B2B Loyalty – Best Practices

Here are the latest trends in B2B loyalty, technology and analytics that can help business build a bond with other other businesses:

How do you transform your company from a mere vendor into a valued partner? By building a loyalty platform on a strong foundation of customer data—and leveraging that platform to identify, understand and influence the consumer behind the account number.

Here are some best practices that you should consider:

IDENTIFY: People, not account numbers

Here’s a look at a few of the predominant models for identifying critical B2B contacts:

Give them some face time.

Jeff Hayzlett, Chief Business Development Officer for Eastman Kodak Company, is intimately familiar    with the B2B identification challenge.Given the vast variety of customer types, Eastman Kodak’s approach is to facilitate meetings and events with end users who value the chance to interact with the company on a personal level. Kodak’s annual Graphic Users Association Conference brings Kodak product managers and software developers face-to-face with their end-users, while a series of customer councils for publishers, commercial printers, and database marketers helps Kodak identify key decision-makers and influencers and give them tools to help them grow their businesses.

Use Web 2.0 tools—but warily.

One in three small-business owners now cultivate leads and choose suppliers based on recommendations from social-networking web sites such as Facebook or LinkedIn.”Increasingly, small-business owners are getting referrals and searching for supplier recommendations through their networks. Direct mail, newspaper and broadcast advertising are becoming less efficient mediums for reaching small businesses.”

UNDERSTAND: It’s the database, stupid

Once you have identified your sweet spot of small-business customers, the next step is to spend some time understanding their current behavior and comparing it to that of your best customers. Your goal is to isolate behavioral gaps that can be overcome with the right offer.

Treat your database like an asset.

B2B data degrades much more quickly than consumer data. While a consumer might keep the same email address for most of her adult life, a small-business buyer might change jobs, get a new title or return to the corporate world. That makes data refreshment a continual challenge.

Thou shalt not live on transactional data alone.

A 2008 Marketing Leadership Council study found that, because the cost of switching suppliers is higher and more complex in B2B, “attitudes”—in other words, the customer’s emotional connection to the brand—are often better indicators of B2B loyalty than pure transactional behavior. Small-business customers can look loyal in the transaction file, but a survey might find pockets of disgruntled customers who could benefit from an intervention.

Become a data conduit.

In the consumer world, data tends to flow one way, from the consumer to the database. B2B marketers, by contrast, can also learn a lot about their small-business customers by reversing the data stream. Small-business credit cardholders who lack accounting departments, for example, can benefit from information on their business purchases. AT&T Universal Business Rewards cardholders not only earn Citi ThankYou Rewards points on all purchases, but also gain access to a wealth of tools to help track and analyze business expenses.

Source: Colloquy

Predictive vs Descriptive modeling – Understanding the difference

Many organizations use historical analytics data as a basis for forecasting future growth, and establishing performance goals and budgets. This applicaton for analytics data can blur the distinction between predictive and descriptive data. Understanding this difference is critical to an effective analytics program.

Predicitive modeling refers to a mathematical model that can accurately predict future outcomes. For instance, I know that if I apply sufficient heat to water, the water will reaach 100 degrees celsius and begin to boil (barring slight variations for altitude which are also predictable). The rate at which this happens and the amount of energy required can be mathematically described.

Descriptive modeling refers to a mathematical model that describes historical events, and the presumed or real relationship between between elements that created them. For instance, yesterday when I went to the store to buy milk, it cost me $1.00 a litre, last month it was 95 cents, last year it was 80 cents.. Based on historical events, I assume it will cost me roughly $1.05 to buy a litre of milk next month.

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Case Study: How Harbor Sweets uses customer data effectively

Harbor Sweets is a Massachusetts-based gourmet chocolate company that offers a variety of premium sweets. In 1973, the company emerged from humble beginnings when Ben Strohecker set out to create the “best piece of candy in the world.” The result was Sweet Sloops, a sailboat shaped piece of almond butter crunch, covered in white chocolate dipped in dark chocolate and crushed pecans. Over the years, the company grew to feature additional candy lines and retail outlets across the U.S. Harbor Sweets also added a significant mail-order catalog division.

Their catalog program was successful in delivering sales during key holiday buying seasons, but the company also wanted to ensure that holiday campaign mailings – the main driver of catalog business – were maximizing sales.Harbor Sweets needed a solution that would efficiently turn existing customer data into actionable next steps. Essentially, Harbor Sweets wanted to find out if removing a single mailing to “active customers” from the holiday mail schedule – which included mailings every month from September through December – would conserve marketing resources, while refraining from negatively impacting revenue or overall response rates.

Harbor Sweets conducted a suppression test, assessing how effective the holiday campaign mailings had been at driving sales. Harbor Sweets mailed the catalog to everyone in December and ran a suppression test for September, October and November. The results were surprising.Harbor Sweets was actually hurting sales by mailing too many catalogs to customers during the holiday season. The results also found that the cadence for the catalogs could be reduced to three, while still achieving similar results.

Additionally, Harbor Sweets learned firsthand that it is imperative to be open to analyzing data and customer behavior in new ways.The more information available on existing customers, the more effective the software is in its results, whether it is predicting customer’s next steps or customers at risk of defection.

The results allowed Harbor Sweet to develop a long-term marketing process for the future. The revenue saved by removing one mailing is now applied to another mailing during non-peak times to provide customers with a more effectively targeted, timely catalog.

Source: DM review