Getting to the bottom of what makes us tick.

Understanding consumer behaviour is key to creating successful and targeted marketing campaigns. In its broadest sense, consumer behaviour analysts seek to understand what drives shoppers to purchase, use, and dispose of goods and services. Initially, the field of study was rooted in ‘utility’ theory which held that consumers make purchasing decisions based on perceived outcomes. If a consumer truly believes that a product will make them happier, healthier, or more successful, they will be inclined to purchase the product. Companies focused on influencing consumers through advertisements which connected a product with a desired outcome. Researchers and marketing departments could measure the success of advertising campaigns through focus group discussions and telephone surveys. The data retrieved via these methods was highly qualitative, but neither quantitative nor comprehensive.

The Little Albert study proved that consumers could be conditioned.

Early consumer behavioural studies were rooted in the discipline of psychology. Trials were invasive, unethical, and somewhat damaging to the test subjects. One case from the 1920s involved a toddler nicknamed ‘Little Albert’ who was trained to associate terrifying noises with the sight of benign objects. By the end of the test, the small child had developed a deep rooted fear of mice, Santa Clause, and many of his wooden toys. This study equipped researchers with the knowledge that humans could be trained. It proved that marketing departments could influence consumer attitude towards products by creating positive product associations. Throughout the 40s and 50s, companies experimented with advertising campaigns and gauged success by sales and surveys. However, understanding product associations was just one cog in the machine of consumer behaviour. With the advent of big data, the way companies assess themselves and the consumer market have changed utterly.



Modern Consumers, Data-driven Algorithms

Technology has made it possible to collect a vast amount of data on consumer motivations and shopping habits. No consumer phone poll or focus group can compete with the ability of mobile applications and sensor data to collect real-time consumer behavioural data while guests are browsing products online and in-store. Modern consumer behaviour studies bring together the fields of psychology, sociology, anthropology, ethnography, marketing, and economics with deep analytics. Complex algorithms pull raw data from physical sensors and digital web trackers to paint a complete picture of consumer demographics. This data collection may seem off-putting but the data collected via these methods is anonymous aside from voluntary information given by consumers through mobile applications and web subscriptions.

Bluetooth Low Energy Data Sharing

Real-Time Consumer Behaviour Analytics

For brick-and-mortar stores, the simplest way to gather ethical data is to make use of location and proximity technology. By ‘ethical’, I mean that the data in question is gathered from customers who opt-in to permissions and requests for a store’s application to use their mobile phone’s location engine. By location and proximity technology, I mean sensing systems which are able to interact with the Bluetooth in consumer’s mobile phones. These sensing systems which detect Bluetooth involve hardware called beacons. Why are stores relying on Bluetooth instead of GPS for location tracking? Simply put, GPS does not work inside of buildings. Bluetooth is really the best bet for indoor location tracking. Lately, the capabilities of Bluetooth have been doubling as new protocols are rolled out by Bluetooth SIG (Special Interest Group).

 Marketing with Bluetooth LE

Bluetooth Proximity Sensing: Real-Time Analytics

Bluetooth Beacons are unique in their ability to broadcast URLs to nearby smart-phones which contain product information, coupons, and marketing messages. Beacons may be configured to broadcast over small areas or over large areas. This allows targeted marketing. For example, when a consumer stands in front of a make-up display, their mobile will pick up the broadcasted URL from a nearby beacon which can encourage the consumer to purchase a product through a special digital coupon. These coupons may require registration which opens up Bluetooth Real-Time Location Systems to gain specific, voluntary consumer data.

There are many methods by which beacons can detect customer location in-store. If the signal between two or more beacons is disrupted, the disruption may be due to a person passing between the beacons. This method of proximity detection is called trilateration and provides anonymous foot traffic data. Other stores have opted to outfit shopping carts with beacons which create real-time maps of customer movement throughout a store. This location data can inform the placement of marketing materials, floor layouts, and bottlenecks. One of the best parts of using beacons for location data is the anonymous nature of the data collection. Beacons are just small radio-transmitting devices and are incapable of gathering information from consumer’s mobile phones. All they are able to do is broadcast a URL which can prompt an in-application action or a Nearby Notification. They are able to sense if someone is nearby, make a guess as to their proximal distance and nothing more.

Reaching customers in the right place at the right time with product information can dispel doubt and drive sales.

Same Goal, Better Tools

Many stores, whether they rely on smart data or not, already lay out stores using consumer behaviour studies. By placing the most popular products at the back of stores, consumers are encouraged to browse through other products which encourages impulse buys. Store applications upgrade the data available to marketing managers. Digital receipts stored in-application associate the consumer, by an anonymous # ID, to specific purchases. If you have ever used Amazon, you’ll understand the power of companies being able to suggest purchases based on customer histories. Applications may be able to suggest recipes based on the grocery items purchased, or remind customers of items which they may have forgotten to buy through algorithmic pairing of commonly purchased together items. Proximity technology works towards the same goals, but gives managers better tools to make smart decisions.

It isn’t just the stores which benefit – the key to successful data gathering and analysis is to provide improved customer services in exchange for data. Applications which gather data achieve this amicable trade off through customer loyalty programs, special offers, and improved access to product information and professional advice. Some applications and their integrated in-store hardware are so useful that shoppers are happy to opt-in and connect. While providing wayfinding services, digital flyers, daily menus, store hours, product information, and suggested products, the application collects data on shopper movements and dwell times

