Reduce Your Dependence on Discounting
Understanding the factors and accurately predicting customer responses to marketing is an area of analytical marketing that allows targeting the most likely customers to buy. These models are built by calculating various customer attributes (such as AOV, Time between orders, time since last order, number of categories bought from etc.), then putting these variables in a statistical predictive model to estimate the probability of customer coming back. The modeling technique is a separate subject, here I'd like to review how these models could be used.
- Determine target depth (How much to spend?)
- Determine target audience (Who to spend it on?)
- Differentiate offers
The topic of this post is to discuss item 3, offer differentiation. For simplicity sake, let's assume that our model produced 3 segments, High, Medium and Low propensity buyers. The average behavior of these people are given below as an illustrative example:
Segment | Population | Probability of Purchase in the next 30 days | Gross Margin Per Order | Cost per Touch (assuming $1.00 cost per piece) | Net Profit (Gross Margin – Cost of Response) | Net Profit From Segment |
High Propensity |
100 |
10% |
$50 |
$10.00 |
$45 |
$450 |
Med. Propensity |
200 |
5% |
$50 |
$20.00 |
$30 |
$300 |
Low Propensity |
1000 |
1% |
$50 |
$100.00 |
-($50) |
-$500 |
Total |
$250 |
In the above scenario, there are two choices to make.
- Accept the fact that low propensity segment is "reactivation" and assume the negative cost as "reacquisition cost", reset the clock on these people and start re-measuring lifetime value on these people
- Find a way to increase the response rate of these people
Let's exercise the impact of a pricing discount to these lower propensity segments. The idea is to selectively discount for these people that minimize margin cannibalization (another important topic on this subject, however, I'll talk about this later)
Segment | Population | Probability of Purchase in the next 30 days | Gross Margin Per Order | Cost per Touch (assuming $1.00 cost per piece) | Pricing Discount | Net Profit (Gross Margin – Cost of Response) | Net Profit From Segment |
High Propensity |
100 |
10% |
$50 |
$10.00 |
0% |
$45 |
$450 |
Med. Propensity |
200 |
8% |
$35 |
$12.00 |
15% |
$23 |
$368 |
Low Propensity |
1000 |
4% |
$20 |
$25.00 |
30% |
-($5) |
-$200 |
Total |
$618 |
Notice four things;
- Due to discount, response rate have increased. This lowers the cost per touch
- Gross Margin per order has decreased due to discounting (here I used 50% gross margin prior to discount, i.e., $100 AOV)
- Net profit per order decreased per order for med. Propensity segment, but increased for low propensity segment. This is where testing will yield the optimal answer of how much to discount to generate the right behavior.
- Even though net profit decreased per order for the med. Propensity segment, since response rate increased, the total profit increased for this and total segments
Of course trial of various offers will make fine tune the outcome. The other thing to consider here is the incrementality of the profitability, which is another area I will discuss.