Data Mining for Media Buying (Media Buying Part II)

January 23rd, 2011

Read part 1 of of my series on media buying.

In my last post, I talked about evaluating the targeting options available in a media buy, and really making smart choices about how targeted you need to purchase your media. Now I’d like to talk about using data to enhance the media buying process at each step of the process. No, I don’t mean the incredibly irrelevant Nielsen data that you have to pay a bunch of money for, nor do I mean the statistically irrelevant traffic/audience measurement tools that are available for cheaper or free (Comscore, Compete, and Quantcast exist, but they are so wildly inaccurate it is not worth making decisions based on their data).

I’m taking about your data. As a business, you likely have some data of customer lifetime value, historical cost per acquisition of a new customer, conversion rates from paid media sources, and repeat purchase rate. If you don’t have that, use assumptions or make numbers up as you go a long (I’ll explain that in more detail later).

As I said in part 1 of this blog post, every vendor should be able to provide some data of theirs to you about a potential media buy. This typically is an impression number. Impression data basically amounts to an estimated number of the maximum number of people who would see your advertisement. Depending on how they calculate this data (always ask), you may want to adjust the number (if they use a very conservative methodology, you may want to multiply it. If they use a shaky method that is not very conservative, you may want to only count a percentage of it). If you’re buying ads on the exteriors of buses for example, some vendors may use the bus ridership data to provide impression data. Those people may be likely to see the ad before they enter the bus, but this data ignores all of the pedestrians and drivers who also may see these ads. Keep that in mind.

Once you have impression data, you also have a cost quote from the vendor attached to the buy. From this, you can calculate a CPM (Cost /(Impressions/1000)). This is the standard cost measure for media buying, so it’s good as a comparison tool. Frequently, if you’re buying different pieces of media from the same vendor, the impression and cost data is broken out by each type of media. This can help you understand what pieces of media are the most expensive and may not be worth the price (more expensive does not necessarily mean more effective for you).

Once you have this data, you can estimate how many people enter your store, visit your website, call your number, or whatever your goal based on these impressions. That should be your conversion rate. If this percentage isn’t very small, you’re probably over-estimating. For example, one media buying/targeting option might generate a million impressions. I could estimate based on previous buys (or just pull a conservative number out of thin air) that 1% of those impressions visit my website, and from there 2% make a purchase. That equals 200 sales. If you don’t have this data or are a new business, estimate using industry benchmarks or whatever forecasts you have (2% is the ecommerce conversion rate average, for example). If another targeting option using that same approach projects to generate 100 sales for the same price, it’s probably not the option you want to choose of the two.

200 sales?! That’s great! Is it? This is where you should look at how much you paid for those sales, how much you earned from those sales, and how many more sales you should expect from those customers. Cost per acquisition measure how much it cost to acquire each customer. This should be compared to the revenue/profit of that sale and the lifetime revenue/profit you expect from that new customer (if it is a new customer. Keep in mind an advertisement may just drive existing customers back). If those 200 sales or the lifetime value of those 200 new customers equal more revenue/profit than the cost per acquisition, you’re possibly looking at a media buy you can pull the trigger on.

If that is the case, from a negotiation perspective, you’re sitting pretty. You have a deal you can bite on, and can just make some attempts to lower the price to make it even more profitable. A good way to make that attempt is to pretend those 200 sales or their lifetime value do not equal more revenue/profit than the cost per acquisition of the buy. If this really is the case (or you’re pretending it is), you can use this data to arrange a price more to your liking. One thing salespeople are not equipped to do is argue with your company’s data. If your data says this buy is not going to be profitable (or you’re pretending the analysis says that), they have to assume they won’t receive the sale unless they make it more enticing. This is one component not covered in Tim Ferriss’s ad buying negotiation tips that absolutely should be. Salespeople are not typically very analytical, so even if there are holes in your data, salespeople are not going to question your assumptions.

Once you’ve negotiated a better deal using data, it’s time to collect real data on how the media buy is performing. Is it profitable? Should I do it again? These are questions I hear a lot from advertisers they can know themselves with a little planning. Most advertising campaigns (except for extremely established brands) are meant to acquire new customers. At any point of sale, website confirmation process, or phone call, you can typically set up your system to tell if someone is a new customer. When you receive a sale from a new customer, just ask how they heard about you. On our website, any time a new customer places their first order, we have a one question survey that asks how they heard about us, and the answers are pre-filled with our marketing mix. 70% of people respond to that. That’s pretty good data. All you have to do is tally up that data to see if you’re receiving enough sales to justify doing the buy again. You can also track if these customers make repeat purchases. Refine your conversion data and lifetime value data for future buys with this data.

Here is an Excel template that can help get you started.

Data can make media buying a regimented, almost automated process that can come close to guaranteeing profitable media buying purchases. So, I challenge any one currently purchasing media today, what data are you using?

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