Move over big data… here comes small data

Although sales performance has been struggling for several years, in some ways sales organizations seem to be on the cusp of a renaissance, thanks to the availability of new predictive tools and automation that promise to re-energize sales by massively improving efficiency.

In “The Data-Driven Rebirth of a Salesman,” Wall Street Journal contributors Shira Ovide and Elizabeth Dwoskin argue that predictive smarts and automation, driven by the availability of big data, promise to push sales into a new “era of efficiency.”

The article uses the example of GuideSpark, a Menlo Park-based software company, to demonstrate the point. Sales employees at GuideSpark use an ensemble of predictive and automation software tools to reduce wasted time and increase sales predictability. One of these tools prioritizes potential customers based on available data, and serves them up one at a time to salespeople in order of most likely to buy. Another tool leaves a voice mail message at the click of a button, freeing the salesperson to move on to the next lead.

Companies like Infer, Hoopla Software, InsideSales.com, and ClearSlide offer a vision of a bright new future for sales, a future in which salespeople are freed from the mundane aspects of their jobs to focus on only the most promising leads. For sales leaders, they promise to offer new pipeline visibility and forecasting capability that outstrips anything previously available. What’s not to love?

How the pursuit of big data leads us astray

One can hardly be blamed for jumping on the bandwagon to try out one of the promising new technologies. The unfortunate truth, however, is that software initiatives aimed at improving predictions and increasing automation often turn into expensive boondoggles that yield more headaches than rewards.

The trouble is not with the software itself, nor even the concept of predictive smarts and automation. The trouble is with the inputs. Many companies dump all of their data into the software, expecting the software to work like magic and convert all that noise into useful insights. Certainly, many of these programs are good at delivering attractive charts and pretty graphs. But the charts are only as useful as the data they’re based on, and if that data is a big mass of unorganized information, the charts will be as useful as a very beautiful sunset. We all love sunsets, but you wouldn’t base a business decision on one.

What’s needed in order to get useful insights and true predictive capability isn’t bigger data. It’s better data.

Better Data = Small Data

Big data is nice because it gives us a lot of mass to work with. But to be effective, organizations need the ability to discern the important details amid the noise of big data. This is what I call “small data”—the little points of information at the crux of what matters.

In the case of sales organizations, small data begins at the heart of the three most important key performance indicators: Deal size, win rate, and sales cycle. These are the KPIs that we have a measure of control over, and they form the foundation of understanding which data is relevant.

Within each KPI is a mass of data, only some of which is relevant. For instance, in regard to the sales cycle KPI, there is the question of when the sales cycle begins, and when it ends. If some salespeople plug sales into the pipeline as soon as a lead comes in, while others don’t plug it in until it’s time to send a proposal, you’ll have discrepancies regarding sales cycle length. If you plug that data into predictive software without examining it first, the output will be contaminated and could lead to worsening results rather than better.

All too often, sales teams do not have a shared definition of a qualified lead. Some sales people may throw everything they get from marketing into the pipeline, while others may wait until they’ve taken the time to qualify them according to their own personal criteria. Without a shared view of what a “qualified” prospect looks like, and a consistent system for entering the data at a consistent point in the process, the best predictive software in the world won’t be able to determine how robust the pipeline is at any given time.

Fortunately, All is Not Lost

Predictive and automation software may not be the silver bullet we all wish they were, but that doesn’t mean there’s no place for them. In fact, most top performing sales organizations either have implemented or are in the process of implementing predictive and automation software. Their potential is real.

What’s needed in order to take advantage of the potential, is a way to systematize the collection of important data, the “small data” that is relevant to the big KPIs, and organize it in such a fashion that the resulting predictions and automation are genuinely useful and beneficial.

How Membrain Helps

Traditional CRMs don’t provide the depth necessary to build in small data collection. In contrast, the opportunity management and sales process capabilities in Membrain helps you systematize your data, collect the “small data,” and tee it up for effective predictive analytics and automation.

To get a sense of how it works, let’s look at what happens in a standard CRM during the qualification stage of a sales cycle. In most CRMs, the definition of “qualified” is often left entirely up to the salesperson, making the resulting data virtually useless for predictive analytics and automation.

In Membrain, when a salesperson inputs a contact into the system, instead of merely asking whether the contact is qualified, it asks the salesperson to follow a set of pre-determined steps, and provides the resources to do so. For instance, one step of the company’s qualification process may be to identify client fit based on certain criteria. Membrain will prompt the salesperson to perform the activity at the correct point in the process, may provide videos regarding the importance of the step or how to do it, and then asks the salesperson to find out the information needed to make needed progress.

Only when all of those steps are fulfilled inside Membrain will it mark the step as complete, and the prospect as qualified or not qualified. Thus, a unified definition of a qualified prospect is achieved, and the “start date” of the sales cycle becomes clear and consistent across the board. Likewise, remaining data points for sales cycle, win rate, and deal size can be set and tracked.

Membrain enables sales leaders to easily see the quality of the pipeline, and to accurately predict sales numbers and close rates well in advance. By collecting and organizing the right data, it prepares the company to invest in additional advanced predictive capabilities effectively… and avoid unfortunate big data boondoggles.

Learn more about Membrain here.