Are you one of millions of marketers/analysts trying to prove that engagement exists, and more important, leads to increased return on investment?

The pick up a broom and start sweeping ... I'll show you a method that loyalty marketers have been using for twenty years to prove that loyalty marketing delivers a return on investment. I use more technical versions of this methodology to calculate the value of loyalty programs for current clients, running a full profit and loss statement on the outcome of this analysis. Hint: It works!

Your audience is comprised of all customers with 1+ purchase (via Step 2).

If "Engagement" is a significant predictor with a positive coefficient, then you just proved that, for the month of November, engagement during October led to an increased probability of a customer purchasing in November.

Ok, here's the SPSS code required to run the Logistic Regression procedure I described above:

Here's the outcome of a trial I ran earlier today:

In this example, the square root of recency is easily most important. Recent orders carry about 5 times the weight of older orders. Large dollar orders result in customers less likely to buy again in the future. And most important (while not terribly significant), customers who were "engaged" in October were more likely to buy in November, after accounting for all other RFM-based variables.

In fact, "engaged" customers were 22.7% more likely to buy again, all things being equal.

Armed with this outcome, we can put a profitability number on "engagement". We'll make the financial analysis terribly simple, for demonstration purposes.

Let's assume that we have 250,000 customers in the database. Of the 250,000 customers, 5,000 are considered "engaged". Let's assume that our 250,000 customer database has a 3% chance of buying in November. And let's pretend that, if a customer purchases, the customer will spend $100. Finally, let's pretend that 35% of demand flows-through to profit, and let's pretend that one employee is responsible for improving engagement, at a cost of $8,000 per month (salary + benefits).

Total Expected Housefile Demand = 250,000 * 0.03 * $100 = $750,000.

Now, we know that 5,000 engaged customers are 22.7% more likely to purchase because they are "engaged", right? So, our calculation changes a bit.

Total Expected Housefile Demand = (245,000 * 0.03 * $100) + (5,000 * 0.03 * 1.227 * $100) = $753,405.

The impact of engaged customers is ... $753,405 - $750,000 = $3,405.

At 35% profit, this translates to $3,405 * 0.35 = $1,192 profit.

But, we hired an individual to generate engagement, and we paid the employee $8,000 to get the job done in November.

How many "engaged" customers do we need to make the effort worthwhile? Well, we have 5,000 engaged customers who generated $1,192 profit, prior to employee costs, or $0.2384 profit per customer. To offset employee costs, we need 8,000 / 0.2384 = 33,557 engaged customers.

So, at this point, here's what we know:

The pick up a broom and start sweeping ... I'll show you a method that loyalty marketers have been using for twenty years to prove that loyalty marketing delivers a return on investment. I use more technical versions of this methodology to calculate the value of loyalty programs for current clients, running a full profit and loss statement on the outcome of this analysis. Hint: It works!

**Step 1:**Create your engagement measure. This will be a different metric for everybody, so there's no sense spending time discussing it, you are the expert at knowing your business. Customers who you consider to be "engaged" receive a value of "1", while customers who you do not consider to be "engaged" receive a value of "0". Only use the timeframe up to 10/31/2011 for your engagement period.**Step 2:**Create RFM-based variables. For each customer, through 10/31/2011, calculate months since last purchase, number of 12-month purchases, number of 13+ month purchases, and historical average order value.Your audience is comprised of all customers with 1+ purchase (via Step 2).

**Step 3:**I will assume that you don't have profitability data, so let's make this really easy. Create a variable called "Future" ... it has a value of "0" for all customers who did not purchase from 11/1/2011 to 11/30/2011 ... it has a value of "1" for all customers who did purchase between 11/1/2011 to 11/30/2011.**Step 4:**Match the query in Step 3 to the query in Step 2. Then, match these queries to Step 1, all at a customer level.**Step 5:**Run a Logistic Regression (you can take this much further if you have profitability data ... Logistic Regression for response, OLS for spend/profitability). Regress Future against Recency (usually Square Root of Recency), 0-12 Month Orders, 13+ Month Orders, Average Order Value, and Engagement.If "Engagement" is a significant predictor with a positive coefficient, then you just proved that, for the month of November, engagement during October led to an increased probability of a customer purchasing in November.

Ok, here's the SPSS code required to run the Logistic Regression procedure I described above:

**LOGISTIC REGRESSION VARIABLES future****/METHOD=FSTEP(WALD) root_recency freq12 freq99 average_order_value engagement****/CRITERIA=PIN(.05) POUT(.10) ITERATE(20) CUT(.5).****execute.**Here's the outcome of a trial I ran earlier today:

In this example, the square root of recency is easily most important. Recent orders carry about 5 times the weight of older orders. Large dollar orders result in customers less likely to buy again in the future. And most important (while not terribly significant), customers who were "engaged" in October were more likely to buy in November, after accounting for all other RFM-based variables.

In fact, "engaged" customers were 22.7% more likely to buy again, all things being equal.

Armed with this outcome, we can put a profitability number on "engagement". We'll make the financial analysis terribly simple, for demonstration purposes.

Let's assume that we have 250,000 customers in the database. Of the 250,000 customers, 5,000 are considered "engaged". Let's assume that our 250,000 customer database has a 3% chance of buying in November. And let's pretend that, if a customer purchases, the customer will spend $100. Finally, let's pretend that 35% of demand flows-through to profit, and let's pretend that one employee is responsible for improving engagement, at a cost of $8,000 per month (salary + benefits).

Total Expected Housefile Demand = 250,000 * 0.03 * $100 = $750,000.

Now, we know that 5,000 engaged customers are 22.7% more likely to purchase because they are "engaged", right? So, our calculation changes a bit.

Total Expected Housefile Demand = (245,000 * 0.03 * $100) + (5,000 * 0.03 * 1.227 * $100) = $753,405.

The impact of engaged customers is ... $753,405 - $750,000 = $3,405.

At 35% profit, this translates to $3,405 * 0.35 = $1,192 profit.

But, we hired an individual to generate engagement, and we paid the employee $8,000 to get the job done in November.

How many "engaged" customers do we need to make the effort worthwhile? Well, we have 5,000 engaged customers who generated $1,192 profit, prior to employee costs, or $0.2384 profit per customer. To offset employee costs, we need 8,000 / 0.2384 = 33,557 engaged customers.

So, at this point, here's what we know:

- We demonstrated that engaged customers are 22.7% more likely to purchase, all things being equal. In this example, Engagement does lead to improvements in customer loyalty. And isn't that what you really wanted to demonstrate?
- We only have 5,000 customers meeting "Engagement" criteria. As a result, we only generated $3,405 of incremental demand.
- After accounting for employee costs, our engagement efforts are not generating a positive ROI.
- Every engaged customer is worth an additional $0.24 profit, per month, to the company.
- If subsequent engagement activities result in having at least 33,557 engaged customers, we can demonstrate that increased engagement can be accomplished at break-even levels.

Statisticians will poke holes in this entire argument, and that's fine ... they can build on or change the methodology to be more appropriate for their needs.

Statisticians, however, are not the audience I am speaking to. I am speaking to you, the Marketing Executive / Analytics Expert. And I just showed you a method that can easily demonstrate the return on investment of "Engagement" ... a method that is more scientific and more accurate than what you're taught out on Twitter.

Now go take this methodology, and do something with it!! Stop talking about how hard it is to measure the value of Engagement ... it isn't hard, I just showed you, for free, how to do it!! No more excuses ... just go do it!!!!

If you'd like for me to do this for you, hire me (click here), and I'll tell you what engagement means to your business.

If you think this was valuable, would you at least tweet is or share it on Facebook or on Linkedin, as a way of demonstrating that Engagement can be linked to ROI?

Thanks,

Kevin

Kevin