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Demand Analytics and the Omni-Channel Customer Experience

By Jeff Bodenstab16 Jun 2016

While much has been written in the trade press about the omnichannel opportunity, somewhat less has been written about the potential issues from a buyer’s perspective.

This new shopping environment is empowering – name your product, name your price, you’re in control! But a complex omnichannel journey can also challenge consumers and present an obstacle to attaining customer satisfaction.  Consider:  More information, more product offerings, more fulfillment choices, and social networks with conflicting messages. Some customers may actually struggle to achieve a positive buying experience.

From a supply chain innovation perspective, what can be done?  One approach is to unlock the potential of the data in your ERP suite, that sits in your clouds, or is a few clicks away on the web. You can leverage this data to model your demand to better understand your customer’s behaviors and to determine the best way to react. At ToolsGroup, we call this “Predictive Marketing”.

The goal is to create a rule-based model that accurately predicts your customers’ next steps, and explains why he or she is doing it. It means modeling both your customer’s future actions (i.e., Predictive Analytics), and also insights on why they are acting (i.e., Prescriptive Analytics).

As you may recall from last week’s blogpredictive analytics extracts information from data to predict trends and behavior patterns. It focuses on what will happen, so supply chain planners can better understand the most likely future scenario and its business implications. Prescriptive analytics suggests the best course of action based on expected future scenarios. It relies both on advanced demand analytics and also rapid re-planning to quickly determine the best course of action.

An Example: Customer Behavior for Pet Food/Care Promotions

A large retailer was looking to understand the behavior of their customers who were buying pet food and pet care items. They especially wanted to improve the effectiveness of their promotions.

The retailer used machine learning to model customer behavior and perform customer segmentation, with segments such as high spending customers, frequent customers, and occasional customers. A data mining technique called “Hierarchical Basket Analysis” identified the relationship between product buying to identify the most successful combinations of offers (For instance, customers who purchased product A get promotional offers to purchase product B). With this approach, they increased their understanding of shadowing, cross-selling and cannibalization effects. They also employed a predictive analytics technique called “ROC curves” to model the response of customers to promotions. With this approach they were able to accurately rank promotions according customer receptiveness (how much more would the customer buy with respect to the non-promotional case?).

Machine learning is able to work with huge amounts of data. In this case, the platform was able to easily and quickly integrate, clean and process transactional data from 43 million sales receipts.

Knowing their customers’ actual behaviors helped the company cut budgets where they were less effective while improving performance. They were able to increase the effectiveness of their marketing and communications strategy by 20 percent. By increasing cross-selling 10 percent, they reduced shadow and cannibalization effects.

The customer experience is important. For buyers, getting it right increases customer loyalty and satisfaction and reduces “time to buy”. For the seller, it means reduced promotional and discount expense, more efficient marketing and selling, and higher advocacy rates. These all impact Customer Lifetime Value, brand awareness and corporate margins. Happy customers. Happy shareholders.

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