| CRM applications like can show you what deals you | | | | Using Analytics to Reduce Pipeline Risk: Sales |
| have in your pipeline, but they don't help you prioritize | | | | analytics helps you reduce pipeline risk in several ways. |
| the deals or identify which ones are at risk. The CRM | | | | For example, using historical information stored by the |
| application can track your forecast, but it doesn't help | | | | analytics application, you can see not just which deals |
| you accurately come up with the forecast in the first | | | | are in which stages of the pipeline, but how long each |
| place. | | | | deal has been in its current stage and how quickly it's |
| This is where focused sales analytics solutions that | | | | been moving through the pipeline. You may feel good |
| are built on a software-as-a-service (SaaS) business | | | | that you have many deals in the pipeline, but your |
| intelligence platform, not a transactional system, are | | | | perspective would be very different if you knew that |
| helping VPs of sales hit their number and avoid nasty | | | | 25% of these later stage deals have been in their |
| quarter end surprises. With the right sales analytics | | | | current stage for more than 75 days, whereas the |
| solution you will get more from your CRM investment | | | | average length of your entire sales cycle is 90 days. |
| by being able to answer these three questions: | | | | 3. Based on my pipeline, what should I be forecasting? |
| 1. Which deals in my pipeline should I focus on? | | | | The third critical question about your pipeline centers on |
| 2. Which portions of my pipeline are at risk? | | | | what revenue you should be forecasting for a quarter |
| 3. Based on my pipeline, what should I be forecasting? | | | | given your current pipeline. An inaccurate forecast is |
| 1. Which deals in my pipeline should I focus on? | | | | created by an inbound assessment of the real size of |
| The #1 reason driving the purchase of CRM | | | | your pipeline, an incorrect evaluation of your real close |
| applications is to increase sales productivity, which | | | | rates and sales cycle times, and difficulty in quickly |
| leads to increased revenue. The best way to do this is | | | | identifying what's changed in your pipeline. |
| by focusing on the right opportunities in your pipeline; | | | | Using Analytics to Increase Forecast Accuracy: |
| that is, the opportunities you're most likely to win, and | | | | correctly assess your real pipeline size, real close rates |
| the one you're most likely to close quickly. | | | | and real sales cycle times. Once you know the real |
| Using Sales Analytics to Increase Revenue: Sales | | | | size of your pipeline using the sales metrics described |
| analytics help you increase revenues by identifying the | | | | above, you need to identify your real close rates and |
| characteristics of opportunities that historically have | | | | cycle times based on the type of opportunity |
| had the largest deal sizes, the shortest sales cycles, | | | | characteristics such as: industry, deal size, lead source, |
| and the highest win rates, so you can focus on the | | | | new vs. existing customer, new vs. seasoned sales |
| opportunities in your current pipeline that share those | | | | rep. |
| characteristics. Some important characteristics include: | | | | By applying these more granular close rates and sales |
| industry, deal size, age of the opportunity, lead source, | | | | cycle times to your current pipeline, you increase the |
| and new vs. existing customer. | | | | accuracy of your forecast. |
| 2. Which portions of my pipeline are at risk? | | | | Correctly Assess What's Changed in Your Sales |
| The next critical question about your pipeline focuses | | | | Pipeline: Once you have increased the accuracy of |
| on identifying which parts of your pipeline are fact, and | | | | your forecast, the next step is to make sure you track |
| which are fiction or fantasy. We've all had the | | | | your pipeline carefully so you know when anything |
| experience of seeing several forecasted deals slip | | | | changes that can threaten your forecast. This is a |
| right at the end of the quarter, which often leads to | | | | challenge, since most CRM applications only show you |
| nasty end-of-quarter surprises, and which occasionally | | | | your current pipeline, not what's changed since the last |
| leads to someone losing their job. There are many | | | | time you looked. |
| causes of this; sales reps having "happy ears" and | | | | Analytic applications that are based a SaaS business |
| falsely believing a deal will close, a sales process that | | | | intelligence platform solve this problem by providing |
| doesn't lead to proper sales opportunity qualification, a | | | | historical snapshots of your sales pipeline so you can |
| competitor starting to win more frequently, etc. But, in | | | | see not just what your pipeline looks like today, but |
| many cases, these deals never should have been | | | | also easily compare it to what it looked like in the past. |
| forecasted in the first place because the facts would | | | | This gives you the ability to quickly identify positive and |
| have shown that these deals were not good | | | | negative trends, and intervene, if necessary. |
| opportunities or at risk. | | | | |