If you’re like many of the companies we work with, the amount of data, both structured and unstructured, at your disposal continues to grow. Data is everywhere, and there’s no end in sight. And it has become among our most valuable currencies. In today’s environment, most every decision a business makes is based on insights derived from data. The key challenge is no longer data, rather it’s synthesizing the data into something meaningful and actionable.

Tom Davenport and Jean Harris in their research found that companies that are analytical competitors are more likely to succeed in the market. Therefore, it is critical that every company, no matter its size or number of customers, that wants to successfully compete in the market be able to put the data it has to work.  How do you make converting analytics into insights easier? Ask one question: what decision do you need to make to help your business grow? This could be a decision about new markets, servicing existing customers, creating new solutions, or identifying different partners.

The decision you need to make serves as your north star.  You will use it to define the data you need and which analytics will be the most appropriate. Some of the data you need may be housed in your internal systems, but data such as competitive intelligence and customer preferences may need to be acquired. Acquiring this data takes skill and often requires investing in research. Don’t let that stop you. There are various ways you can affordably conduct research.  Some of the cost-effective options we’ve explored in previous posts include leveraging customer advisory boards, engaging in reconnaissance at conferences and trade events, and taking advantage of association and industry research.

 

A 5-Step Pattern Breakdown Technique

Once you’ve acquired the data, your next challenge is turning it into action. The key is to look for and identify important patterns. Not every pattern is relevant. Try this 5 step technique we recommend and that our customers find helpful for understanding which patterns are germane.

  • Write one sentence for each pattern that captures an insight about its implications to your business. You might have multiple insights from some patterns.
  • Look for patterns that will facilitate customer-centric business decisions.
  • Put all the statements on a board or wall where you can see them all together.
  • Come back to the board in a day or two and see which of the statements really resonate and are compelling enough to affect the decision you need to make.
  • Agree and document the action you will take based on the insight.

 

Data

 

Good Data Makes for Better Models

This approach helps you make data-derived decisions. Add in analytics to help you identify new innovations, geographies, and markets to pursue. By employing analytics you can boost your revenue, increase your profits, and improve customer lifetime value. An IBM study found that companies that increase their analytics maturity can shift up to four percent of their sales orders into more cost-effective channels, achieving improvements in customer retention, share of wallet, and conversion rates.

If you’re not sure where you are in terms of your analytics maturity, check out Gartner’s analytics maturity model and determine where you are and what you need to do to improve. As your analytics capabilities improve, you’ll be able to create and add important upstream and downstream models.

Analytical models are essential for producing insights, enabling you to surface patterns and identifying relationships from data. Depending on your analytical capabilities, you can build models that can help you describe what is happening, what is going to happen, and even why it’s going to happen. The validity of your models depends on the quality of your data, selecting the right data sets, formatting the data, and choosing your algorithms and variables. Good models should have statistical significance to help you predict the future the better.

 

Analytics Are an Opportunity for Growth

The goal is to create accurate models that enable your leaders to make informed decisions about which customers are loyal, which customers are at risk, and which customers to pursue.  Focus on constructing models that are relevant and developing the skills you need to support interpreting and communicating the

Data work for you

implications of the data patterns to business executives in business terms.

Analytics and models serve organizations well that are keen on organic growth. If growth is a focus for your organization, we recommend you develop and document the following 12 methodologies and models.

  1. Segmentation
  2. Persona
  3. Customer Acquisition
  4. Customer Loyalty/Customer Risk
  5. Predisposition to Purchase
  6. Opportunity Scoring
  7. Touch Point Allocation
  8. Campaign Lift
  9. Attribution/Mix
  10. Pricing
  11. New Product Development
  12. Portfolio Management

Ready to make your data work for you? We can help.

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