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THE 5 TYPES OF USER MATCHING CHALLENGES AND HOW TO SOLVE THEM Cross-Device Cross-Channel Cross-Brand Executive Summary In a perfect world, your shoppers log in and you can easily determine who they are, what they like and offer them a completely personalized experience no matter where they are in their shopping journey. We understand that this scenario as described above, is not a reality today but we’re here to help connect the dots. Getting shoppers to log in is a challenge and we know that most shoppers will only login to make a purchase, and even then, they’ll only do so if there is perceived value.
Offering your shoppers a real-time omnichannel experience that’s merchandised to their individual behaviors and purchases requires common product, product category and user IDs across channels. Most retailers have the frst two handled -- this paper shares how you can leverage user ID matching and RichRelevance features to bring together the data points you need to identify users and motivate them to log in.
There are 5 types of user matching challenges: n Handling anonymous users n Accounts per person and persons per account n Cross-channel (web, mobile app, store…) n Cross-devices (smartphone, laptop, tablet…) n Across multiple websites owned by the same company THE 5 TYPES OF USER MATCHING CHALLENGES AND HOW TO SOLVE THEM: CROSS-DEVICE, CROSS-CHANNEL, CROSS-BRAND
Anonymous Users It would be great if users always logged in and had just one account each, unfortunately that’s not the reality we live in. If it were, we would be able to easily connect their behavior from visit to visit, provide better personalization and increase revenue per visit. In an effort to understand just how many shoppers remain anonymous we analyzed 3.7 billion page views across a dozen countries, on 100 of the largest websites integrated with RichRelevance and found that almost half of page views (48%) and 13% of purchases are anonymous. That is, the website cannot connect the user to a long-term user ID and shoppers visit multiple times before purchasing. Sixty percent of those 100 websites had an average of at least two visits before a visit that included a purchase.
The best, most customer-centric way to personalize anonymous visits is to reduce anonymous visits. Which begs the question, how do we encourage users to log in?
Priceline.com took a novel approach and is running TV ads in the U.S. that simply state, “If you don’t sign in you’ll die.” They’re joking (we hope), but they make a point and one that consumers remember – that Priceline does provide lower prices for those who log in. Like most things in merchandising, the 4 P’s are helpful when brainstorming ways to increase logins, but to further assist we’ve added a 5th P: n Product: Provide better products or features to those who log in. Facebook is a good example. You only see your personalized news feed when you log in so most everyone stays logged in. Retailers as diverse as Amazon, Wine.com, Nordstrom, Williams-Sonoma and JCPenney provide product recommendations pages and even content that is much more relevant when you are logged in. n Placement: Provide personalized features in all channels including desktop web, mobile web, email, mobile app and mobile app used in- store. n Price: Offer better prices or promotions when logged in. Priceline.com does.
1 THE 5 TYPES OF USER MATCHING CHALLENGES AND HOW TO SOLVE THEM: CROSS-DEVICE, CROSS-CHANNEL, CROSS-BRAND
n Promotion: Personalize coupons, special offers and discounts and encourage customer loyalty. Starbucks’ mobile app is a great example. n Preferences: Allow users to indicate preferences that are relevant to your vertical.
Even if you do all of these, many users won’t log in until just before purchasing. Two ways to get the most from personalization for users who aren’t logged in are soft logins and provisional user IDs.
Soft Login
A soft login is when a user logs in on a device, the retailer writes their user ID to a frst-party cookie, the user logs out, and the retailer continues to use their user ID for the current and subsequent visits. Many retailers place an expiration date on the frst-party cookie so that there is a time limit between the most recent explicit login and a soft login. When using a soft login, some website features may not be available, such as viewing past purchases, changing preferences or making a purchase.
Provisional User IDs
RichRelevance uses a “provisional” user ID to handle anonymous users who do have an account and will log in later. The provisional user ID automatically links a user’s behavior before logging-in to their long-term user ID when they do eventually login. Retailers and brands have a choice on implementing provisional IDs: n RichRelevance can handle it automatically using our third-party cookie ID, or n A session ID can be provided by the retailer or brand, as the provisional ID. The session ID can be anything you want, such as a traditional commerce platform session ID that resets after 30 minutes of inactivity, a frst-party cookie ID that expires in 30 days, or a frst-party cookie ID that never expires.
Most ecommerce visits are now on mobile devices, and third-party cookies don’t work well there, so retailers need to give some consideration provisional IDs and determine which is best for them.
2 THE 5 TYPES OF USER MATCHING CHALLENGES AND HOW TO SOLVE THEM: CROSS-DEVICE, CROSS-CHANNEL, CROSS-BRAND Mapping Between Accounts and People
MULTIPLE ACCOUNTS PER PERSON
While having too few logins is a common challenge, the problem of too many accounts per person is another issue to combat. Years ago some brick-and-mortar retail chains used your phone number as your long-term user ID. Whenever you made a purchase, they’d ask for your phone number. Back then shoppers usually gave their home land line number but more recently use their mobile phone number, resulting in two IDs for the same person. Today many shoppers have multiple email addresses so if they can’t remember their password they simply create a new account using a different email address, again resulting in multiple accounts per person. RichRelevance provides a shopper ID mapping feed for retailers and brands to notify our personalization engine when they detect that a shopper has multiple IDs that should be treated as one ID. For example, if you fgure out that users 123 and 789 are the same person and send that to us in the shopper ID mapping feed, then whenever user 123 or user 789 visits, their RichRelevance personalization will be based on the combined behavior of both user IDs. provides a shopper Retailers and brands have a number of ways to detect if two users are the same person, including: ID mapping feed for n Use their own frst-party data such as credit card numbers and billing or shipping address retailers and brands n Send transactions to a third-party service company such as Acxiom to notify our to do the matching n Use online matching services such as Adobe, LiveRamp (an Acxiom personalization en- company), Gigya or Neustar1 gine when they de- DETERMINISTIC THIRD-PARTY MATCHING tect that a shopper SERVICES has multiple IDs that Services such as LiveRamp and Neustar use what’s called deterministic matching. They collect data from many websites and look for cases where should be treated as two users self-identify themselves as the same person. High-traffc sites—like one ID. Match.com and Verizon—are used to collect data on lots of people. Here’s how it typically works. You add the matching service’s tracking tags to your website and send them your user ID for each visitor. For example, when user
1 For a list of more vendors, see “2016 Edition: A marketer’s guide to cross- device identity,” Allison Schiff, AdExchanger, February 29, 2016,
3 THE 5 TYPES OF USER MATCHING CHALLENGES AND HOW TO SOLVE THEM: CROSS-DEVICE, CROSS-CHANNEL, CROSS-BRAND
123 visits, you tell the service, “This is my user 123.” When user 456 visits you tell them, “This is my user 456.” If the service sees user 123 and user 456 both log in to Match.com using the same email address, then they know that they’re the same person. Periodically, the service sends you a fle of matched user IDs, which may be sent to RichRelevance via our Shopper ID Mapping Feed. Some of the services also have the option to get a list of matched IDs at runtime. When you tell the service, “This is my user 123,” it returns user 456 and any other matched IDs. A website could sort the IDs and send the frst one to RichRelevance via our real-time APIs. That way when user 123 visits you send user 123 to RichRelevance, and when user 456 visits you also send us user 123.
MULTIPLE PEOPLE PER ACCOUNT
While B2C ecommerce has the challenge of multiple accounts per person, B2B has the problem of multiple people per account. Consider a small company with 20 employees where all offce supply purchases are shipped to the same address and billed to the owner’s credit card. But the owner doesn’t order much. Most of the orders are placed by the general manager or the offce manager and they each have their own logins. So there are three logins—the owner, the general manager and the offce manager—for one bill-to and ship-to account. B2B companies should consider whether they want RichRelevance’s
FIGURE 1 Multiple People Per Account While B2C ecommerce has the challenge of multiple accounts per person, B2B has the problem of multiple people per account.
4 THE 5 TYPES OF USER MATCHING CHALLENGES AND HOW TO SOLVE THEM: CROSS-DEVICE, CROSS-CHANNEL, CROSS-BRAND personalization engine to view this situation as one or as three users. Maybe the offce manager only orders small items like ink, paper and pens, while the general manager orders printers, laptops and furniture. In that case it might make sense to keep the accounts separate. If you do wish to merge the accounts for personalization purposes, then the same approaches described earlier apply: using our shopper ID mapping feed or selecting one of the three user IDs to always send in RichRelevance API calls. Cross-Channel Matching When a user ID is provided in API calls, user behavior is instantly shared across channels and devices. We store user browse history, purchases, preferences, attributes and segments in a real-time, distributed, cloud-based database shared by all channels and applications. As a result, a shopper can browse on their smartphone, switch to their laptop, and personalization on the laptop will instantly take into account their recent behavior on the smartphone. User history and personalized results can also be instantly shared with sales associates. A shopper can browse on the laptop, walk into a store, browse in-store on their phone, and up-to-date info is available to sales associates and customer service representative. This requires the use of the same user ID or linking multiple user IDs via our shopper mapping feed. In this section we discuss challenges in matching shoppers across channels.
Web browser cookies are little use in cross-channel matching because email opens can’t read cookies, third-party cookies are usually blocked on mobile websites and the vast majority of mobile ecommerce traffc is on mobile apps, not website. So let’s consider the channels one by one.
Again, the best, most customer-centric approach is to encourage logins. If a new customer visits your website, creates an account and opts-in to email, then give them the same user ID in both email and web. If, for some reason, your email and web user IDs have to be different then use RichRelevance’s shopper mapping feed to link them.
Most ecommerce sites also allow anonymous visitors to sign-up for email. What many sites do in that case is assign the user a long-term user ID, immediately start using it in calls to RichRelevance, and use the same ID in email personalization. This is similar to a “soft login” in that the user hasn’t logged-in yet but has been associated with a long-term user ID.
5 THE 5 TYPES OF USER MATCHING CHALLENGES AND HOW TO SOLVE THEM: CROSS-DEVICE, CROSS-CHANNEL, CROSS-BRAND
MOBILE APP
Typically mobile app users are always logged in and thus always have a user ID. If they create a mobile app account and later create a web account (or vice versa), retailers and brands may use a piece of personally identifable information—such as a mobile phone number or email address—to identify that it’s the same user. Ideally the same user ID will be assigned to both but if not, RichRelevance’s shopper mapping feed may be used to link them.
IN-STORE
Loyalty cards, credit card numbers, house-holding with service frms such as Acxiom, email receipts and barcodes on mobile apps are common ways to connect a user to their store transactions. Starbucks’ app is a great example and has achieved wide adoption, driving over 20% of sales. (Rumor has it that the number is now much higher.) The apps main features are quick payment, tracking loyalty points, personalized offers, redeeming points for free items and remote ordering and payment.
If a shopper opts-in for an email receipt and that email address is already associated with a user ID, then that user made the in-store purchase. If it’s a new email address, and the shopper clicks on the email and goes to your
Starbucks Mobile App Review your past purchases and e-receipts
6 THE 5 TYPES OF USER MATCHING CHALLENGES AND HOW TO SOLVE THEM: CROSS-DEVICE, CROSS-CHANNEL, CROSS-BRAND website, and the shopper is logged-in, soft-logged-in or has a user ID stored in a frst-party cookie, then the website can associate them with their user ID. That’s a lot of “ifs” and “ands” but it works. Similarly if they don’t yet have a web user ID then when they land at the website, the website can record their email ID in a frst-party cookie and use it as a soft login.
The new battle for real estate: Cross-Device Matching In 2014 for the frst time, 50% of internet traffc was from mobile devices and in 2015 for the frst time, 50% of ecommerce traffc was mobile. Consumers have shifted to a mobile-frst mindset yet they still purchase on mobile devices only one-third the rate of desktops, but this statistic is growing. US Mobile ecommerce spending is growing at 40% per year compared to the 11% of desktop and 4% for all of retail. The evolution of the smartphone and the ‘google it’ mentality has given consumers the ability to have 24-7 access to make purchases simply by reaching into their pockets. As mobile screen sizes increase, mobile ecommerce shopping feels just as comfortable as the desktop experience and even more convenient. So when it comes to mobile shopping, you know that ‘there’s an app for that’ and whether or not your app makes it to the homescreen is going to be the next big battle, if it
Capacity isn’t upon us already. Nearly half of U.S. shoppers have just three or fewer 57.42 GB 2 Audio Photos Apps Books Other retail mobile apps on their smartphone , in this smartphone real estate 25.2 GB 6.3 GB 18.8 GB 0.38 GB 1.24 GB competition, there is no winning if your customer experiences aren’t personal.
Cross-device user matching is key to delivering good, consistent personalization every time you interact with your customers. The approaches are those we’ve already covered: n Encourage logins by providing a better experience: features, prices and promotions n Use personally identifable information: mobile phone numbers or email addresses n Use your frst-party data: credit card numbers and shipping addresses n Send transactions to a third-party matching service: Acxiom, Harte- Hanks, etc. n Use online matching services: Adobe, Crosswise (from Oracle), Drawbridge, Tapad, LiveRamp (an Acxiom company), Gigya, Neustar, etc. n Send RichRelevance the same user ID whenever possible n Use RichRelevance’s shopper mapping feed to link users who are the same person
2 “Mobile Internet usage skyrockets in past 4 years to overtake desktop as most used digital platform”, April 2015, comScore; “State of the U.S. online retail economy in Q1 2016,” comScore; and “Mobile marketing statistics compilation," Dave Chaffey, SmartInsights.com
7 THE 5 TYPES OF USER MATCHING CHALLENGES AND HOW TO SOLVE THEM: CROSS-DEVICE, CROSS-CHANNEL, CROSS-BRAND Cross-Brand Matching As ecommerce becomes a bigger portion of retail, companies are working to leverage their real-time online user profles across not only all their touchpoints but also across all their brands. With RichRelevance’s cloud-based platform it’s now practical to develop real- time, multi-brand online user profles. Example multi-brand companies are:
L’Oreal Group: Lancôme, Giorgio Armani, Yves Saint Laurent Beauté, Biotherm, Kiehl’s, Ralph Lauren, Shu Uemura, Cacharel, Helena Rubinstein, Clarisonic, Diesel, Viktor&Rolf, Yue Sai, Maison Margiela, Urban Decay, Guy Laroche, Paloma Picasso, Proenza Schouler, L’Oréal Paris, Garnier, Maybelline New York, NYX Professional MakeUp, African Beauty Brands, Essie, Kérastase, Redken, Lancôme and L'Oreal Paris wrinkle creams: Matrix, Pureology, Shu Uemura Art of Hair, Mizani, Essie, Decléor, CARITA, same category and intent but different price points. Vichy, La Roche-Posay, SkinCeuticals, Roger&Gallet, Sanofore, The Body Shop n HBC: Hudson’s Bay, Lord & Taylor, Saks Fifth Avenue, Off 5th, Gilt, Home Outftters n Williams-Sonoma: Williams-Sonoma, Pottery Barn, Pottery Barn Kids, PBteen, West Elm, Rejuvenation, Mark and Graham n Neiman Marcus: Neiman Marcus, Bergdorf Goodman, Cusp, Last Call, Horchow, My Theresa n Barneys New York: Barneys, Barneys Warehouse, The Window
Arrow Electronics: Arrow, Verical, Silicon Experts, ElectronicProducts.com, EEWeb.com, EmbeddedDeveloper.com, Schematics.com, and many more
Arrow Electronics is an example of an interesting trend. They have four ecommerce websites and last year acquired 16 content-based websites in their vertical industry. The 20 sites work together to drive traffc and sales.
Two high-level approaches to sharing profles across websites on the RichRelevance platform are to unify catalogs and treat the whole company as one site with multiple sections, or periodically copy data from user profles on one site to profles on other sites.
Creating a unifed product catalog across all sites requires shared product categories and product IDs. Then each individual site becomes a product category or brand within the catalog. Preserving each site as a category or brand is important so that they can be used in merchandising rules. For example, if a shopper is viewing a Pottery Barn Kids bunk bed then limit product recommendations to other Pottery Barn items. In this approach RichRelevance views the company as one site and uses the same site ID and user IDs across all websites.
The second approach is to periodically copy data from user profles on one site to user profles on other sites. To do that, frst identify matching users across sites using the methods described earlier: RichRelevance’s third-party cookie ID, frst-party data such as credit card numbers and shipping addresses,
8 THE 5 TYPES OF USER MATCHING CHALLENGES AND HOW TO SOLVE THEM: CROSS-DEVICE, CROSS-CHANNEL, CROSS-BRAND third-party service companies such as Acxiom, or online matching services such as Adobe, Crosswise, Tapad and LiveRamp. Then on each website, use RichRelevance’s segment builder to defne customer segments that you wish to share across sites. For example, “bedroom furniture shopper” or “anti- aging concerns.” Next, periodically export the segments to a location where they can be processed, identify the matching users on other sites, and make RichRelevance API calls or a data feed for the other sites to inject customer segment or product view data into the user’s profle on the other sites.
Consider a concrete example of copying data from site to site. L’Oréal Paris has a product category named “Skin Care > Skin Care Concerns > Anti-Aging.” We can defne a customer segment, “anti-aging concerns,” which is users who have viewed four items or purchased one item in that category in the past 180 days. Then schedule RichRelevance to FTP to you a list of users who are in the segment, every 30 minutes. Identify matching users from the Lancôme website. There are three options to getting “anti-aging concerns” information into the Lancôme user profles: n Send the data in bulk via RichRelevance’s segment feed n Send it one user at a time via the sgs parameter of our REST API n Identify the equivalent Lancôme product category (Skin Care > Concerns > Anti Aging) and call RichRelevance APIs to fnd the four top sellers in the category. Then, one at a time for each user and for each of the four products, call RichRelevance APIs to record that that user viewed that product. Probabilistic third- The advantage of the last approach is that low-level event data infuences party matching many parts of the RichRelevance personalization platform while customer segments have a few specifc uses. services use a multitude of data PROBABILISTIC THIRD-PARTY MATCHING and data science SERVICES to determine which Crosswise, Drawbridge and Tapad use what’s called “probabilistic” matching. They’re not only using deterministic matching such as looking for two devices devices are likely logging-in to a website using the same email address, they also collect lots of little pieces of data about each device, data such as IP address, owner used by the same of the IP address (Comcast, AT&T), IP address location, GPS location, person, for example: websites visited, ads seen, device type (iPhone, Android), time of day and much more. They stitch it all together with data science to determine which devices are very probably used by the same person. For example, if you work GPS LOCATION in New York City but live in Newark then both your laptop and smartphone will often be seen in both locations. Conversely, if a smartphone often WEBSITES VISITED visits Esquire.com (a men’s site) and a tablet visits Cosmopolitan.com (a women’s site) then they probably have different users. If there were only a few cities and websites in the world then this approach won’t work too well, TIME OF DAY
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but there are thousands of cities and millions of sites so it works very well.
You might think that deterministic matching is always more accurate than probabilistic matching. Not so. Two years ago comScore measured AOL’s deterministic matching and found it to be 93% accurate, while Nielsen validated that Tapad’s probabilistic matching is 91% accurate and Drawbridge’s is 97%. For applications like personalization where 8% click-thru rates and 4% conversion rates are good, 90% accuracy is more than suffcient. But accuracy is only half of the story. Scale, also known as “reach” in the advertising industry, is also critical. If a matching service accurately matches 1,000 of your users but you have 10 million active users, then the matching service isn’t of much value. Accuracy and reach counterbalance each other: as one goes up the other goes down. Drawbridge’s reach is around 10%. Probabilistic matching reach for retail ecommerce websites active users can vary wildly, generally from 10 to 60%. When considering a cross-device solution, be sure to review both accuracy and reach for your customer base.
Conclusion
n The most customer-centric approach to user matching is to provide goods, services and promotions which encourage and incentivize a user to log in. n RichRelevance automatically copies anonymous user history to their long-term profle when they log in. Temporary, anonymous profles may be stored under a third-party cookie ID or a session ID. Consider which is best for your situation. n Online user matching services are useful for matching customers within a channel, across channels and across devices. They include Adobe, Crosswise (from Oracle), Drawbridge, Tapad, LiveRamp (an Acxiom company), Gigya, and Neustar. KPIs to consider are matching accuracy and reach for your visitors. n Use email addresses and barcodes on mobile apps to identify in-store users. n More than half of ecommerce traffc is now mobile and the vast majority of it is on smartphones. Users prefer mobile apps over mobile websites, but most users have three or fewer retail apps. Retailers must compete for the limited app space on smartphones. n Multi-brand retailers can use RichRelevance’s platform to share user profles between brands.
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ABOUT RICHRELEVANCE
RichRelevance is the global leader in omnichannel personalization and is used by more than 230 multinational companies to deliver the most relevant and innovative customer experiences across web, mobile and in store. RichRelevance drives more than one billion decisions every day, and has generated over $20 billion in sales for its clients, which include Offce Depot, Costco, Marks & Spencer and Darty.
Headquartered in San Francisco, RichRelevance serves clients in 42 countries from nine offces around the globe. For more information, please visit richrelevance.com.
CONTACT US
To request a demo or contact one of our global offces, please visit richrelevance.com/contact.
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