Consumers may be duking it out for Nintendo Wiis in shopping aisles this year, but overall the battle for holiday spending dollars will be between the retailers. With consumers' awareness of a softening economy, retailers will be forced to into stiff competition for consumer dollars, according to the US National Retail Federation. Sahir Anand, an analyst at the research consultancy Aberdeen Group, says this competitive landscape makes business intelligence critical. It represents the work of turning the massive customer and transaction data warehouses into useful guidance on everything from how to attract and deliver a great customer experience to what merchandise to stock and in what balance.
Anand talked to CIO associate online editor Diann Daniel about what retailers should be doing with their business intelligence tools for this essential business season.
CIO: First off, do you think business intelligence is a must for today's retail companies?
Sahir Anand: I think if you want to improve customer retention and sales, in all channels-store, Web, catalogue-at some stage you'll have to adopt business intelligence tools because the transactions and data are so complex nowadays. Without business intelligence tools, it's going to be a lot of gut-feel decisions in terms of how you plan your merchandising strategy to fulfill customer demand. And that just doesn't work well.
Take the example of the holiday season. Business intelligence is important for any seasonal sales, but it's crucial for the high traffic, high-volume Christmas holiday season. Business intelligence combines data and shows patterns and trends to give customer insight, which can then be used to guide sales, marketing and other key areas. Most importantly, BI delivery tools provide the reports and dashboards that are required for retail performance management.
The important thing for any retail segment, especially for the specialty retail segments as they drive a bulk of the holiday sales, is to systematically identify the products your consumers want, and especially to identify new products they may want and make sure that you deliver those new products. In order to make sure you have no gaps between what you offer and what consumers want, you need to identify and forecast trends, and look at the demand pattern over time. Business intelligence integrates the complicated customer, location and product information and enables you to see what patterns are formed. This more effectively helps you plan for the holiday season.
Using business intelligence to establish a merchandise strategy must begin well in advance, at least a year. You need to start planning the best merchandise fit for customers during the key season by looking at comparative trends, internal transaction data and syndicated data, such as consumer demographics from companies such as Nielsen.
Avoiding overstock sales starts at the supply chain
CIO: Last year, many stores ended the holiday season with leftover stock and were forced to mark down a lot of merchandise. Why is getting that merchandising strategy right so difficult?
Anand: Retailers tend to focus on the front end at the expense of the back end. They effectively plan for how their stores, website and catalogue are going to look. But the back-end that involves reviewing and analyzing customer data for taking effective merchandising decisions is always an issue. It's a race against time in the case of several retailers as their data intelligence strategy is not in place or the response to BI is too slow (non-real-time).
Back-end focus is the key towards a successful sales season. Retailers must focus on the entire retail supply chain-product sourcing, procurement, the order-to-time cycle. They must identify which products will actually sell, and create an entire lifecycle management strategy right from product development to delivery nine to 12 months before a key selling season. And business intelligence plays a huge role in it, because it identifies the problem spots for the company in that entire lifecycle management. It also identifies opportunities to improve, in terms of the sourcing of the products, and actually mapping that sourcing with consumer trends.
Problem: Dirty, distributed data
CIO: What is the current state of the retail industry's use of business intelligence and what are some of the challenges?
Anand: Aberdeen research shows that 71 percent of retail companies are using business intelligence, primarily to manage customers, plan business strategy, and for merchandising. Still, only about half of companies are using BI for merchandising, so I think that trend has to improve. The enormous volumes that retailers have to plan and execute can be challenging.
The main challenge that companies face using business intelligence is that their data is not clean enough for analysis. Forty-nine percent of our survey respondents listed dirty data as a top challenge when initiating or implementing business intelligence or predictive analytics. Another associated challenge is that the data is scattered throughout the organization; it's not centralised. Coming up with good sales analysis, space planning analysis, assortment planning analysis, market basket analysis-all of which can help a company create a good merchandise stack for holiday season-requires integrated data. This is isn't possible without a centralised data strategy, and it isn't valuable if the data is dirty.
Data issues are a problem for a number of companies throughout manufacturing, hospitality, and the other service industries. It's not just retail companies that have this issue: Data at many companies is decentralised, it's fragmented in different locations.
CIO: How can organisations fix this problem?
Anand: First off, the mandate has to come from the top-level management, it must have top-level executive support. Top-level executives in companies that are doing business intelligence well-Walmart, Staples, Target and other top retailers-make clean data a priority.
Once a data centralisation strategy is in place, organizations can start with data cleansing projects to improve the data quality. It's a slow process but it's a crucial one. So for example, you eradicate duplicate data for location, products and customer data such as having one person's name in the system in different ways, such as Robert C. Parker and Bob Parker.
Customer information duplication is a especially a problem since the consumer is interacting with different faces of the organization-Web, store and so on-so information in captured in different ways.
The other thing is to limit the fields that you typically look at when you're analyzing customer data and product location data. There are so many fields that organizations have. Some of them are totally redundant. So you should have some core fields where you have the basic demographics and then you have customer transaction size and market basket size. You can then apply algorithms for cross-relational data analysis and do constructive business intelligence.
Take Best Buy, which does business intelligence very well. They used to have immense disparity of sources and fragmented reports. There was no centralised data repository for them, no clear path for clean information to be correlated for analysis and ad hoc reporting of department or business process performance were the norm. But they cleaned their data, integrated those disparate data sources, and built an excellent national scorecard that measures enterprise performance across different retail processes. They've seen effective results come out of it.
And the scorecard is just one example of successful BI usage by Best Buy. Another area where they've succeeded is looking at the marketing and overall company intelligence to guide how and what to sell to the various segments of their customer base.
Making predictions with business intelligence tools
CIO: Much of business intelligence looks at historic data. How do you predict the new items customers will want?
Anand: Forecasting the new items your customer will want is crucial for most retailers, especially for the holiday season.
The key is looking at the research on key new product adoption trends, and using predictive analytics to determine just what your customers will want. I can't overstress how important this is. When the Apple+iPod wave started, the companies that embraced it immediately saw success and had continued success because the customer got accustomed to seeing that particular merchandise at that particular store.
Companies that did not embrace it were the followers and they lost out. If those followers had looked at the competitive landscape and identified that in advance, they would've been able to fulfill customer need. Now customers will simply go elsewhere.
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