How Big Is Your Halo? 3 Ways to Measure the Branding Effect of Your Direct Promotions

Direct marketers take pride in accountability. But as I’ve said before, they can be their own worst enemies when it comes to measurement. They’re good at measuring things that are easy to count—clicks, page views, response rates, cost per lead, etc. But they struggle with measuring the long-term or cumulative effects that the branding in their promotions has on current and future sales—people who buy, but not as a result of a specific promotion, the so-called halo effect.

Direct marketers take pride in accountability. But as I’ve said before, they can be their own worst enemies when it comes to measurement. They’re good at measuring things that are easy to count—clicks, page views, response rates, cost per lead, etc.

But they struggle with measuring the long-term or cumulative effects that the branding in their promotions has on current and future sales—people who buy, but not as a result of a specific promotion, the so-called halo effect.

Consider big direct marketing brands like 1-800-Flowers.com or Omaha Steaks. These brand names have been built through direct marketing promotions over time and, as a result, people self-direct to their Web and phone sales channels.

But most direct marketers don’t know how to account for this halo effect, and when they work with response rates only, at best, they shortchange their results; and at worst, they get fooled by failing to account for those who buy without responding.

Case in point: A few years ago, I analyzed a data set from a multivariate direct mail matrix test that had 12 cells: four list segments, four offers and four creative executions.

Working off of response rates alone, we identified the winning list segment, offer and creative. But digging deeper by matching the solicitation file to the sales file, we discovered that from a revenue-per-prospect standpoint, these response rate winners were not the best revenue producers. Further analysis showed that from an ROI standpoint, they were actually the worst. In fact, the offer with the highest response rate (a free trial) produced a negative ROI when compared with a control cell: People in the control group who did not receive this offer actually spent more than the ones who responded to the offer for a free trial.

Here are three ways you can account for the halo effect:

1. Compare customer sales data to your promotion history. This is a good starting point. See who was exposed to your promotions and purchased without responding

2. Index brand awareness to sales over time. Take a look at this post for a methodology to measure this metric.

3. Create an engagement score that counts brand exposures and index it to sales over time. More on a methodology to measure this metric next time.

Exciting New Tools for B-to-B Prospecting

Finding new customers is a lot easier these days, what with innovative, digitally based ways to capture and collect data. Early examples of this exciting new trend in prospecting were Jigsaw, a business card swapping tool that allowed salespeople to trade contacts, and ZoomInfo, which scrapes corporate websites for information about businesspeople and merges the information into a vast pool of data for analysis and lead generation campaigns. New ways to find prospects continue to come on the scene—it seems like on the daily.

Finding new customers is a lot easier these days, what with innovative, digitally based ways to capture and collect data. Early examples of this exciting new trend in prospecting were Jigsaw, a business card swapping tool that allowed salespeople to trade contacts, and ZoomInfo, which scrapes corporate websites for information about businesspeople and merges the information into a vast pool of data for analysis and lead generation campaigns. New ways to find prospects continue to come on the scene—it seems like on the daily.

One big new development is the trend away from static name/address lists, and towards dynamic sourcing of prospect names complete with valuable indicators of buying readiness culled from their actual behavior online. Companies such as InsideView and Leadspace are developing solutions in this area. Leadspace’s process begins with constructing an ideal buyer persona by analyzing the marketer’s best customers, which can be executed by uploading a few hundred records of name, company name and email address. Then, Leadspace scours the Internet, social networks and scores of contact databases for look-alikes and immediately delivers prospect names, fresh contact information and additional data about their professional activities.

Another dynamic data sourcing supplier with a new approach is Lattice, which also analyzes current customer data to build predictive models for prospecting, cross-sell and churn prevention. The difference from Leadspace is that Lattice builds the client models using their own massive “data cloud” of B-to-B buyer behavior, fed by 35 data sources like LexisNexis, Infogroup, D&B, and the US Government Patent Office. CMO Brian Kardon says Lattice has identified some interesting variables that are useful in prospecting, for example:

  • Juniper Networks found that a company that has recently “signed a lease for a new building” is likely to need new networks and routers.
  • American Express’s foreign exchange software division identified “opened an office in a foreign country” suggests a need for foreign exchange help.
  • Autodesk searches for companies who post job descriptions online that seek “design engineers with CAD/CAM experience.”

Lattice faces competition from Mintigo and Infer, which are also offering prospect scoring models—more evidence of the growing opportunity for marketers to take advantage of new data sources and applications.

Another new approach is using so-called business signals to identify opportunity. As described by Avention’s Hank Weghorst, business signals can be any variable that characterizes a business. Are they growing? Near an airport? Unionized? Minority owned? Susceptible to hurricane damage? The data points are available today, and can be harnessed for what Weghorst calls “hyper segmentation.” Avention’s database of information flowing from 70 suppliers, overlaid by data analytics services, intends to identify targets for sales, marketing and research.

Social networks, especially LinkedIn, are rapidly becoming a source of marketing data. For years, marketers have mined LinkedIn data by hand, often using low-cost offshore resources to gather targets in niche categories. Recently, a gaggle of new companies—like eGrabber and Social123—are experimenting with ways to bring social media data into CRM systems and marketing databases, to populate and enhance customer and prospect records.

Then there’s 6Sense, which identifies prospective accounts that are likely to be in the market for particular products, based on the online behavior of their employees, anonymous or identifiable. 6Sense analyzes billions of rows of 3rd party data, from trade publishers, blogs and forums, looking for indications of purchase intent. If Cisco is looking to promote networking hardware, for example, 6Sense will come back with a set of accounts that are demonstrating an interest in that category, and identify where they were in their buying process, from awareness to purchase. The account data will be populated with contacts, indicating their likely role in the purchase decision, and an estimate of the likely deal size. The data is delivered in real-time to whatever CRM or marketing automation system the client wants, according to CEO and founder Amanda Kahlow.

Just to whet your appetite further, have a look at CrowdFlower, a start-up company in San Francisco, which sends your customer and prospect records to a network of over five million individual contributors in 90 countries, to analyze, clean or collect the information at scale. Crowd sourcing can be very useful for adding information to, and checking on the validity and accuracy of, your data. CrowdFlower has developed an application that lets you manage the data enrichment or validity exercises yourself. This means that you can develop programs to acquire new fields whenever your business changes and still take advantage of their worldwide network of individuals who actually look at each record.

The world of B-to-B data is changing quickly, with exciting new technologies and data sources coming available at record pace. Marketers can expect plenty of new opportunity for reaching customers and prospects efficiently.

A version of this article appeared in Biznology, the digital marketing blog.

5 Steps to Customer Data Hygiene: It’s Not Sexy, But It’s Essential

Are you happy with the quality of the information in your marketing database? Probably not. A new report from NetProspex confirms: 64 percent of company records in the database of a typical B-to-B marketer have no phone number attached. Pretty much eliminates phone as a reliable communications medium, doesn’t it? And 88 percent are missing basic firmographic data

Are you happy with the quality of the information in your marketing database? Probably not. A new report from NetProspex confirms: 64 percent of company records in the database of a typical B-to-B marketer have no phone number attached.

Pretty much eliminates phone as a reliable communications medium, doesn’t it?

And 88 percent are missing basic firmographic data, like industry, revenue or employee size—so profiling and segmentation is pretty tough. In fact, the Netprospex report concluded that 84 percent of B-to-B marketing databases are “barely functional.” Yipes. So, what can you do about it?

This is not a new problem. Dun & Bradstreet reports regularly on how quickly B-to-B data degrades. Get this: Every year, in the U.S., business postal addresses change at a rate of 20.7 percent. If your customer is a new business, the rate is 27.3 percent. Phone numbers change at the rate of 18 percent, and 22.7 percent among new businesses. Even company names fluctuate: 12.4 percent overall, and a staggering 36.4 percent percent among new businesses.

No wonder your sales force is always complaining that your data is no good (although they probably use more colorful words).

Here are five steps you can take to maintain data accuracy, a process known as “data hygiene.”

1. Key enter the data correctly in the first place.
Sounds obvious, but it’s often overlooked. This means following address guidelines from the Postal Service (for example, USPS Publication 28), and standardizing such complex things as job functions and company names. But it also means training for your key-entry personnel. These folks are often at the bottom of the status heap, but they are handling one of your most important corporate assets. So give them the respect they deserve.

2. Harness customer-facing personnel to update the data.
Leverage the access of customer-facing personnel to refresh contact information. Train and motivate call center personnel, customer service, salespeople and distributors—anyone with direct customer contact—to request updated information at each meeting. When it comes to sales people, this is an entirely debatable matter. You want sales people selling, not entering data. But it’s worth at least a conversation to see if you can come up with a painless way to extract fresh contact updates as sales people interact with their accounts.

3. Use data-cleansing software, internally or from a service provider, and delete obsolete records.
Use the software tools that are available, which will de-duplicate, standardize and sometimes append missing fields. These won’t correct much—it’s mostly email and postal address standardization—but they will save you time, and they are much cheaper than other methods.

4. Allow customers access to their records online, so they can make changes.
Consider setting up a customer preference center, where customers can manage the data you have on them, and indicate how they want to hear from you. Offer a premium or incentive, or even a discount, to obtain higher levels of compliance.

5. Outbound phone or email to verify, especially to top customers.
Segment your file, and conduct outbound confirmation campaigns for the highest value accounts. This can be by mail, email or telephone, and done annually. When you have some results, decide whether to put your less valuable accounts through the same process.

Do you have any favorite hygiene techniques to add to my list?

A version of this article appeared in Biznology, the digital marketing blog.

How Many Leads Do You Need?

One key to successful B-to-B lead generation programs is to calculate exactly the right number of qualified leads to provide to sales—as part of your campaign planning. If you generate too many leads, you’ll be wasting precious marketing dollars. If you generate too few, your firm may be at risk of missing its revenue targets, with potentially disastrous financial implications. Moreover, you’ll annoy your sales team by not supporting them properly. So, let’s look at a neat way to figure out in advance how many leads your company needs, so you can invest accordingly.

One key to successful B-to-B lead generation programs is to calculate exactly the right number of qualified leads to provide to sales—as part of your campaign planning. If you generate too many leads, you’ll be wasting precious marketing dollars. If you generate too few, your firm may be at risk of missing its revenue targets, with potentially disastrous financial implications. Moreover, you’ll annoy your sales team by not supporting them properly. So, let’s look at a neat way to figure out in advance how many leads your company needs, so you can invest accordingly.

This easy method uses your sales people’s quotas to back your way into the number of leads required, based on sales productivity per lead. You will need four numbers:

  1. The average revenue quota per rep, in the period, whether it’s a year, or a quarter, or a month.
  2. The average revenue per order, or per closed deal.
  3. The percent of their quota that the sales people generate naturally, without the help of leads. This revenue typically flows from repeat sales, from deeper penetration within the accounts, or from referrals.
  4. The conversion rate from qualified lead to sales.

The first three numbers are likely to come from a discussion with sales management and your finance department. The last number you probably have on hand, from sales and marketing experience.

Here’s an example of how to do this calculation, based on a set of hypothetical numbers that might be common in large-enterprise selling environments. We are saying that each rep is on the hook to deliver $3 million in sales in the period. As a first calculation, subtract out the percentage of that revenue that the rep can produce without any leads supplied by marketing. In this example, it’s 40 percent self-generated, leaving 60 percent, or $1.8 million, that the rep needs help with from marketing.

We divide that remaining revenue by the average deal size, which is $60,000 in this example, to get the number of closed deals that each rep, on average, needs to complete to deliver on the revenue quota. In this example, it’s 30 deals.

Finally, we divide the number of deals required by the lead-to-sales conversion rate, which is 20 percent in this example. Voila. Now we know that each reps needs, on average, 150 qualified leads to make quota.

You can also take this to the next step and calculate the campaign inquiries required by dividing the 150 leads by your inquiry-to-lead conversion rate. With that, you can plan your campaigns to generate enough inquiries for your pipeline that will convert to a known number of qualified leads, and thereafter to the needed revenue.

So, with this simple math exercise, you can avoid waste and keep your sales reps as productive as they can be. Do you use another method that you can share?

A version of this article appeared in Biznology, the digital marketing blog.