Payments and the Convergence of Physical and Digital Commerce

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August 25, 2014

Payments and the Convergence of Physical and Digital Commerce

“Data is the weapon”

Dan Schulman, AXP

  • Consensus is that smartphone penetration (now over 65% of US mobile subscribers) will allow digital brands to extend their e-commerce franchises and data-enabled marketing skills to the physical environment. Google, for example, is executing its mission “to organize the world’s information and make it universally accessible” through using public and permissioned data to construct generalized maps that, like online spiders on the web, can be crawled by robots (cars, drones, glasses etc); and, of course, in this virtual reality Google’s skill advantage in data-enabled modeling of human behavior supports its commercial interests.
  • We believe smartphone penetration will also have the reverse effect: retailers with an analog heritage will mount an “app-and-mortar” challenge to digital brands using real-time, data-enabled marketing of their own. Already, as physical and digital commerce converge around mobile, CMOs have become more intensely focused on digitizing in-store workflows and keeping the resulting data out of the digital common space. The SBUX mobile app, with a core objective of preventing transaction-data leakage into the payments ecosystem, is an early example of this digital enclosure; and the control and protection of data is increasingly shaping physical infrastructure as in the case of the merchants’ payment consortium, MCX, and ChaseNet.
  • Illustrating the mobile, and more broadly “omni-channel”, business case for retailers, WAG reports[1] that customers who use mobile for shopping spend 4x the amount of non-internet walk-ins, and those using both mobile and spend 6x. Meanwhile, in its omni-channel push, WMT plans to match AMZN for online product-range and shipment-speed in 2016.
  • Through digitizing in-store workflows, large retailers will generate transaction data at internet-scale. E-commerce payments are expected to reach $365bn in 2016[2] and, while mobile payments (where a smartphone is used to make an online purchase) will likely be only 10% of this, mobile-influenced offline sales are projected at more than $700bn. WMT notes “the possibility of bringing the web to the store is incredibly disruptive … we have 140mm shoppers in the US and that is internet scale in the offline world”.
  • In pursuit of app-and-mortar strategies and a customer experience that is, in WMT’s framing, “the digital equivalent of the analog experience in store”, retailers will: (i) digitize in-store workflows; (ii) execute data-enabled, digital marketing campaigns; and (iii) view payments as strategic to marketing given the transaction data it generates and the ability of chip-enabled payments devices to act as marketing media (whether customer interaction is through a screen-enabled payment terminal or a ‘phone). The resulting winners include:
  • PAY and NCR who can support retailers, including banks, in enabling “smart” physical infrastructure to engage wirelessly with customers. The reported launch by AAPL in the iPhone 6[3] of a mobile wallet, capable of in-store navigation coordinated through iBeacons, is a likely catalyst for retailer investment in physical infrastructure as a core element of data strategy.
  • ADS and COF who can support retailers in leveraging data into targeted digital marketing campaigns. There is a secular shift in marketing from generic brand-advertising to digital channels, and a convenience-opportunity to integrate e-coupon redemption into the payments stream (through statement credits in a mag-stripe environment and pay-with-points in a digital environment). Private-label credit card issuers are well-positioned because of their retail partnerships; ADS already has strong momentum and Richard Fairbank, CEO of COF, has described the “huge opportunity to really leverage information”.
  • FIS, VNTV, and ACIW who can help reduce the risk of payments-data leakage by routing transactions directly from retailer to issuer hence by-passing the branded networks. This also saves network fees as noted by ACIW CEO Phil Heasley: “direct routing can create efficiency with issuers paying 0.005 cents/transaction vs. at least 5 cents charged by Visa.”
  • The integration of payments and marketing will catalyze structural reform in the card business, and a shift in the payments revenue-model from interchange, traditionally paid by retailers to issuers based on network rules, to bilateral deals for sharing the economics of collaborative marketing. The incentive for this collaboration arises from data synergy: retailers have access to the in-basket (i.e. product-level) purchase data of customers, and issuers have access to the away-spend data (albeit only at ticket-level) of customers at other stores. ChaseNet, announced in February 2013, is an early example with Jamie Dimon noting that it allows JPM[4] “to go to merchants and strike our own [deals]”; we expect other large banks to announce direct-routing schemes in 2015.
  • Visa could respond by insisting, as it has in the past (including with litigation settled in its favor in July 2006 against First Data), that transactions acquired on Visa-branded cards must be processed on the Visa-branded network. However, Durbin does not allow this insistence in debit and large banks may migrate card-spending to debit, reversing the recent trend where consumers have increasingly used credit cards for rewards not financing, if Visa resists in credit.

Overview: The Apps-and-Mortar Challenge to Digital Brands

Physical retailers are responding to the competitive threat from digital brands with “app-and-mortar” strategies, and there is evidence that mobile is providing them with an opportunity to even the tie. In the last holiday season, 50% of traffic at originated from a mobile device, often from a shopper in the store, and this digital engagement helped lift online sales at Walmart by 30% in 2013 compared with 20% at AMZN (albeit off a lower base with WMT online sales of $10bn, or ~2% of the firm-wide total, versus $68bn at AMZN). Online sales are also growing 30% at TGT to just over $2bn or ~3% of firm-wide sales.

Mobile engagement is lifting in-store, as well as online, sales and the focus at retailers is shifting from a concern around show-rooming to encouraging consumer use of ‘phones so that physical and digital commerce converge in an “omni-channel” experience; in 2012, TGT enabled its stores with Wi-Fi, and we increasingly expect stores to digitally engage consumers through Bluetooth-enabled “beacons” (with the reported launch of a mobile payments and shopping app by Apple in the iPhone 6 a likely catalyst) as well as NFC- and screen-enabled POS terminals. The sales lift can be significant: WAG reports[5] that customers who use mobile for shopping spend 4x non-internet customers who walk-in, and customers who use both mobile and spend 6x. Initial reports on beacons are also favorable with an early markting trial at Timberland generating open rates of 75% and redemption rates of 50%[6].

To motivate in-store use of mobile ‘phones, retailers are introducing apps, including around product discovery and price comparison, to offer online conveniences for in-store shopping. For example, in in-store mode, the WMT app has a search-box that is customized for each of the 4,000 US stores to help shoppers find a desired item and place an on-line order if it is unavailable; and the “savings catcher” feature allows shoppers to enter offline receipts and generate an in-store credit if a purchased item is available more cheaply elsewhere. Gibu Thomas, VP of mobile and digital for WMT e-commerce, notes that “the possibility of bringing the web to the store is incredibly disruptive … we have 140mm shoppers in the US and that is internet scale in the offline world”.

Large physical retailers are also looking to combine the convenience of online shopping with the immediacy of in-store fulfilment. WAG, for example, has 8,600 stores in the US so that two-thirds of Americans can pick up an online photo or prescription-order the same day from a store within 3 miles[7]. WMT comments that its combination of digital and physical assets will allow “an e-commerce experience equivalent to anyone in the world”; the firm already leverages its stores and supply-chain expertise to allow pick-up from super-center parking lots (see Exhibit 1), and is extending the fulfilment network through “tethering” which allows shoppers to pick-up from stand-alone facilities that are more local than superstores. More generally, WMT expects to match AMZN in terms of online product range and shipping speed in 2016; meanwhile, at TGT, in-store pick-up now accounts for 14% of digital sales.

Exhibit 1: Drive-Thru Pick-Up of Online Orders at WMT

The Unreasonable Effectiveness of Data

Large retailers are becoming more focused on digitizing the workflow of stores and capturing the data from customer visits for marketing. TGT, for example, notes that “when a guest shops our site, we will suggest products based on their store and digital purchase and browse history”; given 20% of in-store sales are on store-branded RED cards, a significant portion of that in-store purchase data is proprietary. Meanwhile, following this month’s revamp of, WMT reports “the site has much more personalization and each customer’s experience is always changing with fresh content that helps them discover new items … we’ve built our own personalization engine to customize the experience”. Separately, the company is looking to its new WMX marketing platform[8], to leverage data from and store sales to inform media buys both for Walmart itself and its suppliers[9].

WMT comments that WMX allows it “to see more and more the customer before they make a decision and optimize for the demand chain the way we have optimized for the supply chain”. This objective of digitally-engaging shoppers early in the decision-making process is shared by the digital brands with AMZN and GOOG are competing to be the first search box visited by online shoppers; indeed, AMZN’s home-scanning product, Dash, hopes to capture a grocery sale before the customer looks online or leaves home. WMT’s response has included the purchase, announced in May, of Alchemy which helps retailers optimize the use of search terms to drive shoppers to their web-sites[10]. As WMT looks to reach into the online space, the digital brands are looking to reach into stores; a strategic goal of Google Shopping Express (“GSX”) is “to keep more people from starting their shopping-search experience on Amazon[11]” and AMZN CFO Tom Szkutak notes the Fire phone is “a great way to do physical shopping”.

That said, the advantage of digital brands in data-enabled marketing is that online activity is intrinsically digitized, and hence lends itself to data scale which is important not only for processing economies but also for the statistical reliability of behavioral models. Richard Fairbank, CEO of COF, has commented that “the greatest scale is information scale and how it relates to the ability to underwrite [in a lending context] and ultimately do segmented marketing [in a demand-generation context]”. To give a sense of the inordinate data scale of Google, we note that in 2008 it was reportedly[12] processing 20 petabytes of information (approximately equivalent to the contents of 4mm DVDs) each day. For contrast, the National Security Agency (“NSA”)[13] provided a 2013 estimate that it touched 1.6% of daily internet volumes of 1.8 petabytes of information, or 10 petabytes each year[14]; this is about the same amount of information that can be generated each year by the Large Hadron Collider (“LHC”) computation-grid used by CERN to confirm the existence of the Higgs Boson, or so-called God Particle, in June 2014[15].

However, it is possible to achieve remarkable marketing results with much less; for example, as a pioneer in “test-and-learn” behavioral modeling firms for the credit-card industry, COF reported in May 2005 that it had a data warehouse of just 180 terabytes (or less than 0.2 petabytes) and this was then the largest consumer data base by “multiple times”. Charles Horn, CFO of ADS which has pioneered data-enabled marketing for physical retailers, argues that “big data is pretty easy…what is difficult is capturing data from multiple channels, analyzing it and making it usable information [given]… the proliferation of channels, all these different identifiers, all the different data flows”. Unsurprisingly, Google places less emphasis on structuring data with identifiers and more on using whatever data are available. Google researchers[16] argue: (i) that “the first lesson of web-scale learning is to use available large-scale data rather than hoping for annotated data that isn’t available”; (ii) that “simple models and a lot of data trump more elaborate models based on less data”; and (iii) that straight memorization can be best when there is a “a large training set of the input-output behavior that we seek to automate in the wild”. The reasoning is that “in practice we humans care to make only a finite number of distinctions. For many tasks, once we have a billion or so examples, we essentially have a closed set that represents (or at least approximates) what we need without generative rules”.

Google and the Digital Commons; Retailers and Digital Enclosure

Google’s mission is “to organize the world’s information and make it universally accessible” and the firm is pursuing this through digitizing information into generalized maps that can be crawled by physical robots (cars, drones, glasses, thermostats etc.) just as online spiders crawl the web. Of course, in this virtual reality, Google’s skill advantage in data-enabled modeling and automating of human behavior supports its commercial interests. Self-driving cars, and the associated project of creating a virtual facsimile of Highway 101 and surrounding roads, are an example of Google taking advantage access rights to the digital common space of public infrastructure. While accessing the data generated by private infrastructure is not as straightforward given the need for owner permission, the experience of the internet is that this is likely to be forthcoming in return for service. As WMT’s Gibu Thomas puts it “if we save [customers] money or remind them of something they might need, no one says, ‘Wait, how did you get that data?’ or ‘Why are you using that data?’ They say, ‘Thank you!’

The challenge for retailers is that they do not want the transaction data generated in stores bartered for service by a customer or other third-party with digital access in the normal course. This data-leakage is increasingly viewed as a competitive risk that needs to be addressed through a strategy of walling-off data from the digital commons. The SBUX mobile app, with a core objective of preventing transaction-data leakage into the payments ecosystem, is an early example of digital enclosure; and the control and protection of data is increasingly shaping physical infrastructure as in the case of the merchants’ payment consortium, MCX, and ChaseNet. Don Schulman at AXP captures the zeitgeist when he says “data is the weapon”, and retailers with an analog heritage are looking to respond to digital brands by building an arsenal and protecting the armory:

  • Tesco UK is piloting AAPL’s iBeacon technology at its Chelmsford store and the reported launch of a mobile wallet, with Bluetooth-enabled in-store messaging ability, in the iPhone 6[17] could catalyze broader adoption of smart store technology starting in the US.
  • The SBUX mobile app launched in 2009 (by an FIS/mFoundry[18] team under Benjamin Vigier who now has a leadership role in retail mobile payments for AAPL) now accounts for near 15% of transactions and has pioneered the use of QR-codes for point-of-sale payments. Motivated by the opportunity to build customer loyalty through a proprietary app and to control and protect transaction data (which, in a typical Visa- or MasterCard-branded card-swipe or mobile tap ‘n’ pay passes into the payments ecosystem) more retailers are looking to follow SBUX example by adding QR-code readers to cash registers.
  • Payment terminals are being screen-enabled, whether through Square and Square-like “dongles” (including the recently-announced “Local Register” product from AMZN) that turn a tablet into a terminal or next-generation terminals from market-leaders such as PAY, so as to allow shoppers to review, visually and in real-time, offers at point-of-sale and to indicate redemption and tender preferences.
  • Smart ATM’s from NCR allow customers to initiate cash withdrawals from their smart phones, and pick up the cash later at an ATM by scanning a bar-code.

While the goal of capturing data is reshaping customer-facing infrastructure, the desire to protect these data against leakage is reshaping back-end systems. For example, in February last year, JPM announced the formation of its own payments infrastructure, ChaseNet, as an alternative to the Visa-branded network with one of the goals being to keep proprietary the transaction data (and marketing information that will likely be routed along with it). In another example, the merchant payments consortium, MCX, is launching a payments network in partnership with FIS founded on the principle that each member-merchant owns its own data.

The Convergence of Payments and Marketing

Many of the above examples involve the payments process for good reasons. First, payments activity generates “hard” (i.e., high-value) data about customer shopping behavior versus the “soft” (albeit still valuable) intentional data generated by online search and social activity; and, second, the redemption of loyalty offers into the payments stream generates a convenience-benefit for customers. While this is possible on mag-stripe cards (through statement credits, for example), it will become more widespread as the industry transitions to chip cards with the majority of cards and point-of-sale terminal expected to be converted by end-2015. Chip technology turns payment devices into marketing media because, as MA puts it, “EMV [the communication protocol for chips] is a much thicker pipe [than mag stripe]…not only is it a more secure transaction but you can add more data into the stream that can drive things like offers and receipts”.

The shift to mobile will further integrate payments and marketing because the screen provides a real-time means for displaying offers and receiving customer tender and offer-redemption choices[19]. As a result, payments decisions are increasingly being made by Chief Marketing Officers with a focus on data strategy and customer loyalty rather than by Treasury with a focus on card acceptance costs. A clear example is the private-label TGT RED card which is aggressively promoted (to include free shipping for online sales, for example) and which, across debit and credit, now accounts for 20% of tender at US stores. TGT offers shoppers who use RED cards a 5% discount which is double the acceptance cost of a premium Visa credit card and, on an average $40 transaction, nearly ten-times the acceptance cost of a Visa debit card. Why does TGT encourage these higher acceptance costs? Because it sees a 50% sales lift on activated cards.

However, TGT does not provide a template because most marketing programs will likely use payments data to personalize offers rather than providing a flat, across-the-board discount. As Adam Brotman, Chief Digital Officer at SBUX, puts it: “we are seeing an increasing amount of ability for us to learn what is really relevant for our customers because of the data that we have from their card and loyalty purchases … that is driving our ability to drive incremental revenue in our core business through personalized offers and to use more relevant communications in general”. While large retailers, such as WMT and HD, will develop their own personalization engines, smaller retailers may not have the digital or analytics skills and are turning to third-party providers.

The Merchant- Data Driven Payments Model at ADS

One of these providers is ADS which is now the third-largest private-label credit card company with 33mm active accounts (see Exhibit 2). It has generated strong results through using merchant-provided data for targeted marketing; the ROA for the card business, which generates over 60% of firm-wide net adjusted EBITDA, was 5.3% in 2014Q2 with annual growth in both card loans and sales of ~20% (split evenly between core clients and the ramp of new programs). For contrast, COF’s domestic card business, across both private-label and network-branded products, had a return-on-loans of 3.5% with flat loans and annual sales growth of 11%.

Exhibit 2: US Private-Label Card Business

Source: Nilson 1039

Two key differences between the ADS private-label card portfolio and the COF domestic portfolio across both private-label and network-branded cards are:

  • Credit Quality: While the loss ratios on the ADS and COF portfolios are not far apart (4.4% and 3.5% respectively for 2014Q2), this is largely a function of the extraordinarily benign credit environment. The evidence that the ADS portfolio is more tilted to borrowers who are sub-prime (in the sense of having relatively restricted access to credit[20]) is that it generates a gross yield of over 25% while that at COF is 14%. ADS argues that they manage credit risk: (i) by keeping lines tight with an average open-to-buy of $400 versus the necessarily higher amounts of $2,500+ on a Visa or MasterCard general-purpose card; (ii) because adverse selection is more likely in the outbound marketing of general-purpose cards than retailer distribution of private-label cards; and (iii) because use of their private-label cards is limited to discretionary purchases (rather than, say, food or gas or rent) that consumers are more likely to forego at times of financial stress. These are valid risk-management approaches but will apply less as ADS diversifies from private-label cards to Visa and MasterCard “co-brand” cards (such as those with Orbitz and Virgin America, for example).
  • Data Capture: A unique feature of the card business at ADS is that it has contractually negotiated with retailer partners to provide “in-basket” information at the product- or SKU-level whereas a typical card authorization provides only “ticket-level” information, covering the total dollar amount of the purchase and the identity of the merchant, but not the in-basket level information. ADS cross-references this in-basket purchase information with static data on the cardholder compiled from public records (so including marital status and driving information, for example) and purchased information (so including catalog purchases and online/social media activity, for example) to generate targeted offers which are distributed as through a variety of digital channels (based on personally-identifying information including e-mail address and phone number, for example). ADS has accomplished all this in a mag-stripe environment, and the model is likely to become more powerful in a digital environment because of the ability for real-time and, in the case of phone-based messaging, location-based communication. As a simple example, a customers who is notified of a credit-line increase while shopping is more likely to purchase extra items immediately than one who finds out, as is typical today, only at checkout.

The Changing Revenue Model for the Credit Card Business

We believe ADS is in the vanguard of a broader change in the revenue-model for the credit card business which has increasingly come to depend on “interchange”: an amount, traditionally set by the networks such as Visa or MasterCard and averaging approximately 2% of dollar volume, paid by the retailer[21] accepting a credit card to the issuing bank where the cardholder holds the account. Interchange represents over 90% of total merchant fees paid on Visa and MasterCard credit cards (see Exhibit 3); at COF, for example, interchange fees (net of the expense of offering cashback, miles, and other rewards to cardholders) was $535mm in 2014Q2 or 16% of total revenue generated by the card business.

Exhibit 3: US Merchant Card Fees in 2013

Source: Nilson 1041

An important question for the credit-card industry is whether the interchange mechanism represents the most efficient approach to providing activity-incentives to cardholders. Traditionally, issuing banks have focused on driving share gains for their card products, through interchange-funded rewards, independently of where the cardholder shopped; now, however, the marketing potential of the “in-basket” transaction data will likely create a more collaborative arrangement between retailers and issuing banks where the marketing objective is mutual wallet-share gains with cardholders which are, after all, shared customers. Partnership between issuers and retailer is already a foundation of private-label cards which, being limited to use at a single retailer, do not carry the Visa or MasterCard acceptance brands and so are not subject to network rules on interchange. Rather, the issuer focus is on negotiated sharing of economics with the retailer partner rather than capitalizing on interchange transfer-payments. As ADS explains: “we have our own network so we can set the interchange to be whatever we want it to be we tend to set those fees fairly low [because] … we’re wanting the retailer to steer account and growth to our program”. The approach is gaining share since ADS is growing card spend from core clients at 10%+, and at 20% if we include new clients, versus growth in card-spending on network-branded credit products of 7-8%.

The broadening influence of ADS’s merchant-data driven, demand-generation model is indicated by its expansion since 2005 into co-branded cards and, particularly, the success of ADS in winning the Virgin America co-brand card from Barclays with the new cards issued in January 2014. Co-brand cards are a hybrid between private-label cards and general-purpose cards in the sense that they carry a network brand and so have general-purpose acceptance but, like private-label cards, involve a marketing partnership between issuer and retailer with economics shared based on a bilateral agreement rather than network-set interchange. The traditional business case among issuers for co-brand cards was to access that partner’s brand, customer-base, and distribution capability; in the ADS model, an additional element is access to in-basket data and these data may prove transformational for the industry. Indeed, we believe that issuers may enter into bilateral revenue-share arrangements with retailers, and abandon the traditional network-set interchange mechanism, in return for access to in-basket data independently of whether a retailer also offers brand and distribution support. The most striking example of this was JPM’s announcement last February of its ChaseNet private processing network that will negotiate bilaterally with merchants to lower interchange and arrange marketing programs. As CEO Jamie Dimon puts it[22] “[ChaseNet] allows us to go to merchants and strike our own [deals] … we just think it will be a better relationship between us and the merchant”.

A consequence of any shift in the card payments model from economics driven by network-level interchange to economics driven by issuer-retailer collaboration around data-enabled marketing is that the incentives of issuers and retailers will align around lowering processing costs and reducing the risk of data leakage. We believe this will lead to the development of processing infrastructure that routes payments (i.e. communicates authorization and settlement information) directly between retailers and issuing banks rather than over the branded networks. ChaseNet provides an example of this direct-routing as does the use by the merchant payment consortium, MCX, of FIS as a network partner. We expect other direct-routing options to emerge including from acquiring-processors such as VNTV that have, and can build, processing relationships with issuers or their processors; and issuer-processors such as ACIW that have, and can build, processing relationships with retailers or their acquiring-processors.

The Distortions of Interchange and Value of Merchant Data

We are making a strong claim about the value to issuers of in-basket merchant data, and shift in the revenue model for card payments from interchange to data-enabled marketing. A first reason is that interchange fundamentally distorts the payments industry. For example, the impact of regulatory caps on interchange for debit products has been to shift spending to credit cards where interchange is not regulated (see Exhibit 4). In other words, customers who have funds available in their checking account are now using credit cards for purchases (and paying them off in full at the end of each month) to access premium rewards. This frustrates retailers who are willing, up to a point, to pay higher card-acceptance rates where a customer needs pre-approved credit at point-of-sale to complete a purchase, but not to pay these higher rates simply so that the issuer can use merchant-funding for bank-branded marketing and rewards.

Exhibit 4: Banks Have Shifted Promotional Focus to Spending on Credit Cards from Debit Cards

Put more generally, merchants are willing to pay for demand-generation and their complaint with the interchange mechanism is that it blurs the line between the use of payment cards as a transactional convenience (which most retailers will argue merits a relatively low processing fee) and the impact of payment cards in driving incremental demand (which most retailers will argue merits a meaningfully higher fee). To date, despite intensive litigation (around network rules such as honor-all-cards and anti-steering, for example) and successful lobbying for a debit interchange cap to be included as part of Dodd-Frank, retailers have not been able to counter the market power of Visa and MasterCard and this reflected in high and rising credit interchange. However, the potential value of in-basket data (inaccessible to issuers without merchant permission) for data-enabled marketing that can shift shopper behavior in general and card-allegiance in particular gives retailers a stronger negotiating position than in the past.

A second reason that the card payment revenue model is likely to shift from interchange to data-enabled marketing is the sheer size of the marketing opportunity. Nilson estimates total fees paid by retailers for accepting payment cards amounted at over $70bn in 2013 of which the vast majority is interchange; against that, ADS sizes 2016 North American marketing spend at over $400bn and notes that half has already shifted to data-enabled direct marketing. At last year’s Money2020 conference, Ed Labry of First Data provided an estimate of over $500bn in spoken remarks, and we have seen other estimates as high as $750bn (see Exhibit 5 from the excellent blog[23] of payments consultant Tom Noyes) albeit encompassing trade-spend and other verticals that are not included in the ADS figure.

Exhibit 5: 2010 US Marketing Spend (All Verticals): ~$750bn

Furthermore, there is a secular shift in marketing budgets from generic brand advertising to digital marketing and, within that, to ROI-trackable data-enabled marketing. In 2014, for example, spend on digital advertising (comprising about one-fifth of total advertising spend of just over $250bn – see Exhibit 6) is estimated to grow at 14% while other components, including measured media and direct, are flat or down. ADS CEO Ed Heffernan makes clear that he is taking share from Madison Avenue as much as other banks: “what we are basically asking our retailer to do is take $40mm out of the TV budget and move it over to this private-label card business where essentially you are going to be funding big discounts”.

Exhibit 6 – Estimates for 2014 Advertising Spend

Source: Winterberry Group[24]

Richard Fairbank, CEO of COF, has commented on the opportunity for physical retailers to leverage mobile as a means of competing with digital brands around data-enabled marketing: “mobile can actually be the best opportunity to bring the tie [with the digital brands] back in the retailer’s favor when you think about the opportunity for real-time communications, real-time offers, for literally knowing where the person is in the store, for building loyalty programs, for point-of-sale and transactional ease and convenience, and ultimately all the data that can be collected there [at the point-of-sale].” And on the opportunity this presents for his company: “if you want an industry that is in incredibly big need of digital innovation, it is the retailing business. And I think that an opportunity to leverage our own digital innovation in that space is very exciting to us… the ability to use our product design and our marketing machine … and help a retailer with that”.

  9. WMX and Amazon Advertising Platform (“AAP”) are similar demand-side platforms in the sense of using shopper data to inform to media buys for CPG brands; a difference is that WMX is focused on driving sales for WMT, whereas AAP is focused on monetizing traffic since advertisers do not necessarily sell through AMZN.
  14. The NSA added that it reviewed only 0.025% of these data.
  18. FIS acquired mFoundry for $120mm in May 2013
  19. We expect chip cards to be adopted faster than mobile payments (with 2016 chip-card purchases of $1.5tn versus $100bn of mobile payments) creating an interim opportunity for PAY to sell screen-enabled payment terminals along with associated software. See our research note of August 21st “The EMV Opportunity for PAY in Terminal Apps”
  20. We acknowledge that rational consumers may prefer to purchase on an ADS-enabled retailer card rather than a Visa card in order to obtain an offer but the only rational reason for not paying off the resulting balance in full, as opposed to incurring a 20%+ interest rate, is restricted access to funds.
  21. Technically, interchange is paid by the acquiring bank representing the retailer on the card payment network, but the cost is almost always passed through to the retailer.
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