Tackling the Blind Spots in Omni-Channel Merchandising: Research from EKN and Sahir Anand – Part II

EKN, the research arm of Edgell Communications, conducts an annual survey of merchandising trends. Supply Chain Nation discussed this year’s study with Sahir Anand, vice president of research & principal analyst at EKN in anticipation of the August 19 webinar on this topic. In Part I of this series Anand covered key findings and merchandising strategies. Here, he looks at the importance of integration and analytics.

SCN: Your study says 72 percent of respondents cite the lack of integration between existing enterprise systems across channels is preventing them from executing their omni-channel merchandising strategies. Why is integrating planning and execution so critical for omni-channel success?

Anand:  According to our 2014 data, the biggest opportunity for improvement in retail lies in the execution piece. But integrated planning and execution is a challenge for seven out of ten companies. In looking at this lack of integration, the biggest gap is that product affinity is not integrated with merchandise management systems. Price elasticity data is not integrated fully with merchandising management systems. Companies are not balancing localization needs with common merchandising planning requirements across all stores, so it’s hit or miss. Also, for four out of ten retailers, the core order management piece is not integrated with the merchandise management system. It throws off any amount of scenario-based merchandise plans and execution strategies.

This also creates a mismatch between integrated merchandise planning and critical, timely, and agile allocation processes, especially as it relates to suppliers and lead times. This ultimately impacts the type of category-level sell-through, the ability to meet sales trends or the ability to meet demand at the sales level, and it also throws off a number of very specific advertising and promotion campaigns in-season and pre-season. That’s why the marketing and advertising teams, and the agencies that they work with, are not very happy with the reach frequency and conversion they’re getting out of their advertising campaigns.

At the highest level, you don’t have the planning teams working hand-in-glove in very coordinated or collaborative ways with the pricing, promotions, or marketing teams, as well as with space optimization and the overall strategy around reducing markdowns. There is no in-depth integration with the different sources of data, which includes product affinity, price elasticity, sourcing data, and order management data. That’s critical because that shows how supply chain-responsive and customer-responsive your organization is.

The integration challenge has happened because of architectures being extremely complex, having a wide variety of disparate applications. Systems are bought over the years and people aren’t really concentrating on integration because you had people who were working on allocation, people working on pricing and promotions and people who were looking at top-down and bottom-up planning. You had different teams looking at different sides of merchandising, which is a problem.

There are ways to integrate. They include the use of standard space techniques that use SOA architectures to integrate applications at a composite level. You can use XML and other web-based techniques that integrate the various silos, functions and screens that different groups can see. For example, your product design and development team should be able to see what your requirements are, and the planning team should know what is in the pipeline for sourcing of new products that are going to be available for the plan for six months from now, depending on the average lifecycle.

These can be transformative projects. Although this may require a change in platform if your platform is burnt out, it may not require a whole lot of investment. You just need a checks-and-balances process and a complete business process engineered around the retail environment. Those are some ideas that companies can adopt to align planning and execution in more effective ways. That addresses the omni-channel challenge in many ways, too.

SCN: Why is analytics becoming more important to merchandise planning? What are the benefits?

Anand: In 2013 it was a highly mature merchandising environment from the perspective that companies invested in merchandising reporting data insights much before they invested in micro-assortment planning, top-down optimization, or pricing optimization, for example. So while the maturity and the importance is very high, the retailers that we surveyed in 2014— especially the ones that are doing $500 million-plus in revenue—are the people who are telling us that they’d much rather focus on improving the maturity of analytics because their teams are not satisfied with the way the reports are driving action.

I’ll give you a good example. If you have a field strength category performance analysis, or if you have a market-basket analysis, or a general margin uplift, or your top 100 hot seller analysis, all of that is great. That helps you build a level of intelligence. But where it becomes counterintuitive, and even more helpful for retailers, is when you have the ability to predict consumer behavior. That’s where it gets to be interesting, when you can integrate product affinity data into what the consumer is going to buy next week or the following week, or better understand back-to-school or a holiday season based on past-year analysis and predictive analytics.

What’s also important is the ability to integrate price-elasticity data. Elasticity of demand has certainly been one of the biggest challenges in retailing. Price elasticity of demand is something that no retailer can get a good handle on because of the lack of demand forecast accuracy and the ability to address that demand in a timely manner. Integrating price-elasticity data, understanding where the elasticity of demand is and positioning merchandise accordingly for preseason and in-season planning, as well as integrating upstream product-level data from a sourcing and order management standpoint, is crucial.

There are two sets of capabilities required. One is the customer-centric data that looks at cognitive customer behavior and the ability to predict behavior by channel from an omni-channel customer’s vantage point. The other is product-level data, which is going beyond your standard sales trends, margin uplift, and cannibalization to more domestic or international product sourcing data so teams can plan in advance. They need the ability to look at what new product is going to make a difference in the marketplace and have a way to cross-compare based on trends.

They also need access to order management data so they know what is constraining the supply chain, or what is the strength of the supply chain that can assist their merchandise planning. If you’ve got a product that has a lead time which is in excess of eight to ten weeks, you want to plan based on all the attributes and customer data you looked at. If you need something within six weeks, obviously the order-management data tells you that you’re not in a good position to address that demand. Those are the major focus areas that retailers can apply in order to improve analytics in the coming weeks and months.

SCN: Thanks, Sahir, for all of your helpful insights.

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