From Big Data to Smart Data – A practical 11 step cycle to implement

There is much excitement, hype and delusion around Big Data. Everybody is aware of opportunities, but high aspirations are often disappointed. A large number of Big Data projects have missed on meeting expectations.
There are many reasons. One key explanation that companies approach Big Data wrong. Overwhelmed by the massive amount of data (available or imagined), companies through ever increasing computing power and data bases at the growing problem.
I suggest a different approach. Moving from Big Data to Smart Data: Thinking first, then collecting and processing data.
Enclosed I offer an 11 step process that works as endless repeating cycle of planning, execution, learning and improvement:

1. Ask the right questions based on your objectives and develop sensible hypothesizes: focus on business problems and explore opportunities across the value chain; identify ways to leverage digital technologies
2. Collect, create and acquire the right, necessary data prioritized by objectives and head maps)
• Generate data oneself
• Transaction data (Turn over and dynamic turnover sales data, usage and service data)
• Behavior data (customer profiles and core customer data)
• Other data: customer contacts with/ without transactions, customer requests, campaign reactions, movement profiles, social media relationships, benchmarking, need tree structures
• Own databases are often spread silos, but contain much valuable data!
• Gather free or cheap market data through surveys, etc.
• Buy from external sources
• Obtain through exchange with partners. Understand additional customer needs.
• Identify cooperation partners: Suppliers, retailers, and other companies across industries; build long-term network to share data
• Conduct competitive analysis (customer turnover data of competitors)
• Do additional research (total customer demand; promoters across the customer journey)
3. Understand the customer. Combine the often different, complementary and conflicting views from strategic marketing, Product marketing, Customer services, Sales and Logistics and develop one picture. Unite all respective parties across the organization that should be involved to create a commonly agreed pool of socio-demographic, psychographic and transactional data. This allows for an integrated segmentation later. Do multi-variant analysis of customer transaction data as starting point.
4. Segment customers in ideally groups of homogeneous consumer behavior. They should have the same decision parameters for consumption & purchase. Groups should be well distinguishable and separate. The naming and context should be clear and agreed across the entire organization. Apply hierarchical, partitioned and fuzzy cluster analysis or density based techniques.
5. Further correlate and refine segments. Use power questions to further segment. Test and refine. Be aware that changes to segments will need to occur as objectives and priorities change.
6. Focus on 20/80 rule – 20 percent of data generates about 80 percent of insight. Companies have hardly ever all data that they need. And it is not necessary. Companies can get started with available data by looking where there are the biggest results for the effort. A good example is lost-order analysis.
7. Refine USP by understanding customer needs, channel preferences, purchase drivers, share of wallet with new and current customers, campaign selection, offer customizations, improvement of algorithms, enable machine learning.
8. Determine and take action according to specific segments. Test different offering options and improve accordingly.
9. Manage channel mix. Use Envelope Analysis to optimize output level in relation to input variables.
10. Understand and manage customer journey:
• Put yourself fully in the shoes of the customers and define all interaction from their perspective, not company perspective.
• Understand all contact points and the individual experiences of customers at each touch point
• Prioritize customer touch points and take actions. Here, often decides success and failure of companies.
• Identify and establish connections between touch points; improve to speed up the movement from one touch point to the other
• Identify purchase promoters
• Align marketing activities accordingly
• Deploy special techniques such as associated analysis and collaborative filtering to suggest customized product and service recommendations to prospective buyers.
• Establish and manage metrics
11. Learn and refine begin a new circle

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