While there have been CRM systems tailored to specific sales methodologies, I don\u2019t believe that any of them have been commercial successes. Too many sales organizations have their own flavor-of-the-year sales methodology, or think they\u2019ve come up with a completely new one that could not possibly be covered by one of the standard ones. So the smart CRM vendors have kept agnostic, creating a platform that enables any sales methodology, and encouraging vendors to create nice add-on products.\u00a0\nNo matter what sales methodology you currently subscribe to (including none at all), there are key indicators that the CRM can provide that give you significant guidance about what your pipeline is really doing. Good analytics will help uncover process problems and, sometimes, really bad behavior. Some of these analytics may require some significant cleverness in report writing (and variable set up), but here are some sales diagnostics I\u2019m always looking for in clients\u2019 systems (regardless of CRM vendor):\n\nDeal Size:\u00a0 are the deals you win significantly smaller than the deals you lose? That\u2019s not happy news about the past. Are the deals still in the pipeline resembling the size of the losers more than the winners? That\u2019s not happy news about the future.\nZero $ Deals:\u00a0 What percentage of won deals have $0 as the amount? Ought to be zero percent, you\u2019d think. Keep laughing. What percentage of lost deals have $0? Does the percentage of open deals with $0 amounts resemble the winners or the losers? Uh-oh.\nStale Deals:\u00a0 What percentage of your open deals are past their close date? If it\u2019s more than 10 percent, you\u2019ve got an adoption and\/or data fidelity problem that skews your pipeline. Best to just slam this door shut by auto-closing any open deal that is two weeks past its due date. And yes, it\u2019s closed-lost. When the reps whine about this, just say \u201ctough.\u201d\nDeals without Contacts: \u00a0What percentage of your deals don\u2019t have a Contact (or \u201cContact Role\u201d) assigned? What percentage don\u2019t have more than one Contact assigned? This little detail indicates two key things:\u00a0\n\n\nThe sales rep doesn\u2019t know or care to tell management about the people actually involved with the deal\nMarketing will not be able to run their campaign effectiveness reports to fine tune its spending. A telling detail indeed.\n\nThe right answer here is to make the Contact Role a requirement for advancing the deal beyond the first phase, but if you can\u2019t enforce that at least measure the problem.\u00a0\n[Related: CRM backups or audit trails? Yes, please] \n\nDeals without Campaigns:\u00a0 What percentage of deals have no campaign \u201ctouches\u201d recorded against them? Are these really \u201cdeals spontaneously generated by sales,\u201d or is there a data fidelity problem here? (One of my favorite behaviors s is where the sales reps deliberately delete any trace of marketing activities for the deals they win, to show how it was all their doing.) Again, compare the number of marketing touches for the historically won deals, the lost deals, and the current pipeline.\nDeals without Activities:\u00a0 What percentage of deals have no activities recorded against them? What is the average number of activities involved with won deals (in most enterprise deals, it\u2019s going to be at least 5)? What\u2019s the average number of activities for a lost deal? In many sales organizations, they\u2019ve recorded more activities for the losers than the winners. So much for \u201cfail fast.\u201d\nInsta-Close Deals:\u00a0 What percentage of your deals were closed within a day or two of their creation? If it\u2019s over a few percent, you\u2019ve got an adoption and\/or sandbagging problem on your hands. (Of course, in this analysis you need to screen out ecommerce and other system-generated deals that do not involve the sales people.)\nDeal Velocity:\u00a0 Here\u2019s where you\u2019re going to need a pivot table or two, because what you want to do is compare how many days deals spend in each stage of the pipeline, looking for differences between the winners, the losers, and the open deals. You\u2019re likely to find that winners go faster than losers, and you should be able to identify at which stage a deal is most likely to die. So much for \u201cfail fast.\u201d\u00a0 (Ditto the idea of screening out ecommerce and system-generated deals.)\nDeal Regression:\u00a0 What percentage of deals move out in time, backwards in stage, or down in value? If this is over a few percent, it\u2019s an indication of bogusity in the pipeline.\nProbability Accuracy:\u00a0 Most CRM systems have a \u201cprobability\u201d percentage that is set to default values for each stage. So look at the historical pipeline, evaluating deals at each stage to compute how many of them actually closed. The probability is that your probability percentages are optimistic, even at the later stages. Once you determine the average likelihood of winning from each stage, adjust those default probabilities in the system. (Note:\u00a0 my best advice is to avoid using the system standard probability field altogether if the system automatically pushes the percentage to the default it at each stage change. One way or the other, you still need to do the data analysis described here for modeling purposes.)\nForecast Accuracy:\u00a0 What percentage of the final closed amount was in the committed forecast? At what week of the quarter did the committed forecast come within 10% of the actuals (without ever falling back out of bounds)? When did the committed forecast come within 5 percent of the actuals? \u00a0Two percent? Conduct this analysis for each level of management when they have applied adjustments\/amendments to the underlying reps\u2019 pipeline info.\u00a0\n\n[Related: Best practices for Salesforce.com administrators]\u00a0\nIf you\u2019ve got a substantial sales organization with different segments (e.g., US vs UK, commercial vs federal, telecom vs finance, enterprise vs SMB, channel vs direct), you\u2019re going to need to break all these stats out by segment. The aggregate numbers will mask important issues that will jump off the page the instant you drill down into the segments. If you\u2019re using a stats package for analytics, you\u2019ll be able to flag where these breakouts are needed by looking for bi-modality or wide standard deviations.\u00a0\nThis is a process, not an event\nThese diagnostic indicators are interesting when you first run them, but become more important over time, particularly as you evaluate \u201cimprovements\u201d to the sales process, reorganization, messaging, etc. This is a game of continuous improvement and tuning.\u00a0\nBut the indicators by themselves won\u2019t point you to specific fixes, and they mustn\u2019t be interpreted simplistically because the problems can be as much a result of bad policy (like, \u201cour product quality stinks\u201d) as weak sales execution. The metrics simply say \u201clookee here.\u201d\u00a0 What\u2019s amazing is how few organizations routinely do that looking, or evaluate their financial model against the realities of their sales performance.