Several years ago I read a book on corporate strategy. In that book was a small chapter on how to integrate your strategy into your compensation plan and measure the affects. I had the opportunity to have several conversations with the author, in which he confided that in some of the cases where he has advised large organizations, the intended outcome is sometimes missed. Even worse, one must be able to monitor and react to the unintended consequences.
There are two places that are prevalent that I can point this out, one in banking where Wells Fargo had some well known consequences of a very well structured commission plan and corporate strategy, with a general lack of controls. The other is company xyz.
What is the appropriate way to measure this and react quickly based on information gathered, further, what could have been instituted in the case that we referred to. First you have to define what are the key metrics you are changing, what is it that you are trying to achieve, and what are the indicators that something may be going wrong so you can determine quickly how to correct. Let’s go into the following use case to dissect the specifics of what to do. I want to point out that I did not consult the author in these matters and do not claim any of the original work.
Scenario*
The company in question wanted to increase profit margin. They had two products and for the sake of this example, we will call this product A (a low margin, high volume product in most of their customer locations) and product B (a high margin product that was also located in many of the customer locations, but not with the same volume).
The company decided after being advised to focus on the high margin items, effectively decreasing and then removing entirely their low margin high volume products.
What happened?
As one may expect, the worst possible outcome.
The company when under. Lets dissect why.
1. Removing the high volume low margin products reduced the buying power of the customer, thus reducing the profit margin on the higher value items.
2. The big one, by removing the high volume items, they had less touch points with the customers. Having less touch points with the customers opened the client up to competition and effectively replacement.
3. The reduction in touch points and product reduced moral in the sales organization also as a net effect and made the sales people less effective in communicating changes and new products with the customers.
How could this decision been avoided? How could have the issues been discovered prior to the wheels coming off the cart?
Scenario modeling leveraging organizations and data from companies that are in the industry along with possible outcomes at a minimum would have exposed possible anomalies. That and a quick call into suppliers to ask them about what volume discounts you have (which would have been in contracts too). Year over year sales of all the items were considered, but nobody considered the contact points that sales people have with the customers. Thinking about how sales were conducted and other possible data points would have allowed the decision to be avoided, or modified.
Once the decision was made, how could we have noticed a problem. Immediately one could have seen the cost from the suppliers going up by using simple profit margin calculations. Further, suppliers sales people contacting you on why the orders were changing. Sales people quotas sliding, and commissions check reductions along wit the traditional sales people rumbling clearly was missed. The question of whether this customer had a CRM at all is a valid point. The should be outlier reports that can catch this type of situation.
Think about this, when covid hit, all of these bells should have been going off for companies. Many of them reacted quickly, whether it was production of needed items or if it was head count reduction. Many others did not’ react, didn’t change the business model and are counting on governments to help them.
Now that we are here, what are a few things that you can put in place now that may allow you to see the implications of changes? Here are three items that you can look at:
1. Understand Your Critical Data Both Inside & Outside Of Your Organization
You should have a map of influential information to your organization along with things that may look like outliers that you can evaluate. In the case of the above company, one could have looked at weather patterns in the locations that their core product grows to determine possible price shifts. They could have also had something around volume discounts widely seen in the industry which would have allowed them to react.
1. List all the information you use now
2. List information outside of your organization and information that might be tangential to your models
3. Review the integrity of what you have from your data sources
4. Create a data model and a reliable data source list
Now that you have this information correctly planted, you can use it in your modeling techniques. If you are reading this, it is likely you should use a decision tree model of analysis. It isn’t the most optimal model, as I have discussed, I believe RL is, but this will do for a start. Now using the decision tree analysis, you can use it as a large what if modeling technique to determine your outcome model against expected outcome.
2. Outlier reports against expected changes
This is pretty easy and will summerize two outlier reports. One is a deal based profit margin model, taking out any expectations of SG&A along with increased capacity based on corporate decisions. This means, based on what you hold true today, as your cost model, what does it look like tomorrow as you institute changes.
First report simply should be a time lapse over x period reviewing the profit margin, in this case, of the products and lines of products that you are wanting to increase profit for. Correspondingly this metrics should have trigger points plus or minus with a correlation to bottom line profit. For example if we are maintaining profit margin, but our bottom line profit is going down, you have to evaluate if your SG&A is in line with expectations and do you see a corresponding deal/front end opportunities coming into your sales funnel
Second outlier report is simple. You want to track deal velocity to commission. This one will take a little longer, but will give you an idea of sale person beahavior and stated corporate goals. If your deal velocity decreases, that is somewhat expected, given a change in corporate goals. This still needs to be monitored as a complete implosion is a harbinger of problems. Further, sales people usually gravitate towards what they can sell and what makes them the most money. Is that happening?
* More information on this scenario is available in Frank Cespedes’s book Aligning Strategy and Sales: The Choices, Systems, and Behaviors that Drive Effective Selling