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Latency: The B2B Software Example

A B2B software company has an appealing pitch to business - their software makes a company more efficient and saves more money for the company than the software costs.

The software is modular, with a base application and additional add-ons that are specific to certain business challenges. The selling strategy is to under-price the base application to get market penetration and then make a higher margin on the add-ons. The add-ons drive the profitability of the business, as does the installation and customization of these add-ons.

The company has been quite successful with this selling strategy. But lately the CFO has noticed sales of the base application have risen, but revenue from add-ons has not risen in the same proportion. In other words, the company is further penetrating the market and gaining new customers but getting less revenue from each customer.

The CFO thinks:

I can't understand this. Sales of the base application are rising according to plan but overall company revenue is not growing at the same rate. The only thing I can think of that would create this particular situation is fewer basic application customers are buying add-ons. How can I figure out why this is happening?

The CFO calls the heads of business development and marketing to ask about the situation. They both report they are aware of slowing add-on unit sales per customer, but cannot attribute it this to anything specific. The company is simply penetrating the overall market more deeply they say, and as we penetrate further and further, add-on sales seem to have slowed.

The CFO is not particularly satisfied with this answer, and thinks:

If it shows up in my financial statements, it has to be measurable. I'm just seeing this from too high a view. All the sales of the different base applications and add-ons roll up to total sales, so the data I need to better understand this must exist somewhere. The CFO picks up the phone to call the CIO, and then hesitates. The IT people are going to want to know specifically what I am looking for, the CFO thinks. Do I really know?

What is needed here, fellow Drillers, is quantification, some framework for analyzing the situation. What is the real question to be answered here? The CFO knows IT has limited resources to apply to this kind of ad hoc work - if the request just generates information that leads to another question, then time and resources are wasted.

The CFO could ask for monthly product sales percentage by type over the past year. In a lot of ways, this information would simply confirm what the CFO already knows - sales of add-ons have gone soft. But does it answer the core question of *why* they have gone soft? It does not, and that is the real question at hand. Since customers have different LifeCycles, any monthly sales data will contain customers in various stages of being likely to buy an add-on. So raw monthly financial data - the kind the CFO is used to working with - is not going to answer the "real" question. The CFO thinks:

Customers buy the base package and once they get it integrated and tuned up they start to buy the add-ons. During any one-month period, we have customers who just bought the base package, customers who are in different stages of integration, and customers who are buying add-ons. What I really need to know then is this: what is the average number of weeks between the purchase of add-ons, this year versus last year? If this number of weeks is rising, that is where the softness in add-on sales is coming from - customers are simply taking longer to make the purchase decision. If this number of weeks is constant or falling, then something else must be going on.

With a definition of the question at hand, the CFO picks up the phone and calls the CIO. The CFO gets the report on the average number of weeks between the purchases of add-ons. The information looks like this:

Last Year
8.6 weeks

This Year
8.9 weeks

Average Weeks between Add-on Purchases

So it is taking longer for them to purchase, the CFO thinks, and darn it, now I have another question. The IT people are going to have me for breakfast for not thinking this all the way through the first time! I got the information I asked for, but this information is not actionable, I can't do anything with it. There is not enough detail in the information to act.

Fellow Drillers, when you are plumbing the depths of your data, try to think of what you will do with the information you are asking for. Imagine getting back your results, and taking an action based on those results. If you can't imagine the action you would take knowing the information, you are not asking the right question yet. The CFO thinks:

Our add-on modules have different prices and different levels of difficulty involved in their integration. And they are usually installed in a particular sequence. So what I really should have asked for is the average number of weeks between the purchase of add-ons by add-on - the time between base purchase and the first add-on, the time between the first add-on and the second, and so forth. Maybe there are problems with installing one of the add-ons due to changes in the next generation of operating systems, for example, and this is slowing the installation of a particular add-on down. If I can get the average number of weeks between add-on purchases by add-on, I can act on it, because I will know which particular add-on is causing the slowdown.

Next Article: Latency: The B2B Software Example: Upgrade Cycles

This article is taken from the book Drilling Down: Turning Customer Data into Profits with a Spreadsheet. The first article in this series can be found here.

Jim Novo has nearly 20 years of experience using customer data to increase profits. He is co-author of The Guide to Web Analytics and author of Drilling Down:Turning Customer Data into Profits with a Spreadsheet. If you want more visitors to take action on your web site, try using the free conversion metrics calculator, downloadable here. If you need to sell more to customers while reducing marketing expenses, get the first nine chapters of the Drilling Down book free at http://www.drillingdownbook.com.
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