Why Big Data for Sales fails


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The Big Data hype worries me. A lot. Particularly as it pertains to sales analytics. When it comes to understanding the inflection points that should be the determinants of behavior change to improve sales performance, we don’t suffer from an information deficit, we suffer from an insight deficit. Big data is perceived by some as the answer to the question. The problem however is that we often don’t know the question.

It is true that where we are today is a direct consequence of our past actions. You might therefore assume that a singular focus on finding correlations between historical data and results is the panacea to predicting future sales performance or performance hurdles. The difficulty however is that without applying context and experience there is a grave danger of mistaking correlation for causality, being blinded by a seemingly strong linkage between data and results without understanding the true causal factor.

Here’s an example. In some of our customers we have seen a direct correlation between the early identification of budget (allocated to a related project) as the most important indicator of sales success. But when we look at the data for others, budget seems to be less of a factor. When we looked beneath the data to understand the reason for this we uncovered an interesting fact. Early identification of budget is an important factor more often in a situation where the solution being offered could be classified as a ‘nice to have’ as opposed to a ‘must have’. This applies most frequently when the solution makes something better instead of fixing something that’s broken. Context and an experiential compass are important heuristics to apply to divine the meaningful from the obvious. Don’t let the data fool you.

Every day sales managers are struggling to find the answers to make their sales team more effective. Often remote from their sellers, and relying on weekly calls and reports from their CRM systems, these managers find it difficult to identify, interpret and influence the important factors that predict sales success or failure. Sales managers are battling to discover whether their sales teams are doing the right things at the right time to positively impact their sales performance. The volume of data is overwhelming and the insight is missing.

Sales managers can’t manage what they can’t see, and sometimes, even if they can see the data, they don’t know how to find and interpret the most relevant metrics that influence future performance. They grapple to extract pertinent insights that tell them the absolute truth about their sales business – today, and into the future. It’s just to hard interpret the data. And it gets worse. Even if they can find and analyze the data to derive the pertinent insights, the resource required to prescribe effective coaching or curative actions for each sales person, in a consistent and informed way, is overwhelming. But Big Data, as currently being prescribed, is not the answer.

According to a recent Infochimps survey most big data projects fail. According to this recent study, even though 81% of companies have Analytics projects as one of their top priorities, 55% of these projects do not finish. And while we all know that IT projects are not always successful, Big Data / Analytics projects will fail 30% more often.

The most common reason for failure is inaccurate scope. People try to boil the ocean, and assume that more data is better. Unfortunately, that is not necessarily the case. Now that technological advances have made it possible to accumulate colossal amounts of data at an every increasing rate, it has become almost axiomatic that the answer to everything in in the data. But in fact it is not. Companies are making BIG bets on BIG data alone without any qualitative assessment that applies deep domain expertise. That has the potential to lead to BIG decisions being made with BIG confidence that is sadly misplaced. BIG Mistake.

The second issue is lack of business context. Without the right business context it is hard to know what questions to ask – so in that case any answer should do – but of course that doesn’t work. It is understandable though that if there is a separation between the people with the business knowledge and the people with the analytics tools – then success is unlikely. Sales people need to be at the center of any sales analytics project. It cannot be a disconnected project owned by the business analysts or the operations team.

The third point is really an extension of the second. If you don’t have business expertise, domain knowledge, experience and a ‘nose’ for what’s right then you can’t apply any human qualitative input – and that makes it hard to connect the dots.

The most common challenges according to the study are Time and Tools. Now if the tools are hard, and the scope is wrong, then you will of course need a lot of time. We don’t believe it needs to be that way.

The leading sales organizations we have seen are not just using reports, or big-data centric analytics. They are combining targeted smart sales analytics, strengthened with embedded sales methodology knowledge and experience. In the best of cases they are using intelligent automated systems to help expose the relevant sales metrics, gain actionable insights from the data, and provide automated coaching advice to accelerate sales cycles, increase the health of their pipeline and align and motivate their sales teams.

And they are realizing significant business benefits:

  1. Increased performance of the sales team based on more informed sales management and more knowledgeable sales coaching
  2. Improved sales productivity for individual sellers with automated coaching and visualization of results for greater alignment and motivation
  3. Accelerate sales velocity by measurement and analysis of win rate, sales cycle, deal size and pipeline health to reduce risk and take advantage of opportunities

I will follow-up this post with a set of recommendations on how you might approach sales analytics or big data projects – but I’d welcome your thoughts on this. Right now, there is a lot of time and money being spent in this area and most of it is wasted. It doesn’t have to be this way. The life of the sales manager is hard enough.

Republished with author's permission from original post.

Donal Daly
Donal is Founder and CEO of The TAS Group the creators of the Dealmaker intelligent sales software application. Donal also founded Software Development Tools - acquired by Wall Data (NASDAQ: WALL), NewWorld Commerce, The Customer Respect Group and Select Strategies. Donal is author of five books including his recent #1 Amazon Bestseller Account Planning in Salesforce. He can be found on his blog at www.thetasgroup.com/donal-daly-blog or on Twitter @donaldaly


  1. No doubt many of us have felt that big data is a tad overhyped, especially when there is a dearth of useful questions to create actionable insight, a term that is just as overworn as big data.

    When you mention “If you don't have business expertise, domain knowledge, experience and a 'nose' for what's right then you can't apply any human qualitative input – and that makes it hard to connect the dots,” I’m fully with you.

    But you move away from that idea in the next paragraph when you say “In the best of cases [leading sales organizations] are using intelligent automated systems to help expose the relevant sales metrics, gain actionable insights from the data, and provide automated coaching advice to accelerate sales cycles, increase the health of their pipeline and align and motivate their sales teams.”

    One reason that big data struggles to succeed in B2B sales is that events in B2B engagements are idiosyncratic and contextual. Intelligent automated systems and automated coaching imply that the need for human judgement and abstract thinking is obviated, or at least diminished, in B2B sales. I’m not bullish on that idea.

    Nate Silver put it this way in his book, The Signal and the Noise, “As we enter the era of Big Data, with information and processing power increasing at exponential rates, it may be time to develop a healthier attitude toward computers and what they might accomplish for us. Technology is beneficial as a labor-saving device, but we should not expect machines to do our thinking for us.”

  2. Hi Andy,
    Thanks for the comment. I think I do agree with Nate Silver – and probably with you as well 🙂
    I do believe that Intelligent automated systems and automated coaching reduces the need for human input – and I have seen that work well in B2B sales. What these systems can do better than humans is apply consistent analysis and interpretation of results consistently, unemotionally and without ever getting tired.

    Sales managers who, for example manage 10 reps, each working 10-20 deals, can’t get to apply that rigor consistently. They usually do not have the time or energy to do that week in and week out. What they can do better than any system is to imagine outcomes and make judgements heuristically in the context of the moment – based on their experience.

    After Nate Silver argued the pros and cons of systems and humans – his conclusion was emphatically that a combination of the two is the optimum. He cites a number of examples of this.

    In my experience, intelligent automated systems are becoming smarter all of them time and should be applied if you’re looking for a solution that scales. You can make them pretty smart by embedding deep sales domain expertise, and informing the right questions ‘ out of the box’. Then this gives the sales managers the time to apply their own special sauce.


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