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Four steps to analytic success 

Make the case and get started with analytics 

Moving from gut-feel based decision making to an analytics-driven approach isn't easy – especially for small and midsized businesses that often face IT resource constraints, limited analytical talent, tight budgets and technology gaps. But implementing analytical solutions that promote fact-based decision making can be done in a lean organization – and the investment pays off in the end.

The following steps can help you get started on establishing a strong analytical foundation, but don't view them as a simple checklist. At every step of the way you'll need to reiterate the message, point out examples of analytics accomplishments in other organizations, and generate and publicize your early wins to keep the attention of those who make decisions in your organization.

SAS Senior VP and CMO Jim Davis on how to become an analytic organization

What is the best way to become an analytic organization? How can you help introduce analytics into your organization? I enjoy discussing these questions with our customers, and these are the tips I hear most often:
  1. Look for examples outside your company AND outside your industry. For inspiration, look not only at what you can do but also look at what other organizations have done. Create an awareness of what other industries are accomplishing with analytics.
  2. Understand your culture. Assess the personality of your organization to determine whether top-down or bottom-up implementation will work best. Success can happen in either scenario – but you should know before getting started which will work best for your organization.
  3. Determine your baseline. While you're assessing the culture, you should also assess the level of analytic understanding within the organization. A tool like the Information Evolution Model can help you determine where your organization falls on the adoption spectrum, so you know where to start and what to aim for next.
  4. Find a starting point. Once you've provided examples, you need to explain how you will apply those same methods in your organization – and what the results will be. Cross-sell and up-sell is a natural starting point for many businesses.
  5. Give them something tangible. If you associate your initiatives with your business opportunities, you will find support.

No. 1: Seek executive buy-in

Successful adoption of analytics starts with securing executive support. That means getting senior managers' buy-in from the get-go. If this is a struggle, run a pilot program to demonstrate the benefits. The goal is to win over a key decision maker who can serve as the analytics champion.

One way to earn executive buy-in is to invite them to look under the hood. Those unfamiliar with analytics often view solutions as a "black box" where data goes and is mysteriously transformed into "answers." Bruce Bedford, VP of marketing at Oberweis Dairy recommends gently introducing analytics to decision makers who don't have a statistical background. "Take them through a few simple examples like a t-test or chi-square," he says. Then show them how it translates into a business decision.

The benefits of a trial program can't be vague. Don't propose a project that simply cleans data for the sake of cleaning data. Instead, explain how clean data will improve the information you obtain from your customer data to help with a specific goal, like reducing churn. If you associate your data quality initiatives with your business opportunities, you will find support.

A good pilot program has the potential not to just show where the company can earn more or reduce costs, but actually does it, even if in a limited scope. The Wine House chose to look at inventory for its first analytics project – leading it to discover $400,000 in lost inventory. Use a "quick win" of this sort to gain the support of an executive champion, who then can pave the path for adoption of analytical solutions throughout your organization. Another quick win idea: Use analytics to create targeted marketing campaigns that improve response rates. Golfsmith, a golf retailer, increased direct mail response rates as much as 10 to 60 percent by using analytics to segment customers better.

If you are having difficulty settling on the right type of project, look for examples outside your company, maybe even outside your industry. Create an awareness of what other small and midsize businesses are accomplishing with analytics. If you get those with the authority and budget to think about the potential of analytics, the funding will materialize.

No. 2: Establish an analytics culture

People, processes, technology and culture - which of these four are most important for succeeding with analytics? By far, it is culture. This is one area where SMBs have the advantage, as it is certainly easier to make a cultural change in a smaller organization than in a large enterprise.

SAS Chief Researcher Pamela Prentice wrote about how one start-up changed its culture in her blog post from the SAS Power Series event in Chicago: "I particularly like the notion Jayson Tripp and Brian O'Connor from Redbox had: 'When someone is in the desert starving,' Brian said, 'if you feed them a saltine, it tastes like steak.' Brian, responsible for the business intelligence function, initially fed Jayson, in charge of strategic planning, 'saltines' of information as Jayson worked to gain acceptance of analytics at Redbox. Jayson took this information and boiled it down to three PowerPoint slides showing the financial impact to the company - and made inroads in analytics adoption. Getting quick wins in using analytics is important to winning over decision makers."

Just three slides? Yes, because part of establishing an analytics culture is to be able to tell the story in a way that everyone - from the sales clerk to the CEO - can understand. You can't do that with a 70-slide presentation that details the minutia of algorithms and data cleansing.

To extend the analytics culture across the company, Kelley Blue Book created an analytic center of excellence. The resulting improvements in decision making help the company consistently outperform competitors. Both costs and benefits of analytics projects are highly visible, making the power of analytics clear to all.

"We improved agility by placing analytic center of excellence and business team members together," notes Kelley Blue Book's Vice President of Analytical Insights, Shawn Hushman. "As needs arise, analytic experts contribute ideas and answer questions."

No. 3: Figure out at what stage your organization is

In the book, Competing on Analytics: The New Science of Winning, Thomas Davenport and Jeanne Harris list five stages of analytic maturity:

  • Stage 1: Analytical Impaired – Lack of analytical skill or executive interest.
  • Stage 2: Localized Analytics – Uncoordinated activities or silos.
  • Stage 3: Analytical Aspirations – Good intentions with slow progress.
  • Stage 4: Analytical Companies – Widely use analytics internally.
  • Stage 5: Analytical Competitors – Use analytics as a competitive advantage.

Understanding where you are helps make it easier to know where you need to go.

To move from stage one to stage two, walk decision makers lacking a statistical background through a few simple examples to show how analytics translates into business decisions. Start with tactical decisions, such as key metrics reporting or creating basic segmentation for more targeted marketing. Show how tactical decisions can help drive more strategic decisions – such as entering a new market, introducing a new product category based on that segmentation, or expanding production or opening a new plant based on demand trends.

Tie analytical outcomes to the strategic issues of the business. Letting skeptics see the effectiveness of using analytics whets their appetites and gets other people and departments interested. Over time, this can help analytics become pervasive throughout your business. If you can do nothing more, transition the conversation from analytics being about a tool or product to analytics being a component of the business process.

No. 4: Identify your analytical talent

SMBs usually have very few statisticians and analysts – if any. Most staff falls in either the "amateurs" category – those who use spreadsheets and run queries – or in the "semiprofessionals" category of those who can use some basic statistical tools and may be able to program in SQL. Only a few SMBs employ "professionals" who can write their own algorithms.

Don't let the fact that you have only limited analytical pros deter you. Know that the employees that tend to lead analytical initiatives aren't necessarily analytical experts, but ones who understand how to pose analytical questions. The key is to pinpoint that talent in your organization and create processes that enable employees to promote analytic best practices.

Why not just craft a job description and hire analytic talent? Because it isn't easy to hire someone who knows your business and its challenges. While universities are beginning to develop degrees and graduate programs aimed at creating analysts who can quickly get up to speed on an organization's needs, it's not the same as hiring from a top engineering or MBA program. Homegrown really does work best in this situation. Try to identify and nurture talent that is already at your company by looking for individuals with both strong communications skills and technical abilities, or look for a pair of individuals that between them have strong business knowledge, communication skills and technical abilities. Find online training courses and users groups for them to attend. Local SAS® users groups, for example, are very welcoming and encouraging for newcomers to the field.

Growing SMBs might also consider creating the position of chief analytics officer. This person should ideally have both a technical and business background, and be able to tell the story of analytics in order to win over reluctant decision makers. Short of such a position, informally identifying the technical and business users who can pose those analytical questions and encourage them to work together – even if they report up through separate chains in the organization. Even a small company can create a virtual center of analytic excellence, as Kelley Blue Book did, to promote the understanding and adaptation of analytics. At a minimum, you want the analytic talent you do have to get to know and work with each other – even if they are in different departments, or different ends of the country.

Conclusion: It's not all about technology

Overall, you cannot ignore the human relationships and the importance of evangelism at every step of the way. You need to be constantly talking about what analytics can do and what you have seen analytics accomplish in other organizations. There has to be a human element tied back to every aspect of your work, from getting wins early on and catching the attention of those who make decisions in your organization.

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