Commercial Analytics is the kind that makes money. From data to dollars, insights to income, this is all about how to run the business better. To do it and to do it well you need certain capabilities in place. This article builds a map of those business capabilities to help you assess, understand, and plan your business.

Usually we talk about this and we are happy to talk to you about it (just contact us) but we recently had occasion to make a slide pack that covered some of the materials as a stand-alone presentation. This article is based on that pack which is also available for download.

Do you have accurate and timely analysis of the quality of the customers you are acquiring? Most companies carefully track the quantity of new customers by the hour, day, or certainly the week, but it is still less common to track the quality of the inflow as it happens. It is interesting to know that we have acquired, say, 1000 new customers today, but so very much more informative to know that this inflow will bring in £22,000 of revenues over the next year at 35% margin. Break it down by channel and product to see who is performing and who is not, and I as a marketing manager get really excited: I have the tools to do my job!

Monitoring the quality of the inflow and understanding the reasons for change is essential. After all, if your new customers are of lower quality than your existing base, then you are setting your company up for difficulties over many years to come.

Considering how much companies typically spend acquiring each new customer, this really should be a no-brainer. And yet many companies are completely unnecessarily stuck at reporting sales by volume instead of value.

Churn modelling is easy; commercial churn modelling is hard. Let us compare the two to explain what we mean by the latter.

In summary, we make the point that knowing the likelihood to churn and the (most probable) reason for leaving is actionable by the business in a way that knowing only the first component will never be.

This is what we do here at CYBAEA and what we mean by commercial churn modelling: predicting not just that a given customer is about to leave, but what you can do about it right now. We additionally develop analytics that predict changes in the customer’s behaviour after accepting an offer, and therefore the change in revenues and profitability, which is what you need to make a rational commercial decision about what to do with each customer at each moment in time.

Read on for the full article.

We have a mild obsession with employee productivity and how that declines as companies get bigger. We have previously found that when you treble the number of workers, you halve their individual productivity which is scary.

We now re-do the analysis four years later and, just because we can, we are using the leading companies of the London stock exchange instead of the largest American companies.

The results also show in this data set. We called it the 3/2 rule: treble the number of workers and you halve their individual productivity. Large FTSE-100 companies with ten times the number of employees are ¼ as productive as their smaller competitors and if all the FTSE-100 companies achieved their average profits per employee, then the index would generate almost £1 trn of additional net profits for the economy. (But see the comment about the likely selection bias before you rush out to de-merge your company.)

The nice people at Velocity has released The B2B Content Marketing Workbook. It is behind a registration wall which means we wouldn’t normally recommend it but you can just type junk in the fields if you are not comfortable with giving your personal details to a marketing agency. (Think about it....) If you are relatively new in the B2B world, say having joined a professional services or consulting organization, you may find this one useful.

Havard Business School has an interesting study titled Do Friends Influence Purchases in a Social Network?. I would like to get my hands on the raw data (which is from the Korean social site Cyworld), but the outline conclusions seems plausible:

• Highly connected people are negatively influenced by purchases in their network. This is consistent with a hypothesis that they are trend leaders who are always looking for new things to differentiate themselves from their group.
• Moderately connected people are positively influenced by purchases in their network. This is consistent with a hypothesis that these people are trying to "keep up with the Joneses".
• Weakly connected people (48% of the data set) are not influenced by purchases in their network. The paper rather offhandedly dismisses these people as "the low status group".

The financial crisis is all my fault. Or so David Smith from our friends REvolution seems to suggest in his post Physicists, models, and the credit crisis:

I remember working in The City in the late 90's and Wall Street in the early 00's and remarking then that just about every quant had a physics or engineering background. I met very few statisticians. Quantitative models have taken a hefty share of the blame for the credit crisis, but I wonder whether the blame lies more in their application, rather than the models themselves. Statisticians are trained on the limitations of models, and how to detect when models are breaking down, but statisticians were woefully underrepresented amongst quants. Do physicists and engineers get similar training?

I was a physicist who left the CERN research facility to work as a quant in an investment bank in the days before “banker” became a four-letter word, so I do have an opinion on this. I firmly believe that no statistician should ever be allowed out in the wild unsupervised, and this gives me an opportunity to also comment on the current crisis: We have the luxury to be angry with the bankers. I do not want to take that luxury away from us: it is wonderful that we can afford to indulge ourselves. But I would like to remind us that is is a luxury that we can afford, and that we can afford it at least in part due to those bankers.

All too often marketing departments thinks that database analysis is the first, last, and only step in segmenting the base of existing customers. In fact, identifying clusters of common behaviors is only the first activity you should undertake in creating a customer base segmentation.

In this article we identify the five steps you need to follow for success. We also discuss when you can cut short the five step process.

Marc Andreessen, who as founder of Netscape and other companies knows a thing or two about the subject, has a nice little series on The Truth About Venture Capitalists, part 1, part 2, and part 3. We've said something similar before, but Marc puts it very well.

McKinsey writes about Better Strategy Through Organizational Design and come out strongly in favor of Enterprise Social Software and collaboration techniques:

[T]he new element that can help 21st-century corporations create more wealth is large-scale collaboration, across the entire enterprise, enabled by digital technology. ... Digital technology provides the means not just to promote efficient, effective, and large-scale collaboration but also to measure each person’s “assists” and thus motivate employees to collaborate in ways that were not possible in the past.

Their arguments are closely linked to the profits per employee measure we introduced back in October last year. This is the most important metric for the CEO to manage.