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Seizing Sales Opportunities Identified by Big Data Analytics

May 3, 2012


Jeff Vance writes, “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.” [“Big Data Torpedoes Sales Forecasts, Can Cloud Tools Fix the Problem?CIO, 3 April 2012] Pundits, including me, are constantly touting the benefits of Big Data analytics. So why does Vance’s headline claim that Big Data is torpedoing sales forecasts? According to Vance, the problem is with how Big Data is being used. He explains:

“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.'”

Mariners know that you can’t steer a ship by staring at your wake and that’s a good lesson for business executives to learn as well. So using Big Data to extrapolate past activity into the future isn’t a very good plan. Vance also points out that relying on the word of sales people to forecast sales is a bad idea because successful sales people are generally overly optimistic. They believe they are going to close every deal. You don’t want to dampen that enthusiasm; on the other hand, Vance states, “An enthusiastic attitude doesn’t translate into … accurate forecasting.” He also indicates that getting sales people to record their activities is difficult. “When it comes time to collect, enter and improve data in CRM systems,” Vance writes, “the sales representatives will ask, ‘What’s in it for me?’ In most organizations, the answer is not a whole heck of a lot.” To overcome the “participation” challenge, Vance notes that a company could automate its CRM system; but, he writes, automating sales data gathering “can have unintended consequences. … The biggest of these is the escalation of the ‘Big Data’ problem.” That is, more and more data is gathered yet it yields no actionable intelligence nor does it improve sales forecasts. Vance continues:

“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. … Is the possibility of finding obscure sales patterns enough to justify the storage, management and maintenance of such an enormous amount of data? [Jim Burleigh, CEO of Cloud9, a provider of cloud-based analytics], 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.”

In other words, while the improper use of Big Data may torpedo sales forecasts, the opposite is also true — the proper use of Big Data will improve sales forecasts and it could provide other valuable insights as well. Vance indicates that his research helped him conclude that “cloud-based automation [is] the way out.” He continues:

“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.”

Vance reports social media Big Data is also easier to manage in the cloud. He writes:

“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.”

Vance concludes his article with a short lament about privacy. He writes:

“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.”

Privacy issues are likely to increase in the years ahead; which is why companies will increasingly offer incentives, like coupons, discounts, or free samples, to get customers to keep providing them with personal data. Of course, some consumer-related information can be gleaned from more traditional point of sale (POS) data. POS data is just as important for Big Data analytics as any other type of data. The editorial staff at Supply Chain Digest notes that about a decade ago Procter & Gamble began pushing the concept of “Building the Supply Chain from the Shelf Back.” [“Building the Supply Chain from the Shelf Back Research,” 4 April 2012] Although that may sound like a concept that tries to steer the ship by looking at the wake, it’s not. It is based on gathering insights rather than depending on historical data. According to P&G, it is a “moment of truth” when a consumer faces the shelf and makes “the decision whether or not to put a given product in his or her cart.” The important thing is to find out why they made that decision. The article continues:

“One obvious implication: that purchase decision isn’t going to happen if the product is not on the shelf. Building the supply chain from the shelf back, or what others call the ‘shelf-connected supply chain,’ may sound obvious, but that is not how the consumer goods to retail supply chain has really worked in practice until recently. Manufacturers viewed their job as being to get the product as ordered to the right retail distribution center on time. What happened between there and the store shelf was mostly the retailer’s business – even as how good a job the retailer did in that execution could have a big impact on the manufacturer’s level of success. The retailers, meanwhile, usually did a good job of getting merchandise from DC to store, but other elements of store execution, especially in what we like to call ‘in-store logistics,’ were often lacking. Problems included out-of-stocks on the store shelf when the goods were in the back room, planogram compliance failures, and many others. But for many retailers and manufacturers, that is starting to change, as Procter & Gamble and others are demonstrating. They are connecting their supply chains all the to the store shelf – with significant implications for supply chain practice, technology, metrics and much more.”

As the president/CEO of a company that provides Big Data analytic services, I obviously believe that the right system can provide deep insights that companies need to make sense out of the mountains of data that are being collected. Because Enterra Solutions® uses a Sense, Think, Act, and Learn® system, our solutions can discover non-obvious relationships and help deal with everyday challenges like product not available or out of stock situations. Gathering and storing mountains of data may be a challenge for companies; but, mining that data for insights can offer companies real treasure.

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