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August 23, 2011

Analytics in Action - Building Value Through Targeted Solicitation

The Challenge: Managing customer experiences while improving their participation.

We live in a mass customized society – one where consumers want it their way! Global market forces are driving the continual evolution of the food and beverage industry. Changing consumer preferences are dramatically impacting business strategy. In this fiercely competitive marketplace, one must consistently and cost-effectively innovate to meet consumer demand.

One such CRM strategy is the Loyalty Program. Loyalty programs are structured marketing efforts that reward, and therefore encourage, loyal buying behavior; behavior which is potentially beneficial to the organization.

The loyalty industry in United States has a perceived value of $12 billion in 2011, of which the retail sector accounts for 40% of total loyalty program memberships but just 25% of the perceived reward value issued. Retail in particular, therefore largely presents an opportunity to mine buried treasure.

Our client is a leader in food & beverage category, and has more than 10,000 outlets. The client has also, like many others, built a ‘loyalty program’ with multiple reward cards and has around 80,000 members enrolled. Earlier, the client had commissioned a “Usage & Attitude Study” to help them understand motivations behind customer purchase decisions.

The client wanted Marketelligent to develop an actionable customer segmentation framework on the basis of perceptual & behavioral data of these card holders. Our immediate objective was to review the transactional data of customers who had signed for the client's loyalty program. We observed a particular trend in the customer activity, akin to the “Long Tail” behavior. Almost 35% of customers consistently purchase products from one or two categories, while the remaining customers tend to experiment amongst the categories available.

The Approach:
Develop an actionable customer segmentation framework.

The focused research data was tied in with customer transactional details using a technique called Data Fusion (see Exhibit 1). Having understood the transactional data & identified the drivers of customer behavior, we segmented the customers on the basis of their “needs”, visits frequency and average value of a transaction.

"K Means" clustering technique was employed to create the segments (see Exhibit 1). As a result, customers were placed into four segments, differentiated on the basis of 18 behavioral & 12 perceptual variables (see Exhibit 2).

Targeted strategies were then rolled out within marketing & sales division of the client organization; and the marketing team in particular realigned its role to focus mainly around segmentation-driven

Exhibit 1

Exhibit 2

The Result: Managing customer experiences while increasing their involvement.

In order to create growth opportunities, we followed the segmentation exercise with affinity analysis to design up sell/cross sell strategies for each segment.
To lift the monthly transactional value of the “Sweet tooth” segment, we had recommended our client to add packaged offers around dessert & snacks, as we found the product categories to have high affinity. Within 3 months, the monthly transaction for the test group increased by 30% (Exhibit 3).

Exhibit 3

Also, the client expanded its newsletter from one to two, basis the segments; one focused specifically on the products available, and the other on lifestyle & new product information. Specific sections such as “Chef’s Favorites” & “Make your own coffee” now have clickthrough rates three times more than the previous general newsletter.

August 2, 2011

Big Data is Yesterday’s News.

'Big Data' is already here.  Over the past few years, businesses have been able to put in place massive ERP systems, storage devices and data warehouses.  In the process they have spend billions of dollars.  On top of this, they have spent extra millions by putting in place ‘Business Intelligence’ systems to tell Managers what has happened in the past.  So today, a Manager can see exactly which Customer has spent how much, on what date, at which store and on what Product.  Essentially Business Reporting.

Which brings us to the next stage in the information continuum – now that we have all this data and know what has happened, what do we do next?  How can we leverage this data to influence consumer behavior, to manage growth, to predict the future, to optimize strategy, to take actions in almost real-time, etc in a forward-looking fashion.

In effect, to go from the ‘what’ to the ‘who-why-when-where’.  And in the process become smarter about business decisioning.

“Real-time Customer-level Decisioning” will become increasingly important.  Real-time because you want to act on fresh information in the most expedient manner;  lest your competition take advantage.  And Customer-level because each Consumer in unique and wants to be treated uniquely.  Technologies exist today that can deliver on the promise of both.  We have algorithmic trading where actions are taken in milliseconds.  And we have predictive analytics that can deliver on customer-level decisions.

Unfortunately, most typical businesses are not there yet.    For Consumer-focused companies, “Analytical CRM” is the next phase in the evolution of big data, and will be increasingly leveraged by them to manage their competitive positioning.  The “Information” in the IT revolution will then become a reality. 

Analytical CRM can help a business increase revenues by helping design the right products; by identifying those customers most likely to buy a particular product/service, and getting their product to them before the competition acts; by increasing Customers engagement with their Brand by having them buy more of that Product or by cross-selling their other products.  It can also help them optimize expenses by managing advertisement, marketing and operational expenses in the most efficient manner.

Big data is yesterday’s news.  Now let’s leverage this data for business optimization.