Based on my blog post from last month, hopefully you took a shot at bringing together and cleansing your B2B data over the past few weeks. If you did, congratulations! You have started the journey of moving data to actionable insight.

The first time through the data-connecting process is a lot of work and full of surprises. Did anyone beat my record of finding 28 different versions of a single company?

Properly mined and merged data offers insightful information about customers.
Properly mined and merged data offers insightful information about customers.

Regardless of whether you did or didn’t make progress connecting your data sources, let’s move to putting the data to work. We’ll work through a real-world first project that can be done with or without a single view of the customer.

The project is a B2B analysis to uncover some subtleness in using year-over-year customer spending patterns to show broader patterns.

The bottom line? Let’s do something!

The data for this analysis project is an extract of all advertising by customer for a three-year period. Two years of data can also work if need be. Once you have the data in hand (Excel, Access, or SQL database), the first thing to verify is that the finance or advertising departments were consistent in their categorisation codes from year to year and with all coding you plan on using to slice and dice the data.

I worked with a company that decided to isolate its major accounts mid-way through a year. It made year-over-year look terrible for the local accounts until the coding change was “mapped” into the analytics.

With the data in hand, ask what the sales management is interested in learning about customer behaviour year-over-year. You can give them some of the ideas below to start the conversation. The analysis can be used internally to help the sales team understand how the monthly goal was set, who should be contacted, and which businesses are “lost” and will have to be replaced to reach the goal.

Representatives can also use the information to help the individual businesses. As a value add, the reps can position the data to fit the clients’ perspectives.

Your advertising sales reps position themselves as a proactive agency would by showing the client prior year(s) marketing spends and then build to a longer-term, multi-channel marketing (advertising) plan. (Sure, the bigger businesses will have their spending numbers, but it can’t hurt to show that you paid enough attention to them to build out a budget recommendation.)

With merged data, here are a dozen questions you can ask. All of these can be done in Excel with pivot tables and slicers over the charts and graphs.

  1. What is the industry segment average (SIC/NAICS Code) spend?
  2. How does the business size (number of employees/total revenue) change within a segment?
  3. What is your total revenue by segment?
  4. What is your percent of total revenue within the segment (Borrell merged data)?
  5. What months are peak/valleys in each segment?
  6. Did the year-over-year spending by advertiser stay steady by month over the years?
  7. Can you spot dollar shifts from print to digital by advertiser?
  8. Are the local preprint/we-print dollars in a different trend pattern than the nationals?
  9. What businesses are gone that contributed a large revenue stream? By month, what is the loss exposure?
  10. Has a rate bundling (revenue contract) changed your average rate?
  11. Can you isolate and understand high-rate yield customers and low-rate yield customers?
  12. Sort the data to show a long-tail view of the advertisers and consider the following:
    • Are the high spenders local, regional, or national companies?
    • Where does their predicted spending shift and how fast (Borrell data)?
    • How many advertisers sit above the knee of the long-tail curve? Are you paying attention to them?
    • What is the average spend? What is your cost to service an account with a personal visit to the business?

And so on.

The analysis slicing and dicing becomes endless. However, after you finish with the initial analysis, build a process to deliver the reports the sales folks will use to move the revenue. There are probably about three or four report versions that can be used every week or month. Automate those so you can continue analysis work rather than generate operational reports.

Build long-term and daily analysis reporting from this quick and dirty analysis. Listen to the sales managers and listen to the sales reps. They will guide you to the few reports they need — not only the just-because-you-could-so-you-did reports.