Without question there is an urgent need among direct marketers to prove that their investments in technology (databases, websites, social media, email, SEO, kiosks, call centers, catalogs and mailings, …) are more than paying for themselves. How, then, should companies that transact and communicate with their customers through multiple channels evaluate the cost-effectiveness of their multi-channel marketing strategy?
In this article we suggest two metrics that managers should use to better understand how well their multichannel efforts are paying off:
- 1. Cost to serve – The customer specific marketing and servicing costs typically incurred by multichannel marketers to initiate and maintain a business relationship with individual customers. Examples are: freebies and promotions (shipping and handling costs, two for ones, cents or dollars off), fees and commissions (to affiliates, retailers, etc.), customer service and support (returns, call center support usage), loyalty costs (miles redeemed, gifts), etc., etc.
2. Realized revenue – The revenues actually realized by the company from a given customer. This is determined by subtracting the cost to serve from the invoiced, or the contracted, price (which itself can differ by channel, retailer, or if the product was bought through an online auction).
The realized revenue from customers who routinely buy only when products are being promoted, return goods frequently, or require heavy levels of support services could be much lower than the invoiced revenue – severely impairing the lifetime attractiveness of such a customer.As the number of channels through which customers communicate and transact with companies continues to explode, the number of offers and communications companies present to their customers has grown exponentially.
New database and CRM technologies make it possible to track customers by revisit behavior, allowing targeted promotions for newer versus existing customers, or for particular products. Additional offers still are communicated to various segments through e-mails, print and mass media ads, and direct mail pieces.
While differences in offers have always existed, CRM technologies and new media have greatly increased the numbers of offers presented to customers. Left unmonitored, such complexity has the potential of severely increasing cost to serve, eroding realized revenues and greatly impairing profitability.
Consider the situation described in the table below. While both customers, A and B, paid the invoiced price of $100, the realized revenues from customer A were only half as much as those from customer B. Further, notice that while costs such as promotion discounts would normally be visible to the manager, others such as affiliate fees and costs of returns are often missed in assessing the value of a given customer. New database and eCRM technologies make it possible to track these costs, often at the individual customer level.
The tracking system can be implemented by building your own software to tag each cost category with a unique customer I.D. Reports such as the one above can then be created using standard business intelligence tools. Third party software and services are also available (Return.com, ReturnBuy, etc.) that provide software or hosted services designed to monitor customers and their return habits, granting return merchandise authorization numbers, and reducing cases of fraud. Others such as CommissionJunction and Linkshare provide services related to affiliate marketing programs.
Cost to Serve and Realized Revenue
|Credit Card Fees||3||3|
|Shipping and Handling Discounts||25||22|
|Customer Service Contacts||7||3|
Once created, such a breakdown of the paid and realized prices can provide several meaningful insights. For example, suppose now that the columns marked A and B represent the same customer (or cohort of customers), but at different points in time.
The evident improvement in realized price would, of course, represent welcome progress for the company. But, more importantly, such a table also shows progress with respect to each of the components of cost to serve. The decreases in returns and affiliate fees probably indicate that the customer is more satisfied with the products bought, and relies less frequently on affiliate sites to find the target site. These component level trends can then be compared versus target levels for each of the costs across time. Necessary corrective action could then be taken to bring aberrant costs under control.
The analysis can also help the company develop the appropriate strategies for enhancing customer satisfaction and profitability. Based on the separate tables for each customer (where necessary, some of the costs could be inferred at the segment level), it is now possible to create a map such as the one shown below. In this map, the horizontal axis represents the cost to serve and the vertical axis represents the revenues realized. Each customer can be plotted as a point in the cost-revenue space. Each of the four quadrants, then, becomes the basis for creating a segmentation scheme.
Cost – Revenue Strategy Map
For example, the customers in the yellow “watch-out” quadrant have not yielded a great amount of realized revenue, but have cost a great deal to serve. These might be customers who demand a lot of call center services, use coupons extensively, and manage to convince the telesales rep to throw in free shipping.
They may have high invoiced revenues, might even have bought more than once, but are very expensive to maintain as customers. The company may want to consider teaching them how to use automated/online support and services. Alternately, they might be aggressive users of returns, discounts and promotions because such customers do not see real value in current offerings. Instituting “low-cost” marketing research approaches to better learn the kinds of products and services that represent real value for them should help the company improve realized revenues. But, not understanding how many such customers there are, and failing to devise the appropriate teaching, learning, or divestiture program for them will certainly prove unprofitable for the company.
The “keep-em” customers in the top-left quadrant are obviously the most desirable. Programs aimed at retention, such as providing preferred services, and (especially in business to business applications) joint development of new products and services should be important.
Because the realized revenues are not out of line with the cost to serve, both of the remaining two quadrants are in balance. However, the high cost to serve customers in the top right quadrant suggests that ways of automating purchase orders (these are frequent buyers) and customer service, and replacing their use of discounts with attractive rewards for loyalty should result in significant bottom line gains. Finally, customers in the bottom left quadrant should be given incentives to increase the size of orders, or be cross-sold. But unless there is clear indication of high potential for future sales, failing to control cost to serve will have immediate negative bottom line impact.
Much to the dismay of some (and gleeful satisfaction of others), technology based multichannel marketing is neither free nor easy. Because it is not free, it is imperative to understand how each customer impacts the bottom line. Fortunately, the same technology that has created so many ways of communicating with customers (each a potential money sink) also permits the marketer to record much better individual level data about the relevant costs and revenues over time. However, as many have discovered, from the gigamounds of bits generated, pulling the relevant data together to yield actionable results is not easy. The approach described here provides:
– a simple way of summarizing the relationship between marketing activities, customer responses, and the company’s bottom line. The approach emphasizes the importance of going beyond the invoiced price to the revenues actually realized from each customer
– a useful tool for monitoring and controlling the various costs incurred in selling and servicing individual customers over time
– a strategic approach for creating four distinct segments of customers which yield actionable recommendations based on the value each customer provides the company