Why It Pays to Use Credit Data
For the most part, whenever the possibility of using credit data elements in a marketing file is mentioned, the reaction of most marketers can be boiled down to a couple of standard responses. One is a blunt "I couldn't care less." Another is the explanation that "My customers always pay by credit card." The third popular reaction puts the responsibility for ascertaining the creditworthiness of current customers and prospects in the hands of the credit manager and not theirs.
Segmenting customer and prospect lists using credit information is not widely practiced, with the exception of financial service companies. So if you are inclined to one of the standard responses referenced above, before you stop reading, please peruse the following possibilities. You may just be in for some enlightenment.
For example, what if you could:
* Reduce marketing costs 4 percent to 8 percent by eliminating high-risk prospects and/or customers?
* Offer levels of service or credit appropriate to the need and capability of the customer?
* Target prospects or customers who have demonstrated a willingness to purchase products or services?
* Maintain and manage an interactive relationship with your customers?
* Be proactive in offering new levels of service or credit?
The Broader Value of Credit Data
Traditionally, credit elements are associated with risk management. However, these elements can also be used to proactively and appropriately segment prospect and customer data files. Let's take a closer look at each of the opportunities identified above.
With the downturn of the economy over the past few years, some businesses have closed their doors and many have laid off employees. However, the economic situation also has resulted in the creation of new businesses. As professionals found themselves unemployed, many have chosen to start up their own businesses, as reflected in a 3.5-percent increase in the number of businesses started in 2002 over 2001, according to ABC News.
In fact, there are now nearly 5 million businesses that are less than a year old. Their owners are looking to establish credit, purchase goods and services and establish themselves in their respective industries.
At the same time, the credit ratings of established businesses are declining. About 2 million busi-nesses are currently involved in derogatory legal processes, representing a 2-percent decline in credit ratings that are considered to be reasonable-to-good. The number of businesses in collections has increased by nearly 7 percent in the past year, bringing the total number to approximately 900,000.
During this tumultuous period, savvy business people have to work hard to proactively manage their relationships with their prospects and customers.
Eliminate High-Risk Prospects
Let's start with risk management, the most obvious use of credit data. According to a study by Experian, 8 percent of businesses are currently facing some form of derogatory legal process. By looking at derogatory indicators (bankruptcies, liens and collections) and excessive days beyond term (DBT), marketers can avoid targeting high-risk prospects and even the high-risk customers currently in their own databases.
For example, assume you want to send a mailing that costs $.75 per piece to 100,000 targets. At first glance, weeding out the 8 percent would result in savings of $6,000 in mailing costs. Not bad. However, if the company was mailing to a million businesses, the marketing expenses would be reduced by $60,000. If mailings occur more than once a year, then the savings really become significant.
The example above does not take into consideration costs involved in credit investigation of new prospects. If this business does not pull credit reports on new prospects, the back-end costs on default could easily increase the overall costs significantly, depending upon the size of the sale. The good news is that you have at least eliminated the high-risk prospects.
Remember one of my opening comments about marketers who leave the responsibility of credit screening to credit managers? Let's look at a situation where the total costs of the salethe mailing costs and the cost of credit investigationare taken into consideration. The credit department needs to pull credit reports on all offers, especially where significant risk is involved.
For this example, a business conducts a mailing campaign targeted to 45,000 businesses at a cost of $.85 per piece and receives a 4-percent response. The credit manager then needs to pull credit reports at $20 each for the 1,800 respondents, totaling $36,000. At this point the manager determines the level of risk, and the negotiations begin between the salesperson and the prospect.
However, before conducting the campaign, a credit score is added to the 45,000 records at $.12 per record for a cost of around $5,400. Based upon the credit score, the mailing only goes to 80 percent of the file, eliminating the high-risk accounts with net savings of $7,650 in mailing costs only.
However, because the credit scores are available, the credit manager does not have to pull credit reports on the low-risk accounts, and the high-risk accounts have already been eliminated. He only needs to be concerned about pulling reports on the middle tiers, or what is now 75 percent of those responding (or on 1,350 prospects for a total cost of $27,000).
Consequently, he has reduced his department's costs by $9,000 by not having to pull reports on all prospects. The saving for the total campaign is $16,650which includes the savings from the mailing and costs for a credit investigation.
And there is a huge added benefit. The sales cycle is shortened and offers are made with the inbound sales representative already knowing the terms to be offered based upon a credit score predetermined by the credit manager. Savings are achieved on the cost of mailings overall and on back-end costs by not having to pull reports on as many sales prospects.
Note: There are some businesses and financial institutions that specifically target only high-risk prospects. The principle for that activity is the same as above, except that the focus is only on high-risk accounts.
Offer Appropriate Levels of Credit
The example as illustrated above is the most simple risk management tactic. A business can also segment its prospects and customers based upon credit scores, and then make pre-approved offers of credit or terms of payment based upon levels of risk, creating an ongoing "win-win" relationship.
Several financial institutions offer levels of credit based upon credit scores, while some businesses provide goods or equipment by offering tight or flexible terms of payment based upon levels of risk. In this instance, the customer is brought into the decision loop earlier by knowing the terms of the offer up front.
For long-term customers with excellent credit, one can be proactive by offering a higher ceiling and terms of 60 days. Whenever it appears that risk is increasing, one also can be proactive in managing that risk by offering appropriate terms for the current situation. This benefits both the business and the customer by turning away everyone with a credit risk, but offering terms that are acceptable to each party. Again, a win-win situation is created and at the same time, the risk involved is minimized.
This flexible and proactive tactic does require ongoing maintenance of the marketing file with appropriate updates of credit elements. However, even with additional updates to the file, the savings to the company as represented in the total cost of sales is significant.
Demonstrated Willingness to Purchase
Credit elements also can be used to target businesses with a willingness or propensity to purchase goods and services. In many cases, businesses choose not to extend credit if several inquiries are shown on a credit report.
But consider another view. Perhaps this is a business that is looking to establish credit for purpose of purchasing goods and services. In this scenario, marketers are seeing a business that is more active and looking to invest, rather than one showing no credit activity, and thus no apparent interest in purchasing.
In this situation, one also will want to examine additional credit elements beyond a credit score, such as DBT. A new business with a number of inquiries but with no additional credit elements is one with a propensity to purchase. Similarly, a mid-sized business with a good credit history and a recent increase in credit inquiries may be willing to make new commitments and investments as a part of a growth strategy.
The next level of predictive modeling involves utilizing credit elements in a propensity-to-buy model that takes into account a credit score provided by major credit bureaus or financial institutions, and other elements such as DBT and derogatory indicators. These elements, combined with purchase history, can create a robust predictive model. However, modeling scores need to be rerun frequently to adjust for significant changes in purchase history and/or credit history.
All of the previous activities are initiated whenever marketing or sales begin a campaign. However, in the customer relationship management (CRM) environment, communications are started and offers made based upon customer-initiated activity, and not solely by the business' need to produce sales.
In this scenario, the customer has recently made a purchase (an offer within 60 days of a purchase has a greater possibility of producing a sale than at any other time) or there is a change in demographic/credit history. Perhaps the customer has moved, paid off a purchase, demonstrated a change in credit risk (either positive or negative) or sought credit. Each of these activities could signal the opportunity to interact with that customer.
Based either on developing triggers in the marketing file or systematically running a profile, a customer list may be produced that signifies a relevant change in statusin this case either an improvement or reduction in credit risk. An acknowledgement and offer to purchase under new terms may be extended to those customers whose risk has improved by a preset amount. This activity demonstrates to a customer that the relationship is being monitored and that the business is responding to good or bad news. The interactive relationship with the customer base is propelled forward and furthers aspirations to build a loyal base.
Efforts Bring Reward
In summary, credit data elements can be very useful in eliminating or reducing high-risk situations, targeting customers with a propensity to purchase and developing positive, proactive situations that promote interactivity with a customer base. One note of caution: As credit elements are used in a broader manner, more diligence and time must be invested in the management of the customer file. However, such efforts will be amply rewarded. Money will be saved, sales will be increased, and along the way, a strong, loyal customer base will be developed.
Jerry Hawkins is product line manager and consultant at Experian Business Marketing Services. He brings to Experian more than 20 years experience in research, marketing database development and targeted marketing both in the business-to-business and business-to-consumer arenas. He is past chairman of the Direct Marketing Association (DMA) Business-to-Business Operating Council and past chairman of the DMA Southern California Chapter. He can be reached at (800) 509-5604.