"Data-Driven Thinking" is written by members of the media community and contains fresh ideas on the digital revolution in media.
Today’s column is written by Chris O’Hara, vice president of strategic accounts at Krux.
If you think about the companies with perhaps the least amount of consumer data, you may automatically think about consumer packaged goods (CPG) manufacturers.
Hardly anybody registers for their websites or joins their loyalty clubs. Moms don’t flock to their branded diaper sites and they are at arm’s length from any valuable transaction data, such as store sales, until well after the fact.
So, with little registration, website or offline sales data, why are so many large CPG firms licensing expensive first-party data management platforms (DMPs)?
While CPG companies will never have the vast amounts of point-of-sale, loyalty-card, app and website data that big box retailers 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.
Private equity 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 Wanamaker 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 the CPG marketer actually has in abundance – media exposure data – can be leveraged to the hilt.
The Move To Purchase-Based Targeting (PBT)
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, where big consumer marketers have written many tactics into their data-driven playbooks.
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 two 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, gleaned from loyalty cards and coupons. Yet until the emergence of data management technologies, they have struggled to activate audiences based on such data. Now big marketers can look at online coupon redemption or build special store purchase segments using information from Datalogix, Nielsen Catalina, News America Marketing and other data collection firms 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 automated through the use of algorithmic modeling and machine learning.
CPG has seen the future, and it is using purchase-based targeting 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).
Whom to target? It’s an interesting question, and one answered more decisively with purchase-based sales data.
Take a large global soda company as an example. The average amount of colas its typical 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 two to three 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 two to three messages per user each month) and bring light buyers to higher levels of purchase consideration (up to 20 messages per month).
Moreover, they can segment these buyers based on their category membership and adjust the creative based on the audience. Heavy buyers get messages that reinforce why they 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 make decisions on media investment by both channel and partner. By using DMPs for cross-channel measurement, CPG companies can apply even small insights 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, what if a CPG company mixed in a few “Buy Corn Flakes now at Kroger and save!” creatives afterward and saw a huge impact on its display performance?
Or a large CPG company may see massive lift in in-store coupon redemptions by running branded display ads on desktop throughout the week in addition to 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.
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?