Monthly Archives: October 2014

Programmatic’s place at the top of the marketing funnel

By Eric Picard (Originally Published in iMedia – October 11, 2014)

For decades, modern marketers have developed significant marketing plans with detailed analysis of target audiences. Often before products are designed, significant amounts of market research have been developed and applied against the product or service development process.

When a brand decides to spend millions of dollars to create a product or service, it typically then spends tens to hundreds of thousands of dollars on market research and product planning to get ready to launch it.  And then hundreds of thousands to millions of dollars to market the product.

Most of that market research and product strategy folds over into the marketing plan. And as part of that process, typically very detailed marketing personas are created — sometimes a handful, sometimes more than a dozen. These marketing personas are decomposed into the marketing plan and drive many of the media mix decisions that are used to divvy up budget among channels. And often these do get distributed to the media agency as part of the marketing plan’s translation into media planning and strategy.

But in my experience, it is fairly common that by the time the media buyer gets the media plan from the planners, the marketing personas have been stripped off. And this is even more true when we bring programmatic media into view. As an example, consider a conversation I had this past year with a media buyer at a major trading desk.

This trading desk handles the media buying for a major home improvement retailer. And when I talked with the trading desk buyer about how the company approaches this customer’s media buys over its DSP partner, the buyer looked a little puzzled. To that person, it was about only two things:

  • Buying the “home improvement” segment
  • Setting the rest of the budget to optimize spend against CPA on its web pages and letting the DSP figure the rest out

The problem with this approach is that it’s extremely one dimensional — and loses much of the value that exists within the systems used. It’s like using an F-16 to commute to work. Or an aircraft carrier to run to the store.

I haven’t seen the marketing plan for the client, but I can imagine (having seen a lot of them over the years) that the retailer has several different ones. I’ll make up a few that probably exist in part, and explain how I’d have approached the campaign using a DSP.

Persona 1: Reggie is a 28-year-old single male who lives in a major metropolitan area in a condo that he owns. He makes more than $50,000 a year and mostly shops at the client’s stores to buy décor items, fans, DIY project materials, and probably will buy things like air conditioners, painting supplies, hand tools, etc.

Personas 2 and 3: Sophie is a 35-year-old stay-at-home mother who lives in the suburbs of a major metropolitan area and is married to Tim, a 35-year-old executive who works in the city and commutes. Together they own a house that is more than 4,000 square feet and has at least half an acre of land. Tim is a weekend DIY warrior, who takes on various home improvement projects. He’s likely to take on light construction projects, buying building materials, painting materials, plumbing and electrical, and lots of landscaping tools such as riding mower, blowers, etc. Sophie is an avid gardener who buys numerous plants and gardening materials, and takes frequent courses on design and gardening at the store.

Persona 4: Arthur is 65 years old. He is retired, lives in a modest home in the suburbs, which he owns outright. He is in the process of getting ready to sell the house as he and his wife are looking to move to a smaller place or a retirement community. But he has three adult children who own homes nearby, and he frequently putters and does projects around their houses. He’s likely to buy building and painting materials.

Although I just made up these personas, they’re fairly typical of the kinds of personas I have seen over my career — if anything, they’re a bit light. Additional information that would typically accompany a persona includes the numbers of each of these personas that exist in each DMA in the U.S., perhaps even broken down by ZIP code within each DMA. And then marketing teams typically will use whatever tools are at their disposal to begin matching against mechanisms like PRISM clusters and do some media mix modeling about how to reach these audiences.

At the handoff to media agency partners for digital media, the planners at that point begin using various tools to determine what sites have traffic that matches their target audiences, and an overall media plan and strategy is devised.

Once the plan is handed off to media buyers and their trading desk partners, the thinking is usually quite distilled. Buyers going directly to publishers will send over an RFP that simplifies the media plan (they may also send the media plan) for sending to publishers. They then wait to hear back regarding what inventory is available. The trading desk partners typically decide what audience attributes align against available data segments for their goals.

Now let’s go back to the example I used above about the trading desk with a major client in the home improvement retail space. Given its customer personas, I’d have recommended a few other ways to engage and find audiences.

Perhaps it could target users who own homes of a certain size or homeowners who have been in their home for a certain number of years. It could target each of these segments by age and geography. It could differentiate both creative and offer by each of these. It could vary what products to highlight in its advertising based on some of the criteria, such as age, gender, and other elements. It could target households with children differently than households with adult children not living in the home. It could even target based on the age of children, assuming parents of college age students might be moving kids into apartments or dorms at the end of summer or fall. Or it could target urban apartment dwellers with fans in the summer and suburban homeowners with leaf blowers in the early fall, snowblowers in the late fall, and lawnmowers in the early spring.

In programmatic, we far too often fall into the trap of only feeding the portion of the purchase funnel that is focused only on CPA at low costs of media plus data. As a market, we need to expand how we see programmatic media and really try to dig into the market for data and the use of sophisticated DSP platforms.

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