forecasting

  • March 26, 2013
  • Marketing Performance Management Isn’t Hard, It’s Good Business

    Sales has always enjoyed a quantitative edge over marketing but today that’s changing.  Sales managers measure many things like sales calls per rep per week, forecasted revenue, the time a deal stays in the pipeline or in a particular deal stage and much more.  Forecasts are often tallied in spreadsheets and they always involve an impressive array of revenue numbers and probabilities of close.

    Pity the poor marketer.  Marketing has been at a quantitative disadvantage because they have tracked response rates, click-through rates and many other qualitative measures of interest that can be as reliable as fickle customers.  Worse still, the rest of the C-suite speaks the language of costs and profits while the CMO talks about things that don’t directly result in revenue.  It doesn’t matter that some sales numbers, like probability of close, are just as qualitative.

    In the past all marketers could do to arrive at “serious” numbers was to add up marketing campaign expenses and divide them by the number of leads and revenue that came in.  This macro approach didn’t take into account which campaigns did the best job of attracting the customer initially or which one pushed the deal over the top.  As a consequence, marketers couldn’t tell if one campaign or style of campaign was better or worse at doing a specific job and resource allocation was hit or miss.

    But what if there was a way to define and track marketing metrics that more closely track revenue?  For many years marketers couldn’t hope to track those metrics but thanks to the confluence of big data, analytics, social techniques and CRM, marketers can track the data their campaigns give off and make measurements that can stand on an equal footing with sales metrics.  This reality has made the marketing funnel a real and important part of the overall sales process and spawned the discipline of Marketing Performance Management of MPM.  Full Circle CRM provides a good example of an MPM solution.

    A marketing department that tracks data on its activities can put itself and its company on a path to having greater certainty about its pipeline and revenue forecasts and greater influence in the C-suite.  Every marketing campaign generates valuable data from the raw number of prospects it attracts to the time it takes to close a lead and even to knowing how many prospects with initial interest make it all the way to closure.  So the issue for marketers no longer revolves around which data to collect or how to do it.  Instead emphasis has shifted to which calculations to make and which metrics to apply.

    If a marketing department tracks spending, dates of transition through the steps in the marketing funnel and number of leads generated — by each campaign — it can calculate many meaningful measures of performance that will make anyone in the C-suite smile.  Here are some metrics that every marketer who is intent on improving MPM should consider.

    1. Immediately, the cost of marketing becomes clear with simple metrics like cost per lead, cost per revenue dollar and conversion rates by each campaign.
    2. A slightly more sophisticated measure can calculate cost per lead based on campaign type — trade show, direct mail, social campaigns — whatever.  This can tell you the best sources of leads by volume and it can identify the best mix of campaigns by cost per lead and quality of lead.
    3. Capturing the date when a customer first raised a hand and date of close (from the SFA system) averaged over a number of leads in a specific time range gives the average sales cycle.  It also gives the overall velocity of the sales funnel — the speed from first contact to closed deal.  Further identifying leads by campaign type will also show which campaigns produce the most sales ready leads.
    4. You can use the deal velocity calculation on leads from specific campaigns too.  This will tell you which deals might be accelerated to help ensure sales plans are met.
    5. By capturing dates for transition from one funnel or pipeline stage to another marketers can tell conversion rates by stage and, most importantly, if and where deals get stuck.  This will naturally also show the kinds of campaigns that might be most effective at getting the funnel flowing again.

    All of this data can be captured and stored in the CRM system.  Many of these metrics depend on establishing historical norms or averages but that’s easy to do and the norms get refined over a short time.

    So, tracking data on a relatively small number of attributes and applying the right math can significantly improve marketing’s visibility into the funnel — that’s what sales does and marketing can do the same.  Of course, plenty of consideration ought to be given to the vagaries of each marketing department including overall budgets, product type and customer types.  Marketing organizations therefore need ways to customize weightings for various programs and scores for resulting leads.

    So when shopping for modern marketing automation solutions, keep an eye out for the performance management side of the equation and include marketing performance management as part of your shopping list.  It can easily mean the difference between success in your new approach to marketing and remaining at a quantitative disadvantage to sales.

    Published: 5 years ago


    I spent most of last week in Boston at the Enterprise 2.0 conference where I was honored to be the sales and marketing track chairman.  Next year it will be called E2 Social and will bookend the other conference that has been held in Santa Clara and will become known as E2 Innovate.  There’s good symmetry here.  I can’t think of another purely social show or one focused on innovation.  Most shows today are vendor sponsored which is good but different.

    Our track had some cool presentations on social marketing from IDC mavens Gerry Murray and Joe Farentino, revenue performance management from Phil Fernandez, CEO of Marketo, and an intriguing discussion from Pam Kostka, a fellow Crusader and CMO of VirtuOz, a company that makes virtual agents.

    If you are wondering, a virtual agent is a software robot that you can talk to regarding sales, marketing or service issues just like a person.  These agents are a happening thing and promise to do away with wait times and improve service.

    There were also two panels, one on M&A activity that we put together last minute with the able assistance of Sameer Patel, Josh Greenbaum and Louis Columbus.  As is so often the case with these things, serendipity played a role and caused more than a few people to walk away with the idea that this kind of thing ought to happen again.  Thanks guys, the panel was outstanding and a good example of the talent pool that lurks in the Enterprise Irregulars a group with a low profile (that ought to be greater) and an inversely proportional IQ factor.

    The other panel, which I want to focus on, was illuminating to me for an unexpected reason.  I invited some of my brain trust including Thor Johnson, Cary Fulbright, Derek Peplau, Columbus and Murray mentioned above.  Toward the end we had a discussion of big data and someone mentioned a large company that had converted from one CRM system to another and had deleted many years of sales data in the process rather than bring it along and try to figure it out.

    Initially I thought throwing away all that data was folly but I came to see it as smart but for reasons that I think are different from the consensus of the panel and audience.  One audience member got the analysis right, in my opinion, when he said simply, “There’s nothing in it,” by which he meant there was a great deal of data but that it was devoid of information content.  How could this be?

    Very simply, most CRM systems either have fields or enable you to create them to capture important data like product interest, deal size, projected close date and much more.  All of this is valuable but CRM’s point of failure is that these fields can be overwritten and there is no provision for storing historical information.

    Now, you’ve heard my sermon on historical data before most likely.  But at E2.0 I had an insight about the difference between sales and marketing that reflects the difference in the data we collect and analyze in each space.

    In marketing we collect data once from a large sample.  If you run a program against a list you collect data from a large number of people one time.  You analyze the data and perhaps discover people who are interested in a product now or in the future and you process accordingly.

    Sales is different.  The universe of data sources is smaller but the sources give off data constantly through a sales cycle.  Sales reports — pipelines and forecasts — show a single cross section of the data and they are equivalent to the individual frames of a movie.  Most of the time it’s hard to say much about how a film ends by examining a random frame.  Sometimes you get lucky and the random frame shows the butler with a knife in the in the library etc. and you can make a deduction.  But most of the time you aren’t that lucky.

    Unlike the movie, which is a succession of stills projected in rapid succession to give the illusion of movement, the sales forecast is a one and done thing.  Worse, making the report necessarily destroys the old frames.  So, getting back to the company that threw away old data, I would throw it away too.  The old data was simply the last frame depicting the end state of a deal and usually the end state is a loss.

    There’s almost nothing you can discover from the end state but if you have all the frames that led to the end then you can apply analytics to it and find out things you didn’t know.  Analytics lets you play the movie back and forth to find the aha! moment.  But you need to keep all the frames.  The point is that in marketing you can apply analytics to a single state of the market but if you try to do the same with sales you’re toast.  Sales data is different from marketing data and so are the ways we analyze it.

    In the panel I moderated last week that idea was not in evidence and it shows how we need to re-think and maybe find new people who think differently about selling and sales data.  Without new thinking we’re liable to not be able to figure out the importance of social tools and selling will continue to be a hard nut to crack because it remains more art than science.  It doesn’t have to be that way.

    Published: 6 years ago


    Forecasting and pipeline management don’t get nearly the attention they deserve and that doesn’t make sense.  Of all the parts of CRM, the forecast is one of the few things many companies still leave to manual systems, i.e. spreadsheets.  Even sales compensation has a higher place in heaven as companies like Xactly have blazed a trail away from spreadsheets to a system with a database and analytics, with excellent results.  You’d think that sales people would be willing to invest as much in the forecast as they do in counting their commissions.

    Part of the challenge with forecasting and pipeline management is that some professionals might resent conventional forecasting systems for the same reasons they like compensation systems.  Confused?  You shouldn’t be.  Both types of system reduce uncertainty to certainty as much as possible.  But while that’s a good thing when you are counting existing money (your commissions), it’s a problem when figuring out the future because the future is anything but certain.

    That’s why last week’s Cloud 9 Analytics user meeting was so important.  At their third annual user conference, CEO Jim Burleigh, talked about the importance of understanding the probabilities when forecasting.  It’s no coincidence that Cloud 9 now boasts a forecasting user interface that uses probabilities but also acts like a sales manager.

    If you’ve spent any part of your career in sales then you know there are deals and there are DEALS.  Some deals are like racehorses, they practically sprint from first call to closure while others plod along and maybe even stop.  That’s an extreme situation and it’s easy to spot the real winner.  But consider two deals at a 90 percent completion stage.  They might look the same numerically but each took a different path to that 90 percent mark.  One might have taken twice as long, one might not have enough money budgeted, one may be run by a C-level officer on the customer side the other might be managed by a director.

    These differences in the history of the deal add up and a seasoned sales pro knows they are important.  But conventional pipeline and forecasting tools (e.g. spreadsheets) make no use of history, which might help explain why only nine percent of organizations we’ve surveyed have a 0.9 correlation between the forecast and reality.  The rest?  Foregtaboutit.  When it comes to forecasting these deals, the sales pro might favor one over the other for reasons that add up to gut instinct.  So, it’s no surprise that the pros create three flavors of forecast — the best case, worst case and the most probable.

    The genius of Cloud 9 today is that they’ve found a way to take the best of what analytics can do to track history and spot trends and combined it with a forecasting user interface that enables a professional to apply common sense to arrive at best, worst and most probable scenarios.  Some people call it gut instinct and I suppose that’s as good a term as any, but really, it’s not gut — it’s applied intelligence and experience that just happen to be hard to put into words.  At any rate, the new forecasting UI is straightforward and looks easy to use and it will remind professionals of their beloved spreadsheets, but with a lot more intelligence behind it.

    Getting sales people to put aside the pure spreadsheet approach and go with something with more rigor behind it may still be a challenge.  But Cloud 9 has demonstrated that it both understands the challenge in all its dimensions and that it can turn its knowledge into very serviceable product.  Like the compensation managers before them, Cloud 9 has replaced the spreadsheet with something that makes more sense, is easier to use and should result in better results all around.

    Published: 7 years ago


    I have been studying sales forecasting and forecasting tools a lot recently and I have come to the conclusion that we need better tools as well as better ways of using them.

    There is a lot that can be said about forecasting, its current state and how to improve it and I don’t want to leave anything out but I will try to be brief.  First off, how we forecast says a lot about our views on economics.  Given that most of us are not economists, our views of economy are most likely derived from what we see and hear on a daily basis, much like our view of the weather.

    For over thirty years our view of economics has been increasingly colored by the ascendant views of the New- or Neo-Classical school of economics.  To over simplify, it is a view that goes back to Adam Smith, of supply and demand and a belief that economics is a hard science governed by equations as rigorous as Newtonian physics — wishful thinking I’m afraid.

    The most germane idea for our purposes is Say’s Law.  Say was a French economist, very much in the Classical school who said that “production creates its own demand” and from that we derive the famous supply side economics of the last thirty years.  Supply side economics corresponded nicely with another phenomenon in our world, the introduction of the CPU chip in 1968 and the cascade of new products that ensued over the coming forty years, roughly the high-tech era.

    Increasing CPU power followed Gordon Moore’s famous dictum, now Moore’s Law, of increasing CPU power and decreasing cost, and it created a special circumstance that governed supply and demand for technology goods.  Moore’s Law made Say’s Law work like a charm.  A corollary to Say is that all markets clear, i.e. all supply is eventually absorbed at some price — but maybe not a premium price.

    Moore’s Law ensured that a fresh supply of technology goods that superseded the earlier generation would arrive and drive demand thus ensuring Say’s Law would operate as advertised.  But if Say’s Law requires something like Moore’s Law to operate smoothly, then it must be said that Say’s Law is a special case, not an iron clad law of economics.

    What’s that got to do with sales forecasting?  Quite a bit.  In the special case of selling into a market with undiminished demand, sales forecasting need not be a lot more complicated than determining where we are in the sales cycle.  If we’re ninety percent through the cycle we ask for the order and there is a reasonable chance that we will get the business — no guarantee, but a reasonable chance.

    It hardly matters that our ninety percent is not really a probability derived statistically but really just a milestone in a process.  In an expanding market there are enough deals percolating that reasonably diligent effort will result in on-quota performance.  But on-quota performance is not what it once was and forecasting is in disrepute in many places.

    According to Jim Dickey and Barry Trailer at CSO Insights, only about fifty-eight percent of sales people manage to make or exceed quota.  Also, according to my research less than ten percent of sales forecasts have an accuracy of ninety percent; the rest aren’t worth the time and effort it takes to compile them.

    What’s happening to sales forecasting is not surprising.  With Moore’s Law slowing down and with so many formerly new market niches filled with products, we are transitioning from an era of expanding markets to one of zero-sum conditions.  In a zero-sum situation, if you are going to win business you need to do it by displacing another product.  If you are a customer in a displacement game it is always easy to do nothing and wait for a better offer and continue using an existing product that might not have all the bells and whistles you want but fills the need nevertheless.

    A zero-sum economic environment has a lot of uncertainty in it.  You might use the words uncertainty and risk interchangeably but they are not the same.  Risk is something that is unknown but knowable.  If a deal forecast is at risk a sales representative — frequently at the urging of the sales manager — can ask more questions, get more data, and piece together an answer.  There are many issues in sales that are simply unknowable or mostly unknowable, for example, the details of the bid your competition makes.

    When uncertainty — not just risk — enters the picture, our forecasting paradigm that relies on milestones in the sales process becomes useless.  We need better tools if we are to forecast in the face of uncertainty and those tools exist but few of us have taken them up yet.  For example, prudent managers might start with the territory planning process.  How much white space is in the territory?  What percentage of that white space is likely to churn this year?  What is the overall economic forecast?  Given our market share what is the probable share of that white space that we can capture?  Is that enough to sustain quota for one or more people?  How should we incentivize them?

    Sales forecasting will always be an inexact science but we can do better than we are currently.  We could persist in basing our forecasting ideas on Say’s Law but inevitably it is a race to the bottom, to pure competition on price.  The airlines do that but none of them makes any money.

    Published: 9 years ago