From Density to Destiny: Using Spatial Dimension of Sales Data for Early Prediction of New Product Success

One of the main problems associated with early-period assessment of new product success is the lack of sufficient sales data to enable reliable predictions. We show that managers can use spatial dimension of sales data to obtain a predictive assessment of the success of a new product shortly after launch time. Based on diffusion theory, we expect that for many innovative products, word of mouth and imitation play a significant role in the success of an innovation. Because word-of-mouth spread is often associated with some level of geographical proximity between the parties involved, one can expect “clusters” of adopters to begin to form. Alternatively, if the market reaction is widespread reluctance to adopt the new product, then the word-of-mouth effect is expected to be significantly smaller, leading to a more uniform pattern of sales (assuming that there are no external reasons for clustering). Hence, the less uniform a product's distribution, the higher its likelihood of generating a “contagion process” and therefore of being a success. This is also true if the underlying baseline distribution is nonuniform, as long as it is an empirical distribution known to the firm. We use a spatial divergence approach based on cross-entropy divergence measures to determine the “distance” between two distribution functions. Using both simulated and real-life data, we find that this approach has been capable of predicting success in the beginning of the adoption process, correctly predicting 14 of 16 actual product introductions in two product categories. We also discuss the limitations of our approach, among them the possible confusion between natural formation of geodemographic clusters and word-of-mouth-based clusters.new products, innovation diffusion, spatial analysis, complexity

Customer Referral Management: Optimal Reward Programs

Sellers who plan to capitalize on the lifetime value of customers need to manage the sales potential from customer referrals proactively. To encourage existing customers to generate referrals, a seller can offer exceptional value to current customers through either excellent quality or a very attractive price. Rewards to customers for referring other customers can also encourage referrals. We investigate when referral rewards should be offered to motivate referrals and derive the optimal combination of reward and price that will lead to the most profitable referrals. We define a delighted customer as one who obtains a positive level of surplus above a threshold level and, consequently, recommends the product to another customer. We show that the use of referral rewards depends on how demanding consumers are before they are willing to recommend (i.e., on the delight threshold level). The optimal mix of price and referral reward falls into three regions: (1) When customers are easy to delight, the optimal strategy is to lower the price below that of a seller who ignores the referral effect but not to offer rewards. (2) In an intermediate level of customer delight threshold, a seller should use a reward to complement a low-price strategy. As the delight threshold gets higher in this region, price should be higher and the rewards should be raised. (3) When the delight threshold is even higher, the seller should forsake the referral strategy all together. No rewards should be given, and price reverts back to that of a seller who ignores referrals. These results are consistent with the fact that referral rewards are not offered in all markets. Our analysis highlights the differences between lowering price and offering rewards as tools to motivate referrals. Lowering price is attractive because the seller “kills two birds with one stone”: a lower price increases the probability of an initial purchase and the likelihood of referral. Unfortunately, a low price also creates a “free-riding” problem, because some customers benefit from the low price but do not refer other customers. Free riding becomes more severe with an increasing delight threshold; therefore, motivating referrals through low price is less attractive at high threshold levels. A referral reward helps to alleviate this problem, because of its “pay for performance” incentive (only actual referrals are rewarded.) Unfortunately, rewards can sometimes be given to customers who would have recommended anyway, causing a waste of company resources. The lower the delight threshold level, the bigger the waste and, therefore, motivating referrals through rewards loses attractiveness. Our theory highlights the advantage of using referral rewards in addition to lowering price to motivate referrals. It explains why referral programs are offered sometimes but not always and provides guidelines to managers on how to set the price and reward optimally.Referral Rewards, Customer Referrals, Customer Delight, Word-of-Mouth

Linking Customer Assets to Financial Performance

As more firms adopt a customer asset management approach to their business, it has become increasingly important to understand how customer management efforts relate to the financial performance of the firm. Of specific interest to shareholders is the relationship between traditional financial measures and customer-centric measures. The customer-centric measure that has received the most attention is customer lifetime value (CLV). In this article, the authors argue that the basic CLV model represents a useful foundation from which to begin to fill the gap between marketing actions and shareholder value. However, much work remains to be done before appropriate models can be developed that reflect the true value of a customer to the firm. Specifically, this article elaborates on how factors such as risk associated with customer behavior dynamics, social and competitive effects, and the effect of the product life cycle can be incorporated into the basic CLV model