Data is everywhere.  Social media activity, online activity, in-store point of sale (POS) transactions, online transactions, web analytics, mobile devices, and the list of data collection channels go on.  Nearly every action we perform today generates valuable data.  According to IBM, we create 2.5 quintillion bytes of data per day — so much that 90% of the data in the world today has been created in the last two years alone. 

In this era of Big Data, collecting customer data is key; however, not having an effective customer analytics strategy could be a giant blunder for organizations of any size.  Creating an effective customer analytics strategy will not only help drive top-line growth, avoid unnecessary costs and increase customer satisfaction, we also know from significant research that it will spur an increase in customer retention and conversion rates.

In a recent whitepaper from IBM’s Institute for Business Value, IBM has taken years of research and identified a conceptual framework that defines four stages of customer analytics strategies. 

Stage 1 – Cost Reduction

The Information Cost Reduction Stage is defined by organizations that have the capability to gain insight from BigData to develop a deeper understanding of the customer and a focus on various data quality tactics in an effort to reduce cost and realize incremental gains in revenue. 

Organizations who implement this strategy can potentially increase customer retention by up to 9 percent, capture 2 percent more wallet share and convert an extra 3 percent of inbound contacts into a cross-sell event, while shifting up to an additional 4 percent of their sales orders to more cost-effective channels.

Stage 2 – Sharing

The Information Sharing Stage is defined by organizations that have the capability to share information internally and across the value chain with customers using any digital device or channel they choose and a focus on a clear customer analytics strategy to enable information sharing.

The most sophisticated marketing organizations who implement this strategy focus on the application of analytics for marketing event optimization in an effort to optimize their direct marketing events over a given time period over multiple channels.  Analytics scoring models are used to detect purchase “patterns” and dynamically create events to optimize the customer relationship.

Stage 3 – Responsiveness

The Information Responsiveness Stage is defined by organizations that have the capability to move from reaction to prediction through the use of advanced analytics around raw data, such as social media, and a focus on tactics toward greater speed and predictive actions for response in order to increase dialog among consumers.

Organizations that have been able to perform real-time analysis of external data combined with rules-based actions have experienced average conversion rates of 16.9 to 38.2 percent.

Stage 4 – Demand

The Information On Demand Stage is defined by organizations that have the capability to adapt business models that enable faster creation of value and customer communication online in real time using the customer preferred channel and providing personalized guided selling or guided customer service and a focus on an advanced analytics-driven approach called multichannel next-best action (MNBA) to create a two-way, real-time dialogue with consumers to improve communication relevancy and inspire brand loyalty.

Research by IBM has demonstrated that organizations who use predictive analytics and execute across multiple channels have been able to increase top-line growth up to five times more than other, less sophisticated, businesses. The addition of the ability to execute a multichannel next-best action strategy has an average converted response rate of 24.1 to 64.3 percent.


Every organization collects BigData.  It’s not only important to simply collect this data, but to enhance your marketing efforts, it’s also an imperative to understand your business' customer analytics maturity level as well as develop and implement a solid customer analytics strategy to drive organizational change and gradually increase your organizations maturity level to better understand and engage customers in a more personalized way.

If you need help in identifying your customer analytics maturity level, developing a customer analytics strategy, and putting some of that BigData you’ve been collecting to achieve higher productivity, output and a better understanding of your customers, SourceLink has experts in Customer Intelligence, Analytics, Data Modeling, Database Marketing and Marketing Strategy. Please reach out and let us use our experience to help define and implement your customer analytics and marketing strategy.