By: Chris Davies
How do you reduce fraudulent incentive claims within your organization? Most companies have an audit protocol in place so let’s ask this question: on what percentage of your spiff and/or rebate claims do you detect fraud? The generally accepted number we have found is that most companies figure on around 2-3% fraud in a campaign but the average actual number we detect in the first 90 days of operating a brand’s campaign is more like 8-9% or even higher. On a campaign that pays out tens of millions of dollars, that’s a lotta jack!
When these types of channel or partner marketing programs are implemented, a company’s marketing team or in many cases, the marketing manager implements a system of random audits for the claims being submitted. Here’s the thing about random audits though: they do not work.
Realistically, after a few hours of doing this some key elements start to fall by the wayside just so that huge pile can be wiped off someone’s desk. When this happens, the potential for paying out on a fraudulent claim ramps up dramatically. How can one individual possibly remember that John Smith submitted a spiff claim last month for $100 and has repeated that same claim for another $100 having only changed the customer’s last name? Sometimes salespeople aren’t even aware that they’ve submitted a duplicate claim or it happened accidentally and in this case, it’s not really anyone’s fault, but it still costs the brand real money.
Compare Data To Data
The true key to detect fraud is by measuring data against even more data. Sales spiff programs and rebate programs have very basic rules in order for the claim to be paid out. Most programs require a copy of the store invoice with an invoice number, the customer’s name, address, postal or zip code, and most importantly, the model and serial number of the product sold. This last key item is probably the easiest way to detect a fraudulent claim. However, how can anyone expect one individual to remember every invoice with a serial number that comes out of that pile of claims? By implementing a system that automatically references the submitted serial numbers, you can easily reduce the amount of bad claims being paid out. This can be as simple as entering the data from all claims into a computer spreadsheet and searching for duplicates, the viability of which varies by campaign reach of course.
Another way to detect a bad claim is by using the submitted invoice. Look at the store’s address: is it actually possible that the salesperson sold a product to someone who lives over 100 miles away or even in another state or province? Sure, it is possible, but it is not a normal practice. Using the store’s postal or zip code against the actual consumer’s address is another good way to prevent fraud.
The customer’s name is another key piece of data that can help prevent paying out fraudulent claims. Watch for claims that are submitted where the invoice shows a different version of the consumer’s name, e.g. John Smith bought the same product this week as J. Smith bought last week and Jonathan Smith bought the week before. Hmmm…does our friend Mr. Smith really have use for 3 of the same item at a single address?
A hugely valuable way to keep fraudulent spiff or other sales incentive claims at a minimum is by using a company’s field sales representatives. Active, engaged FSR’s are the eyes and ears of your brand. The reps can usually indicate who the top performers are within their respective areas. If Mike is normally the top sales person in a particular location month after month, but all of a sudden John seems to be outselling Mike one month, this is usually worth a closer look.
So there are four easy ways you can start reducing sales incentive and rebate fraud right away, today. If you would like more detail on this topic, I recommend downloading our e-book Fraud: The Most Overlooked Opportunity. It even includes a worksheet to help you get going on fraud busting today!
Chris Davies is a sales channel incentive specialist at 360Incentives.com. You can connect with him on LinkedIn here.