A Data-Driven Approach to the Marketing & Sales Funnel: Part 1

The goal of this series of posts is to provide value to the conversation around measuring marketing efforts and their contribution to the overall business goals of a company, and to make recommendations about how analytics and marketing can best work together to drive those goals. This will entail a bit of background on different marketing metrics, what their strengths and weaknesses are, and how they are commonly used, but is not meant to be a fully exhaustive list. Some points will draw on industry best practices, some will be technology based, and some will be statistical, but in an effort to be broadly useful, overly detailed or complex issues will be in separate posts.

Thought Process:

The first thing to try and do is (from a metrics/statistical standpoint) to stop thinking of customers and purchases in the traditional ways. Essentially, every entity, whether an individual or a company, that has a need for our product (i.e., the total IT training market) has what I will term a ‘potential for conversion event’ which is also conveniently the same initials as a ‘probability of conversion event,’ or a PCE.

(Thinking this way allows for some really beneficial logical progressions, one of the biggest being that there is no distinction to be made between new and renewal customers, or monthly and annual customers, from a qualitative standpoint, but more on that in a moment)

Every PCE or cluster of PCEs is just that: a business opportunity that has some chance of experiencing a ‘conversion event,’ which just means a transaction of some kind. For this endeavour, we are going to consider three different levels of contact with PCEs, the culmination of which is a ‘conversion event,’ which can happen unlimited times.

• ‘Conversion Event’ - a transaction of some sort, regardless of amount or type
• ex., trial sign up, first purchase, annual renewal, adding licenses, monthly auto-renew, etc.
• ‘Event’ - a deliberate, non-transactional interaction initiated by a PCE or involving two-way communication
• ex., email sign up, webinar sign up, phone call, chat, social follow/conversation
• ‘Exposure’ - any interaction with the brand that is non-individualized or falls short of the other interactions
• ex., see a display ad or Facebook status, read a blog post, visit the website, watch a YouTube video, etc.

The reality is that every purchase or conversion event is the cumulative result of a complex and infinitely mutable series of micro-interactions. As a result, we have to recognize that no conversion can be attributed to a single interaction, and that trying to do so will lead to erroneous assumptions. So the key to all measurement will be in the ‘P’ of PCE.

The goal in measurement will be, quite simply, to estimate as accurately as possible the effect of each interaction on the probability of an entity’s next conversion event, whether it is the first or tenth.

So, to look at what all this means, let’s go back to our initial definition of PCE, and examine ‘real world’ implications of this

Imagine that there are 10,000,000 individuals/companies in the world who need IT training:

• The probability that someone who has no awareness of our brand will have a conversion event is 0%. You have to know about us to purchase
• The probability that someone who knows that we exist, but nothing else, will have a conversion event is, say, .00001%
• The probability that someone who knows us and follows us on twitter, and had read a few blog posts, is .001%
• The probability that someone who has looked around the site, watched our YouTube videos, and discussed us on Reddit will have a conversion event is 1% (trial)
• The probability that someone who has signed up for a free trial (experienced first conversion event) will have another (become a paying customer) is 20%
• The probability that someone who has been on the site, gets our emails, did a free trial, and chats in to ask about subscription options will have a conversion event is 65%
• The probability that someone who has had 7 users on annual licenses for the past three years, who combined watch an average of 16 hours of video per month, uses coaching, and has had two phone calls from a sales person about their account expiration date is 85%

Now, these are example values (though some of them are not far from real), but the point is that the only number that truly matters, from a revenue standpoint, is the probability of their next conversion event, which, combined with the size of the opportunity, can give us the expected value of an event.

• i.e., a monthly customer in his/her second month, based on x interaction data, has a 78% chance of a $299 conversion event  20 days from now, with an expected conversion value of $233.22)
• Of course, we can take this further. Expected value allows us to chain events this way:
• for monthly customers, we know that they, as a whole, renew at some constant rate, with each renewal being a conversion event; this is a conditional probability (chances of X, given that Y)
• Looking at everyone who buys a monthly then, we start with a total conditional probability estimate that looks like this:

So we would start by saying that the average lifetime value of someone who purchases a monthly subscription is a function of the summed probabilities of each number of purchases in the chart above, times $99

• i.e. in the first column, we see that the probability of one purchase of a monthly is 1.0 or 100% (which makes sense, since our condition is the purchase of at least one monthly)
• then the lifetime value would look like this:
• (1 x $299) + (.8 x $299) + (.7 x $299) … (.025 x $299) = $1422.68

That’s great, but it lacks a lot of detail and context, and more over it is looking at the lifetime picture, when what we care about is the next conversion probability. However, we can still use a part of that in our model. For instance, we would make our subset [PCEs who have already renewed twice], who we know in aggregate will make the next purchase 60% of the time.

• What we want to know, however, is of those PCEs with two previous renewals, what if they had been exposed to a marketing campaign? How much does that affect the probability of their next purchase? Does it go up to 62%? 70%?

Note: This model/way of thinking is not limited to marketing, but should be considered for all means of contact between a brand and a PCE (potential converter):

• Talk to a sales rep? That will affect conversion probability
• Received an automated system email based on behavioral triggers, or reminders? Yup
• Use a feature of a product? Read a review on a blog or see a funny tweet from the brand? Yes, all of them

It’s also important to keep in mind that this isn’t model that simply marches towards a probability of 1 from 0, with every touch a positive contribution increasing likelihood of conversion. You can have inputs that add nothing, or even decrease the probability, which is what we need the models for. What models you use, how you select the variables, and what you do with the outputs is a whole other story, but that will be explained later. 

For now, start asking yourself questions about how you might be putting your understanding of customers and their journeys into the wrong boxes. A dollar of renewal revenue is just as good as a dollar of new revenue, right? So all you care about is the probability of each one, and how much effort it takes to change those probabilities. How can you increase the likelihood of getting the outcomes you want? How do you even know what events and exposures lead to conversions? We will look into that in the next post.