Getting Real with Data-Driven Innovation

This article, our latest on data-driven innovation, was originally published in Chief Executive (yesterday).

According to a 2014 IBM study, three out of five mid-market executives have gained a competitive advantage from information and analytics.  But with all the noise in the market about analytics, and unclear ROI from investments in data initiatives, it can be challenging for CEOs to right-size investments in analytics and create more data-driven organizations. One helpful practice to focus efforts is to simply “get real” by finding other companies similar to yours and looking at analytic approaches that have worked before making big investments in people, technology and processes for data projects.  Here are a few examples of companies doing it right:

  • FleetRisk Advisors helps trucking and logistics organizations and commercial fleets improve their performance. They have access to mountains of data about their drivers and vehicles.  Predictive analytics has helped the company achieve a minimum of 20 percent reduction in the overall accident rate and an 80 percent reduction in severe accidents.  Identifying risk factors–especially those that contribute to driver fatigue–has helped them provide a more reliable service for their customers, protect valuable cargo, and most importantly, keep drivers and other road-users safe.
  • Plymouth Rock, a automobile insurance provider, faces an increasing competitive market from national brands known for low cost services. They needed to focus on a specific niche, with messaging that resonated to the market.  By doing a series of online multivariate tests targeted to educators, they increased online quotes 300% and reducing cost-per-quote by 33%.  Personally, my experience with multivariate testing is similar and these kinds of results are very achievable with affordable new technologies.  Multivariate testing tools enable marketers to quickly test hundreds (or thousands) of versions of a web page and find winners that produce quantifiable gains in key metrics.
  • Iron Mountain. They assist organizations across 36 countries and five continents with storing, protecting, and managing information. With complex client needs and countless ways clients use to interact with Iron Mountain, account management tactics were more reactive than proactive. By incorporating a predictive data model, account managers are now alerted if they have clients at risk of leaving, and they’re given specific recommendations within their CRM system to help retain the client.

The number of cloud based solutions that help companies achieve results similar to the ones above are proliferating, yet according to the IBM Study, only one-third of midmarket companies have adopted cloud technologies to achieve data-driven insights.  Here are some tips on how to bridge this gap and “get real” with data-driven initiatives:

  • Take inspiration from other companies in the market who have similar challenges as yours. Charge your team with “getting outside” and proactively seeking out examples from other companies worth emulating.
  • Start small. Before investing in a big data infrastructure, have a specific use case in mind and conduct focused experiments to solve particular problems.
  • Identify key data gaps. It’s vital that your organization “know what you don’t know” about your sales prospects, current customers and markets.  We recommend a structured process to proactively identify and fill key data gaps.  This can yield golden insights and dramatic gains in sales and marketing effectiveness.

For additional perspective or guidance on how your company can “get real” with sales and marketing analytics, drop us a line at 917-373-74351.

By |October 17th, 2014|Uncategorized|0 Comments

Innovation by the Numbers

In a recent study of entrepreneurial CEOs,  Gallup found that CEOs of the fastest growing companies (the Inc.  500), are twice as likely as other chief executives to seek in-depth information and use knowledge as a competitive advantage. Data driven innovation

Yet “knowledge seeking” activities like data collection and research are still sometimes seen as old school.  Some believe that market disruptions result from creativity unbound by the “death by data” process large companies apply when vetting opportunities. The nimble new disruptors that are eating the lunch of entrenched legacy players are seen assimply going for it, often without worrying about pesky roadblocks like total market opportunity, adoption timeline or – boo, hiss – profits.

While there’s a kernel of truth to this, the fact is that the opportunity to win can actually come by leveraging data during the innovation process.  So how do we successfully leverage data to innovate, without getting bogged down?

What Is Innovation, Anyway?

The need for data begins before inspiration sparks.  As leaders, we’re really looking for the Right Idea, not the Big Idea.  To enable our teams to innovate effectively, we need to provide them with strategic focus.  Make sure they understand the strategic position on entering new markets, timeline to profitability and what “Big” means for the company.  Most important, the team needs to know the capacity for project funding after testing is complete.  If we can’t spend a million dollars to scale a new product, we owe it to everyone to say so upfront.

Testing, Testing . . .

One of the reasons many start-ups scoff at data is that they often begin without any more of it than a frustration with what is, or an inspiration of what could be.  No hard numbers, just “jeez I hate having to …”  They come up with an idea and shop it around.

Successful early stage ventures don’t spend a fortune on initial product introduction, but they do collect data and cycle through many more go/no go decisions. The key is not to get ahead of your data. For example, one of our clients, a Google-backed venture, just turned down a meeting with a big potential partner because the venture wants to evaluate current partner performance data before bringing on more of the same partner profiles. The idea is to quickly nail the product/market fit and then scale.  Bottom line: test, measure and test again and make sure to allow tests to run their course so you can learn from them, which is why keeping them short and understanding what you’re trying to get out of each test is critical.

So what can we learn from this?

  1. Test ideas on the cheap without a lot of expectations except data gathering.
  2. Know what data is important and how you want to see it, so you are sure to get the right information.
  3. Allow more, smaller cycles of test-and-evaluate versus going from Beta to World Domination in 6 months.
  4. Look the data in the face: understand when a “go” decision is quantified, and when it’s not.

The Right Stuff

While it makes sense to project revenue and expenses, these figures too often are all that’s required to approve new initiatives.  But, these numbers may be less important than “Readiness” metrics.

For example, I worked with a product manager several years ago who (really, really) wanted to build an app to complement our online ordering process.  While at that time having an app was a good idea for PR purposes (“aren’t we the forward-thinking company!”), our customers weren’t actually using apps, or interested in using apps.  What they were interested in was a revitalized take on online ordering.  It took real discipline not to waste time on the app, but it made sense because our customers weren’t ready for it.

Have you ever noticed how tough it is to sell a new product, no matter how ready the market seems to buy it?  That’s often because External Readiness is higher than Internal Readiness.  Is our salesforce compensated in a way that makes it worthwhile for them to pitch the new product?  Are they hunters as opposed to nurturers?  It helps to think this through ahead of time.  I recommend a structured process to quantifying Internal and External Readiness factors to help prioritize all innovation projects.

Take Me to Your Leading Indicator

If we’re looking to our existing market data for inspiration, we need to understand the difference between Leading and Lagging Indicators.  While it seems like a no-brainer to make market and revenue assumptions based on our “average customer,” it’s better to think about who’s been lining up lately to buy our products.

Let’s say and our biggest market is Segment “A” which has an average contract value of $5,000. If we use this information to prioritize innovation – and expectations – we’d likely focus new offerings on Segment A.   But what if our new business – and current pipeline – is skewed toward Segment B which has an average contract value of $10,000?  Wouldn’t that change our focus somewhat?

This principle extends beyond internal focus.  Your existing customers may be industry leaders but becoming less relevant in their industry as their own disrupteos emerge.  Recently I redirected a product manager who was building a new product for the print publishing industry.  While that industry has stabilized somewhat – and isn’t going away, by any means – it’s not a growing segment of the knowledge ecosystem, and wasn’t a place to concentrate product development.

Doesn’t all this data build fences around game-changing creativity?  Generally, no. The amount of focus data provides enables effective innovation, versus an “anything goes/nothing gets done” atmosphere that results in wasted time, frustration and lowered morale.  So, while it’s way past time for mature companies to be more innovative, don’t let the myth of the seat-of-our-pants startup cloud your judgment past all logic.  Rather, learn from how successful startups use data to find funding, fans and footholds.

Diane Pierson is Principal – Innovation at Boundless MarketsEmail or call +1 917-373-7451 to learn how to improve your innovation process and deliver more revenue through data-driven sales and marketing.

By |October 3rd, 2014|Uncategorized|0 Comments

Thought question: Refreshing use of data?

Refreshing Use of Data?

By |October 1st, 2014|Thought questions|0 Comments