Building Analytical Frameworks

Fri, May 2, 2014

In Focus, Industry Speak

Consumers and organizations create 3.5 quintillion bytes of data daily. In fact, more than ninety percent of data in the world today has been created in the last two years. The data come from everywhere: posts on social media sites, point of sale – are only a few examples. Thanks to affordable connectivity and cloud services, the world is a networked society. Today, most business organizations understand the value of collecting customer-related data. However, many struggle with the challenges of leveraging the insights from this data to create a dynamic customer relationship. They are unsure how to effectively use their customer data to make decisions that turn insights into sales growth.

Organizations need to deploy and use analytical strategies as a competitive differentiator and as an engine for sales growth. For this perspective, there needs to be a conceptual framework that enables organizations to implement analytical strategies for sales growth and cost reduction.

Analytical Framework

Most of the organizations start from stage 1 and the optimal shift for analytical strategy is obtained from stage 4. At stage 4, organizations have the capability to adopt business models that enable faster creation of value. The organization can make this shift via information sharing , stage 2, or through information responsiveness, stage 3.

In the first stage of the framework, the focus of the marketing organization is on tactics to better target addressable mail, like catalogs and direct mail and therefore reduce postal costs and in effect increasing profits[1]. The marketing efforts focus on segmentation efficiency and information cost reduction to reach operational efficiency. This enables the organization to gain insights from the information explosion.

Organizations in the second stage of the customer analytics framework share information throughout the value chain through external data and hence create a consistent customer experience over multiple channels and benefit from increased loyalty, improved sales conversion rate and better cross sell. Analytical models detect purchase “patterns” the customer has exhibited in the past and then simulate the customer segmentation. The customer analytics strategy of information sharing and the horizontal marketing approach better align the focus of a business firm with its customers’ needs[1].

At stage three, the organisation develops information responsiveness through internal and external data interchange. The organizations focus on identifying the questions that – if answered – will impact their business the most. This acts as a filter on data collection and helps an organization avoid the task of collecting all sundry data and then deciding what to do with it[1]. The process of standardization adds to a major percentage of cost while analysing data. The organizations at stage 3 enhance capability of the organization from reaction to prediction.

In the fourth stage of the customer analytics framework, the most successful marketing organizations execute a strategy that enables information on demand and an analytics-driven approach[1]. This helps the company and customer to communicate online in real time using the customer’s preferred channel. This also enables the company in providing a personalized guided selling and customer service experience. Organizations start engaging with the customer from the point of needs identification and continue to do so throughout the buying cycle . Faster creation of value for end customer is the key focus here, there by improving direct sales.

Improving analytical capability is key for organizations who intend to smartly use the vast customer data they collect. Analytical capability enables organization to know their customer better and reduce the marketing and sales forces. Organizations differentiate themselves through knowledge of their customers and this is one key successful attribute of improving analytical capability. Information is a business, we are still far from quantifying this in the financial books but the time is now to use this asset for accountable business and customer communication.

References

1. http://www.ama-atlanta.com/files/documents/IBM-Customer-Analytics-Pay-Off.pdf


Saurabh Prasad
M.B.A. 2013-2015

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