Gaining a Better Understanding of Your Customer’s Buying Behaviors: The Buyer Matrix

The Buyer Matrix helps you to develop marketing campaigns that will build your business and improve profits, while helping you to forecast sales in future years.  This technique is available to any business that keeps manual or computer records of each customer’s transactions.

How many customers purchased from you last year?  On average, how many orders did each of those customers place?  What was the average value of those orders?  How many new customers did you acquire last year?  How many of them do you expect to buy from you this year?  What have been the trends of these metrics for your business?  What marketing programs are most effective in improving these trends?  Based on these metrics, what should your sales be next year?

Marketers are frequently asked to forecast sales, and identify what marketing programs can improve sales.  But the process of forecasting sales and projecting marketing program results has been generally approached as an “art”.  While there is always some level of uncertainty in forecasting sales, the Buyer Matrix methodology will help to reduce many of the errors associated with predicting future business.

The Buyer Matrix was developed out of the need to understand how key customer behavior trends are related to future sales.   There was also a need to measure what effect Marketing Programs had on sales, and if those programs were delivering sufficient sales to justify their expense. 

The Buyer Matrix is based on the following principal: if you can predict how many buyers you will have in the future and how much will they spend, you should be able to forecast what your sales will be.  But there is a great deal of variability in predicting buyer counts and their behaviors.  After years of experience I have found the best method of projecting buyer counts is to assign your buyers into unique cohort groups and measure the group’s behavior across time.  Experience has shown that cohort groups defined by the date a customer first purchased from your company works very well.   Each of these cohort groups exhibits a unique predictable behavior pattern.  When all the cohort groups are rolled up, a good estimate of the number of buyers for the forecast period is available.  Let’s examine a specific example to help understand this concept.

ABC Company was started in 1999.  The company sells supplies to other business across the United States.  All transactions are captured in an order entry/shipping system.  In 2002, management of the ABC Company increased the advertising budget dedicated to acquiring new customers by 20%.  As the business continued to increase in 2002, management increased the budget for 2003 by another 20%.  Management was forecasting another banner year for sales in 2003, a gain they based on the ever-increasing sales volumes since 1999.  In late 2003 however, the company suspected they were falling short of expectations, and did not know why.

In early 2004, in an effort to understand what was happening to the business, the company utilized the Buyer Matrix methodology. The transactions were extracted from their system and the following Buyer Matrix report was created:

Year of	Buyer Placed Order
First Order	1999	2000	2001	2002	2003
1999	100	50	40	32	25
2000		110	55	44	35
2001			110	55	44
2002				100	50
2003					95
Totals	100	160	205	231	249

The matrix report shows that in 1999, the company acquired 100 new customers.  In 2000, the company acquired 110 more new customers, and 50 of the customers who were acquired in 1999 returned to purchase.  By the end of 2001, ABC now had 205 total buyers, consisting of: 40 from the group acquired in 1999, 55 from the group acquired in 2000 and 110 newly acquired customers in 2001.  In 2002, total buyers grew to 231, consisting of 32 from the 1999 group, 44 from the 2000 group, 55 from the 2001 group and 100 newly acquired in 2002. 

The matrix shows the volume of acquired customers fell as the acquisition-marketing program was rolled out in 2002 and the negative trend continued into 2003.   Management did not realize the customer acquisition program was not working effectively until early 2004, and by then the acquisition budget for 2003 had been exhausted.  Profits were negatively affected in 2003 as a result of not having a measurement system in place.

Many companies have adopted loyalty programs (rewards programs), but without a Buyer Matrix mechanism to measure historical customer retention rates and measure changes in those rates, companies have no way of knowing if a loyalty program is effective and delivering value.  If the hypothetical ABC Company started a new customer loyalty program in 2002, with the goal of increasing the number of buyers who have had activity since 1999, would the same matrix suggest the program was effective?  By using the same Buyer Matrix report, and reading it differently, the answer can be found.

The 1999 cohort group had 50 customers return in 2000.  Then in 2001 only 40 customers returned to buy, a fall-off of 20%.  Then in 2002, when the loyalty program was initiated, the 1999 cohort group fell to 32 buyers, another 20% fall off.  The program did not change the buyer behavior pattern in 2002.  In 2003, the trend was still not affected by the loyalty program as the buyers continued to fall off by over 20%.  These metrics were the same for the 2000 and 2001 cohort groups, indicating the loyalty program was not effective.  What if the loyalty program’s goal was to increase the activity of the buyers as opposed to the number of buyers, would the program then be effective?

In the buyer matrix below, the measurement has been changed from buyer counts to orders per buyer:

Year of	Orders per Buyer
First Order	1999	2000	2001	2002	2003
1999	1.20	1.50	1.50	1.60	1.70
2000		1.20	1.50	1.60	1.70
2001			1.20	1.50	1.60
2002				1.20	1.50
2003					1.20
Totals	1.20	1.40	1.45	1.55	1.65

In 2002, the order per buyer for the 1999 cohort group increased from 1.5 orders to 1.6.  The trend continued in 2003 with orders per buyer increasing to 1.7.  The 2000 and 2001 cohort groups show similar order per buyer gains too.  These measures show the loyalty program is working, assuming the goal is to increase orders per buyer with the existing customers.

Another key metric in the Buyer Matrix is the average purchase amount.  Similar to the Orders Per Buyer report above, this report shows the trends of the average purchase amount across time for the respective cohort groups.  This is a useful metric for marketing programs designed to increase the average order value, such as premium and discount offers.

When these three key metrics are combined, you have a customer behavior based sales forecasting tool.  Sales are broken down into the equation: Sales = Buyers * Order per Buyer * Average Order Value.

For a marketer to make an accurate sales forecast, the buyer counts, orders per buyer and average order value metrics associated with each cohort group are forecast out.  The assumptions associated with each group’s specific behaviors should be a reflection of past performance for that specific metric, adjusted for any marketing campaigns designed to affect the metric.  In adjusting for campaigns, behavior trends should not be changed significantly beyond historical trends.   The illustration below should help to clarify the process.

2004 Forecast based on Buyer Matrix
		Buyers who placed an Order
Year of First Order							Forecast
		1999	2000	2001	2002	2003	2004
	1999	100	50	40	32	25	20
	Returning Pct.		50.0%	80.0%	80.0%	78.1%	78.0%
	2000		110	55	44	35	28
	Returning Pct.			50.0%	80.0%	79.5%	79.5%
	2001			110	55	44	35
	Returning Pct.				50.0%	80.0%	79.0%
	2002				100	50	40
	Returning Pct.					50.0%	80.0%
	2003					95	48
	Returning Pct.						50.0%
	2004						100

		Orders Per Buyer
Year of First Order
		1999	2000	2001	2002	2003	2004
	1999	1.2	1.5	1.5	1.6	1.7	1.7
	2000		1.2	1.5	1.6	1.7	1.7
	2001			1.2	1.5	1.6	1.7
	2002				1.2	1.5	1.6
	2003					1.2	1.5
	2004						1.2
		Average Order Value
Year of First Order
		1999	2000	2001	2002	2003	2004
	1999	$90	$100	$101	$102	$103	$104
	2000		$95	$100	$101	$102	$103
	2001			$97	$101	$102	$102
	2002				$97	$101	$102
	2003					$97	$101
	2004						$97
		Sales
Year of First Order
		1999	2000	2001	2002	2003	2004
	1999	$10,800	$7,500	$6,060	$5,222	$4,378	$3,448
	2000		$12,540	$8,250	$7,110	$6,069	$4,872
	2001			$12,804	$8,333	$7,181	$6,027
	2002				$11,640	$7,575	$6,528
	2003					$11,058	$7,196
	2004						$11,640
Total Sales		$10,800	$20,040	$27,114	$32,305	$36,260	$39,711

ABC Company’s sales forecast for the next year is $39,711, assuming all key behavior metrics are consistent with 2003 historical rates.  If the company developed a new customer acquisition program they believed would work substantially better than the old program, adjusting new customers acquired to 120, instead of the 100, might be appropriate for 2004.  These kinds of what-if assumptions can be easily analyzed to understand the possible value of new marketing initiatives.   The matrix provides key measures to goal marketing programs against, providing companies with an early indication of a program’s success, or the need to re-think it.

The Buyer Matrix is a proven mechanism that has helped many companies understand what marketing programs are effective, and how those programs impact overall business.  The matrix cohort groups can be based not only on date of first purchase, but also on any easily identified characteristic that is a differentiator of your business.  Some other cohort grouping include:

• First product purchased (can be a product line or a specific product)
• Source of the new customer (could be from certain ads or web site)
• Promotional customers (purchase from sale or discount offer)

When this technique is combined with in an in depth understanding of why customers purchase from you, top line performance is maximized, and ensures marketing program investments are delivering sales consistent with expectations.