Data and Audience
Solving some of a marketer’s biggest challenges by understanding a persona’s next moves.
Omnichannel attributioniSimply put, marketing attribution is identifying how a customer became familiar with and/or purchased your product or service. The goal of attribution is to enable marketers to understand which messages and channels had the greatest impact on the consumer's next step. A good example is a consumer gets information about a product in an email blast, but doesn't make a purcha... Read More is one of the most difficult problems to crack for marketers. Measuring the value of campaigns and channels that reach potential customers is rarely straightforward, and easier said than done. But the solutions to these marketing obstacles could come from an unlikely origin — a Russian mathematician.
Named for late-19th and early-20th Century mathematician Andrey Markov, so-called “Markov Chains” are mathematical frameworks of possible events that sequence the probability of each event depending on the step that came before it. In marketing vernacular, Markov Chains use machine learning to model paths to conversioniConversion Path is the sequence of steps a consumer took before it turned into a customer or completed another desired action. It is important for marketers to understand the conversion path of its customers so they can gain better insights into what prompted the target audience member into take action. A conversion path could consist of a consumer opening an email, comple... Read More across hundreds of thousands of potential customer journeys. By utilizing the full power of Markov Chains to understand each touchpointiA touchpoint is any time a consumer comes in contact with your brand over the course of the buyer journey. Examples of common touchpoints are social media posts, website clicks, email messaging, search engine searches or even online reviews. Touchpoints are a very important aspect to consider when studying the customer journey to ensure that every possible interaction with... Read More before an audience converted, brands can ultimately make more informed decisions on future marketing initiatives.
To get a better sense of how Markov Chains connects to marketing efforts and customer conversion, we spoke to Amsive’s VP of Data Analytics, Ray Owens.
Amsive: Take us through our Markov methodology.
Ray Owens: The Markov Chain is an algorithm that tracks the order of marketing channels customers encounter, and whether the journey ended in a conversion. We also frequently use the process to track creative rotation and online behaviors that result in conversions as well.
For example, take our health care vertical — if a prospect is searching around for information about the flu, or maybe looking for school physicals for their kids, we serve them ads for our urgent care clients based on collections of those triggers. The machine learning Markov Chain tracks which action, or actions, they exhibited that hopefully drove an eventual conversion for that urgent care client.
We previously spoke about how there are so many different approaches to attribution floating around. Where does the importance of Markov Chains factor in this equation?
It’s less about attribution of channels as it is about capitalizing on what decision drove the client to make a purchase and what influences our advertisingiAdvertising is digital or printed communication paid for by a business or industry that is directed toward a consumer with the intention of promoting or selling a product, service or idea or influencing purchasing decisions. Advertising is important because if done well, ads can have a direct and lasting impact on brand recognition and ultimately a company's bottom line.... Read More had.
When we push prospect data into our data management platform, we are pushing a highly predictive audience. We identify a good amount of different triggers that we can equate to desired behaviors that positively help drive conversions. For urgent care, for example, we can set a general audience with any in-market intentiIntent signals are like little breadcrumbs that consumers leave along the way as they search to purchase something online. A good intent marketer will pick up on these signals and use them to their advantage by creating more relevant experiences that convert prospects into customers. Those breadcrumbs are actually important bits of data about a consumer's intent to buy. Un... Read More, but we can also single out people who are new movers to a certain area, or if these people research health-related topics, if they’re searching on destinations like WebMD, and so on, as triggers.
We can also see that a group might exhibit certain behaviors, and they didn’t become a customer. Or they exhibited a particular behavior or range of them, and they did become a customer. It’s about pinpointing what combination of signal(s) in the Markov Chain throughout the journey generated the actual conversion or lack of conversion, and turning that around into actionable takeaways.
So should clients be interested in this methodology because it fills in some of the gray areas surrounding true ROI?
Absolutely. It answers the question: What is working in my advertising? It digs down to take the guessing game out of whether to run a general ad all day long and ignore other factors, or whether to target a certain audience based on their progression along with certain behaviors with one, two, or even more triggers.
At the end of the day, our clients have invested in their marketing spend, and they rely on us to justify that spend. What we’re able to do is show them, through our research and experience, that we picked the right creative, we picked the right behaviors, and it’s paying off. Ultimately what it does is help us directly prove the effectiveness of the spend.
How do Markov Chains compare to attribution approaches like last-touch, first-touch, and linear-touch?
The consequence of taking typical heuristic methods like first-touch or last-touch, and giving one or more of them equal weight, is that we could potentially end up with skewed metrics.
For example, let’s say we’ve calculated all of our first-touchpoints, and the top two ROIiReturn on investment (ROI) is simply the measure of what was spent on a marketing campaign compared to the sales that were a result of that marketing campaign. More simply put, for every dollar a company spends on marketing, how much money is that effort generating in sales. On a larger scale, ROI is a key metric used to not only determine success of markteing efforts, but... Read More drivers were a general ad and a back-to-school message trigger. What would have overwhelmingly been the major contributor to the conversion with these two at first-touch actually fall to the middle of the pack with a Markov Chain algorithm that ran through all the probabilities across the path to conversion.
We wouldn’t want a client to put all their spend on only first-touch points when the Markov chain method adjusts for the true effect of the journey that got them to conversion. We want to make sure to pinpoint the right behaviors that will pay off.
But there’s also value in showing what could be removed as well. How does a lack of triggers or behaviors drive the effectiveness of conversions?
The removal effect is actually the heart and soul of proving how valuable each step is in Markov chains. If we have all possible paths with a Markov Chain, we can also calculate what completely removing a step could have on conversion results.
Say we had an audience that exhibited five different behaviors through the chain to conversion. If I were to take out everyone who converted, I could see who would have converted had they taken a certain different path. It’s like Jenga — you remove some of the pieces and see whether it stops the tower from standing up. In this case, you pressure test whether you would still get to conversion by strategically removing certain paths while justifying the effectiveness of the different triggers.
Are there any drawbacks to utilizing Markov Chain methods?
If you think about the sheer volume of the potential paths to conversion, we can’t calculate that much data without machine learning. It takes more effort to do machine learning methods like this, and you might have to invest in more resources. But it ultimately pays itself back by more efficient reporting and attribution.
We found it to be so versatile. We now do this for all of our urgent care clients, and we’re rolling this into real estate, financial services, and more because of the ability to capture multiple signals and their effect on conversion. The payoff is the amount of confidence our clients receive from having more visibility into what’s actually working with their marketing spend.