Client Overview
Viking Office Products was a publicly traded office supply retailer (NASDAQ: VKNG) known for its extensive network of regional distribution warehouses, a footprint that later made it an attractive acquisition target for Office Depot. Prior to that acquisition, Viking engaged Verity Marketing to bring analytical rigor to one of its largest and least efficient line items: catalog mail.
Viking mailed more than 191 million catalogs per year, a strategy that had historically driven strong revenue but carried an enormous and largely unexamined cost. Leadership wanted to know whether the mail file could be trimmed without giving up the sales those catalogs generated.
The Challenge
Viking asked Verity to reduce catalog mailing spend by 20% while holding sales flat. The mail list had grown organically over years, with no systematic way to distinguish a customer worth mailing indefinitely from one who would never order again. Mail selections relied on recency, frequency, and monetary; a common direct marketing strategy. However as technology evolved so did options for developing a mail plan.
Compounding the problem, Viking’s catalogs featured spend-based premiums on the cover, offers like a free mug, toaster, or cooler for hitting a minimum purchase threshold. These offers were popular and easy to point to as a driver of order volume, but nobody had tested whether they were attracting the right kind of customer.
Methodology
Segmenting Customers by First Purchase
Verity’s approach centered on a simple hypothesis: a customer’s first order contains a signal about their long-term value. Verity analyzed the historical purchase history of Viking’s customer base, employing logistic regression to identify propensity to buy again from 1st purchase indicators.
The pattern that emerged was clear and, in hindsight, intuitive. Customers whose first order was for a commodity item, paper being the clearest example, went on to become long-tenured, repeat purchasers. Office paper is a recurring, predictable need, and a customer who buys it once is signaling an ongoing relationship. By contrast, customers whose first order was driven primarily by the cover premium, the free mug or toaster, behaved like one-time bargain hunters. Many of them appeared to be employees chasing a gift rather than building a purchasing relationship with Viking, and the large majority never ordered again.
Rebuilding the Mail File Around Predicted Value
With that segmentation validated, Verity scored Viking’s mail file for predicted future value rather than historical mail-file inertia. Premium-driven, one-time customers were identified and removed from the recurring catalog file, directly reducing print and postage costs. Commodity-first customers and other segments with strong predicted LTV continued to receive full mail cadence, since they were the customers driving durable revenue.
This reframed the mailing budget as a targeting problem rather than a blunt cost-cutting exercise, allowing Verity to remove low-value volume while protecting the revenue base Viking cared about.
Extending the Model: Regional Pricing
The first-purchase LTV work surfaced a second opportunity. Because Viking operated distribution warehouses across many states, delivered paper cost varied meaningfully by region. Verity used this insight to move Viking to inkjetted, region-specific paper pricing within the catalog, aligning price to local delivery economics rather than a single national price point.
Since paper had already been established as the product most associated with high-LTV, long-tenured customers, optimizing its regional pricing had an outsized effect on gross margin without disturbing the acquisition and retention dynamics the segmentation work was designed to protect.
Extending the Model: Predictive Attrition
Building on the success of the LTV segmentation, Verity later applied logistic regression to Viking’s best customers to get ahead of churn rather than react to it. The model flagged a customer as at-risk when they missed an order within their established average buying cycle, comparing each top customer’s ordering rhythm against their own historical pattern rather than a generic threshold.
The model identified likely attrition at the 85% confidence level, giving Viking’s team a reliable, actionable signal well before a customer would otherwise have been written off as lost. Flagged customers were routed into a proactive outreach program, a direct service call timed to their predicted attrition window rather than a generic win-back campaign after the fact.
Results
● Reduced catalog mail volume in line with the 20% budget-reduction target by removing low-LTV, premium-driven names from the file, while preserving sales from the retained, high-value customer base.
● Identified first-purchase category (commodity vs. premium-driven) as a reliable, low-cost predictor of customer lifetime value, usable at the point of first order.
● Implemented region-specific paper pricing that improved gross margin by aligning price to Viking’s actual regional delivery costs.
● Deployed a logistic regression attrition model that flagged at-risk top customers at the 85% confidence level based on missed orders within their normal buying cycle.
● Proactive outreach triggered by the attrition model saved more than 75% of the customers it flagged, converting a reactive retention posture into a predictive one.
Why It Worked
The throughline across all three phases of this engagement was treating customer behavior as a source of prediction rather than description. A first order didn’t just record what someone bought, it forecast what they were worth. A missed order didn’t just mean a quiet month, it forecast a customer about to leave. In both cases, Verity built the analytical infrastructure to act on the forecast before the outcome had already happened, turning what had been a blunt, cost-driven mailing cut into a durable, margin-positive targeting and retention system.