on target
How to predict sales prospect results
Prospects are surely one of most precious assets and, at same time great resource consumers. How many lost opportunities took lot of your time and effort? What if you could assess your real chance with a prospect before investing your resources? What if you could identify the factors that actually matter to direct your marketing and sales efforts over valued targets?
Business Analytics can help you to do that, as you will see in this two-parts post. In first part (this one) you will see which data to collect and how to organize it. In next part you will see how to visualize data to identify the factors that really matter.
Organizing your data
In order to predict the future, you shall look at past (even intuitive assessment is based on past experience). You shall only keep track of key prospects characteristics and final outcome.
Such information can be conveniently organized in a table (a spreadsheet is an excellent alternative). You can usually merge data coming from your CRM with other data provided by other sources, compute other data and, of course provide the final outcome. Final outcome is usually a simple alternative (won/lost), some numeric value (amount sold, profit generated) or any combination of them.
Each row corresponds to a prospect, and each column to a prospect characteristic. In data-mining language, rows are usually called “cases” and columns are called “dimensions” or “features”.
Let's see an example:
|
Prospect |
Industry |
Employees |
… |
Result |
|
ACME Corp. |
Chemical |
12000 |
... |
Lost |
|
Softsource |
IT |
2700 |
... |
Won |
|
Telemart |
Telecom |
5400 |
... |
Won |
|
... |
... |
... |
... |
... |
First column (prospect) identifies each prospect, while following columns are features: the industry to which client pertain, the number of employees and so on. Last column is the result.
About features
Features come in two basic flavors:
discrete – can assume a value within an finite set of values. for example industry, sex, day of week … and the result in our case (lost/won). Logical features are discrete features with two possible values: true and false.
continuous – can assume a numeric value for example employee count, yearly turnover or even dates (that can be translated to elapsed time).
As you will see in next post, the type of feature will determine the kind of visualization you will use.
A second important consideration about features is their manageability, this is: what we can do about them. Consider, for example, the number of employees. If we discover that large organizations with many employees produce better result, there is little thing we can do but directing our effort to larger prospects. If, on the other side we discover “live demonstration” influences result, we can try to make live demonstration to a larger number of prospect. On a third case, if we detect that the presence of a specific competitor reduces our success chance, we can refine a strategy tailored to its eventual presence.
What to collect
Now that you have seen what features are you shall consider which features to collect. This will obviously depend on your specific case, but here is a list of common B2B features you would like to consider. The list is for inspiration: despite too long is not exhaustive. In practice you will use this list to identify 6-12 features you consider important in your case.
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Data Source |
Features |
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The client – Most information is usually available from on-line sources and catalogs.
For B2C clients you usually consider personal factors: age, education, professional position, social class, marital status, clubs and associations ... |
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The product – this is the solution you will be proposing (if different for different prospects). |
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Qualifying questions – usually politely asked by sales rep when qualifying it as a prospect. If prospect qualification is not part of your sales process, you are urged to review it before thinking in whatever else! |
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Competition – This information is possibly not easily available but is very important, and you shall make your best effort to obtain it. |
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Sales Rep – Sales rep is usually a key factor in success determination. You shall usually plot twice your data, both considering and not considering sales rep features. This way you can determine, for example which kind of training is more effective. |
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Lead source – Lead source can influence results. Search engine advertising, for example, usually generate price raider prospects that can produce poor results if you are not very competitive in terms of price. |
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Opportunity and evolution – usually extracted from CRM |
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Analog choices – excellent predictors! The principle is simple: suppose you sell luxury houses which prospect is better: one that comes on a Bentley or another that comes on a Chrysler? Same rules applies to technology. If you sell software solutions its database, web technology and network technology are predictors of how much prospect will match your offer. |
Technology (example):
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Take some spare time to choose about 20 of your last concluded prospects with both positive and negative result, and some of their features. In next post you will see how to visualize their interrelationship.
Interested in partnership?
Would like to try or coach this technique with your clients, do you need any additional or technical detail? Please let me know! Component Bases Solutions has great interest in partnership with consultants. We can help you automate your proposed solutions in a very short time. We can also help to increase your visibility through links from many management tools we make freely available on the Web. Please contact for more detail.
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Posted at 11:57PM Mar 04, 2010 by admin in Sales & Marketing | Comments[1]
Posted by on target on March 23, 2010 at 11:09 PM UTC #