Midmarket companies are beginning to experiment and utilize data to compete and win using their CRM data assets by implementing useful analytics.

The term “big data” has been floating around for many years and creating a new era of data.  We consult in the middle market with some customers on the verge the large end of the market definition and others in small end of the midmarket.  Currently, we are not observing the “Big Data” business impact quite yet.  However we believe there is potential for our customers to make strides in their operations with the data they have today.  We closely watch the firms and industries in the enterprise tier, especially retail, finance, manufacturing and banking, to get a handle on the mega-trends heading to the midmarket (think social, virtual, cloud, mobile and the like).  One such growing trend is called analytics and in particular business analytics.  There are many different types of analytics and they fall along a path on a capability maturity ladder.

This article will introduce some basic analytic terms, provide visuals for analytics maturity, and offer suggestions on how your company can prepare to utilize your systems to make gains before your competitors make the leap first, build up their capabilities, and leave you in the dust with smarter and faster decisions.

First, here are the basic terms for analytics without taking into consideration emerging trends like cognitive analytics and robots etc.  There is always something new.

• “Descriptive Analytics” – To summarize what happened. (Business intelligence or data mining) which provides a hindsight look.
• “Diagnostic Analytics” – To summarize why did it happen, provides insight
• “Predictive Analytics” – can only forecast what might happen in the future to a certain probability, what is likely to happen (forecasting) which provides foresight.
• “Prescriptive Analytics” – a type of predictive analytics, recommending one or more courses of action — and showing the likely outcome of each decision, what should be done about it. (Optimization and simulation)

Source: Fractal Analytics

Foresight is always better than hindsight.  Companies are looking for a competitive advantage and are looking to get to the tipping point in markets faster.(The industry or focus is really irrelevant).  Whether you are in the educational market, manufacturing, healthcare, service, retail, banking there is a competitive advantage in taking data and using it for decision making (in a faster smarter way).  An interesting quote is, “We’re entering a new world in which data may be more important than software. –Tim O’Reilly”  So data is growing and becoming more important to our value chain.  Secondly, let us focus on how your organization can prepare for analytics and think through areas for experimentation.  There needs to be a focus on data quality and data management.  Once there is a proactive and reactive treatment utilizing data governance practices using information quality techniques, then there is a foundation for building on a “data platform” to leverage various levels of business intelligence/analytics.  Then you need the right talent to help make sense of your data.  Some of the objectives might be to detect trends, clusters, and outlier exceptions in data to various processes and operations to improve them (from data to decisions). The reasons for experimenting in various areas of business improvement would be increased revenue, increased profits, increased earnings per share, and increasing the operating margins for starters.  Below are some Sales, Marketing, and customer service related areas for analytic experimentation and planning today:

• Segmentation/stratification of customers
  • enables companies to accurately target tailored marketing messages to customers who are most likely to buy their products
• Opportunity sizing
• Acquisition modeling
• Intelligent Lead scoring
• Cross-sell/next best offer
  • People who liked this books like these books two (recommendation offerings) like Netflix and Amazon.
• Personalized Marketing Campaigns
• Response modeling / Customer Retention Action Planning
• Customer Lifetime Value (CLV)

Having data to help tune and shape your business makes sense.  Working from a business model that is driven by data and positively impacting the bottom line can help improve decision making, bottom line results, and achieve better customer insights.  In other words, the business drives the tools and not the other way around and the customer and finally the company wins the race to deliver value.

“It’s tough to make predictions, especially about the future.”
― Yogi Berra who turned 90 years old on May 12, 2015 and passed away this year Sept 22nd.  We honor his life with “yogisms”

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