Big (BAD) Data! Or, What Is My Average Sales Cycle Length?

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Big Data

I don’t know if sales professionals spend much time checking what’s going on in technology that might profoundly affect their working lives, or not. I should throw in a caveat. Right now, what I’m talking about applies mostly to what’s going on with big companies using technology for competitive advantage. Small companies are typically “late adopters,” so checking in on what Global Enterprise is doing might give a heads-up on where small business will be in five or ten years from now.

There are a lot of things that the big guys are fooling around with at the moment, and I want to comment on a couple of them. First is big data, and second, by default—analytics. Don’t get me wrong, my domain in my professional career has always hovered around small companies, but I am convinced that learning from analysis of carefully collected information can be invaluable, and should be one of the driving forces for all companies, no matter what size, to implement CRM.

Big data expands the collection of internally derived customer data to include information from internet, content, and social marketing initiatives. Big data comes from the amalgamation of inside and outside customer related information. It’s possible for small businesses to participate because tools and services are now available to make them fully-fledged netizens. It’s easy and cost effective to tap into web sites and social networks to pro-actively check out where customers go and how they are behaving in digital space.

But there are two things to watch out for in grabbing bigger slices of data, even if it becomes economically viable to do it. The scope of available information that could be relevant is huge. Even the data load from your company’s direct customer experience can be daunting. I once did a back of the napkin calculation to see how many significant interactions the world’s population of salespeople might have with their customers each year. I came up with a figure of one and a half trillion. My philosophy on what customer information to gather and keep is outlined in Chapter 5 of my book. (Get the free Part One e-book here.)

How do you prevent an avalanche of incoming data that can swamp any ongoing effort to mine it for strategic intelligence? The only answer is to make sure you have the technology and the stamina to do it. Bite the project up into chunks and take a methodical, systemized approach to collecting the right stuff and making the correct analyses.

Avoid collecting bad data. Collecting bad data is double trouble. It takes valuable resources to amass it and time to figure out that it is garbage. The problems are compounded by using the garbage to make bad decisions. Internally, this can be avoided by being diligent about the goals, rules and data maintenance of your CRM system. This is shared information we are talking about—there can be no exceptions. Some participants in the project may be lax about the way they collect the data, but in this case there has to be zero tolerance.

Data is useless unless it is mined, or analyzed. There has to be a clear vision about the results expected and the value to the company. I have a personal interest in applying analytics to sales process data—information on the sales cycle is a good example. It is useful to know the average sales cycle for different products. Salespeople and sales managers both profit from this kind of intelligence. Sales cycles will accumulate around a typical length, but will also have spread around that point. Good sales methods encapsulated in well designed and implemented CRM systems can provide that. But, and it’s a big but, don’t even think of retrieving this kind of information if your sales force is confused about what the sales cycle is.

Our ASPEC sales process model says that the sales cycle has a beginning and an end. Most salespeople would accept that almost as a given. But in twenty-five years of working with customers on the design and implementation of CRM projects, I would wager that less than half of them actually record the beginning of the sales cycle. Recording the end is easy, it’s the point that you won or lost the sale. But if you don’t have a beginning, forget sales cycle analysis. I could go on a long time with this one, but I think that is something for future posts.

Big data and analytics are very important. Don’t be intimidated into thinking this is the realm only of Big enterprise. Tackle the project in manageable chunks, provide sensible and clear objectives, and set the rules and adhere to them rigidly. The potential rewards in the sales and marketing departments are well worth the effort.

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