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EMC Journal Authors: William Schmarzo, Greg Schulz, Jeffrey Abbott, Mat Mathews, Cloud Best Practices Network

Related Topics: EMC Journal, Cloud Data Analytics, Big Data on Ulitzer

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Holiday Experience and #BigData | @CloudExpo @Schmarzo #IoT #Analytics

Retailers are considering how this holiday season, and the resulting wealth of data, can be converted into new analytic insights

The holiday season is nearly upon us (I’ve already heard Christmas songs being played…really?) and retailers are usually the big winners during the holiday season. However, leading retailers are already thinking beyond the current holiday season, and not just from marketing and merchandising perspectives. These leading retailers are considering how this holiday season – and the resulting wealth of customer, product and operational data – can be converted into new analytic insights that can be used to optimize key business processes, uncover new monetization opportunities and create a more compelling, more prescriptive user experience all year round.

The holiday season provides an opportunity for retailers to accelerate or jump start their processes for gathering, harvesting and exploiting customer, product and operational insights that can pay financial dividends year round. In the end, the holiday season can provide a catalyst for organizations seeking to become more effective in leveraging big data and analytics to power their business models.

However, any organization looking to exploit the business benefits of customer, product and operational analytics, needs to embrace a “thinking differently” approach with respect to how they exploit the economic potential their data and analytics. Organizations need to transform their business culture from treating data as a cost to be minimized, to embracing data (and the resulting analytics) as strategic assets to be gathered, harvested, shared and ultimately monetized across the organization.

This requires business leadership to beyond Business Intelligence “analytics” that monitors or reports on what happened with descriptive (BI) analytics. Business leadership must embrace analytics as a business discipline and cultivate an analytics-driven culture that embraces the “fail fast / learn faster” data science mentality that seeks competitive advantage from predicting what is likely to happen and prescribing what actions to take.

Start with Your Business Use Cases
So how does a retailer make this transformation happen? How does a retailer take advantage of the upcoming holiday season to accelerate their move towards data and analytics as a unique source of business differentiation? The starting point for that process is an understanding of the organization’s key business initiatives; that is, what the business trying to achieve or accomplish over the next 9 to 12 months.

For example, Retailers can leverage customer loyalty data, combined with web clicks, social media, mobile data and publicly available data sources coming out of the holiday season to create detailed customer Analytic Profiles that captures and quantifies each customer’s preferences, behaviors, tendencies, inclinations, trends, interests, passions, associations and affiliations. These Analytic Profiles can be used across a variety of customer-centric use cases including:

  • Customer acquisition
  • Customer activation
  • Customer maturation
  • Customer up-sell/cross-sell
  • Customer retention
  • Customer satisfaction (sat)
  • Customer Likelihood to Recommend (LTR)
  • Fraud
  • Customer Lifetime Value (LTV)
  • And many others…

An understanding and prioritization of the use cases is the starting point for organizations seeking to exploit the economic potential of data and analytics. The use cases form the linchpin for identifying, prioritizing, and gathering the data and creating the analytics that support the organization’s key business initiatives.

Exploiting the Economic Potential of Data and Analytics
As I have covered in previous blogs, data (and subsequently analytics) are unusual business assets. Data and analytics 1) not only are business assets that appreciate (not depreciate) with usage, but 2) data and analytics can also be used simultaneously across multiple use cases. Data and analytics actually become more valuable, more accurate and more complete with more usage.

There are no assets on your balance sheet that exhibit these unique behaviors; that can be used simultaneously across multiple use cases and whose value increases with usage. Consequently, data and analytics may be the most important assets in which organizations can invest.

In order to determine the potential economic value of the organization’s data, start by mapping the potential data sources (both internal as well as external or publicly-available) to each use case (see Table 1).

Table 1: Mapping Data Sources to Use Cases

Table 1 shows a mapping of the data sources required to support each use case. If the organization can master the organizational discipline of focusing on one use case at a time, then the organization can build out its data (and analytic) assets one use case at a time, with each use case driven by its own financial Return on Investment (ROI).

But this is where it gets really exciting. If use case #1 is Customer Acquisition, then the ROI on that use case likely covers the cost of acquiring and integrating the required data sources (Point of Sales, Social Media, Store Demographics, Local Events, Local Economic). Then use case #2 (Customer Up-sell/Cross-sell) can leverage the data sources used in use case #1 at no marginal cost. Use case #2 only has to pay for the data sources that are unique for that use case (Market Baskets, Product Margins). Use case by use case, we identify, prioritize and build out the data in the data lake. And as we advance from use case to use case, the cost of the data already integrated into the data lake are free for all subsequent reuse.

I’m sorry if my examples are not as clear as they could be, but it’s worth reading the above two paragraphs again, because you only pay the cost of acquiring and integrating data into the data lake once. After paying that price, the margin cost of reusing that data across additional use cases is zero. The economic potential of that can not be under-stated.

Analytics share the same behavior and the same iterative process, but with a small, very important wrinkle (see Table 2).

Table 2: Mapping Analytic Profiles to Use Cases

In the same way that use cases prioritized what data was to be loaded in the data lake, use cases also drive the prioritization of what analytics to build and capture in the analytic profiles.

Analytic Profiles are structures (models) that standardize the collection, application and re-use of the analytic insights for the key business entities at the level of the individual (human or physical object). We build Analytic Profiles for each individual business entity. The Analytic Profiles for The Disney Company, for example, could include guests, talent, rides, shows, attractions and operators.

In Figure 1 (below), the first three use cases result in the development of the following analytic scores:

  • Use Case #1 (Customer Acquisition) builds Customer Behavioral Segments that will be used to identify and target marketing campaigns against highest potential prospects
  • Use Case #2 (Customer Up-sell/Cross-sell) creates a Customer Loyalty score (version 1.0), but also updates the Customer Behavioral Segments (now version 1.1) taking advantage of new data required to create the Customer Loyalty score (see Table 1 for list of new data sources required for use case #2)
  • Use Case #3 (Customer Retention) creates two new scores (Customer Frequency 1.0 and Customer Recency 1.0) but also updates Customer Loyalty score (now version 2.0) leveraging new data (see Table 1 for list of new data sources required for use case #3)

Figure 1: Building out Analytic Profile Use Case by Use Case

The big difference is that as the analytic scores evolve and get more accurate, the previous use cases that used those analytic scores reap the benefit of an improved score without having to incur any additional cost. That is, the earlier use cases benefit from the addition of new data sources and the evolution of the analytics without having to incur any additional costs. The benefits of such a model can be economically staggering!

After working through several use cases, the customer analytic profile might look like Figure 2.

Figure 2: Updated Customer Analytic Profile

Leverage the Holiday Season to Build Out Your Digital Assets
I can understand why retailers in particular look at the holiday season as just survival time. For many retailers, a significant portion of their entire year’s sales and profits occur during the holiday season.

But leading retailers see the upcoming holiday season as the catalyst to jump-start the collection of the modern organization’s key digital assets – data and analytics. At the end of the day, the holiday season can help organizations to become more effective at leveraging data and analytics to power their business models – all year round!

Happy Big Data Holidays!!

The post Big Data Driving Customers’ Holiday Experience appeared first on InFocus Blog | Dell EMC Services.

More Stories By William Schmarzo

Bill Schmarzo, author of “Big Data: Understanding How Data Powers Big Business”, is responsible for setting the strategy and defining the Big Data service line offerings and capabilities for the EMC Global Services organization. As part of Bill’s CTO charter, he is responsible for working with organizations to help them identify where and how to start their big data journeys. He’s written several white papers, avid blogger and is a frequent speaker on the use of Big Data and advanced analytics to power organization’s key business initiatives. He also teaches the “Big Data MBA” at the University of San Francisco School of Management.

Bill has nearly three decades of experience in data warehousing, BI and analytics. Bill authored EMC’s Vision Workshop methodology that links an organization’s strategic business initiatives with their supporting data and analytic requirements, and co-authored with Ralph Kimball a series of articles on analytic applications. Bill has served on The Data Warehouse Institute’s faculty as the head of the analytic applications curriculum.

Previously, Bill was the Vice President of Advertiser Analytics at Yahoo and the Vice President of Analytic Applications at Business Objects.