Optimization means getting the right offers to the right people on the right channel at the right time to drive a profitable increase in purchasing during a single trip – and if you do it well, potentially lengthening the tail of that optimization to ensure longer-term effects. It involves everything from the simple list of available offers all the way to comprehensive shopper behavior analysis. Consider that you have millions of shoppers, thousands of offers and content communications, as well as several channels (digital and others).
Offers need to be broken down into products and economics. With regard to product, you need to think about product popularity and correlation. How popular is peanut butter, and how might sales increase when paired with a jelly offer? Do peanut butter sales drop or increase during summer vacation?
Economically, you need to analyze how to value and format the offer. Consider how attractive the discount is, compared to the product’s recent prices, and decide how it will be formatted: discount; fixed price; discount based on total basket; buy X, get Y; two for one, etc. Since we’re talking economics, don’t forget about price elasticity and sensitivity – the product’s sales performance at different price points. Unless eggs jump to $10 per dozen, most people will buy eggs, whereas premium ice cream may sell only to $5 per pint, with fewer sales at higher prices.
Once you’ve got the right offer, you need to find the right shopper. Behavior analysis is critical at this step. The shopper breakdown needs to be much more specific than basic demographics. It needs to consider the frequency, basket size, and patterns over visits.
You also need to consider the relevancy of the offer – is the shopper you have chosen similar to the people who regularly buy the product? Also, consider the individual’s price sensitivity to special offers – will they buy anything with a coupon, or only use coupons for brands they already buy? The basket effect will be low on products they normally buy, while cross-sell and up-sell offers will be higher.
Brand loyalty is also a factor when considering customer behavior. How loyal is the shopper to the brand or competing brands within the category, and what is her normal purchase behavior? Make sure the profits outweigh any cannibalization effect.
Optimization cannot happen without a specific focus on your constraints and your needs. During the negotiations with your suppliers, what kinds of offer levels were you able to secure? How does that limit what you can offer? In which stores is this product available, and which locations do you want to make the offer available? Should some stores offer it and not others? Do you have stores under threat that you need to drive traffic to?
We all remember the medium is the message. Channel and timing analysis are critical to ensuring that future campaigns have ever-increasing success. When did the customer receive the offer, and how long did it take him to act? Did shopper X see the offers through channel Y? What was the redemption rate per channel? For each channel, you need to do an in-depth analysis of the maximum shopper quantity allowed, cost per issuance, typical engagement levels, the basket effect, and, of course, ROI.
True optimization is a continuous feedback loop, examining the results on a shopper, offer, and campaign level. On the shopper side, look at their response rate to individual and similar offers, the channel of delivery, and how well your campaigns delivered, all in light of ROI. With automated solutions and AI science it’s possible.
