Data Management Platform

CPG goes DMP


I often wonder how Wayne Gretzky feels when he looks at this photograph. Maybe he’s like, “man, that blazer was totally boss.”

If 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?




Advertising Agencies

Classic Wrap Up Article with Typical Next-Year Guru Predictions


Everything I predicted came true, but I still cannot grow a manly beard. 

2015 was a fantastic year for many data-driven marketers, with data management platforms (DMPs), consultancies and marketers getting something nice under their trees.


Unfortunately, 2015 also saw legacy networks, supply-side platforms (SSPs) and some less nimble agencies receive coal in their respective stockings for failing to keep up with the rapidly changing paradigm as marketing and ad technology merge.

In the great tradition of end-of-year prediction articles, here’s my take on the year’s biggest developments and what we’ll see in 2016, including a rapid technology adoption from big marketers, a continuing evolution of the agency model and an outright revolution in how media is procured.

Agency Ascendant?

I thought 2015 was supposed to herald the “death of the digital agency model.” As agencies struggled to define their value proposition to big marketers that were increasingly bringing “programmatic in house,” agencies were reputed to be on the ropes. Massive accounts with billions of dollars in marketing spend were reviewed, while agencies churned through cash pitching to win new business – or at least trying keep old business.

The result? Agencies swapped a ton of money, but were abandoned by no serious marketers. Agencies got a lot smarter, and starting spinning new digital strategies and DMP practices to combat the likes of system integrators and traditional consultancies. And the band played on.

In 2016, we will continue to see agencies strengthen their digital strategy bench, start moving “marketing automation” practices into the DMP world and offer integration services to help marketers build bespoke “stack” solutions. Trading desks will continue to aggressively pursue unique relationships with big publishers and start to embrace new media procurement methodologies that emphasize their skillset, rather than the bidded approach in open exchanges (more on that below).

Marketers Hug Big Data

Marketers started to “cross the chasm” in 2015 and more widely embrace DMPs. It’s no longer just “early adopters” such as Kellogg’s that are making the market. Massive top-100 firms have fully embraced DMP tech and are starting to treat online data as fuel for growth.

Private equity and activist investors continue to put the squeeze on CPG companies, which have turned to their own first-party data to find media efficiency as they try to control the one line item in the P&L usually immune to risk management: marketing spend.

Media and entertainment companies are wrangling their consumer data to fuel over-the-top initiatives, which put a true first-party relationship with their viewers front and center. Travel companies are starting to marry their invaluable CRM data to the anonymous online world to put “butts in seats” and “heads in beds.”

If 2015 saw 15% of the Fortune 500 engage with DMPs, 2016 is when the early majority will surge and start to make the embrace of DMP tech commonplace. The land grab for 24-month SAAS contracts is on.

Busy Consultants

It used to be a that a senior-level digital guy would get sick of his job and leave it (or his job would leave him), leading to a happy consultant walking around advising three or four clients on programmatic strategy. In 2015, that still exists but we’ve seen a rise in scale to meet the needs of a rapidly changing digital landscape.

Marketers and publishers are hiring boutique consultancies left and right to get on track (see this excellent, if not comprehensive, list). Also, big boys, including Accenture, Boston Consulting Group and McKinsey, are in the game, as are large, media-centric firms, such as MediaLink.

These shops are advising on data strategy, programmatic media, organizational change management and privacy. They are helping evaluate expensive SAAS technology, including DMPs and yield management solutions, and also doing large systems integrations required to marry traditional databases with DMPs.

Match Rates (Ugh)

Perhaps unpublicized, with the exception of a few nerdy industry pieces, we saw in 2015 a huge focus on “match rates,” or the ability for marketers to find matches for their first-party data in other execution systems.

Marketers want to activate their entire CRM databases in the dot-com space, but are finding only 40% to 50% of cookies that map to their valuable lists. When they try to map those cookies to a DSP, more disappointment ensues. As discussed in an earlier article, match rates are hard to get right, and require a relentless focus on user matching, great “onboarding services,” strong server-to-server connections between DMPs and DSPs (and other platforms) and a high frequency of user matching.

This was the year that marketers got disappointed in match rates and started to force the industry to find better solutions. Next year, huge marketers will take bold steps to actually share data and create an available identity map of consumers. I think we will see the first real data consortium emerge for the purposes of creating an open identity graph. That’s my big prediction – and hope – for 2016.

Head For The Headers

2015 was the year of “header bidding,” the catch-all phrase for a software appliance that gives publishers the chance to offer favored demand-side partners a “first look” at valuable inventory. I am not sure if “header bidding” will ultimately become the de facto standard for “workflow automation,” but we seem to be relentlessly marching back to a world in which publishers and marketers take control of inventory procurement and get away from the gamesmanship inherent in exchange-based buying.

Big SSPs and networks that have layered bidding tech onto open exchanges are struggling. Demand-side platforms are scrambling to add all sorts of bells and whistles to their “private marketplaces,” but the industry evolves.

Next year, we will see the pace of innovation increase, and we have already seen big trade desks make deals with DMPs to access premium publisher inventory. It’s nice to see premium publisher inventory increase in value – and I believe it will only continue to do so.

2016 will be the year of “second-party data” and the winners will be the ones with the technology installed to easily transact on it.

2015 was a great year for data-driven marketing, and 2016 will be even more fun. Stay safe out there.

This post originally appeared in AdExchanger on 12/17/2015