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.
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:
These topics require rigorous data modeling to address. And sales’ Big Data can help!
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.
Learn more about how Alexander Group’s insights with big data can transform your organization.
Originally published by: Ian Zhao