The Diffusion of Innovation bell curve illustrates how the use of a new product spreads through a population. Ideally, marketers want everyone in a market to immediately run out and buy our new products, but this is not realistic, because consumers differ in how willing they are to accept the risk of buying and using new products. It often takes months or years for most of the population to accept new products.The Diffusion of Innovation is a valuable tool for marketers because it helps us to forecast sales, penetration timeline, and the life cycle of a new product.
1. Forecast Sales. Let's assume we live in a village with 100 residents and we just invented a new tool. Some residents in our village will run right out to buy our new tool. We call these residents "innovators" and based on the Diffusion of Innovation, there are 2.5 innovators in our village (ignore the fact for now that we can't really sell to half of a customer). If the average price of our tool is $10, then the innovators generate $25 in sales (100x0.025x10). Already, we can predict the total sales of our product for all other types of customers in our village and for all stages of diffusion. Here are some examples:
A. If Innovators account for $25 in revenue, how much revenue will the Early Adopters generate? In other words, if 2.5 residents spend $25, how much will 13.5 residents spend?
If 0.025 = $25, Then 0.135 = x, x = (0.135($25))/0.025 = $135
B. If total product sales to Innovators is $25, what will total product sales through the Early Majority stage be? In other words, if total sales to 2.5 residents is $25, what will total sales be when 50 residents buy the product?
If 0.025 = $25, Then 0.025+0.135+0.34 = x, x = (0.5($25))/.025 = $500
2. Forecast Penetration. Customers differ in how willing they are to accept the risk of buying and using a new product. The 100 residents in your village have different levels of know-how, budget, and experience. Some residents can begin to use your product immediately, while others will require more information, more hands-on experience, and maybe financial incentives. If all Innovators purchase your new tool in 10 weeks, then we can forecast how long it will take for all other types of customers to use your product.
If you begin selling your product on January 1, 2011, and all Innovators buy your tool in the first 10 weeks it is available, when will all Early Adopters buy your tool, i.e. when will you begin to sell to all Early Majority customers?
If 0.025 = 2 weeks, Then 0.025+0.135= x, x = .16(2 weeks)/0.025 = 12.8 weeks, which means mid-April .
We use this data two ways. First, to track our weekly progress toward the goal of getting 13.5 new customers by mid-April. If after 9 weeks we have 10 customers, will we achieve our goal of selling to all Early Adopters by mid-April? We also use this data to remind us to look at our decision-making process. If we fall behind in our penetration and revenue goals, it means we need to pay closer attention to some steps in the customer decision-making process.
3. Forecast Product Life Cycle. All products, no matter how innovative, will eventually lose momentum and sales will shrink. The savvy marketer is constantly watching individual product sales to determine when to reallocate marketing dollars to launch new products and make up for revenue lost from declining products. To do this, we need to match the diffusion stages to phases in a product's life cycle. These are separate curves. The diffusion curve represents various stages of a product's commercialization. During a product's emerging phase, the product is being developed and tested - no revenue is generated. A product's emerging phase is, therefore, a pre-diffusion stage. To match the diffusion
stages to life cycle phases, lets use the data from the 100 villager example to calculate the stage sales and sales growth for each phase of diffusion.To calculate the revenue growth rate, determine the change in revenue and divide by the lower number. For example, if sales in January were $25 and sales in February are $135, then sales grew 440% ((135-25)/25) from January to February.
We can tell by looking at the revenue growth rates in the far right column that the diffusion stages clearly match up to the growth, maturity and decline phases. The growth rates for Innovator, Early Adopter and Early Majority stages were greater than 10% and this corresponds with the growth phase in the product's lif cycle. The growth rate for the Late Majority phase was less than 10%, but not negative, and the product did generate revenue, so this stage matches with the product's maturity phase. Sales in the Laggard phase were negative compared to the prior phase, which means the product is in decline.
Self Test. To see how you did, send your results and work to mcapone@godmncapone.com.
A. Assume the village has a population of 1,763 customers and your new tool costs $29. Forecast total sales to all stages.
B. If you begin to market your product on March 1, 2011 and it takes 4 weeks to sell to Innovators, when will you begin to sell to each of the other diffusion customer types if your penetration rate is constant?
C. Use your answers to the above questions to predict when your product will enter the decline phase, i.e. when you have to have a new product ready for commercialization?
D. If you take a 14% portion of the product's profit earned during maturity and invest it in new product development, how long will you have to get your new product from ideation to testing?
E. If each stage of diffusion takes 20% longer than the previous stage, when will your product begin to decline?
