If you ask a typical consumer what channel they prefer to shop in, you'll probably be met by a blank stare. Shoppers don't think in terms of 'channels'; they think in terms of price, convenience, products, availability, and service. They often don't really care about the channel they use, so the key for today's retailer is to provide them with a seamless experience no matter which channel (or combination of them) they use, says Paul Hancocks, value development consultant for Brickstream. This article is copyright 2014 The Best Customer Guide.

Consumers will happily shift between making online, mobile or in-store purchases depending on what their needs are at any given time - whether it's the ease of clicking and shipping holiday gifts from the comfort of their sofa, or the desire to touch and try a favourite department store's new collection of fall boots.

And while most retailers have a wealth of data at their fingertips about what's working and what isn't across their online and mobile efforts, accurate in-store insight is often lacking. This blind spot is a problem: according to a 2014 A.T. Kearney report, about 90% of consumer purchases still occur in brick-and-mortar stores. Emerging in-store analytics technologies for physical retail locations are changing this, but a well thought out strategy is needed to drive a successful deployment.

Looking to the marketing department?
A Brickstream survey of 124 global retail executives shows that businesses are looking to the marketing department to take the lead on in-store analytics efforts. This makes sense, as retailers of course want to understand how marketing and promotional strategies both outside of and within the store impact foot traffic, and ultimately, conversions and purchases.

With this in mind, here are three things that marketers should consider when developing a strategy for in-store analytics:

  1. Know what you want to measure
    Before embarking on an in-store analytics initiative, have an objective in mind: What do you want to measure, and why? Raw data is of limited use if it can't be transformed into actionable insight, so it's important to be as specific as possible about what questions need answers and what information needs to be captured to answer those questions accurately.

    For example, traffic data enables stores to measure the all-important sales conversion ratio, which is the percentage of shoppers visiting a store that make a purchase. This helps stores move beyond POS data to not only get a read on how many shoppers bought something, but also how many shoppers visited but didn't buy. But if your objective is to understand how a new department layout or promotional display might be impacting those conversions, more detailed data on shopper behaviour is required. For example: Which areas of your stores are shoppers visiting once they enter the four walls? Is your investment in a new department providing the consumer with a reason to visit? What is the conversion in an area to buyers? Are shoppers looking at promotional displays or passing them by? If they stop, do they just look or do they buy? Are they price checking (i.e. showrooming) on their mobile phones while shopping?

    In-store analytics technologies that use techniques such as path tracking and heat mapping can help marketers capture highly granular data about shopper actions taken throughout the store and correlate those actions to changes in product positioning, signage, promotional displays and other strategic initiatives. Using these technologies successfully requires careful thought about how data will be collected, where to position data capture devices and how much coverage is needed to collect the metrics needed. These decisions depend on first having a clear definition of what you want to measure.

  2. Accuracy matters
    There are a growing number of technology options available for retailers who want to collect and analyse data in the store - from basic infrared beam counters, to different levels of video analytics, to Wi-Fi and BLE proximity analytics and more. When choosing a solution, accuracy and scope should be top considerations. As stated above, data is only useful if it's actionable, but actions taken based on in-store analytics will be wrong if your data is incorrect or imprecise.

    For example, if traffic counts are off because your data capture technology cannot correctly identify single shoppers as well as shopping units (i.e., people shopping together, such as parents with children), then your sales conversion ratio will be too low. And a low conversion ratio may indicate that store performance is poor, when the reality is just that a high number of shopping units made purchases. (This might be the case during a back-to-school promotion, for instance.) Coverage counts too: using proximity analytics and Wi-Fi or BLE data alone to measure in-store behaviour only captures the actions of shoppers who have phones with the Wi-Fi signal turned on, leaving out a significant percentage of store visitors.

    Key questions to ask when evaluating in-store analytics solutions include the following: Can the technology distinguish between adults and children and between humans and inanimate objects like carts? Is it able to tell whether a shopper is exiting or entering a location? Can it deal with environmental factors that might obscure data such as low light, sunshine coming through store windows or dense crowds? Can it count only a portion of shoppers or can it capture data on every person that comes through the door? Accuracy is critical to transforming in-store data into truly valuable knowledge and insight.

  3. Have a growth plan
    While intelligence about how marketing and promotional activities impact shopper behaviour is a clear benefit of in-store analytics, there are a number of other functional areas within the retail enterprise that can take advantage of this technology to improve decision-making and performance. Brickstream's survey of global retailers also shows that operations, merchandising, and loss prevention are seen as key departments that will see value. For example, real-time in-store data can help operations more effectively manage staffing needs as store traffic ebbs and flows, and loss prevention can use behaviour insights to identify fraudulent activities at registers. In-store analytics can also be used to provide insights for merchandising on product placement and the performance of end caps.

    This indicates that retailers would be wise to consider the scalability and broad accessibility of their in-store technology investments. During a recent roundtable that Brickstream participated in with RetailWire and executives from national department stores, drug stores and big box chains, one participant talked about the importance of "freeing the data" by making it available and transparent to all levels, from the CEO to part time workers. This requires a platform approach to in-store analytics, with a focus on solutions that can integrate multiple sources of data (including multi-channel data), and leverage multiple types of emerging technologies, whether 3D video, Wi-Fi, BLE devices and more. In the long term, an analytics platform will be more efficient, cost effective and valuable than deploying a number of one-off, single-focus point solutions that can't be easily extended.

Assumptions about what factors are drawing people to stores and moving the needle on sales are just that. An ability to capture and analyse accurate data in stores will help marketers understand if their investments are truly paying off, and ultimately help optimize multi-channel promotional strategies.

In-store analytics present an opportunity to drive benefits beyond simple marketing; If the marketing department takes the lead, it is critical to consider how other stakeholders may leverage chosen solutions across the organisation. A coordinated effort that takes into account the needs of operations, merchandising and loss prevention will ensure greater long-term benefits and ROI.