At the end of any quarter, the last thing CFOs want to hear is that more than half of forecasted sales did not close. Unfortunately, this scenario is quite common. CFOs blame sales staffs for faulty forecasts, while sales teams try to shift the blame to IT for not giving them the right tools to turn fuzzy forecasts into actionable data.
According to Ventana Research, most organizations are missing one essential piece of the sales puzzle: visibility into the sales pipeline. More than 60 percent of the organizations Ventana polled say they plan to invest in sales analytics in 2012, meaning that finding sales insights hidden in “Big Data” will be a top priority this year.
There’s a trap here, though.
Gaining visibility into the sales pipeline is meaningless if your organization doesn’t have a formal sales process in place. “Companies spend far too much time trying to normalize their existing sales data not realizing that historical sales only tells you what you sold, not what opportunities exist,” says Melissa Scheppele, CIO of Cooper Industries . “Today, forecasting is based on a sales person in field saying ‘I’m going to close deal.’ Those falsehoods trickle up.”
Before we rush to blame sales teams for bogus forecasts, it’s important to step back and consider an important contributing factor: the personality of successful sales people. Sales managers want optimistic sales people. They want a team that believes it will close a high percentage of open leads. They stress positive thinking. That kind of attitude can certainly translate into success. What an enthusiastic attitude doesn’t translate into, however, is accurate forecasting.
Many sales teams have access to tools that are supposed to provide visibility, but there’s no guarantee that the sales force will use them — or if they do, that they will use them properly.
“CRM [Customer Relationship Management] is one of the few apps where target users, sales reps, don’t want to use it,” says Jim Burleigh, CEO of Cloud9, a provider of cloud-based analytics. “CRM is about preserving institutional knowledge and ensuring institutional processes.” When it comes time to collect, enter and improve data in CRM systems, the sales representatives will ask, “What’s in it for me?” In most organizations, the answer is not a whole heck of a lot.
“There is no incentive, no compensation for getting the data right,” Scheppele says. “The compensation is for closing the deal.”
Sales Automation Can Add to the Problem
Getting sales representatives to embrace automation can have unintended consequences, however. The biggest of these is the escalation of the “Big Data” problem.
Consider this: The average sales representative has anywhere from 20 to 50 open sales opportunities at any given time. For a small organization with a sales team of 10, they have to keep track of a manageable 200-500 opportunities. For a large organization with a 100- or even 1,000-member sales team, the opportunities start spiraling out of control. A 1,000-member team with a modest number of opportunities, say 25 each, will generate data on 25,000 leads.
But it’s not just each lead that is the problem. If the CRM system doesn’t archive records but simply overwrites them when changes are made to a lead, valuable information will be lost. For instance, e-signature company EchoSign (which was recently acquired by Adobe) discovered an interesting pattern in lead-nurturing emails.
When you sign up for a free EchoSign account, you will receive three nurturing emails. The first will thank you for signing up, while the next two will offer usage tips. Of course, within each email users are given links to premium, paid services.
As with most email campaigns, the first email generates the most clickthroughs. Click rates drop with each subsequent email — but only to a point. “I decided to test another series of emails a month out after users hadn’t heard from us in a while,” Loretta Jones, EchoSign’s head of marketing says. The results surprised her. The first email sent a month later generated better results than all but the initial welcome email.
If in her recordkeeping, Jones simply updated each record, overwriting the results from the first series of emails, this pattern would have never emerged. However, patterns like this require storing lots and lots of data. Basically, you trade a lack of visibility for Big Data woes.
Sales Visibility Triggers an Avalanche of Data
If every single prospect or customer event is recorded, data starts to grow exponentially. In some sales cycles, records can be updated dozens of times before the sale is closed or abandoned. Each time a prospect attends a webinar, downloads a white paper, stops by a booth at a trade show, the record must be updated. The time lags between subsequent events are important, too. Over the sales lifecycle, there could be hundreds of versions of each record.
Remember that a 1,000-member sales team will easily have records on 25,000 prospects or more? If changes are tracked inefficiently, as they so commonly are, the already unwieldy 25,000-record database will balloon to hundreds of thousands or even millions of records.
Is the possibility of finding obscure sales patterns enough to justify the storage, management and maintenance of such an enormous amount of data? Burleigh of Cloud9 believes it is. “Deals and even the sales reps themselves tend to follow patterns,” Burleigh says.
“Certain events, in combination, will increase the likelihood of a sale, and some of those triggers will be unpredictable.”
Similarly, sales representatives will behave differently. Some will be more successful than others, and there will be reasons for that. A good sales manager will want to know how successful sales people behave. Over time, patterns will emerge, patterns that can be turned into best practices and accurate forecasts. “A good sales manager will want to know when the data indicates that a sales person is veering away from a proven pattern of success,” Burleigh added.
Something as simple as when a prospect sees a product demonstration can lead to vastly different outcomes. “Most organizations collect this information, but they have a hard time seeing it. For instance, it’s common to know that a prospect saw a product demo, but it’s rare to track when and at what stage,” he said.
If the demo comes too early in the sales cycle, you’ll likely lose the sale. If you do it later, but not too late, you have a good chance of winning that sale.
Using the Cloud to Dig out
I attended the Online Marketing Summit in San Diego a couple of weeks ago. When I informally asked attendees about these issues, pretty much everyone recommended cloud-based automation as the way out. Obviously, there is serious selection bias here, but that doesn’t mean these people are wrong. Cloud-based tools are more affordable, often easier to use, and will be less likely to exist in siloes than on-premise solutions.
There are caveats, though. If you decide to leave Salesforce.com, good luck getting your data out. Even if you worry about Salesforce.com being a locked “cloud silo,” though, rest assured that it’s much less so than on-premise suites. The ecosystem that emerges around successful cloud tools means that data will be shared more easily across applications. At the same time, as new tools emerge, you won’t necessarily need to install a new system, but can simply plug the tool into an existing app.
EchoSign, for instance, uses the cloud-based marketing automation solution from Marketo, which is tightly integrated with Salesforce.com. Using Marketo, EchoSign can rank certain behaviors and provide the sales team with a score. The higher the score, the more likely the prospect will become a buyer.
The integration of marketing and sales tools means that EchoSign’s head of marketing Loretta Jones can give the sales team SLAs as a step in the sales cycle. If a lead is, for instance, ranked above a certain threshold, it will trigger an alert for a sales representative to follow up that very day.
Another advantage of cloud-based sales and marketing automation is the fact that many of them have hooks into social media. Burleigh of Cloud9 believes that social media is the next frontier of sales and marketing.
“If you can pull a social map together, your results will go through the roof,” Burleigh says, and he made no secret of the fact that this is the sort of thing Cloud9 has on its roadmap. “If you understand who a person is, who they deal with, and who they trust, you can influence them through people they already know, like and trust.”
This type of forecasting is happening already. If you “like” certain companies on their Facebook fan pages, your friends will likely see a picture of you saying that you’re a fan of anything from NPR to the NRA. These activity updates are really testimonials. This sort of user manipulation upsets privacy advocates (this is why I refuse to install any of the Facebook readers), but it is a road we’ve been traveling on for quite some time.
In the free-content era, we won’t pay to read newspapers, listen to music, or watch movies, but we don’t think twice about giving up privacy. For marketing and sales teams, this provides them with a goldmine of social data, but it also means that as soon as Big Data is solved there will likely be a Big Privacy challenge following right on its heels.
Jeff Vance is a Los Angeles-based freelance writer who focuses on next-generation technology trends. Follow him on Twitter @ JWVance.