Creating predictable revenue is one of the hardest things that any business can do, but it is also one of the most important. When it comes to revenue, surprises are bad. The worst surprise is missing your revenue plan but overachieving one quarter and underachieving the next can be nearly as problematic. It is very difficult to resource a company appropriately if you don't have a good sense for the amount of business you will bring in. If you miss plan and are over-resourced, you'll lose money and if you exceed plan you may be under-resourced and your company may start disappointing your customers. It's therefore not very surprising that according to AMR Research, every three percent increase in sales forecasting accuracy leads to a two percent increase in profit margin.

So how do we create a sales pipeline that is not too big and not too small, what I call the "goldilocks pipeline forecast"? That creates predictable revenue we can plan appropriately for? The answer to this lies in two key activities, both of which have data at their center.

  1. Marketing and Sales Alignment

A highly aligned sales and marketing team think of pipeline as a continuous flow from the first moment a customer hears your company's name to the moment they stop being a customer. When teams think like this they create something very powerful. A transparent pipeline. Aligned teams know what the impact on each stage of the pipeline will be of each amount of marketing resource employed. This is because they align on two critical factors. A common vocabulary and an unwavering commitment to share data with one another.

The common vocabulary allows aligned teams to understand every single gate in the pipeline process from suspect, to marketing qualified lead, to sales opportunity and so on. Everyone on both teams understands and agrees upon what these terms mean.

From a data perspective both teams also share precisely what is happening at every stage in the funnel and use this information to continuously reforecast and brainstorm new tactics in a collaborative and non- accusatory way. What that means for instance is that marketing soon knows that they need to spend $X on Google ads in order to generate a specific number of sales qualified leads. They will also know how many deals those leads will turn into and have some ability to determine what revenue those deals will bring in for the company. At this point a company has reached what I call "revenue nirvana" where you know for every dollar spent what you will yield. However, I am sure you are incredulous about the ease with which this can be achieved and you should be. An aligned team is nowhere near enough to create "revenue nirvana," a critical missing component is the forecasting methodology used to assess the conversion rate of the pipeline at each stage.

  1. Create a World-Class Forecasting Methodology

There are many ways to forecast pipeline conversion, each with different pros and cons. The greatest accuracy is created by using multiple methods and triangulating the results to a consensus estimate. I will share my two favorite methods that we use at my company Velocify.

Method 1--The Simple Method

The Simple Method is sales rep driven, it harnesses and focuses their "gut feel" for the probability of if and when each deal will close in a fairly scientific way. The way I like to do this is to allow reps to make two decisions about each opportunity in their pipeline on a weekly basis. First, in what month do they think this deal will most likely close. Not when would they like it to close but if they were only paid commission on the deal if they accurately forecast the month that it closes which month would they choose. Second, we talk about which of five categories each opportunity sits in. At Velocify the categories are, "Long Shot," "Best Case," "Committed," "Won," and "Lost." Each of these categories are then assigned a probability that they will turn into a deal. Again in Velocify's case this is 10%, 50%, 90%, 100% and 0%.

What the "Simple Method" ultimately gives your sales and marketing team is a clear view on the probability adjusted pipeline of deals many months out and highlights by what amount you are currently above or below from your planned revenue. Thus indicating how marketing efforts need to be focused.

The greatest advantage of this method is that it captures things a rep knows but they have not documented in your CRM system. It also provides a good forum for a sales manager to coach a rep in their weekly pipeline review sessions.

Method 2--Predictive Pipeline Analysis

This is a potentially very scientific way of analyzing pipeline. Using "Moneyball-type" techniques, data scientists look at two aspects of each opportunity in order to determine its probability and time of closing. The similarity of the opportunity to other opportunities that have been won in the past is the first aspect and then the pattern of interactions that have occurred on that opportunity and how they relate to previous successful sales patterns is the second aspect. Using this information in a predictive pipeline tool can fairly accurately determine what percentage of pipeline will close each month.

Despite being a potentially fabulously accurate methodology for forecasting pipeline, the obvious disadvantage of this technique is that it requires a heavy lift from a data science team to build the model. Luckily there are a number of companies such as 6sense and Angoss that have technology and teams capable of doing much of that heavy lift for you.

The best of all sales teams use both pipeline forecast methodologies and managers review the delta between the two. However the best of all companies don't stop there. A critical component of the sales pipeline forecasting process is designed to provide a 360 degree feedback loop between marketing and sales. Filling pipe and closing pipeline becomes a collaborative enterprise between the two teams and the company ends up with a "Goldilocks" pipeline which is neither too hot for the operations of the company to handle nor too meagre to meet the company's financial goals.

Published on: Mar 9, 2016
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