Month: November 2015

The Missing Signal

My Circuit City store in Silverdale, WA being demolished to make room for a Trader Joe's
My Circuit City store in Silverdale, WA being demolished to make room for a Trader Joe’s

When I took my first Sales Manager job with Circuit City at a store in Silverdale, Washington, I was made aware of a report available to store managers that I hadn’t previously seen. I learned the true reason that employees were forced to use the side entrance: to avoid skewing the close rate. On either side of the main entrance were two small devices that counted any time the beam was broken. This kept a count of roughly how many people entered the store during business hours, and was compared to sales transactions to calculate a “close rate” and metrics such as average spend across all visitors. We were held accountable to the metrics that used it, regardless of our level of trust in the data. 

Counting the number of potential customers walking through the door, and comparing it to transaction data, is an interesting exercise in trying to divine what went through the minds of those who passed through the door but failed to buy anything. In Circuit City’s case, they wanted to understand if stores were capitalizing on the opportunities generated by local marketing efforts to generate store traffic. If a store only “closed” 15% of customers who entered, but their district averaged 19% and the total company averaged 21%, there would be some tough questions. 

Retailers still track customer traffic, but the sources of non-purchase signals have continued to grow over time. We now have web traffic, email views, Tweets, Likes, and even Bluetooth and WiFi tracking using in-store beacons. All of these technologies generate data that is a demand signal, but not typically considered part of the demand signal. The reports reviewed daily within retailers are dominated by summarizations of transactions. As mentioned above, some analysis is done on traffic data, but it’s rarely incorporated into the data that drives decisions on a daily basis. In this regard, eCommerce is slightly different. Instead of “close rate”, conversion rate is used. The number of views, add-to-carts, and purchases are typically published with similar importance to sales data. This helps merchants better understand if products are resonating with customers that view them, and if they’re getting the views they need to begin with.

Despite this incorporation of additional elements of the demand signal, something is missing. The typical data requirements for forecasting, demand planning, and optimization systems used by merchants includes transactional data only. This isn’t to say that these additional demand signals are entirely ignored by science-driven tools, it’s just that those tools are mostly targeted specifically toward optimizing specific elements of eCommerce visual merchandising such as page layout, design, images, and direct marketing—elements that drive engagement and improved user experience on the storefront, rather than the larger world of optimization solutions available for price, promotion, assortment, and inventory. 

This provides an opportunity to improve merchant and marketer tooling by finding novel uses for the rest of the demand signal. Knowing what a customer viewed on the storefront before they made the purchase is valuable. It can tell you things about affinity and cannibalization that are hard to derive from sales data lone. Understanding the journey a customer took across social media, your storefront, and app that finally led to an in-store purchase can help the merchant better manage multi-channel pricing and promotions. These data can bolster models that sometimes strain to find meaning in sales data, and provide powerful insights that address more than customers and extend to prospects.

What are some examples of these missing demand signals? The most readily available for retailers with an eCommerce storefront is the browse path taken by the customer prior to purchase. A customer may perform a search on Google for a type of product and follow a link to the storefront. From there they may click on a few recommended products, or perhaps use the site’s search functionality. Eventually, hopefully, they add a product to their cart and complete the purchase. Today, that transaction tells us one thing: Product X was purchased for price N. What else could we learn from this?

Why didn’t the customer purchase the product they found through Google search? Why didn’t they buy a product that appeared in the recommendations, and instead had to use the site’s search functionality? Was the product ultimately purchased substitutable or comparable with the one viewed initially? What search term was used on Google to find the first product, and what search term was used on-site to eventually find the purchased product?

Considering data generated through direct email marketing, coupon redemption, social media, third party shopping portals, and much more, the world of underutilized demand signals begins to look like an endless buffet of potential insights and discoveries.

All Retail is eCommerce

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A merchant at a large omni-channel retailer sits and reviews the prior week’s performance on a Monday morning. Sales are broken out by department, then channel. She notices that the ‘WEB’ channel is about 20% of her department’s total revenue for the week, at a much lower margin than the in-store channel. But even then, the in-store channel’s margin is lower than usual, a trend that has persisted for some time now. The average selling price is coming in substantially below plan.

One floor down and on the other side of the building, a web marketer reviews competitive information scraped from a number of competitors earlier in the day, and sorts through the direct email marketing those same competitors sent out that morning. He consults a spreadsheet containing the currently active web promotions and decides to send out a department-wide special offer to the email list.

eCommerce retailing was born of the rapid advancement of the internet as personal computers began to commoditize in the 90s. First-movers saw this as a new channel to both conduct commerce and identify and target new customers. The web became a channel of sales, just like telephone and catalog sales had been accompanying (and in some cases pre-dating) the in-store channel for most of what could be considered modern retailing. But as internet connectivity reached saturation, and ‘the web’ became something users have access to anywhere at any time, does the definition of eCommerce as just a sales channel hold up?

In our opening scenario there are a few factors at work. The merchant is viewing sales through the website as distinctly different than the stores, and is puzzled at the margin erosion experienced in store. The web marketer is hyper-aware of what competitors are doing online, and has the authority to create promotions targeted to web shoppers. It’s a safe assumption that those promotional offers are causing the in-store margin erosion. We know that customers purchasing in either channel frequently utilize multiple channels as part of their journey to purchase, so it makes sense that marketing efforts in one channel will impact the performance in another. We also know that almost half of customers use their smartphones to enhance their shopping experience while in-store, meaning that they are literally shopping in two channels at once.

Is eCommerce just a channel? No. It’s much more. eCommerce is the new retail. Having a paper price tag on the shelf is no longer an excuse for not being competitive. Having different merchant groups managing your storefront is no longer an excuse for an isolated, disconnected cross-channel shopping experience. Retailers have lamented the growth of retailing online as promoting behaviors such as ‘showrooming’, going so far as to work with manufacturers to introduce confusing model variations to make research and price comparisons more difficult. This has come at the expense of the customer experience, one of the few things ‘B&M’ retailers claim sets them apart.

Simply reconciling your sales channels is insufficient. This approach still considers them to actually be independent channels. Instead, retailers must find ways to integrate the different shopping experiences they offer to their customers. Sales channels become sales tools, and suddenly the customer experience means something again.

How can retailers embrace this? In my previous post, “Put a ‘Buy’ Button on That!”, I discussed the new shopping model being introduced by social media platforms and search providers, and how we might be best equipped to help them make the most of it. Could ‘Buyable Pins’ be considered another sales channel? Is it really an entirely different sales model? Perhaps it’s best to consider it simply another part of one large channel, ‘commerce’, and put it on the board along with store, web, and phone as methods for a retailer to sell a product.

Take what we used to consider channels and put them on the board as equals. Now we have in-store, web, mobile, platform-enabled (‘Buyable Pins’), telephone, and catalog as the major selling tools. Let’s also consider these to not just be selling tools, but also buying tools. While retailers leverage each of those to drive sales, users leverage them to discover, learn, and make purchases. Knowing that our customer is using many of those tools, frequently at the same time, means a merchant should consider tactics that leverage this fact.

And with that, the walls between the-tools-formally-known-as-channels come down. Instead of defining each by the things that make them different, retailers should embrace the fact that customers consider them the same. This point has not gone unnoticed in the market. Apple, and others, are promoting in-store beacons that allow systems to identify customers when they enter the store, and even locate them within the store itself, while powerful analytics comb through information about the customer to figure out the best way to improve their experience and close the sale.

The proliferation of Electronic Shelf Labels (ESLs) in other countries (due to a combination of tough consumer protection and environmental/labor concerns) has begun to blur the lines between web and in-store further. Retailers are looking for capabilities that squeeze the most out of these systems by changing prices mid-day to remain competitive, or capitalize on intra-day demand patterns.

Much as rapid innovation and availability of technology created the world of online retailing, so too will it further enable the retailer to blur the lines between in-store and online. These technologies will be met with high demand, and along with that demand, software and systems to help retailers make the most of these new opportunities.

Across the web one can find bits and pieces of capabilities and concepts starting to form, but not yet coming together. Retailers aren’t sure what to ask for and software providers are ramping up development based on an extension of today’s capabilities, tactics, and methods. The puck is still moving, and today it’s passing by intra-day in-store price changes and beacon-enabled targeted mobile offers for in-store visitors. Where will it be tomorrow?