Let’s Talk About the HiPPO In the Room. Five Steps to Activate Data-Driven Sales and Marketing

Increasing Data and Analytics Usage is the #1 Marketing and Sales Priority for B2B Companies

No one will be surprised to hear that – according to a recent Forrester study1 – 82% of B2B companies believe that increasing the use of data and analytics for marketing and sales is a top priority over the next 12 months – with 22% saying it is critical!

What may surprise many—according to the same study—is that 48% of B2B companies still use their intuition over data to guide their decisions. In today’s world of Big Data, Machine Learning and AI, why do so many still rely on intuition over insight?

Perhaps for some it is the HiPPO effect – the Highest Paid Person’s Opinion – and a latent organizational authority bias.2 In many organizations, decisions often still come down to the dominant HiPPO in the room, where authority trumps data and insight.

Of course, the study identified a number of more “traditional” organizational challenges in activating data-driven decision-making (since I’m guessing HiPPO was not a category on the survey!).

Commonly cited challenges included:

  • Lack of executive sponsorship (hey – maybe these are the HiPPO’s after all!)
  • Lack of mature analytic capabilities
  • Poor data management practices and data quality issues
  • Multiple technology platforms resulting in data silos and challenges in activating multiple channels with any resulting insight
  • Complexity in managing the volume and velocity of data inside and outside the organization
  • Organizational silos and inefficient processes

And the other 52% who are using analytics and insight to make decisions instead of relying on their HiPPOs? Those analytics leaders reported significant improvement in key sales and marketing metrics over the laggards: sales cycle time, return on marketing investment, customer retention and loyalty, etc.

Avoiding the Trough of Disillusionment

The analytics view certainly appears to be worth the organizational climb based on the results that the industry leaders are achieving.

That said, the Gartner Hype Cycle suggests that Predictive Sales Analytics, Predictive B2B Marketing Analytics and Machine Learning, (and other Data Science capabilities) are just now entering the Trough of Disillusionment.

So how can companies making those increased investments in data and analytics for sales and marketing in 2019 avoid the Trough of Disillusionment and move rapidly up the Slope of Enlightenment toward the Plateau of Productivity?

Collectively we refer to this as the Activation Challenge: How do organizations extract greater value and return from their substantial investments in data and analytics, and overcome the HiPPO effect by activating data-driven decision-making directly into their sales and marketing processes?

Here are five best practices to get started with analytics operationalization based on our experiences working with clients in this B2B space over the last ten years:

1) Think Big, But Start Small

As our Chief Analytics Officer so eloquently noted in an earlier blog post, Small Data is still where a lot of the ROI is hidden in B2B Sales and Marketing. Many of the best problems out there today—the ones that will yield the most incremental lift, in terms of leads, opportunities, loyal customers, dollars, etc.—have to deal with small data.

Evaluate additional data sources carefully, and only add them in when they generate significant gain in the underlying models. Work slowly, getting wins on the board (and publicizing them) based on business impact, not the “best data” or the “coolest models.”

2) Lean on Agile Best Practices

The core tenants of the Agile methodology are pretty simple: Stay close to your customers and understand their stories; work in short-cycles; don’t overplan; and show your work aggressively and often. Successful marketing analytics organizations follow these to the letter. Unfortunately, many marketing analytics organizations we see exist in an isolated, siege mentality, only coming out for air and water when they need more data. This almost guarantees that analytics won’t make their way into the parts of the business that are actually talking to customers. Specifically, gather feedback from your pilot participants on a frequent basis (we typically do debrief calls with all pilot participants every other week during a pilot program).

Agile is a product development framework, and marketing analytics is really about building analytic products. They’re not applications with screens and buttons (although they can be), but they are assets that are continually improved. Taking a “product-centric” approach to your data science and analytics capabilities ensures that scale is built over time, and that results will be reproducible back to data using source code.

3) Connect the Dots

Organizations that spend all of their time integrating data don’t get anything done, but the reverse is also true. Try doing analytics when you’re spending six hours a day hunting down csv files from Joe in Accounting. Fortunately, new techniques in data integration—for example, the semi-structured “data lake” concept using cheap cloud storage—allow companies to build an integrated view of their prospects and customers an order of magnitude faster and cheaper than was possible even 18 months ago.

Creating an integrated view of customer engagement touchpoints will enable your organization to see how your customers are engaging across all of your channels and will allow data scientists to ask and answer better questions. Some of the best organizations create an Insights Center of Excellence that is responsible for the “data step”, allowing data scientists to work on solving problems, not munging data.

4) Translate For Your Internal Audience

Several years ago, we developed a killer Likelihood to Purchase model for a large Asset Management firm. Each month, we scored all customers on their “likelihood to purchase” in the next 30 days. We piloted it with half of the sales force.

The client measured the model results for six months (thereby avoiding any “fox-in-the-henhouse” measurement bias). At the end of the pilot, the client reported that 98% of the advisors we predicted were going to purchase in the next 30 days actually purchased in the next 30 days! The model far exceeded their expectations. In fact, I remember telling the head of distribution that had I come into our first meeting telling them we could predict with 98% accuracy who would purchase in the next 30 days, they would have likely thrown me out.

But an interesting thing happened on the way to the predictive analytics Plateau of Productivity with this client – the pilot sales teams did not significantly change their call patterns even when armed with this killer insight and knowledge

Why, you may ask? As I have long said, “the sales force has an infinite capacity to absorb all productivity gains.” In this case, the highly-educated, highly-compensated salesforce was a bloat of HiPPOs who knew better, and simply continued to call on who they always called on. (Did you know a herd of hippos is called a bloat? I didn’t, but it certainly seems fitting in this case!)

We learned that we needed to do a much better job educating the sales force on what to do with the insights, and why, in terms they could understand. To be successful, data and analytics investments must also be accompanied by active enablement of your sales (and marketing!) channels to ensure that insights are translated to meaningful actions, without requiring everyone to have a PhD.

Without this translation layer, even the best models will not produce any outcome at all!

Enablement and translation come in many forms, but some examples that work include:

  • Converting analytic output into business context that marketers, salespeople, and partners can understand and internalize
  • Aligning appropriate messaging and content with “who / when” models so that “the right touch” isn’t killed by “the wrong stuff”
  • Embedding analytics into the existing sales and marketing workflow so that minimal process change is required
  • Consolidating cross-channel customer engagement dispositions into a single view, to minimize switching windows and applications and to provide a holistic view of customer engagement
  • Reporting positive business outcomes back to users so they believe in and trust the insights

5) Work With Trusted Partners

Sounds self-serving I know, but according to Forrester, analytic leaders rely on trusted external partners for both data and analytic services.

Every client we work with has their own data science team(s). We are not their competitors, we are their partners. We are often referred to as an accelerant for driving results in market. A Champion / Challenger approach to analytics should yield greater results.

Avoid “black box” solutions that cannot be brought in-house or internally managed by your own data science team.

And as noted above, without appropriate alignment and activation of your sales (and marketing) channels, even the best data and analytics will not deliver results if you can’t get your sales and marketing resources (and eventually your customers!) to change their behavior.

Ensure your partners have experience in enabling your sales and marketing channels with analytics and insight, content and messaging – not just in developing analytic models.


Banishing the HiPPOs from sales and marketing will ultimately lead to less waste, better growth, happier customers, and happier employees. Getting there, however, doesn’t happen by installing the latest machine learning technique alone—it happens when analytics is translated into a context that internal users can understand, so they don’t fall back on intuition or opinion alone.

1 The B2B Data Activation Priority: A Forrester Consulting Thought Leadership Paper Commissioned by Dun & Bradstreet, May 2018

2 Forbes.com, Data-Driven Decision Making: Beware Of The HIPPO Effect!, Bernard Marr, October 26, 2017

Hype Cycle, Trough of Disillusionment, Slope of Enlightenment and Plateau of Productivity are trademarks of Gartner