Giving credit where credit is due has always been tricky for marketers. That’s what has caused so much tension between marketing and sales over the years.

Our philosophy is to always make the complicated as easy as possible. You could call it a “hacker” mentality. So you want a simple solution to sales attribution – but we can’t reduce the complexity to the point where the model doesn’t work.

Years before the Internet, when direct marketers would run campaigns, it was clear that companies with powerful brands had a great advantage over those that were unknown. How much impact did a brand awareness push have on direct marketing efforts? Marketers had benchmarks, but it was impossible to know specifically.

Now we have sales attribution models designed to help close that gap. In theory, marketers should be able to determine how much each marketing effort contributed to the conversion to a sale. When you can understand that, you can optimize your media mix more efficiently.

Several models have emerged in the past few years. The easiest to measure – and, therefore, the most disparaged – is “last-click” attribution. The marketing effort that immediately preceded the sale gets all the credit.

It’s easy to see why marketers don’t like last-click attribution very much. It overvalues search engine marketing and other promotions where the customer is already sold on the product and is just asking to be pointed in the right direction.

It’s as if McDonald’s decided to put its entire marketing budget into training retail clerks to say, “Do you want fries with that?” and called it a day.

Another relatively simple model is “first-click” attribution. This is more popular with lead-generation sales models. The marketing effort that first got the customer to click gets the credit, and all the lead nurturing that goes on afterward doesn’t count.

You may also find some business-rule-based attribution models out there, where every interaction gets a portion of the value attributed to it. These models may give every contact equal weight or customize the value – for example, giving more recent interactions more weight.

Algorithmic models take this to another level, using regression or probability to weight the contacts.

But didn’t we say we wanted to make the complex easy?

Unfortunately, it gets tougher. The complexity grows exponentially when you move beyond digital media and attempt to attribute the value of traditional media.

At least marketers have a better chance of accuracy with measuring the impact of multiple marketing messages on current customers, because they have a customer record in place. They can know either what messages they have sent to that particular individual or the promotion code the customer used at purchase.

But is it possible to extend an algorithmic sales attribution model to customer acquisition?

It’s possible with a prospect database that covers the marketing universe. Then you track every solicitation – generally for non-addressable media, such as DRTV, and specifically for targeted direct media, such as email and direct mail – and every response.  Then look for links within the database to evaluate attribution. It may even be possible to infer when solicitations are passed along to someone who wasn’t targeted.

With this degree of detail, marketers can use a cross-channel algorithmic attribution model to plan the online and offline media mix and even their creative campaign strategy for the greatest effectiveness.

Setting it up is challenging – but the results are more dependable than with any other model, making the approach far easier to deal with in the long run.