Quick Wins for Your Analytics Engine

Joe Skorupa
Editor at Large
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By Joe Skorupa
What happens when one of your best customers reaches gold status in your loyalty program? Does your analytics engine recognize the milestone, automatically push a coupon to her preferred channel, and automatically enter her into your Facebook gold community? It could and should. Here’s what you need to know to rev up your customer analytics engine for all-channel commerce.
Good things happen when your customer analytics engine delivers reports in near-real time, incorporates omni-channel data, and extends the flow of data to the tools used by key line-of-business departments. Some of these ‘good things’ include sales growth, ROI, competitive differentiation, and customer satisfaction. But your analytics engine will cough and sputter unless you design and build it for quick wins and rapid adoption by power users.
Analytics software is the most important tool in retailing today for driving hard, measurable performance gains, but it is also the most complex. Plus it is only effective when used by well trained and guided executives. For an in-depth Thought Leadership report on this topic titled "Rethinking Customer Analytics for the Age of All-Channel Commerce" click here.
Avoid the Ready- Fire-Aim Syndrome
Customer analytics tools can be deployed in weeks and positive results (even ROI) can happen within months, but only if careful planning takes place at the foundational level. Typically, this will occur with the aid of an expert BI consultant, a choice that is highly recommended. Few retailers have cutting-edge analytics experts on staff and fewer still have use them to train line-of-business executives to tap into the new world of advanced BI possibilities.
Start with a strategy that is aligned with your individual organization’s core mission and business goals. Then you create a blueprint beginning with your current analytical capabilities and then, step by step, flow all the way out to the horizon line of objectives you want to achieve.
Quick wins along the way can come from leveraging current infrastructure and capabilities while simultaneously redefining database criteria and attributes that will provide deeper insight into your customer segments. The key is to organize your data at the earliest stage so it is relevant for specific departments and their ecosystems.
Another important step along the way is to determine who owns the data. For example, it may not be the marketing group that should be in charge of aligning data from such new channels as social media, especially if the goal is to ensure data and reports are useful for merchandise planning or store operations.
When organizing data it is important to make sure the end result is so accurate and actionable that it can be embedded into automatic workflows. For example, when working at the market basket level it is important the system recognizes a basket driver item and automatically suggests a higher-margin equivalent to substitute. This higher-margin item can be promoted to customers who have purchased the driver item in the past. In this way, a retailer can plan market basket sales with a layer of customer data and deploy promotions to increase demand for higher-margin items.
Cross-Channel Analytics at the Operational Level
Many retailers are still trying to master a single view of the customer. When complete the next step is to tightly couple inventory and customer activities across all  channels into a single record. This is important because the shopper journey may start online, continue in the store, and finalize with a purchase on a smartphone or tablet. Or it could happen in any combination of these or other channels. As a result, retailers need to be able to look at shopper behaviors across all channels as well as within them.
To be able to do this it all goes back to the beginning of the project at the item level master and how the data is defined, tagged and structured. If you haven’t baked everything you need into the cake right from the start, you won’t get the results you are expecting when it comes out of the oven.

Related Story:

Rethinking Customer Analytics for the Age of All-Channel Commerce


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