Predicting the Effectiveness of Sales Support Resources (Part 2)
Posted by Ian Zhao
When it comes to investment decisions, timing is everything. In theory, deciding when to start investing and when to stop should be an informed process. In practice, however, investment decisions rely heavily on intuition and trial and error. The key to making such decisions in a more precise and scientific way …and you may already know what I’m about to say… is through thoughtful, rigorous data analysis.
Let’s return to last week’s topic - deciding whether or not to add Sales Support headcount to an existing sales force. In this case, we find that it’s best to take two distinct measures of performance. The first measure, which I discussed last week, is taken during the initial “test phase” and is used to predict whether a full-scale implementation will be worthwhile. The second measure, and the focus of this week’s post, takes place after full-scale deployment and sheds light on when to stop investing further, as the optimal point has reached.
As I mentioned last week, AGI helped a medical device client decide whether to roll out a clinical specialist program. Once they had rolled out the program to the entire sales force, they settled on a near 2:1 support ratio, i.e., two sales reps for every clinical specialist. At this point, AGI stepped in to conduct its second measure of performance. We found that, while the results of the general deployment were still very positive, the average increase in rep productivity had fallen from our initial measure of $230K (during the test phase) to $180K (sales force -wide implementation). It seems that the law of diminishing returns was at work here. So, should the client stop investing now? Or should they continue ahead full steam, bringing the CS-Rep ratio all the way to 1:1?
The answer would be found in a time series analysis of individual rep performances. We constructed a panel set of data with two records for every rep – one detailing their revenue performance during the experimental period six months prior, and one detailing revenue performance after the full-scale deployment. Using time series regression, we calculated that the effect of a clinical specialist on revenue was between $150K and $500K – a wide range indeed. But exactly where in that spectrum the true number lay would determine whether or not to push on with the program.
By isolating the differences between the two records for the same rep, we created a new set of variables with which to run another regression. The result showed that having CS support should increase a given rep’s revenue by an average of $154K. Therefore we advised the client to cease further investment in clinical specialists, as the theoretical benefit of a clinical specialist was fast approaching the actual cost of one.
This serves only as one example of how to gauge when to stop investing in sales resources. Other methods, e.g., regression with data in logarithmic format and maximum likelihood calculation, would have given us the same answer. But when simpler calculations can get the job done, what’s the point in show-boating advanced statistical knowledge? To achieve the stated goal using the most efficient method – that’s the commitment of AGI consultants.
Predicting the Effectiveness of Sales Support Resources (Part 1)
Posted by Ian Zhao
For many sales leaders, adding support headcount is like walking a tightrope: too little support may not budge the top line, but too much support could hurt the bottom line. Striking a balance is so tricky because support resources have only an indirect impact on revenue. If one could tell for sure that, say, adding X support headcount would increase revenue by Y dollars, then adding support headcount (or not) would be a snap. As it turns out, it’s possible to do just that by leveraging the right sales analytics techniques in three steps…
First, construct two sample groups. Divide your top-performing reps into two groups: an experimental group and a control group. The groups should be large (30+ reps) and similar with regard to demographics and average rep performance (use random assignments to accomplish this). The experimental group will be given the level of sales support that you’re considering making available to the entire sales force. The control group will continue with the current support level without change.
Second, collect data. After a short while has passed, gather information on every rep across both groups, including but not limited to:
- Quota and quota attainment
- Pipeline movement
- Revenue flow
- Sales activities and time allocations
Quota, revenue, and pipeline information should be easy to get, as sales departments track them regularly. Sales activities and time allocations can be captured by the use of a survey.
Third, analyze the data. Compare the groups’ results using two statistical methods: hypothesis testing and regression analysis. Hypothesis testing will tell you whether or not the experimental group’s performance is significantly different than the control group’s; regression analysis goes further to map out the causal relationships between the factors at play (obviously, “whether or not a rep has full sales support” is the most interesting variable here). For a more thorough test, include other variables such as education level, tenure, etc. into your regression analysis.
Of course these are just general steps. But eventually you’ll wind up with an equation which predicts the effect of each variable on overall rep performance. Then you can judge for your organization whether or not adding sales support headcount makes sense.
AGI recently ran these analyses to evaluate the effectiveness of a clinical specialist program for a large Medical Device company. The client had a traditional "lone wolf" sales model. Sales reps spent much of their time in operating rooms, limiting their ability to field other sales opportunities. In considering whether to offload OR activities to less expensive clinical specialists, the client brought in AGI to assist the transition. We advised the client on a two-phase implementation: first, involve only a small sub-set of sales reps; then if (and only if) the reps’ performance improved dramatically, implement a large scale deployment.
Initial results were encouraging. Reps with clinical specialist support brought in 28% more revenue than those without, more than enough to cover the added costs. But revenue increase alone didn’t necessarily indicate that the difference was a direct result of the clinical specialists – it may have been due to other factors that set the experimental group apart from the control group.
To investigate, we performed a regression analysis which treated time allocation, tenure, and having clinical specialist support or not, as independent variables. The result showed that, after controlling for other differences, a rep with a clinical specialist should bring in $230K more revenue annually than a rep without one. The positive impact of clinical specialists on revenue was loud and clear. The client went ahead with the full-scale implementation, and AGI was retained as an advisor to measure the final result and disembarked on a journey to find the optimal point for sales support, which I will discuss next week.
(to be continued)
The Eight Killer Sales Analytics Every Sales Leader Should Know
Posted by Paul Vinogradov
by Paul Vinogradov
Analyze This, the humorous 1999 film featuring Robert Di Nero 
and Billy Crystal, focused on psychiatric therapy for a troubled mob leader. At one time, sales was considered more art than science. A VP of Sales was just as likely to hire a shrink as they were a sales ops leader or sales effectiveness consultant. Today, the term “sales analytics” is one of the hottest executive team buzz words. But as sales leaders wrestle with ways to analyze markets, products, and their sales people, which sales analytics really matter? To find out, here is our latest research highlight: The Eight Killer Sales Analytics Every Sales Leader Should Know.
In the film, Billy Crystal had a challenging task before him. He had to get inside the head of a troubled mafia leader, Paul Vitti (Robert Di Nero) who was battling the Feds, competing gangsters, and his own panic attacks and virility lapses. Maybe a lot of VPs of Sales feel like Paul Vitti at times, chased and beat up by customers, competitors, superiors, and even their own reps. And while a good psychiatrist might help them get a grip, what they really need is a set of comprehensive analytics on their market, products, and sales force so they can plan and execute their next moves with confidence. Here are the eight “killer” sales analytics, in the order in which they should be conducted:
Number One: Sales Potential Estimation (SPE). This analysis uses market data and current sales to estimate sales potential at the account level. This is the “headwaters” of sales strategy and planning. In addition to providing market sizing information, it serves as one of the primary inputs for segmentation and targeting for coverage decisions.
Number Two: Buyer Needs Analysis (BNA). This evaluates customer preferences, requirements and cost to serve It often provides the second dimension or axis alongside SPE for defining and targeting customer segments. The explosion of data available on the internet is lifting data limitations that typically hamper the value and depth of this analysis. Customers with really high potential typically also have really high needs, some may not be profitable. Combining #1 and #2 allows you to determine which customers to focus on and which to avoid or drop.
Number Three: Sales Cost Analysis (SCA). This evaluates the channel effectiveness, coverage ratios, management ratios, and overall spend on sales compensation, enablement, and infrastructure, leading to targeted investment decisions for those limited sales budgets.
Number Four: Sales Process Analysis (SPA). This examines time and productivity at the role and sales activity level. It provides direction on role definition, sales time inhibitors, and sales process improvement.
Number Five: Sales Force Sizing Analysis (SFSA). Sizing analysis determines the optimal headcount by role for the Sales Force. Done right, it leverages all four of the previous analyses to determine an aggregate headcount based on the sales capacity required to meet plan.
Number Six: Sales Territory Analysis (STA). Determining the “patch” for each seller should take in to account several factors including number of accounts, current revenue, sales potential, density, proximity, and others. Mapping software tools can be used for geographic territories. This is typically a highly sensitive and tedious exercise fraught with internal selling and favoritism. But good analytics can drive the design of a more even playing field.
Number Seven: Pay for Performance Analysis (PPA). This is the bread and butter of Sales Operations – analyzing quota attainment and earnings correlations to determine the health of the sales organization and to gain insight into root causes for gains and drains in productivity.
Number Eight: Sales Incentive Cost Analysis (SICA). Last but not least, it’s always good to forecast with some accuracy what the incentive program will run you. Statistical modeling and scenario testing enables sales and finance to agree in advance on what they’ve committed to the organization.
Well there you have it – the eight “killer” sales analytics every Sales Leader should know. No matter how well you may know them, The Alexander Group specializes in helping our clients not only learn them, but leverage them effectively. Analyze that!
Learn more about Alexander Group's Sales Analytics and Benchmarking Practice.
About the Author
Paul Vinogradov is the Vice President of AGI’s San Francisco Office and head of its Sales Benchmarking Program.
SaaS Assessment — Standing on the Shoulders of Giants
Posted by Ian Zhao
by Ian Zhao
When praised for his contribution to science, Sir Isaac Newton said, simply, “If I have seen further it is by standing on the shoulders of Giants.” Of course he tried to be modest, but he also revealed the divine secret of scientific research: first, gather previous knowledge; second, create your own.
Our SaaS (Software as a Service) assessment series is an example of a Newtonian approach to business analytics and industry research. In it, we leveraged our knowledge of other industries, became customers ourselves of several SaaS platforms, and learned to understand the dynamics of the customer-vendor relationship, all in an effort to gather previous knowledge and create actionable insights for the study.
When we started the study two years ago, there weren’t many prominent or profitable SaaS companies. To borrow from statistics– the sample was small and the data scattered. Not knowing where to begin, we naturally looked to conventional software companies for inspiration. After all, the first S in SaaS stands for “Software,” and many of these companies were considering developing their own SaaS offerings.
It didn’t take long to realize the error of our strategy. Whereas the average software company spends 26% of its revenue on sales, the average expense-to-revenue ratio for SaaS is closer to 40%-50%. Furthermore, we went on to calculate that the similarity in cost allocation patterns between SaaS and software companies is less than 30%. With such a wide gap between the two, conventional software clearly made a poor benchmark dataset for SaaS.
Lo and behold, we found a much more reliable comparison group in telecom and data services companies. With sales cost allocation patterns resembling SaaS’s to the tune of 60%, we had as close a match as we could get. One key difference between the two industries, however, is that most telecom/data services companies cannot replicate the stellar growth enjoyed by SaaS companies. So what drives that SaaS revenue? We gained insight into this question from our knowledge about recurring revenue and customer support in service-related industries. In layman’s terms, periodical cash outflows serve as constant reminders to executives of the need for ROI; and when one has an urge to act, service firms provide support resources to help make it happen. These observations, in turn, helped us design targeted questions around pay frequency and customer support, which led to our conclusion that successful SaaS companies are able to leverage customer service for longer average contractual commitments. In fact, leveraging our previous experience with telecom companies, we could mathematically gauge the health of a SaaS company simply by looking at their price level and mix of customers by payment frequency. Now that’s standing on the shoulders of giants!
Our next round of SaaS assessment is currently under way, and we expect to discover more powerful insights into the typical SaaS coverage model. So stay tuned.
If you are a SaaS sales leader seeking actionable insights to inform your sales strategy, you should participate in our 2012 SaaS Benchmarking Assessment.
About the Author
Ian Zhao is the senior manager for AGI’s Sales Benchmarking Practice.
Focus, Alignment, Consistency
Posted by Paul Vinogradov
by Paul Vinogradov
Just moments after we passed out our binders, creating an impressive “thud” factor that represented the body of our work, one of the executives turned to me and said, “Paul, I’m sure this report is great. But can you summarize your findings in 25 words or less?” It was one of my first true tests as a consultant. I responded, “I can summarize it for you in three words: focus, alignment and consistency.” I went on to share how these three words captured the essence of what our many weeks of ride-alongs, interviews and sales analytics had found this sales organization needed. Now, fifteen years into my consulting career, I’ve learned how these same three words continue to serve as a guide for sales leaders regardless of industry or company size.
Focus: Focus increases effectiveness. But how can you ensure that your selling effort is focused on the right customers and opportunities? This requires analysis of the market to understand potential within existing accounts and new accounts. Most start-up companies get this. They know that A and B round investors need to hear a clear and logical case for how the company is going to succeed. It’s surprising how many large, well-established companies lose sight of this basic principle. In some cases the core product is doing well and growth relies almost entirely on penetrating the existing customer base. In other cases new customers are fueling the growth while existing customers are dropping like flies.
A simple yet incredibly powerful sales analytic for determining where sales people should focus comes in the form of a cool acronym: CPR, which stands for Conversion, Penetration, and Retention. This analysis breaks down a company’s revenue sources between new and existing customers and new and existing products. The client from 15 years ago was experiencing a 45% churn rate. In other words, they were only retaining 55% of their business from existing customers year over year. To realize growth they had to make up the remaining +45% through a combination of further penetration in the accounts that stayed and new customer acquisition. Our data showed that similar organizations could expect a customer retention rate closer to 75% for the commercial segment and close to 95% for enterprise. The CPR analysis created a rallying cry among the executive team for change. We did CPR analysis by segment, by product, by region, even at the rep level. This revealed a host of issues, which pointed us toward a set of recommendations around customer focus.
Alignment: Once you know which customers and products to focus on, you must align your selling resources and effort up against them. More recently I worked with a mid-size software company that felt paralyzed by their existing deployment model for fear that change would be too disruptive. Never mind that they might have five sales reps covering Florida and only one covering the entire state of Texas. This seems to be a common scenario. Clients struggle to change territory or account assignments for a variety of reasons (or excuses?) including a) fear of losing their top performing reps who have worked hard to earn the privilege of covering those accounts and might stand to lose commissions, b) fear of losing customers, who don’t want to deal with a new rep, even if a new one could serve them better, or c) the VP of Sales fear of change in general. Another simple yet powerful sales analytic is Workload Analysis, which evaluates the workload at the territory level using sales potential, number of accounts and sales time data. Companies that take the time to do this right and have the courage to re-align territories accordingly may have to carefully manage the changes, but can reap huge rewards as a result.
Consistency: Once sellers are focused on the right customers and products, a sales leader needs to lead the sales force to execute consistently. It begins with clear communication of goals and visibility of progress to motivate reps and drive accountability. Many sales leaders underperform and fail simply because they did not clearly communicate the goals or they communicate too many goals (see Focus). It’s also important to have a dashboard of metrics with both leading and lagging indicators to measure success on a frequent basis. The best leading indicators include pipeline quantity, quality and velocity revealed through detailed Pipeline Analysis. The best lagging indicators are sales productivity (revenue per head) and quota attainment, examined with careful Performance Analysis. The Alexander Group recommends that a healthy sales force is one in which 55% to 60% of the sales force is at or above quota. If the number is much lower or much higher there is reason for concern and deeper examination. Either the sales force is not properly focused and aligned, has too many poor performers, or has major issues that are outside of sales’ control or influence.
If your sales organization is struggling, maybe starting with a few simple questions about focus, alignment, and consistency will help point them in the right direction. The Sales Benchmarking and Analytics team can help. Contact us to learn more about a host of sales analytics here at AGI, as well as an up-coming Sales Analytics Certification Course we will be offering later this Spring.
Stay focused (and aligned, and hopefully consistent too)!
About the Author
Paul Vinogradov is the Vice President of AGI’s San Francisco Office.
Balancing the Past with the Future: Lessons from Statistical Compensation Cost Modeling
Posted by Ben Koupal
by Ben Koupal
Any undergraduate economics student will tell you, “The past is 
not an accurate predictor of the future.” It’s one of the basest mantras of the entire field of study. Historical returns on dot-com stocks in the late 90’s gave no clear indication when the bubble might burst [1]. The steady decline in year-over-year GDP growth seen since 2009 in the US didn’t even point in the same direction as the modest bounce we experienced in 2010 [2]. Using past years’ data as one’s only indicator of future performance is clearly an ineffective management tool.
In a recent project for a high tech client, AGI built a statistical model to predict future variable compensation expenses based on, among many other factors, historical performance against quota by the global sales organization. A thorough analysis of the sales force’s past data laid the foundation for a model which let us simulate this year’s performance under similar circumstances. Not surprisingly, our simulations produced estimates in line with last year’s results. Rather than serve as a portal into the future, a mathematical model built upon sound assumptions is well designed to do nothing more than “predict” past datasets [3]. Something was clearly missing.
Abandoning one’s reliance on data is not the answer; on the contrary, it's an invaluable source of guidance for many reasons.
- First- it’s objective. It will not bend or sway in the face of trends, fads, gut instincts, etc.
- Second- it’s abundant. Our post on Big Data asserts that even small firms already have more data than they know what to do with.
- Third- it's verifiable. All data has interesting stories to tell. Once elicited, analysts can test and re-test those stories ad infinitum.
Data is the key, in this discussion, to understanding the causal relationships underlying what it is one's trying to model. But to make more useful predictions about next year’s sales activities, one must strike a balance between what past data tells us happened last year, and what business leaders think may happen next year.
What was missing from our model was an ability to do “scenario testing”! By combining the objective underpinnings of a data-based model with the ability to “slide the scale” on its key assumptions, sales leadership can see what’s likely to happen under different sets of circumstances. With extra information about how “good” or “bad” things could be (and not simply what a black-box model says they’re going to be), sales managers can hope to avoid unexpected bumps down the road, along with the “knee-jerk” reactions that sometimes accompany them [4].
About the Author
Ben Koupal is an associate consultant for AGI’s Sales Benchmarking Practice.
Geometric Mean and Sales Performance Ranking
Posted by Ian Zhao
by Ian Zhao and Mike Pap Rocki
When in school, statistics is easily the most mind-numbing subject we had to learn. Remember being bored to tears while the professor gleefully demonstrated on the blackboard how to calculate a “geometric mean?” You might have asked yourself, “Why do I need to learn this?” For many of us, finding the average of a set of numbers through the n-th root of their product was nothing more than an ostentatious display of math skills that serves no practical purpose.
This all came back to us on a project we did for a medical device company, which tasked us to come up with a performance ranking mechanism for their field sales representatives. Our initial analysis showed that some reps’ quota attainment fluctuated dramatically year-to-year. A rep who attained 140% of quota in 2010 may have ended up achieving only 60% in 2011. Even though that rep’s two-year average is 100%, he performed less consistently than a rep who attained 100% in both years. And consistency counts!
At this point, most consultants would have stopped thinking and started ranking reps by arithmetic averages, using standard deviation as a tie-breaker. We went one step further to find an intuitive metric that combines average and consistency into one – by using the geometric mean. Looking at our hypothetical rep above, we see that the geometric mean of 140% and 60% is 91.7%, while the geometric mean of 100% and 100% is, still, 100%.
In the end, we were able to provide a more scientific measure for sales reps multi-year performance, and therefore provide a more meaningful recommendation to our client. Obviously, the selection of statistical tools is a tiny portion of any AGI consulting project. Taking the time to pick the most suitable measure, though, gives us the most intuitive and powerful story to tell a VP of Sales. That’s what we strive for.
About the Authors
Ian Zhao is the manager for AGI’s Sales Benchmarking Practice. Mike Pap Rocki is a business analyst at AGI’s San Francisco Office.
Preparing Data Before Sales Analytics
Posted by Ian Zhao
by Ian Zhao and Manish Jindal
In Napoleon Hill’s story “Three Feet from Gold”, a gold prospector stumbles upon a shiny ore, but as he digs deeper, the vein of gold mysteriously disappears. The man drills on desperately, but eventually gives up. Later, a mining engineer re-examines the site and finds that since the poor man hadn’t grasped the concept of fault lines, he had stopped just three feet from striking it rich!
Managing an effective sales force is a lot like gold mining in this respect. Persistence is key, but understanding your environment well enough to know exactly where your reps stand in relation to “gold,” or sales deals, is a game-changer. That takes experience and insights from data.
Mining existing sales information can yield great insights. Even for small sales forces, the amount of data accumulated over a year can help answer:
1. Which products sell better than others
2. Who the most effective sellers are, and what they have in common
3. What type of customers are interested in which products
Unfortunately, many companies have not even attempted the basics – not because they don’t have the will or skills, but because analysis is constantly hindered by the ubiquity of “dirty data,” just like fault lines making the vein of gold ore disappear.
Data for sales analytics are often gathered without proper quality assurance, resulting in duplicate customer records (e.g., “Cisco Systems” vs. “Cisco”), incorrect units (e.g., $15 instead of $15,000), simple misspellings, and incomplete information... AGI estimates that 15% of CRM entries contain errors. No wonder sales executives often (and understandably) lack confidence in their data, much less the insights garnered from it.
To address data issues, we have many options. Master Data Management is a newly-popular enterprise approach. When time is pressing and a systematic data audit is infeasible, a more practical strategy involves a right amount of duplicate check, missing data imputation (see below), and outlier removal procedures.
Using a recent project for a software company as an example, AGI ran into data issues when modeling customer spending potentials and share-of-wallet. The client data was incomplete and did not map out parent-child relationship among accounts consistently. So we rebuilt the entire account hierarchy by leveraging an external database. But still there was missing data for 20% of customers. That’s when we used the technique of “missing data imputation” to carefully fill gaps in the data with respective industry averages. In the end, we validated our assumptions by comparing the model’s output to actual total market size. The work we did in this project helped our client formulate the right coverage strategy for the right accounts, with a revenue upside exceeding $190M annually!
It also demonstrated that, with the right mix of data preparation and validation techniques, imperfect sales data can still yield impeccable insights to drive sales results.
About the Authors
Ian Zhao is the manager for AGI’s Sales Benchmarking Practice. Manish Jindal is a consultant at AGI’s San Francisco Office.
Companies expect high growth in 2012
Posted by Gary Tubridy
The results are in for the Alexander Group's 2012 Sales Pulse Survey. We asked over 100 sales leaders across a number of industries about their outlook for the coming year.
High growth companies are investing more in sales and focusing on customer relationships as a key driver of growth in 2012. Neil Isford, VP Business Analytics & Optimization at IBM, joined me on Monday for a discussion of the New Rules governing sales for 2012. You can listen to our discussion here.
Big Data in B2B Sales: It’s Arrived!
Posted by Ian Zhao
Big Data has become a hot tech trend in late 2011. The term “Big Data” refers to those datasets which are too big to fit into a traditional relational database, and therefore require special tools for data storage and processing. Ten years ago, the only businesses that had Big Data were a handful of financial service, consumer retail, and giant Internet firms, like CapitalOne, Google, Amazon, etc. Those companies relied on massive amounts of data for decision-making. They also had the resources to perform Big Data Analytics properly. With the availability of new IT technologies, especially cloud storage and mobile computing, Big Data is no longer a remote concept for many industries. In fact, it could change the way many businesses are run, comparable to what the PC and Internet have each done in the past.
The Opportunity:
Among all corporate functions, sales may be the one most intertwined with the reality of Big Data. Sales departments generate a lot of data. Let’s use as an example a mid-sized software company’s sales force of 50 reps: one year’s worth of data on things like annual sales contacts, opportunities, and transactions could very easily reach beyond one gigabyte in size. Adding information about products, sales promotions, sales rep compensation and all the various interactions between data points to this mix, it’s feasible to grow that amount of data to a full terabyte. And this is just one year of data! If we retain five years, or ten years of historical records, we are rapidly approaching the limit of some commercial database servers. As you can see, Big Data is closer than one might think. For sales, it may have already arrived without notice. The data is there; the sooner we can dig in to begin exploring its richness, the better we can help answer the types of questions that keep VPs of Sales awake at night, such as:
- Natural market segmentation
- Optimal resource deployment
- Precision quota setting
- Accurate sales forecasting
These topics require rigorous data modeling to address. And sales’ Big Data can help!
The Challenge:
Opportunity and optimism notwithstanding, Big Data also brings serious challenges to sales organizations, especially with regard to sales analytics.
1) Big Data will aggravate the chronic analytical capacity issue. The average ratio of sales reps to sales operations staff is around 25:1. Since sales analytics is a sub-function of sales operations as a whole, we would have no more than two FTEs of analytical resources for a sales force of 50, on average. Those analysts typically have responsibilities encompassing quota setting, territory design, sales credit tracking, pricing approval, answering various questions from the field, etc. What’s left for non-routine analytics is at most 5% of their work time: roughly two hours per week. Even in those precious two hours, sales analysts still need to answer ad-hoc questions from VPs of sales and/or fulfill various data requests from management consulting projects. This is a rather passive modus operandi. No wonder sales operations staff often cite bandwidth as their biggest obstacle to productivity. Without additional investments in analytical headcount, it’s unthinkable that sales effectiveness can improve markedly in the New Era of Big Data, as sales analysts are expected to struggle to keep themselves afloat in the rising ocean of sales data, leaving less and less time for strategically important discovery analysis.
2) Big Data also necessitates the call for major upgrades to sales analytical tools. At present, Excel is still the primary, if not the only, analytical software available to sales departments. Marketing teams, on the other hand, have been using statistical packages like SAS and SPSS to process large datasets and perform automated, specialized analytics (e.g., conjoint analysis, etc.) for more than 10 years. Excel does a decent job of summarizing data and illustrating key statistics using charts. It also has serious limits. By and large, it’s a generic analytical tool which requires a great deal of manual interaction. When data models get too complex, or data changes become too frequent, Excel quickly becomes very clumsy. And complex data models and frequent data changes happen to be two prime characteristics of Big Data. In the face of increasing data variety and velocity, it will be more difficult for sales analyst to deliver timely analytical results using the same set of tools.
3) Big Data requires increased Sales leadership trust in analytics. A study by the Corporate Executive Board indicates that 50% of senior managers from all corporate functions either 1) do not question their data, or 2) do not trust their analysts’ results. Sales leaders tend to fall into the latter bucket. Based on our experience, they are far more inclined to make decisions based on intuition, embracing analytical results only when they support their assertion, and quickly dismissing when they don’t, often on the grounds of “insufficient” or “irrelevant” data.
The Bottom Line:
Properly leveraging Big Data with the right tools and processes in place will effectively disable the ‘insufficient-data’ excuse completely. In order to balance intuition with data-driven conclusions in the presence of Big Data, sales leaders must not only have their sales content knowledge down cold, but also know how to perform basic statistical analyses and database-related IT tasks (e.g., SQL queries, etc.) with speed and confidence.
Sales, statistics and IT are the three essential realms of knowledge for today’s sales leaders, because Big Data has changed the game. Sales may be a people business, but is also a numbers’ game; and just like professional baseball, it needs to be managed by the numbers.