Youtube: Foretuit and The Future of Sales Analytics

Michael’s background with IBM and in the VC world gives him great perspective on the needs of the financing world when it comes to the start-ups they fund. He also knows the needs of the enterprise at a level that few can top. Foretuit is his baby, he’s the CEO and Founder, and I’m convinced that this is going to become a major change agent in the way smart companies manage their sales force.

And according to this blog post, Foretuit are enjoying a robust relationship with Salesforce.

Note: Repost from Selling Power.

Back to the future.  Was Dreamforce a corporate event, or a counter-cultural San Franciscan summer of love replete with shouts for an “Enterprise Uprising” (aka Arab Spring)?  Hard not to get caught up in the fervor.

Now that I’m returning to Earth from my Metallica-induced ‘cloud high’,  I find myself searching for not just the meaning of life, but the means in which to make the grandiose vision of the Social Enterprise a grounded reality — and to enable the kind of hard metrics that most senior executives crave in order to rationalize the required investment and resource allocation.

Yet with all the hoopla, one inescapable truth remains.  How is it that companies spend over $18 billion per year on CRM solutions, but studies show they are spending less and less time selling?  Afterall, isn’t the essence of social selling, well, social? And further, isn’t this CRM spend only the beginning, not the end?

These days the average cost of a direct sales rep is in excess of $250,000, fully burdened (meaning, all-in).  Uplift that fixed expense by another 20-30% to cover the cost of sales operations and management — and you get to appreciate the significant day one investment most enterprises are dealing with when it comes to reaching out and touching their customers.  Add to that the burden of feeding the machine — that is, demand generation to create a 10x deal flow so that the organization can run a 2-3x coverage model in-quarter — and the expense makes your CFO’s head spin.  It’s no wonder that Sales and Marketing expenses can run upwards of 50% of revenue in some organizations.

Thus, that first $18 billion needs to be as efficient and effective as possible to yield the kind of operational integrity that leverages the investment in manpower and demand gen.  If not, then why even try?  Creating the right discipline for managing the sales process means helping sales reps to close more business, faster.

But creating the ideal environment for the perfect discipline is challenging.  The best approach should detect real-time winning practices that can be used to quickly guide all reps to the right set of actions and, over time, be used to enhance sales procedures overall.  But how do you learn the most effective practices and then help enable them across an organization?  How do you assemble the most effective deal teams for any given sales situation?  How do you ramp new reps into existing opportunities?  How do you embrace personal style over company style? How do you locate the ‘real’ decision maker (not the most senior title)?  How do you help existing reps find the most lucrative path forward, and the right experts at the right time in the deal cycle?  And lastly, how do you give management the kind of transparency and visibility they require?

And do all this without adding yet more burden onto your sales reps, who already rebel at the level of manual data entry they are asked to do?

The over-riding goal then is to create a set of robust metrics that matter.  Based on our research, here are the top 5:

#5:  Reduced Organizational Costs

Reduce the overhead of administrative tasks and redirecting high value resources to generating revenue not managing process.  Yes — there’s a novel idea — have sales people in front of customers instead of doing mundane chores.  How much time is spent each week in your org on sales reviews?  Some organizations spend upwards of two days each week, allocating hours to understanding what is happening with each forecasted deal.  This burden keeps resource from being with customers which is why you hire a direct sales org in the first place.  The goal should be to reduce the weekly process to minutes, not hours or days.  Do you have metrics in place for meeting time reduction?

#4: Improved Sales Efficiency

Support automated task planning, streamlining labor intensive and error prone activity.  What is the compliance rate of associating information within opportunity records?  How many clicks does it take to add a task or associate an e-mail?  And what is the currency of the information?  If some actions take upwards of 30 clicks, then is it any wonder why adoption, compliance and currency are so low.  What if you handle the same load in one, two or no clicks?  Wouldn’t that yield significant improvement?

#3: Increased Leverage/Effectiveness

Better target sales effort at prospects that are winnable and redirect resources away from lost causes as early as possible.  On average, losing deals stay in the pipe twice as long as winning deals.  Orgs are loathe to ‘de-clutter’ the pipe of what can only be referred to as “coverage fodder”.  For some reason, big coverage factors on a 1x target seems to make management happy, that is, until deals begin vaporizing at quarter’s end (they never slip, now do they?).  Having a clean, clear pipe helps allocate resource better but requires a degree of organizational integrity to let go of the failed opportunities.  Lowering deal maturity days is critical.

#2: Better Risk Management

Reduce the need to build in margins and improve the accuracy of projections – projections that are critical to right-sizing production and other upstream and downstream decisions.  Intellectual honesty in forecasting can be difficult to achieve primarily due to subjective and highly interpretive nature of deal stage maturity assessment.  Making the process fact-based has been elusive at best, but can be done with the right pipeline management capability and transparency.  What’s your forecast accuracy?  How is it measured and tracked?   Do you use standard measures of deal maturity?  Are they uniformly applied?

#1: Improved Employee and Process Assessment

Provide the opportunity for detailed performance assessment and continual sales process improvement, not to mention allocate incentive compensation more fairly, to those most responsible for winning deals.  Many organizations find attributing contribution difficult and thus overpay incentive compensation two and three times over.  With a clear understanding of actual contribution to the deal and an understanding of behavioral characteristics associated with internal and external activity — an organization has better odds of improving productivity and process, not to mention retaining the most valuable performers.  Critical to this effort is understanding and measuring key quantitative metrics surrounding individual contribution across different roles in the transaction, inclusive of actual contribution and frequency of contribution, responsiveness and timeliness of interaction, throughout the entire deal lifecycle.

Extreme ROI

Bottomline, modest improvements in sales efficiency and effectiveness can yield significant short term ROI — not including higher deal win rates which would boost the return exponentially.  Take a solution that costs an incremental $45/per user/month but yields a certifiable 25% increase in sales operation efficiency and a 10% improvement in management time.  Based on average comp, you would likely see a 400% ROI within 12 months.  Now you can rationalize the underpinnings of a socially connected enterprise, one that engages with customers using less, more-focused, resources.  Seems like a huge win for the Social Enterprise.

All together, an organization that is not only able to create these metrics, but improve these metrics will become the envy of their industry, and the overall market leader, if not already.  But, to quote one industry pundit, “it’s not about using media, it’s about being social, to engage with customers through multiple means and touch points.”  For any leading enterprise to be social, its sales people must become more effective and efficient — in other words, they must spend more time in front of and with customers.  Now that’s love.

Note: This is the long form of a guest post in Selling Power.

Growing up, I was surrounded by women who were deeply enthralled by the soaps, particularly General Hospital and the whole saga of Luke (Tony Geary) and Laura (Genie Francis).  Luckily, I was left in blissful peace reading my comic books and looking at the ads for x-ray glasses … and sea monkeys.  Now, I didn’t believe that x-ray vision (or, for that matter, sea monkeys) were possible, but it didn’t stop any one from selling them or, I imagine, buying them.

Grown-ups in c-suites in most companies have much in common with my younger self.  They want foresight, that is, vision and transparency into objects that they desire.  Management would like to peer through functional walls and down corporate hierarchies.  They want to know where and when to focus and, or perhaps more importantly, where not to focus.  The benefits of x-ray vision are significant to any company. And more so, within an environment of capital constraints where management must create higher yield from the same or less investment in selling resource.

Now new behavioral analytic techniques are possible given the growth of both data and computing power.  Such analytics have the potential to streamline any unstructured business process by identifying deficiencies and excesses allowing resources to be better allocated and managed.  This is no more true than across the “front office” (as opposed to the “back office”) and creates an opportunity to automate previously manual processes.  If so, are we on the verge of a new industrial revolution powered by analytics applied to social business, specifically front office operations?

If the weatherman said that there’s an 90% chance of showers today, would you carry an umbrella?  Salesmen are often characterized as “rain-makers”.  As such, how certain are you about your  company’s “weather forecast”?  Is there a downpour in your future?  Or a drizzle?   What would it be worth to you to know how your quarter will fare – with 90% accuracy?  Up to 3 months in advance?

Advances in science and technology have given unprecedented precision into nearly all aspects of forecasting.  Meteorologists are able to make accurate predictions of the path and strength of hurricanes to avert disasters and save lives.  Missile and rocket paths can be targeted with pinpoint accuracy.  Even the stock market has been changed by automated algorithmic trading systems.  Businesses are just starting to take advantage of the wealth of data that has been available due to the increasing use of technology in the workplace, and the availability of lower cost cloud-based platforms.  Transactions of all kinds from phone records, emails, shared document repository records, Internet usage, and other records are now stored by the terabyte on on-site and off-site computer servers.  This large volume of unstructured records necessarily precludes manual processing for anything other than the most basic, search-friendly tasks. Yet, this same digital data is a treasure-trove of social behavior, and predictive and institutional knowledge.

As with other forecasting technologies, tapping into this data can help corporations avert disasters and hit their targets with pinpoint accuracy.  A challenge has been how to get all this unstructured data into a digestible format that machine-learning algorithms can use.  Such representations need to be flexible enough to absorb new forms of data, yet robust enough to be accurate.

What’s been missing?  The first is a top-down approach where a model is used and iteratively refined with insights from the second, bottom-up approach.  This second approach uses a proprietary machine representation, pattern recognition, and decision engine that discovers its own models based strictly on the data.  The collaboration allows the method to quickly converge on useful models that provide the greatest predictive abilities.

Sales offers a unique challenge with an equally unique prize.  Sales is a highly human-centric endeavor.  All good salespeople know that their relationship with their customer can seal or break a deal.  The old adage about selling ice to Eskimos is a testament to the very social nature of sales.

Accurately predicting sales opportunities directly affects the entire corporation.  Much of a company’s asset allocation, its budgets and resources, are set based on the predictions from their sales department.  Unfortunately, current predictions from sales are more like fortune-telling.  The reason is simple: humans process this data intuitively; therefore it is difficult to come up with quantitative values.

Until now, that is.  Foretuit brings modern science and technologies using big data and analytics to better predict sales outcomes.  Think of the possibilities when applied to your own business!

When people connect in a social sense, they are “friends.”  There’s no more simple construct as they are literally peers — friends, neighbors, relatives.

On the flip side, when people connect in a business sense, they are “colleagues.”  People that work together and have a defined role.   Their role defines their social standing in an organization.  They are worker, manager, sales person, marketer, pricer, boss — you get it.

More important, each colleague has a need to collaborate to perform a specified task at a particular time during a particular process. This is hugely important.  Why?  Well, understand the role and purpose, you can understand the business.

This is the very nature of collaborative structures and it lies at the heart of our approach. When applied to a specific context — like sales, you begin to understand our method. Foretuit leverages these collaborative structures to identify patterns of unstructured activities within the chaos of daily business life.

For sales, it creates a real-time model for deal flow, and maps that deal with other deals to create a knowledge base of deal sequencing – like a sales genome.  Understand the genome on an individual, group, organization, industry or global level, and you have achieved the holy grail to determine deal maturity, and the confidence to know the likely outcome, positive or negative.  In fact, our alpha results have been extraordinary and indicate a high likelihood of correctly predicting outcomes at a very early stage.   Thus, we are on the verge of greatly reducing friction in the sales process thereby eliminating the subjective nature of deal proctology.

Think about the impact.  Today, most experts, board members, executive management believe the sales process is entirely ineffective and totally inefficient.  The amount of resources (think capital) required to operate the sales process is burdened by significant over investment in lead generation, pipeline coverage and operational oversight.  Yet with all that investment, deals don’t just slip at the end of the quarter — they vaporize as if they never existed in the first place.

Thus, the environment is dysfunctional at best as it breeds distrust and lives on regular interrogation. The investments required to generate 10x lead flow and 3x pipeline coverage are huge, and the outcomes hit or miss.  Organizations spend over $18 billion per year on CRM applications alone.

A small relative improvement in deal cycle management could yield significant upstream and downstream benefits.  Not just from better allocation of management and operations time, but in improved upfront targeting and resource allocation.  Plus, if you live in a product organization, think about the benefits to downstream production and alignment with supply.

Sales resource is expensive and precious.  With improved foresight, comes better allocation of sales resource.  And with the amount of time compression at the end of any quarter, there is a critical need to understand how best to utilize scarce resource.  Most customers skew their purchase cycle to the closing weeks of any quarter.  With the proper foresight, management would know where and when to focus and, perhaps more importantly, where not to.  More importantly, this benefits the sales rep as sales quota obtainment would be higher and thus compensation and job satisfaction much greater.

Thus it’s in everybody’s interest to improve deal lifecycle management.  Today’s tools and applications tend to enable the current dysfunction but do little to change the game.  Our approach is certain to change that — and that’s a quick overview on how Foretuit does what it does.

The answer is actually pretty simple: Sales tools are selected by management for managers – usually without thinking of the impact on the daily life of your reps. So, you give your reps the shiny new tool, and the reps think “Great, one more thing to waste my time. Why can’t I just spend my time selling. That’s what I’m being paid to do!”

It’s no wonder that the data in a typical CRM is not much better than a heap of trash. There’s nothing in it for the reps. Nothing to make their ability to sell easier, faster, or simpler.

If we want to improve sales productivity, we need to put a fresh set of eyes on the problem because it’s clear that focusing solely on the needs of sales managers is not actually solving the problem. We need to think from the “front line” back. What can we put in the hands of our reps to make them sell better? Because, what’s good for reps, is good for their managers.