Best Practices for Credit + Sales Collaboration
Ah, the credit and sales relationship. We’ve talked about this for years at Dun & Bradstreet, the need for these two departments to work together, and not too see each other as the opposition.
The credit and sales relationship can sometimes seem like a culture clash because the two departments often operate at cross purposes. Salespeople work on commission and may push to extend easy credit to maximize sales. In contrast, credit teams aren’t rewarded for sales, but may be reprimanded for lax credit standards; and therefore apply credit rules cautiously to minimize the risk of slow payers or those who might default altogether.
These differences may promote tension and conflict rather than cooperation. However, credit and sales have more in common than they think, as the two departments share many important goals. Both want their company to be successful. Both want to keep their customers happy and not to take their business elsewhere. Each brings to the table unique customer knowledge that can help the other succeed, particularly when their data is combined in rigorous, ongoing analysis of the credit-to-cash cycle. (You knew this would be about data, right?)
Leverage Data to Improve Credit + Sales Collaboration
There is no perfect way to improve the credit and sales relationship. Each organization must shape its activities to reflect its unique culture, operating model, processes, and business goals. Credit departments possess a wealth of data that can be mined to identify new business opportunities. With the help of new technologies, credit can work with sales departments by tapping into customer data and sharing insights for increasing revenue opportunities. In talking with clients who’ve worked with Dun & Bradstreet to help transform their credit departments through our solutions, I’ve identified four best practices have been identified to help overcome traditional obstacles to cooperation.
1. Shorten the Sales Cycle with Automation
Automation helps avoid the inefficiencies and problems that can arise with manual processes, such as the many manual credit applications, phone calls, e-mails, rekeying of data, ad hoc credit reporting and inconsistent application of credit limits and other policies—all of which increase the potential for errors and delays.
Widespread prescreening of credit applicants can be made easier with third-party services that can supplement a company’s own firmographic data about prospects. In addition, third parties can provide robust financial and risk measures, which can be calibrated to a customized set of credit rules. Companies can also create separate prescreening processes and rules for new customers, thus helping minimize risk.
Automating prescreening and approval processes essentially pushes the company’s internal credit policies all the way to the actual point of sale, whether the sale occurs at the office, on the company website, or in the field. In fact, automation significantly increases the benefits of a mobile sales force. Customers are better served by the swift and efficient approval process, as are the credit and the sales teams, who are empowered to sell more with preapproved credit limits.
Not all decisions will be automated, but automation will enable companies to quickly address the clear-cut approvals and disapprovals, so they can begin doing business immediately with promising customers. Equally important, sales personnel won’t waste time chasing prospects that can’t or won’t pay on time or within terms, while the credit department can focus on resolving more challenging credit issues.
This approach also lends itself to creating an online credit application. Many companies already can receive e-signatures and securely store credit applications and other related documents. An online credit application with e-signature capabilities helps to create a consistent and more compliant process that improves sales and customer satisfaction by enabling an instant answer. In addition, by labeling certain fields as “required,” you’ll reduce the number of incomplete applications you receive and help increase speed to credit decision and sale.
2. Optimize Credit Limits with Customer Insight
Credit limits are often set based on a customer’s initial order, rather than on their future needs or capacity. Typically, once credit limits are established, they often remain fixed unless the customer requests an increase. Fixed credit limits are sometimes not reflective of a customer’s ability to buy and pay. Over time, growing customers may find themselves hamstrung by unnecessarily low credit limits, and some may even inadvertently exceed their limit and cause their account to be put on hold, leading to a frustrating scramble to get the order released.
As a result, companies—and their credit and sales teams—are potentially leaving money on the table and causing dissatisfaction because they don’t fully understand their customers’ needs, capabilities, or risk profile. Credit departments can help rectify this problem by providing sales with data and analytic insights about each customer, including which ones are reaching their credit limit and/or have the ability to spend beyond their current limit. By constantly monitoring the risk levels of their portfolios and customer buying potential, credit teams can optimize the credit limit and terms as appropriate.
3. Find Up-Sell Opportunities to Help Generate New Business
Providing sales with data and analytic insights about each customer not only helps ensure profitable customers will have access to the credit they need, it also provides up-sell opportunities for sales. For example, business and risk-based intelligence can apply a Credit Limit Analysis to help identify low-risk customers who are under-utilizing their credit limits, thus making them good prospects for increased sales. Third-party data can also help sales staff understand which customers are expanding their businesses and have the resources to increase their sales.
Another valuable source of insight is a customer’s corporate linkage. An understanding of corporate linkage, which is the legal ownership relationship between different companies within a corporate family tree, can reveal previously unknown connections among customers. It is often used to determine global corporate exposure. With this information, sales can see who their biggest and best customers are and develop promising leads of prospects that are affiliated with those customers, such as a subsidiary. In addition, sales can allocate resources more efficiently, identify cross-selling and up-selling opportunities and determine the appropriate service levels for the biggest and best customers. By forging a stronger partnership with the sales department, credit departments can help sales forge stronger relationships with their customers.
4. Improve Integration of Information Systems
Most companies possess a wealth of information about their customers, but it’s often trapped in multiple systems, such as the ERP, CRM and accounts receivable systems. By cleansing and integrating your internal data, companies can create a powerful 360-degree view of their existing and potential customers.
However, it should be noted that this step depends on accurate and complete matching (the official term for this is “entity resolution”) of the different businesses within the databases. Many companies use name-recognition software programs, but these often fail to accurately consolidate all the ways a company may be represented—or misrepresented. That is, a single company could have separate entries for its corporate name, trade name, acronym, abbreviation, or misspelled versions of these names. (A common example is IBM and International Business Machines.) Matching software that cannot effectively reconcile business names and data will yield fewer customer insights, multiple erroneous company records, and perhaps even provide misleading guidance to decision makers.
Once internal data is cleansed, leveraging third-party data to enrich and provide more robust insight helps to create an optimized customer database. Companies can use an analytic model, called a Lookalike Model, to help build profiles of what their best customers look like using analytics that examine firmographic data, risk levels, profitability, predictions of future risk, and other variables. In addition, segment these profiles according to industry, geographical location, business size, years of operation and other business categories. By understanding risk and profitability across these segmentations, credit teams can expand their collaborative network beyond sales to work with marketing and business development to target prospects that look just like their best, most profitable customers.
Although the plan is simple, not all of the activities within each step are necessarily easy. Matching and integration can pose a significant challenge for many companies. Devising the right analytics can demand persistence, creativity and perhaps new skills. Nevertheless, the time and investments is far exceeded by the payoff in improved customer service, increasing numbers of high-quality prospects, and a bigger bottom line.