Predictive Targeting: Managing Complexity


Personalization, one to one, predictive targeting, whatever you call it. Serving the optimal digital experience for each customer is often touted as the pinnacle of digital marketing efficacy. But if predictive targeting is so great, why isn’t everyone doing it right now?  

The reason is that while targeting can be incredibility valuable, many in the industry haven’t fully grasped that targeting ALWAYS leads to greater organization complexity, and that greater complexity means greater costs. 

Complexity
Informally, complexity describes both the number of individual parts, or elements, a system has, as well as how those parts interact with one another. Transitioning your marketing system (web site, call center, etc.) into one that can perform targeting will increase the number of elements the system needs to keep track of and increase the ways in which these elements interact with one another.

Targeted Marketing: The Elements
First, let me give you a quick definition of targeted marketing.  By targeting, I mean delivering different experiences to customers based on attributes of that customer. There are three main requirements that a marketing system will need to address in order to deliver targeted customer experiences :
1) Customer data. Nothing new here, this is what we all tend to think of when think about data driven customer experiences. However, we have additional issues to consider with targeting that we don’t need to deal with when only using the data for analytics. We need to:

1) ensure that we have accurate user data; AND
2) that it is available to our marketing systems at decision time.

That means that not only do we need to source the data, but we also need to ensure its quality and set up the processes that are able to deliver the relevant data at the time that the marketing system needs to select an experience for the user.

2) The User Experiences (differentiated content, different flows, offers, etc): Anyone who has done any AB testing will tell you that the hard, costly part isn’t running or analyzing the tests. It is the creation and maintenance of multiple versions of the content. Even if there is no marginal cost to create additional experiences (say we are doing a pricing test, it doesn’t cost more to display $10.99, than $20.99), we still need to be able to manage and account for each of these options.

3) Targeting Logic – This is a new concept. In order to link #1 and #2 above we need a rule set, or logic, that links the customer to the best experience. A set of instructions that tells our marketing system that if it sees some particular customer, it should select some particular experience. Most of the time the conversation around personalization and targeting is about how we come up with the targeting logic. This is where all of the talk about predictive analytics and machine learning etc comes in.  But we need to consider that once we have done that work, and come up with our targeting logic, we still need to integrate the targeting logic into our marketing system.

In this way, the targeting logic should be thought of as a new asset class, along with the data and experience content. And like the data and experiences, the targeting logic needs to be managed and maintained – it too is perishable and needs refreshing and/or replacement.

In the pre-targeting marketing system, we don’t really need to deal with either #1 or #3 above to serve customers experiences, since there is really just one experience – all customers get the one experience.

Obscuring Introspection
However, in the targeted marketing system, we not only have these extra components, we also need to realize that these elements all interact, which significantly increases the complexity.  For example, let us say you have a customer who contacts us with a question about a digital experience they had.  In the pre-targeting world, it was fairly easily to determine what the customer’s experience was. With a targeted marketing system, what they experienced is now a function of both their ‘data’ and the targeting logic that were both active AT THE TIME of the experience. So whereas before, introspection was trivial, it is now extraordinarily difficult, if not impossible in certain contexts to discover what experience state the customer was in.  And this difficulty only increases as the system complexity is a function of the cardinality (number of options) of both the customer data and experiences – the finer the targeting the greater the complexity.

This is important to consider when you are thinking about what machine learning approach you use to induce the targeting logic.  While there has been a lot of excitement around Deep Learning, the resulting models are incredibly complex, making it very difficult for a human to quickly comprehend how the model will assign users to experiences.

Data Ethics
This can be a big issue when you need to assess the ethics of the targeting/decision logic.  While the input data may be innocent, it is possible the output of the system is in someway infringing on legal or regularity constraints. In the near future, it is entirely possible that automated logic will need to be evaluated for ethical/legal review. Human interpretable logic will be more likely to pass review, and help to instill confidence and acceptance of their host systems.

It is one of the reasons we have spent a large amount of our research here at Conductrics on coming up with algorithms that will produce targeting rules that are both machine as well as human consumable.

Marginal Value of Complexity
None of this is meant to imply that providing targeted and personalized experiences isn’t often well worth it. Rather it is to provide you with a framework for thinking about both the COSTS and the Benefits of targeting. This way you and your organization can ensure success once you do embark on predictive targeting by keeping this formula for the ROI of complexity in mind every step of the way:

Marginal Value of Complexity

In a way, you can think of your marketing system as one big computer program, that attempts to map customers to experiences in a way that you consider optimal. Without targeting, this program is relatively simple: it has fewer lines of code, it requires fewer data inputs to run, and you know pretty well what it is going to spit out as an answer. When you include targeting, your program will need many more lines of code, require a lot more data to run, and it may be very difficult to know what it will spit out as an answer. 

So the question you need to spend some time thinking about before you answer, is if/when the complex program will be worth the extra cost than the simple one. 

And once you do, please feel free to reach out to us to see how we can help make the transition as simple as possible. 

Feel free to comment 


3 Comments

  1. Posted February 15, 2016 at 2:56 pm | Permalink

    I really like this way of thinking about this. It highlights that just adding “one” element (targeting), you’re inherently bringing another element (#1) into play, and that new element (#3) is an “integration” asset. And integration is always complex.

    I actually think #1 could be split into two different sub-groups: 1) profile data, and 2) behavioral data. The first sub-group is “the customer record” — who they are, RFM, etc. That’s the traditional customer record. But, a lot of the hope/desire around digital is that behavioral data — what people *do* — opens up all sorts of additional insight and possibility. The kicker is that behavioral data is pretty messy. And has a relatively short lifespan.

    So, if you’re tackling #3 and don’t even have #1 really in order… yikes!

    • Matt Gershoff
      Posted February 15, 2016 at 3:22 pm | Permalink

      Good point!

  2. Gregg Hamilton
    Posted February 17, 2016 at 4:06 pm | Permalink

    To complicate matters further, and perhaps beyond repair: if a visitor is unrecognizable (uses a new browser or device or clears their cookies), then it may be impossible to preserve the integrity of their customized treatment. The system might have presented the “A” offer on their initial visit, and then defaulted to the generic offer on their return.

    Also, the marketer must design appropriate content and messaging and rules for all the potential paths and experiential branches that visitors may traverse. Has anyone mastered this challenge? Are our marketing teams large and clever enough to do so?

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