10 Key Ways Data Analytics Can Help Advance True Omnichannel - Part 1

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I recently co-wrote a paper “True Omnichannel: Aligning Marketing and Merchandising through Analytics” with RetailWire, a retail industry think group. The premise of the paper is that while customers don’t differentiate between various channels, retailers struggle to provide a consistent omnichannel experience due to lack of alignment between the “buy” side and the “sell side activities.

In this paper, we highlighted 10 key ways analytics can help retailers synchronize their marketing and merchandising efforts for “true” omnichannel excellence. I am sharing five of those 10 ways in part one of this blog.

1. Get one comprehensive, cross-channel view of the data: “True” omnichannel requires visibility across customer, inventory and order systems data – so marketing and merchandising can align their demand generation and fulfillment efforts. Easy-to-use analytics can help business analysts combine all relevant data across disparate customer databases, with cross-channel order management and inventory systems data --without long; arduous IT projects -- for one view of the customer, product availability, location, and order status.

2. Personalize messaging and promotions: Success of omnichannel marketing depends on reaching customers with relevant messages and promotions through the media and channels of their preference. Data analytics can help marketing analysts blend cross-channel customer data and segment customers based on their browsing behavior, purchase history, path-to-purchase and social media preferences so they can align promotional messages and delivery channels for better campaign response.

3. Target like customers to expand the customer base: Research shows that customers who buy from multiple channels tend to spend 3-3.5 times the amount of customers buying from just a single channel. Analyzing customer data can help isolate the key attributes/identifiers of these cross-channel customers. Once profiles of this choice customer group are established — typical browsing habits, paths to purchase, income, life-stage, etc. —retail marketers can use attribute modeling to identify and target the potential best new customers.

4. Optimize the media mix: On one hand, retailers want to reach customers with the right promotions and right messages through channels of their preference. On the other hand, the marketing budgets continue to stay flat, or worse shrink. Understanding customers' path-to-purchase, social group affinities, and lifestyle variables and more, can help retailers gain insights into their preferred channels of communication. Marketers can then use that information to align channel/media buys with target segments.

5. Align product promotions and assortment plans via affinity analysis: Analyzing customer past transactional history, path-to-purchase and inventory movement data can help retailers better understand customer-channel and product-channel affinities. They can then use this information to shape demand, promote the right products through the right channels, and optimize channel specific assortments. The results are reduced stock-outs and markdowns, lower inventory expediting or transportation costs between channels, and improved margins.

You can read the next five of the 10 ways analytics can help you be omnichannel ready in my blog next week or download the paper now for the whole list.

Let me know where you are in your omnichannel readiness, and if you would like to learn more about our multi-channel analytical capabilities.