoring deals and offers to the customer.

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It should be noted that PreciseTarget does not share data, rather we are “only” a provider of recommended product sets based on the aggregated metadata associated with each synthetic identifier. We’ll recommend a product set from your catalog, to a specific consumer, but we won’t tell you why our engine believes it is the highest-scoring product set, nor would we share data provided by our retailers and publishers. We’ve designed our system such that we aren’t able to identify the PII about customers. Nor can we share specific sales data from other retailers.

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Reactivation: Customers choose from thousands of retailers, and it’s easyfor them to shift their allegiances to other retailers. Conclusive studies have revealed that it is far less expensive to reactivate a customer than acquire a new customer. Reactivation campaigns are designed to catalyze the customer to come back to your site or store, by presenting a high-scoring offer for the specific customer.

The PreciseTarget system enables customers to programmatically acquirenew customers, expand customer relationships, and reactivate inactive customers. The Taste Platform is based on machine observations of over five billion taste events. A taste event can originate from a product purchase, a product click, or other expression of interest. The taste system weighs the importance of individual taste events. For example, a taste event linked to an actual product purchase may carry a higher score than a taste event related to a product click. At this writing, over five billion of the system’s taste events are related to specific consumer purchases. The system is based on a probabilistic methodology. The taste computations are based on scored probabilities using the training sets provided by its partners. If the system predicts a synthetic idea for a person scores high for Patagonia outerwear, it is because her previous taste events led to her predicted taste in outerwear. The prediction makes use of product similarity computations, and look-alike modeling of people based on their attributes. The people similarity computations are conducted by Equifax, using their company-owned data, serving to shield PreciseTarget from being able to gain knowledge about specific consumers.

How it Works

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Putting Your Data to WorkWe begin retail relationships by becoming an authorized affiliate of each retailer. This provides us with access to catalog feeds, so we immediately begin matching your off-the-shelf deals and offers to consumers in our network. At this stage, our relationship is a typical advertiser/affiliate relationship. We’re simply algorithmically matching your deals and offers with consumers in our network. The matching is based on scoring, and we’ll deliver unique product sets, from your catalog, to each consumer. Our publishers, whether a deal site, content site, or a credit card offers program, typically sees a 100 to 300% conversion improvement by leveraging our ability to match product offers to customers.

The next phase for retailers involves the retailer programmatically targeting specific customers for acquisition, relationship expansion, and reactivation. The retailer will provide us with de-identified purchase history data with no exchange of PII. This data exchange enables the PreciseTarget engine to understand your relationship with the customer and programmatically make recommendations to grow your share of wallet, increasing revenues and profits. Keep in mind, the customer is de-identified, eliminating all risk you are violating customer privacy.

The de-identified customer data enables the engine to create clones of your best customers, and determine the appropriate similar people in our base of 400 million synthetic identities for acquisition. The engine will also determine the right product sets to expand your relationships with the sporadic customer, with the goal of increasing your share of the customer’s wallet. It will also, based on your rules, identify the inactive customers and recommend the right prodct sets to catalyze the customer to become active again.

Most retailers protect their data out of fear of being exploited

by a nefarious competitor. The perceived threat: the competitor

will target your customers for acquisition.

In reality, your data is far toosparse for use by a competitor.

If your data indicates a customer purchased a pair of shoes nine

months ago, and a dress last month, it’s useless data for

a competitor.

Humorously, your marketing department already knows this, given that no major retailer has sufficient data to fuel an email

marketing recommendation engine. In fact, your data might

hurt your competitor rather than help them.

We ran a series of tests on our 5 billion taste events to predict

future purchases based on history. Our data indicates that a

consumer is no more likely to re-purchase a brand than the

overall population of similar consumers. If a 30-year old

woman purchases a MichaelKors dress, she is no more

likely to repeat that purchasethan other 30-year old

women in her cohort.

Don’t Lose Sleep if a Competitor

Steals Your Data

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The system provides you the ability to target specific consumers, for bothonline and in-store offers, on a pay-for-performance basis. We have theability to deliver unique, one-time-use, in-store codes. The in-store code isassociated with the synthetic identifier, providing retailers with theconfidence that the code is only usable by the specific targeted consumer.

PreciseTarget partners with deal sites, content sites, and major credit cards for targeted offer delivery. Our targeting engine substantially increases conversion for our publisher partners, creating a win-win for both the retailer and the publisher. This provides our retailers with a much lower cost option for reaching consumers when compared with targeting advertising. Our pay-for-performance business model is based on our ability to deliver highly relevant offers with the right underlying product set.

How to Get Started with Programmatic Targeting Level 1- Affiliate: Add PreciseTarget as an affiliate. This will enable thecompany to begin understanding your products, and begin matching yourdeals and offers to consumers.

Level 2- Initial Programmatic Campaigns: Meet with PreciseTarget tocreate tests for acquisition, expansion, and reactivation offers. We’llhelp with the exchange of de-identified data, and begin the work of delivering targeted deals and offers that align with your marketing goals.

Personalization, Beyond Your E-commerce Site

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Level 3: - Rules-Based Programmatic Campaigns: Provide PreciseTarget with specific rules that align with your marketing strategy. This will enable you to programmatically target consumers with automation. For example, you can create rules about when a customer is deemed “inactive,” and you can create a rule indicating the discount level the engine uses to reactivate a customer with a compelling offer.

PreciseTarget is a retail data company focused on helping retailers drive higher conversion by using our innovative Taste Graph. The company has profiled the product tastes of over 200 million U.S. adults in the largest retail categories, including apparel, footwear, cosmetics, home goods, and electronics. Our customers have trusted us with over 5 billion SKU-level transactions, and more than 200 major retailers provide us with daily data feeds. Retailers, agencies, and ad-tech companies use our audiences and customer profile data for e-commerce, in-store, and customer insight applications. To learn more, please contact us at [email protected].

About PreciseTarget


Our FounderOur team is led by Rob McGovern, an experienced entrepreneur who founded Careerbuilder.com. He has previously served in executive positions at Hewlett Packard and Legent Corporations, and has held many board directorships at private and public companies. He’s a graduate of the Smith School of Business at the University of Maryland, and in his free time is a cyclist, airplane pilot, and mentor to young entrepreneurs.

white paper programmatic targeting · 2019. 10. 10. · programmatic targeting, for three types of...

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