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How Many Pipelines Do You Have?

How Many Pipelines Do You Have?

Analytics should be able to tell you the likelihood of each deal's completion in time for you to take action if the prediction is unfavorable. If each deal is somewhat different, then comparing each with the historic deals most like them will provide a truer indicator of close probability than simply comparing deals through something as general-purpose as deal stage. The difference is important.

By Denis Pombriant
07/16/14 8:35 PM PT

It's general practice to have multiple sales forecasts, and that typically means low and upside potential as well as what's committed -- but the idea of teasing those threads apart only at forecasting time might be old school and no longer applicable.

Long before you compile a forecast, you have a good idea of which deals might be prone to a typical divergence -- so it makes sense to group like with like so that you can formulate strategies.

Before now, that was hard to do, because forecasters relied on instinct and there was too much data for instinct alone to act on. Also, typical reporting tools, like spreadsheets and BI, are great at telling you what happened but not how to accomplish what should happen.

The Portfolio Analogy

Now, advances in analytics and raw processing power make it possible to do the segregation early, with positive benefits for the forecasting process overall. Rather than thinking a lead is a lead is a lead, consider that leads and the deals they represent can cluster into families with similarities, and each family has unique characteristics and closing behaviors.

I prefer to think of these families as "portfolios," a term recently introduced to forecasting by Aviso, an upstart in the predictive modeling space. They're just like an investment portfolio that tries to balance risk and reward. The big difference is that you can pick your investments, but often you don't have the same control over the deals that get into the pipeline.

The portfolio model makes a lot of sense, and if you look at the past deals that you won you will probably recognize the portfolio types in your customer base. If you apply what you learned through those deals to the current portfolios, you should be better able to close more business.

Good analytics can do this kind of segregating -- especially if, through machine learning, the algorithms can churn through a few years' worth of your deal history first to confirm your most common portfolio types.

Another way to think about it is modeling. Portfolios represent the few models of successful purchase and sales within your business.

From Predictive to Prescriptive

The purpose of analytics isn't simply to predict a revenue number, though that's obviously important. Analytics also should be able to tell you the likelihood -- or "probability," to use a technical term -- of each deal's completion in time for you to take action if the prediction is unfavorable. That's prescriptive.

If each deal is somewhat different, then comparing each with the historic deals most like them will provide a truer indicator of close probability than simply comparing deals through something as general-purpose as deal stage.

The difference is important. Consider this: Let's call your best and easiest-to-close deals "Vanilla" and the next easiest "Chocolate." Each has its own portfolio. Deals in the Chocolate portfolio take a bit more work -- they might involve more steps, for example, greater exposition of the ROI, or something else.

Chocolate deals always seem to surprise you. Maybe you've even begun to notice them in the pipeline -- and for that reason, you tend to place them in the upside forecast simply because they don't behave like Vanilla deals.

Now, let's call a third portfolio of deals "Rocky Road," because it's summer, and ice cream is in fashion again. In the Rocky Road portfolio, deals are more difficult to close, and they are opaque. There's always something that makes you reach for the Prilosec when you consider them, and so you exclude them from the committed pile because you can imagine a world where they suck up resources, don't close quickly enough, and leave you with a downside forecast and a miss.

OK?

Adjust Your Strategies

Now, the Vanilla, Chocolate and Rocky Road deals all come into your organization the same way, and you never know the distribution. Sometimes your pipeline may be just a big Vanilla portfolio, and you think that maybe, with a few Chocolate deals thrown in, you'll make the upside forecast.

At other times, the quarter is a long slog down a rocky road (pun intended) and the forecast suffers.

Clearly, you're running three distinct pipelines and it's their confluence that makes up the real forecast, but the constitution of the pipeline changes every reporting period. Sometimes the pipeline looks like nothing but Rocky Roads.

The best way to figure out which is which is to compare today's pipeline deals with those in your recent past -- by type or portfolio -- to figure out how the quarter will end AND what you can do in time to change or ensure the outcome.

If experience tells you that Chocolate deals do best with a reassuring call from your CFO, why not make this standard procedure? Similarly, if Rocky Road deals close better in Q2 or Q4 -- a distinct possibility -- then arrange your efforts accordingly. It's easy, once you start seeing deals for their distinct characteristics.

The point in all this is that with an accurate model and the right algorithms, all of these fine points can be made clear from past history, and you can adjust your expectations and strategies to do the things that each deal type demands.

Maybe you've noticed that Rocky Road customers like to wait till the last minute to give the order, because they perceive that they have greater bargaining power that way. Or perhaps your Chocolate customers look for value in a total package approach rather than simply going for a low price.

On the other hand, Vanilla deals are about raw technology Visit the VMware Tech Center. Running pipelines by the differences found in each portfolio gives you added leverage and insight into customer behaviors.

By adjusting your approach for each portfolio, you can identify the types of deals in your pipeline that not only will help you close more and better, but also will help dampen the variability in forecasting that comes from having an excess of one type of deal.

Also, if you are consistently adding too many Rocky Road deals to the pipeline, you might need to better coordinate with marketing to adjust the lead generation and qualification processes.

It's like navigating a ship. A compass can tell you your direction, but only sonar will tell you what's submerged and in your path.


Denis Pombriant is the managing principal of the Beagle Research Group, a CRM market research firm and consultancy. Pombriant's research concentrates on evolving product ideas and emerging companies in the sales, marketing and call center disciplines. His research is freely distributed through a blog and website. He is the author of Hello, Ladies! Dispatches from the Social CRM Frontier and can be reached at denis.pombriant@beagleresearch.com. You can also connect with him on Google+.


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