As it turns out, you can plot the two as the axes of a matrix on which, theoretically, you could plot any purchase. The four quadrants would be, starting in the lower left and going clockwise: low risk/low reward, low risk/high reward, high risk/high reward and, finally, high risk/low reward. Let's take a deeper dive in each quadrant to see what kind of purchases fall into each.
Low Risk/Low Reward
This is the stuff of everyday life. If you're a "to-do" list kind of person, these types of purchases would probably be on that list. Think of household supplies like toilet paper and laundry detergent, or the milk, dry goods, etc. that make up a large percentage of your grocery list. This is the world of consumer packaged goods. The only real exceptions are those products that represent personal indulgences, like a steak or your favorite premium ice cream.
There is a huge piece of the B2B market that falls into this category as well: office and industrial supplies, parts and other often-purchased items.
There is no gas pedal and no brake on these purchases. While the low prices remove any real risk, these are also not the types of shopping trips you look forward to all day. You simply have to get them done. This means the personal engagement with the actual act of purchasing will be minimal. Here, we are creatures of habit. We go to the same places to buy the same things because we really don't want to invest any more time than is necessary to get the job done. If you compete in this space, you have one strategy and one strategy only: provide the fastest and easiest path to purchase.
Low Risk/High Reward
Here, we have our little indulgences; the day-to-day treats that make life worth living. The entire premium consumer product industry lives squarely in this quadrant: premium desserts, pre-made meals, beauty care products, wines, craft beers and, moving into slightly greater degrees of risk, clothes, accessories, shoes, costume jewelry and electronic gadgets. This is also where you'd find CDs, DVDs and books. It's in this quadrant where Amazon rules.
These purchases are all gas and little brake. If you ever make a purchase on impulse, it's almost guaranteed to fall into this part of the behavioral matrix. When women plan shopping trips, it's to indulge their reward center with these types of purchases. But men are also vulnerable to the siren call of the indulgent purchase: gadgets, tools, sporting goods, electronic games -- and, for the metro-men amongst us, clothes and accessories. By the way, manicures, pedicures and spa visits all qualify, along with movies, concerts and dining out.
This quadrant is particularly timely this time of year, because when you buy a gift for someone, you hope you've hit this quadrant. The tough part is knowing your recipients well enough to figure out what will kick their nucleus accumbens into high gear.
While the degree of risk doesn't merit a lot of intensive research, here the buying can be as much fun as the owning, which generally means a higher degree of engagement on the part of the buyer. Shopping environments that enhance the reward part of the equation will be attractive. Buyers are susceptible to suggestion, especially if it comes through our social connections. And brand affinities are powerful here.
In my next column, I'll provide some examples of the other two quadrants to see what kind of purchases fall into each. Then, we'll see how each of these buying scenarios might map on the online consumer landscape.
This methodology was used in practical applications by the Western Electric (of AT&T) back in 68-72. At that time work was done on mapping outcomes based on events in a 2D graph.
The X axis measured success-failure
The Y axis measured reward-penalty
By using various measures of a company's metrics you would come up with different curves that indicated how firms reward their employees and shareholders based on performance.
Recently, in behavioral targeting, the same algorithms can measure which attributes respond differently from others on similar axis.
Experiments in analyzing casino gaming attributes from loyalty reward membership demonstrates that plotting attributes in 2D, shows that a simple low-high risk |low-high |reward graph is not very useful for driving just in time and location based offers, discounts, etc.
A 3D x-y-z attribute plot gives more insight. Then again, simple StatPlot or SPSS in the right hands is the best approach rather than simplistic model proposed in the article.