<iframe src="//www.googletagmanager.com/ns.html?id=GTM-KJ4LCR" height="0" width="0" style="display:none;visibility:hidden"></iframe>
digitaslbi logo

Back to School Season is Signal-Harvesting Season

DigitasLBi

Brett Leary

Back to School Season is Signal-Harvesting Season

We’re nearing the end of yet another major shopping season in Back to School (B2S) and two things are clear. 1) Shoppers are clearly comfortable moving between physical and digital shopping experiences and 2) Retail and brand marketers have an amazing opportunity to gain a more holistic understanding of their customers by learning from the data signals these shoppers generate.

The Age of Convergence

Various B2S and general commerce-focused studies support the notion that today’s digital-savvy shoppers are continuing to converge their digital and physical shopping activities. Deloitte’s recent Back to School survey points to expectations that in-store spending will double that of online this year ($288 in-store vs $103 online, on average). The survey also highlighted that more than two-thirds of shoppers expect online and physical channels to complement each other. The NRF’s 2017 B2S study pointed out that 54% of parents who shop online would use buy online-pickup in store. These findings correlate with general commerce studies that highlight rising cross-channel shopping behaviors and heavy digital influence regardless of channel.

Need more evidence? Look at Amazon’s realization that online domination is not enough anymore. The company has recently kickstarted a number of initiatives that merge their digital prowess with physical experiences. See Whole Foods, Amazon Instant Pick Up on select college campuses, the Amazon Go Concept store and Amazon bookstores. (I predict that next B2S season we will see pencils, glue sticks and Nike sneakers sold in Whole Foods.)

Your Shoppers: Same, But Different

Cost, convenience and value have long been the primary driving forces behind shopping behaviors and this doesn’t appear to be changing any time in the foreseeable future.

What has changed are shopper’s expectations. Today’s shoppers have and therefore expect real-time access to information and the ability to instantly purchase just about any item imaginable. They also expect to have access to a buy button wherever they are, and they want their goods as fast as possible - all while incurring little to no shipping costs.

If a customer’s expectations can be met by visiting the local store versus browsing Amazon they will take that path, or vice versa. Today’s path to purchase will always vary and smart brands and retailers that best understand and anticipate the customer’s decision-making processes will win.

Worlds Collide - New Signals Are Released

As shoppers go down their various purchase paths, they bounce back and forth across physical and digital channels, giving off both passive and explicit signals - lots of them. These signals come primarily via their mobile phones, but also increasingly via their wearables, voice assistants, cars and interactions with in-store tech.

The available signals are numerous and can reveal such things as intent: take, for example, a customer searching for shoes on the Target app. This search can also reveal the shopper’s surrounding context: they’ve been dwelling for 5 minutes in the shoe department at the Target located on Main Street. The signals can even reveal the shopper’s emotion: they appear happy when looking at the in-store digital shoe display.

The challenge that many multi-channel retailers and brands face is threefold. 1) The identification of what key signals their customers are making available to them. 2) How to collect these signals, and 3) How to interpret and act upon the various signals when they are mashed together.

Retailers and Brands - Think "Sensor Fusion"

The term “sensor fusion” refers to the aggregation of data from several different sensors, which helps provide a more accurate representation of something than could be determined by any one sensor. For example, your smartphone can combine data from its embedded GPS, Bluetooth, Wi-Fi, barometer and magnetometer sensors to help determine your location within a multi-story department store.

Retail and brand marketers can apply this same concept to create a more holistic picture of their customers as they interact with their digital and physical ecosystems. To get started with harvesting of these signals, companies need to take an inventory of what data capturing sensors they have in place today, and what signals they’re missing. Here are some tips on this audit process:

  • Map your customer journeys. A thorough map across your customer’s online and physical worlds will help identify gaps where data collecting sensors could be applied. Sensors can be any customer touchpoint e.g. the brand’s mobile site, or a passive data collector, like a geofence around the store parking lot that shares data with the retailer’s app.
  • Retailers should leverage a service-level design mapping approach. It can help surface data collection opportunities via customer service touchpoints and physical store elements such as video cameras.
  • Use a cross-functional team to work on the mappings. The combined expertise of user experience pros, marketers, technologists, store operations and data scientists works best. A cross-functional mindset will streamline the necessary fusion of sensors, digital experiences, technical systems and operational requirements.

Making Sense of it all Via The Context Graph

Once a “sensor network” is in place across your online and digital realm, the final step towards a clearer customer profile is to make sense of the aggregate data.

A sound approach is to begin looking for patterns in the relationships between the customer signals. These aggregate relationships make up what we call The Context Graph. Every customer has a unique graph; the purpose of the graph is to help identify which pattern of contextual cues can be tapped to best influence an individual’s shopping behaviors. Understanding a shopper’s context graph will not only help reveal a more accurate customer profile, but also provide insights on how, when and what to deliver to that person in moments that matter most. 

Machines Are Key

To help unlock these types of insights, marketers will rely on advances in machine learning technology. The technology’s combined ability to recognize patterns of intent in a sea of contextual data and predict behavioral outcomes make it the right tool, but companies first need to harvest the right fuel i.e. the data to feed it.

To Recap:

  1. Your customers are channel agnostic, your organization should be, too
  2. Map your customer journeys across digital and physical worlds
  3. Use a cross-functional team to plan your sensor-network
  4. Leverage The Context Graph to help make sense of your customer’s signals
  5. Utilize machine learning to find actionable patterns in your customer data

To learn more about back to school and back to college shopping trends, download DigitasLBi’s infographic here: http://bit.ly/BTS-Trends 

Brett Leary

Brett Leary

SVP, Commerce Innovation Lead

Brett is the North American lead for Commerce Innovation at DigitasLBi. He provides strategic counsel and thought leadership on Commerce trends, tactics and technologies across the U.S. client base. He's also the creator and managing director of DigitasLBi'​s CONVERGENCE Showcase - an experiential lab featuring the latest mobile solutions influencing today's path to purchase from emerging mobile media, in-store experiences and mobile payments. Brett was the 2016 winner of the Publicis90 entrepreneurship award.

UP NEXT

Native Advertising 101: The Importance of Keeping Brands Honest