The Miller Heiman Research Institute, led by Joe Galvin, EVP, provides research-based thought leadership to the Miller Heiman customer community on B2B sales performance. In this interview, Joe offers his perspectives on how auto-analytics improve sales performance by reducing the risks of surprisingly poor results from instinctively useful efforts. Key points he makes:
- auto-analytics capture the details of what efforts produce what results
- they do so with precision, capturing data automatically ‘in the flow’ of the work
- this creates a new level of transparency that replaces intuition with performance facts
In a nutshell, Joe contends that auto-analytics represent a shift away from a world in which B2B sales performance is guided by what we think works towards a world in which performance is guided by what we know works.
Knowing why a deal DIDN’T close is just as valuable as knowing why it did. What are the missing pieces in the chain to conversion? What is one sales member doing that another is not that is leading to more closed leads?
Pat: great points. IMO, as non-obvious differences in sales practices become obvious for all to see, performance gaps narrow. When better practices are in clear view, they get practiced more often. The value of auto-analytics in all this? They create this clarity, quickly. – John
Well, this troubles me, because Joe’s approach clearly subscribes to the idea that data has unerring explanatory power. While I believe that you cannot run a sales organization today without collecting performance data, you can’t abdicate all of your insights to what the data tells you – or, more insidiously, what you think the data tells you. Or, even more insidiously, what you want the data to tell you. Anything can be gamed – ‘auto-analytics’ is no different.
Sales managers are paid for effective judgments, for ferreting out nuances, and for collecting and sharing tacit knowledge. Not every effective sales behavior or management decision can be – or will be – exposed through passive collection of information.
The Fit Bit example is a case in point. The device does an excellent job of counting steps, measuring heart rate and exertion, keeping track of wakeful and restful states. No argument. But it’s data. We know about an individual’s physical performance and activities, but there’s a longer list of even more important stuff that we DON’T know – that can’t simply be offloaded to a clever piece of technology. To name just a few, we have little insight into his or her state of mind. We don’t know why a person chooses one activity over another, or how he or she makes activity decisions. We don’t know how they form good habits, or how they extinguish bad ones. Data supports this knowledge, but it doesn’t replace it.
I recognize in a time when most give accolades to “running things based on the numbers,” it might sound heretic to make a case that there needs to be boundaries for using them. Joe says that with auto analytics, “you can monitor everything [salespeople] do without having to be there.” I disagree. As Nicholas Carr wrote in his book, The Shallows, every technology offers something new, but there’s also something that’s given up. The unmeasurable buyt indispensible observations that managers get from “being there” is one of them. It’s an outcome that every executive must consider before he advocates making decisions predominantly “based on the numbers.”
Andy, there’s a tremendous amount of data already collected in B2B sales — most of it used badly! Which are the right bits to pay attention to? Those that, if improved, would lead to improved performance?
For example, the rule of thumb is a pipeline should be 2X quota (or 3X or 5x or ?). If the rep doesn’t have a pipeline the requisite size or better, warning bells go off, emails fly and — no surprise here — the pipeline magically improves. With made up deals, in some cases.
Analytics is not a substitute for good judgement. It should be an *input* to better judgement.
Sales managers (or any managers) that run only “based on the numbers” should be fired. But I say it’s time to also get rid of those that say, “my gut feel is always right.” Nobody is that good.
Regarding gaming, yes, you’re probably right that every system can be gamed with enough effort. So, the answer is to collect no data, do no analysis, post no numbers and just hope for the best? Measurement and reward systems need regular tuning because people are remarkably adaptive.
But still, I think it’s worthwhile to find data that can be collected unobtrusively in the natural course of business, rather than relying on what reps enter into a system. For similar reasons I’ve argued that companies should collect and use behavioral data in Voice of Customer programs, because what customers say is just part of the complete “truth.”
Andy: thanks for chiming in with your views. My feedback on yours:
>> … data has unerring explanatory power …
Don’t believe that’s his point. But, rather, that it was enormous clarifying value. Particularly in complex situations [like sales] where the dynamics of what’s going on and with what impact are normally fuzzy [if not impossible] to detect.
IMO the potential of ‘auto-analytics’, done right, is in letting Reps and their managers see, with greater clarity and speed, what’s occurring, and with what impact on outcomes. You want to ‘game’ your Fitbit and be the one person who takes 20,000 steps per day and doesn’t lose weight. Go ahead. The results you’re getting from the inputs you’re ‘seeing’ will be seen, by all, for the aberration that they are. With precision. And speed.
>> … don’t know how they form good habits …
Partly by seeing, with precision and speed, which habits, if any, are bad ones that need eliminating. Partly by coaching, with focus, that encourages new, healthier, practices. I agree with your premise that data, alone won’t cause new habits to form. They are, however, important in providing needed feedback that encourages [and sharpens the individual resolve for] healthier behaviors.
>> … making decisions predominantly “based on the numbers.” …
IMO, sales is a profession starved for better performance data. Data that so sharpens our understanding of what’s driving or inhibiting performance that, with learning and practice, performance actually improves. Not robotically. Not instinctively. With practice. Informed in new ways. That ensure we’re practicing practices worth learning with practice.
Trust this adds some value. – John
Bob: thanks for your contribution to this conversation. My feedback on yours:
>> … worthwhile to collect [new data] unobtrusively in the natural course of business …
Completely agree. Where the complexities of what’s going on and with what impact are hardest to discern [such as in B2B sales] the clarifying value of better data on performance is going to be greatest.
Data’s role in such situations is to reveal with greater clarity, the underlying ‘patterns of performance’ and, in so doing, spark more learning, from more experimentation. Put another way, the value of auto-analytics will be in informing and facilitating what Hagel advocates in ‘The Power of Pull’ – small moves, smartly made, that can set big things in motion.
As with Moneyball, early adopters who discover the learning and performance advantages of such analytics in sales performance are going to be the talk of the town. They’ll leave others wondering what kind of luck they had on their side to achieve seemingly impossible performance lifts. Pssst. They’ll have played the game differently, by seeing and understanding it differently.
Not everyone will see nor understand the value. Some will have seemingly valid reasons for opposing it. It won’t matter. Auto-analytics that accelerate learning are an early adopter advantage, both for individuals and the firms they work for. The early adopters will be the big winners.
Trust this adds some value. – John
Andy and Bob – Thanks for contributing to the discussion. John, as always, well said and always pushing the thinking.
Its an interesting topic because unlike SFA, where utilization and adoption remain a challenge for most sales organizations, Auto Analytics extracts sales usage and activity data from the multiple systems sales regularly uses and connects it to outcomes. 3X pipeline is a classic sales myth and a reasonable rule of thumb. Its what we believe at some basic level based on years of selling and what has been passed on to us by our sales managers. It analogue thinking – manually connecting the dots based on what we have see, heard and read. Its accepted because its accepted, not because its statistically proven.
Auto analytics will never replace judgement in decision making. Sales is a game of strategy played out at the opportunity, account and organization level. The dynamics of each interaction and decision is unique. However, when an organization can categorize and track the activities, messages and outcomes across their sales team, patterns of success (and failure) will emerge. Myths will be busted and better, quicker decisions will be made. Sales people will learn quicker. Sales organizations will get smarter – quicker. Sales people will spend less time on deals they can’t or won’t win and more time winning the competitive deals.
Auto analytics will pull all the activity a sales person does across all the applications they use, Email, SFA, knowledge management, proposal generator, etc and connect the cavity, message and outcome. Unlike SFA which requires the individual interpretation of the customers context, concept and decision process to create data, auto analytics will extract the data from various systems they use naturally in the course of selling and combine them with the SFA outcome data… In effect connecting activities, behaviors and messages with outcomes.
It’s where big data meets sales. It demands high levels of forecast accuracy and funnel confidence for the outcomes. We are in the very early days of learning what this data means and what it might tell us. Organizations that figure it out first will have an advantage with this data until everyone else figures it out.
Early days indeed, but for those who choose to apply it to their strategic decision making process at the opportunity, account or organization level, it will be a major advantage creating the next level of transparency.
What I see with the promotion of analytics is a boomerang effect. Companies that sell analytic technologies tend to be enamored of the potential, but less commonly do their executives discuss limitations or caveats. I understand that. I’ve done it too. We all love our products, and while that passion is a good thing, it also causes many of us to ignore the boundaries for what our products can do.
As Bob mentioned, I too am leery of managers who eschew analytical reports, insisting that their proven instincts and years of industry experience are all they need to govern decision making. In most industries, that doesn’t cut it anymore.
But I have seen firsthand what happens when managers believe they can run a sales organization by looking at cells in an excel spreadsheet from the comfort of their corner office, without having to ever hop in a car and sit in sales calls with reps in the field. It’s not pretty. I’ve participated in many meetings when managers espouse self-satisfied conclusions “based on the performance data” that were way off the mark.
It happens every day. I think Joe is going down a wrong path when he says “you can monitor everything [salespeople] do without having to be there.” You can monitor much of what my CAR does by putting a black-box recorder in it, but even then, you still can’t monitor everything. The notion that you can “monitor everything without having to be there” is not something I would recommend to any company that’s measuring human performance – especially in an environment as complex and varied as B2B sales. It shows way too much love for “the numbers” and not enough recognition that “management by walking around” has a pragmatic, and vital, purpose.
Andy, let me clarify, we are in the very beginning days of auto-analytics. There is no path to go down because it doesn’t exist yet. What we are seeing is the beginnings of how technologies ability to handle massive amounts of “big data” can be converted into fresh insights… the next level of transparency. Visibility to things that couldn’t seen with the naked eye. Like the microscope changed science, auto analytics will do the same for selling.
I see examples emerging everywhere. Fitbit is a perfect example. Moneyball and “21” tell stories of how data, when analyzed in detail, produces a level of transparency and awareness, that when incorporated onto the decision-making process, produce better results – consistently. Spreadsheet data v. the crusty old baseball scout’s pure “judgement” and “gut feel” opinion.
I came across this just yesterday. A long but interesting read about the application of advanced auto analytics in the NBA. Playing basketball with its multiple player variables, fluid – continuous rate of play and layers of dependencies makes digitizing the game really hard. (unlike the standalone events of a baseball pitch or the flip of card) But, they are beginning to crack this code and with it, coaches and players will have another highly trusted source of knowledge to consider. Not replacing – rather adding to the decision making process.
http://grantland.com/features/expected-value-possession-nba-analytics/
Data will never replace the judgement of a sales professional, frontline sales manager or sales leader. What it will do is add new facts and a deeper level of awareness for them to consider when they develop their account strategy or next opportunity interaction tactic. The sales professional will be served meaningful and relevant content based on the analysis of similar selling situations. The frontline sales manager will focus on the account or opportunity strategy as all sales data is auto-populated and compared against performance curves to suggest high probability outcomes. The sales leader will be better able to allocate resource and pre-load new hires. The potential is endless for everyone – and it will never replace the human element of strategy, execution and customer interactions.
Today we call that sales experience. It can’t be taught and will always be valued. Its the aggregation of all we have learned. The difference in an auto analytics world is they will not have to only depend on just their own judgement, they can leveraged the wisdom of their crowd – the 10’s, 100’s or 1,000’s of sales people in their own organization selling similar capabilities to common buying centers going through similar decision processes. Think of the impact to new hire time to productivity. New product launches, competitive intelligence… it adds the next level of transparency to everything the sales professional does – everything. It speeds learning.
And all they have to do is use the technologies they are given. Everything we need to know about a sales person, sales role, activities, messages, objections…. everything is already captured in the various, disconnected bits of technology we use. Its called “dark data.” Email, voicemail, SFA, knowledge management, calendars, mapquest, web search, word-excel-powerpoint… its all there. Collecting and connecting the digital dots will be a major undertaking. As I said we are in the early days and we are starting to see early examples emerge.
Luddites and libertarians will decry these advances. SFA has no value – I don’t want people looking at my stuff… and technology marches on. In the infamous words credited to Thomas J. Watson, “I think there is world market for maybe five computers”. …the world he must have been referring to is my house.
I personally think that all of the responders have made some very strong points and I do feel that Joe is approaching sales management progressively. Having been a sales manager in the past, and one that believes in focused allocation of human resources for the greatest return, I have discovered something that has been absent in the comments.
Every sales organization has strong performers. I have studied them and developed insights from interviews that identify the behaviors they feel make them successful. Once that data is drawn together, you can idenify common success traits of top performers.
If you then determine not only why a prospect DID buy from you, as well as why they DID NOT buy from you, you have the first reflection of success.
If you now marry that results, with the behaviors, and the characteristics of an account that match the company’s appetite for type of customer, you can begin to develop a logical marketing approach that should result in a predictable production rate.
No, data alone does not do the trick, but married with successful behaviors you have the beginnings of a hiring, coaching and sales management strategy that can be predicted and have an expected ROI.
This model can not only benefit a company from a sales results perspective, but will also allow for staff evaluation and development. It is not a “silver bullet” but it more closely approximates a success “model” a marketing organization can use to grow sales, reduce staff turnover and provide long term focus on the customer segment(s) that will create the greatest profit.
Gary: brilliantly put. Especially like your point re: the multiple ways in which this kind of holistic approach can deliver its ROI. Not just better sales results, but also reduced turnover and better talent retention that comes from having a team perform to their potential. Thanks for joining the conversation. – John
A really interesting discussion folks. Gary, I think you summed it up nicely.
I am in this space and for me this is all got to be about improving both the productivity of the resources within the revenue generation function and the ROI of initiatives you’ve already invested in – whether it’s CRM, SFA, skills training, coaching etc. They are all valuable, regardless of the brand or provider.
Anything that can increase the use of valuable tools and methodology and drive stronger efficiency, effectiveness, productivity from people is gold and will deliver the growth and margins so many are struggling to achieve.
CSO reported in their 2014 Sales Performance Optimisation Study that in 2013 only 58% of sellers made quota and that only 83% of organisations hit their revenue targets. Just as concerning, over 94% of the 1,200 organisations surveyed are lifting their revenue numbers in 2014!
Something has got to give! Everything the revenue function does and undertakes has to be in the context of improved performance and productivity at the organisational and individual level.
To achieve this you need productivity gains – to achieve productivity you need ‘authentic data’ to make the right decisions on resource allocation and utilisation – not gut or emotion. This data is not housed in the CRM and nor in the contact data base or SFA system. This lives in all the myriads of systems that Joe refers to.
Authentic data is data that is democratised, unlocked then rendered in a simple and easy to consume way from all the disparate systems where it sits – only then can you map it against pipeline and forecast to get a truly accurate and predictable view of the activities and resource utilisation that deliver the best results.
In the short to medium-term this technology will never replace human judgement, it will improve it as Joe highlighted. However, long-term who knows? Organisations such as InfoCepts, using a MicroStrategy BI platform, have made some remarkable inroads into minimalising the need for it with some very impressive solutions.
This technology does exist and continues to evolve. Companies such as DOMO, C9, InfoCepts, DataHUG, Birst and our technology platform partners Intilecta and SeeMoreData have been developing and delivering different variations of mapping, connecting and binding data for sales to great effect for some time now. Not to mention some of the BI platforms.
This discussion is focussed on the ‘sell’ side. You can’t overlook the most critical asset in the revenue generation function – the customer. Unless you are measuring the productivity and performance of the customer, in their eyes / their view, not yours then you are limiting any gains.
Adam: thanks for chiming in. Great to hear of the successes you’re seeing + the types of technologies behind those successes.
Agree with you on the importance of bettering the ROI from investments in sales performance. IMO, the biggest of those investments will *always* be with people, not technologies. And bettering the ROI in sales people requires approaches that re-think the path to that ROI.
One example: getting a better ROI from sales training. To me this requires approaches that narrow the gap between what’s taught and what gets learned, with practice, in the doing of the actual work. Lots of room here for analytics to provide the proof, and the behavioral nudges needed to ensure better ROI. – John
Since I’ve already taken one counter position in this discussion, I’ll go ahead and take another. The missing voice in this conversation about productivity measurement is the customer’s.
Seems ironic, since customers are the central object of the efforts that salespeople make. While we present a unilateral view about streamlining processes and squeezing more productivity out of the sales workforce, what is happening to customer experience? Are the outcomes or results of the process improvements better for customers – about the same – or worse?
My experience has been that sales organizations have a checkered reputation in this regard. I wrote about this issue back in 2008 regarding the retailer Ann Taylor’s productivity measurement system, and questioned whether the singleminded focus on ‘the numbers’ would improve outcomes for customers, the company, and its employees. The article, “Can Sales Productivity, Ethics, and Shareholder Interests Coexist?” is on CustomerThink. In the article, I quoted a B2B sales VP regarding his thoughts on the topic. I’ve copied his quote here, because I believe his opinion offers a unique perspective for this discussion:
“Understanding what causes sales to happen and managing metrics is critical to success, but sometimes it’s easy to get so caught up in metrics that you lose sight of the big picture . . . Effectively managing your team’s performance requires a balance between hard metrics and business instinct. I have found that there is no substitute for frequent, intensive one-on-one meetings with reps and sales managers, where you hold them accountable for understanding and articulating all aspects of their business and how they are tracking toward their revenue goals. Metrics are only one component of that discussion, and the key there is to develop practical metrics that really do lead to sales, communicate them clearly, and then hire disciplined sales people that are smart enough to understand their importance.”
John – I couldn’t agree more with you on your points. Interestingly, the ROI on sales training was the founding purpose of our organisation, TRED International.
It came about through a group of sales and marketing leaders (I am ex Managing Director of Huthwaite) who believed there was a better and more effective way, through technology, to generate a stronger ROI from skill training and better measure and enable the behavioural change from such initiatives.
Joe describes it as ‘Auto-Analytics’. We refer to it as ‘Revenue performance Intelligence’.
Andrew – I wrapped up my early blog with “This discussion is focussed on the ‘sell’ side. You can’t overlook the most critical asset in the revenue generation function – the customer.
Unless you are measuring the productivity and performance of the customer, in their eyes / their view, not yours then you are limiting any gains.”
So I’m in agreement about ensuring the customer is a core piece of the equation. At TRED, we are advocates of trust and use customer trust diagnostics called ‘Customer Experience Analysis’ as part of our customer analytics piece and process. Trust is the most underrated competitive weapon there is.
Buyers follow their own process, which will render any sales process low value. So how do you know what activities sellers are undertaking and what the outcomes of those activities are if it’s not being recorded? Even if they are, how do you take the ‘emotion’ out of the data?
Unless you are using an advanced form of internal data collection, rendering information from numerous enterprise platforms, activity tracking including communications activity data such as Outlook and social media your data is of low value.
Please read more here in this blog – http://tredinternational.com.au/measuring_metrics_sales_productivity-115
Adam
Outstanding comments and observations from all.
You are all spot on regarding the importance of the customer. After all (as the old saying goes) “nothing happens until someone sells something”. No resulting sales outcomes, no data to measure, no service “wheels” to turn, etc.
The key income is the customer and that where I feel the value of auto analytics lays. If it is understood why the customer bought a product, the actions that a salesperson took to motivate the buying process, then the process could be replicated with customers with similar profiles and needs. In effect, the veil of mystery can be lifted.
Truth be told, there are a few rules for selling. 1) you don’t sell anyone anything; people buy – so, if you understand the “what and why”, you can lead them through the process, 2) no one buys anything they don’t want, no matter how cheap it is – price is not a driver in all cases and customer buying decisions are directly related to perceived value, 3) offer customer options that suit their needs then take their money – simple sure, but you have to first know what a customer might want.
Auto analytics may not be the “holy grail” of selling, but if the data is gathered properly it will fill sales pipelines or funnels with prospects of like kind. The key is the right data. Which brings us back to the customer. Forensic sales analysis that focuses on buying triggers and impulses is the intangible that companies cannot gather from CRM, spreadsheets, production data or sales numbers alone. The only way to get this gold is to ask the customer and backfill the statistical data that best matches customer behaviors.
In short, auto analytics is one more step to creating “sales systems”. It is worthwhile to remember that customers do make the buying decision, so while data will support the logic of the sales process it is still the people in the process and their approach that needs to be part of the analysis. Couple the human side of sales with the human side of a salesperson and you will have the key to repeatable lateral selling in identified markets of high return and profit for the sales organization and high satisfaction for customers.