CPG goes DMP

a95d8358e93c679917a7785d9cce4a35If you think about the companies with perhaps least amount of consumer data, you would automatically think about consumer packaged goods (CPG) manufacturers. Hardly anybody registers for their website or joins their loyalty clubs; moms don’t flock to their branded diaper sites; and they are at arms-length from any valuable transaction data (store sales) until well after the fact. So, with little registration, website, or offline sales data, why are so many large CPG firms licensing an expensive first-party data management platform?

While CPG companies will never have the vast amounts of point-of-sale, loyalty-card, app, and website data that a big box retailer might have, they do spend a ton of dough on media. And, as we all know, with large media expenditures come tons of waste. Combine this with the increasingly large investment and influence that activist investors and private equity companies have in CPG, and you can see where this leads. PE companies have installed zero-based budgeting that forces CPG concerns to rationalize every penny of the marketing budget—which, until lately, has been subject to the Wannamaker Rule (“I know half of my budget is working, but not which half”). Enter the DMP for measurement and global frequency control, cutting off and reallocating potentially millions of dollars in “long tail” spending. Now, the data that the CPG marketer actually has in abundance (media exposure data), can be leveraged to the hilt.

This first and most obvious CPG use case has been discussed extensively in past articles. But there is much more to data management for CPG companies. Here are just a few tactics big consumer marketers have written into their data-driven playbooks:

 

The Move to Purchase-Based Targeting (PBT)

Marketers have come a long way from demographic targeting. Yes, gender, age and income are all reliable proxies for finding those “household CEOs,” but we live in complicated times and “woman, aged 25-54, with 2 children in household” is still a fairly broad way to target media in 2016. Today, men are increasingly as likely to go grocery shopping on a Thursday night. Marketers saw this and shifted more budget to behavioral, psychographic, and contextual targeting—but finding cereal buyers using proxies such as site visitation sharpened the targeting arrow only slightly more than demography.

Packaged goods marketers have long understood the value of past purchases (loyalty cards and coupons), but until the emergence of data management technologies, have struggled to activate audiences based on such data. Now, big marketers can look at online coupon redemption or build special store purchase segments (Datalogix, Nieslen Catalina, News America Marketing) and create high value purchase-based segments. The problem? Such seed segments are small, and must be modeled to achieve scale. Also, by the time the store sales data comes in, it’s often far too late to optimize a media plan. That said, CPG marketers are finding that product purchasers share key data attributes that reveal much about their household composition, behavior, and—most interestingly—affinity for a company’s other products. It may not seem obvious that a shopping basket contains diapers and beer—until you understand that Mom sent Dad out to the store to pick up some Huggies, and he took the opportunity to grab a cold six-pack of Bud Light. These insights are shaping modern digital audience segmentation strategy, and those tactics are becoming more and more automated through the use of algorithmic modeling and machine-learning. CPG has seen the future, and it is using PBT to increase relevant reach.

Optimizing Category Reach

CPG marketers are constantly thinking about how to grow the amount of product they sell, and those thoughts typically vary between focusing on folks who are immensely loyal (“heavy” category buyers) versus those who infrequently purchase (“light” or “medium” category buyers). Who to target? It’s an interesting question, and one answered more decisively with purchase-based sales data.

Take the large global soda company as an example. Their average amount of colas their customer consumes is 15 a year, but that is an immensely deceptive number. The truth is that the company has a good amount of “power users” who drink 900 colas a year (two and a half per day), and a lot of people who may only drink 2-3 colas during the entire year. Using the age-old “80/20 Rule” as a guideline, you would perhaps be inclined to focus most of the marketing budget on the 20% of users who supposedly make up 80% of sales volume. However, closer examination reveals that heavy category buyers may only be driving as little as 50% of total purchase volume. So, the marketer’s quandary is, “Do I try and sell the heavy buyer his 901st cola, or do I try and get the light buyer to double his purchase from two to four colas a year?”

Leveraging data helps CPG companies not have to decide. Increasingly, companies are adopting frequency approaches that identify the right amount of messaging to nurture the heavy users (maybe 2-3 messages per user, per month) and bring light buyers to higher levels of purchase consideration (up to 20 messages per month). Moreover, by using DMP technology to segment these buyers based on their category membership, creatives can be adjusted based on the audience. Heavy buyers get messages that reinforce why the love the brand (“share the love”), and light buyers can receive more convincing messages (“tastes better”).

Increasing Lift through Cross-Channel Messaging

CPG marketers have some highly evolved models that show just how much lift a working media dollar has on sales, and they use this guide to decision on media investment by both channel and partner. With the power of DMPs for cross-channel measurement, CPG companies are finally able to apply even small insights they can to tweak sales lift.

What if the data reveal that a 50% mixture of equity and direct response ad creatives lifts coupon downloads by 200%? In other words, instead of just showing “Corn Flakes are Yummy” ads, you mixed in a few “Buy Flakes now at Kroger and save!” creatives afterwards, and you saw a huge impact on your display performance? Sadly, this simple insight was not available before data management platforms corralled cross-channel spending and associated it with an individual, but now these small insights are adding up to appreciable sales lift.

In another example, a large CPG company sees massive lift in in-store coupon redemptions by running branded display ads on desktop all throughout the week—but giving a “mobile nudge” on the smartphone on Friday night when it’s time to fill the pantry. This cross-channel call-to-action has seen real results, and only involves grabbing a brand-favorable consumer’s attention on another device to create a big impact. Again, a simple tactic—but also impossible without the power of a DMP.

CPG marketers have been able to achieve a ton of progress by working with relatively sparse amounts of data. What can you do with yours?

 

 

 

Data Triangulation: How Second-Party Data Will Eat The Digital World

bearsharkMarketers are getting frustrated with spending up to 60% of their working media dollars to fund intermediaries between themselves and their publishing partners. By the time a marketer pays his agency, trading desk, exchange, third-party data provider, and subsidizes the publisher’s ad serving stack, dollars turn into dimes. Marketers want less fraud, more people, less ad tech, and to put more media dollars to work to drive performance. Quality publishers, who for so long sacrificed control for access to an always-on stream of programmatic cash, are now seeing balance return, as shady sources of inventory leave the ecosystem and start to create scarcity for premium supply.

Publishers with desired audiences are starting to leverage hacks like “header bidding” and private marketplaces to get more control and capture more revenue from transactions. But they are also starting to look at data-only transactions among trusted demand-side partners. Now that marketers are catching up with DMP technology, securely sharing audiences becomes possible, opening up a new era where “second party” data is poised to reign supreme. Before we talk about how that happens, let’s first define some data terms:

A Primer

First-party data is proprietary data that marketers and publishers have collected – with permission, of course – and, therefore, own. It can be cookies collected from a site visit, offline data onboarded into addressable IDs and even data from marketing campaigns. Second-party data is simply someone else’s first-party data. Second-party data gets created any time two companies strike up a deal for data that is not publicly available. The most common use case is that of a marketer – say a big airline –getting access to data for a publisher’s frequent travelers. Big Airline might say to Huge News Site with business travelers, “Let’s user match, so every time I see one of my frequent flyers on your site, I can serve him an ad.” Huge News Site may decide to allow Big Airline to target its users wherever they are found (a “bring your own data” deal) or make such a deal incumbent upon buying media. Either way, Big Airline now has tons of really valuable Huge News Site reader data available in its data-management platform (DMP) for modeling, analysis and targeting.

Despite the much heralded death or merely diminution of third-party data, it is still a staple of addressable media buying. This is data that is syndicated and made available for anyone to buy. This data could describe user behavior (Polk “auto intenders” of various stripes) or bucket people into interesting addressable segments based on their life circumstances (Nieslen “Suburban Strivers”), describe a user’s income level (Acxiom or Experian) or tell you where a user likes to go via location data (PlaceIQ or Foursquare). Most demand-side platforms (DSPs) make a wide variety of this data available within their platforms for targeting, and DMPs enable users to leverage third-party data for segment creation – usually allowing free usage for analytics and modeling purposes and getting paid upon successful activation. Data Quality And Scale So, which kind of data is the best? When asked that question by a marketer, the right question is inevitably, “all of it.” But, since that’s an annoying answer, let’s talk about the relative scale and value of each type of data. It’s easily visualized by this wonderfully over-simplified triangle:

TRIANGLE OF DATA

First-party data is the most limited in scope, yet the most powerful. For marketers –especially big CPG marketers who don’t get a lot of site traffic – first-party data is incredibly sparse but is still the absolute most valuable signal to use for modeling. Marketers can analyze first-party data attributes to understand what traits and behaviors consumers have in common and expand their reach using second- or third-party data. Retail and ecommerce players are more fortunate. A Big Box Store has first-party data out the wazoo: loyalty card data, point-of-sale system data, app data, website registration data, site visit data and maybe even credit card data if it owns and operates a finance arm. It can leverage a DMP to understand how media exposure drove a store visit, where customers were in the store (beacons!), what was purchased, how many coupons were remitted and whether or not they researched their purchase on the site. Talk about getting “closed loop” sales attribution. The power of first-party data is truly amazing.

The biggest problem with third-party data is that all of my competitors have it. In programmatic marketing, that means both Ford and Chevy are likely bidding on the same “auto-intender” and driving prices up. The other problem is that I don’t know how the data was created. What attributes went into deciding whether or not this “auto intender” is truly in-market for a car? There are no real rules about this stuff. A guy who read the word “car” in an article might be an “auto-intender” just as someone who looked a four-door sedans three times in the last 30 days on reputable auto sites. Quality varies. That being said, there is huge value in having third-party data at your disposal. Ginormous Music App, for example, has built a service that is essentially a DMP for music; it knows how to break down every song, assign very granular attributes to it and delivers highly customized listening experiences for free and paid users. Those users are highly engaged, have demonstrated a willingness to buy premium services and are, by virtue of their mobile device, easily found at precise geolocations. Yet, for all of that, the value to a marketer of a Maroon Five segment is rather small. Everyone likes Maroon Five, from grandmothers to tweens to Dads. A Maroon Five segment provides little value to an advertiser. Yet, if Ginormous Music App could push its app-based user data (IDFAs) into the cookie space and find a user match, it could effectively use third-party data to understand the income, behavior and general profile of many Maroon Five fans. And that’s what their advertisers like to buy. That’s pretty damn valuable.

So, how about “second-party” data? These are the “frequent business travelers” on Huge News Site and the “Mitsubishi intenders” on Large Auto Site. These are real users, with true demonstrated intent and behavior that has been validated on real properties. One of the most valuable things about audiences built on second-party data is that there is usually transparency regarding how those users found their way into a segment.

The ironic and kind of beautiful thing about the emergence of second-party data is that it is most often merely a connection to a premium publisher’s users. However, it can be uncoupled from a publisher’s media sales practice. Marketers, increasingly sick of all the fraud and junk in the programmatic ecosystem are turning toward second-party data to access the same audiences they bought heavily in print 30 years ago. This time, however, they are starting to get both the quality – and the quantitative results – they were looking for. On the flip side, quality publishers are starting to understand that, when offered in a strict, policy-controlled environment that protects their largest asset – audience data – they can make way more money with data deals than media deals.

Put simply, second-party data is heralding a return to the good old days when big marketers depended on relationships with big publishers as the stewards of audiences, and they created deep, direct relationships to ensure an ongoing value exchange. Today, that exchange increasingly happens through web-based software rather than martini lunches.

[This article originally appeared in AdExchanger on 1/25/16]