It is a widely known fact that the key to business success relies on a happy and satisfied end-user. For the retail sector, customers are key to business outcomes. A way to ensure retailers are meeting the needs of shoppers is by understanding them and their behaviour.
One way to be on the front foot is through in-store data analytics. Not only do data and analytics provide insight into the customers entering a store, smarter and more informed business decisions can also be made.
Analysing customer behaviour
In order to stand out from the crowd and attract customers, retailers must know and understand customer behaviour – determined by acknowledging and understanding: who the customers are, why do they visit a store, what do they purchase, where are they visiting from, what made them enter the store and how do they go about making a purchase.
In-store analytics can help retailers collect information to find out the answers to these key questions. Once store owners gather data and insights, they can leverage the information available to evolve in-store and business-wide strategies to better satisfy and attract customers.
Developing a better understanding of customer behaviour
One way retailers can gain a better understanding of the diverse types of customers visiting a store is by utilising technology and pre-existing infrastructures. BLE beacon networks, door-to-people counters, video sources, web and social platforms, for example, allow retailers to not only the analyse behaviour of anonymous shoppers, but also gain deeper insights about the audience and shopping behaviours of those who opt-in to free internet connectivity.
As consumers connect to guest Wi-Fi in store, retailers are able to gain insight into invaluable data, such as the age, gender, postcode, likes and dislikes of consumers. Similarly, social media analytics and online activity can also be gathered to provide retailers, with a correlation between in store visitation to social sentiment, offering retailers with powerful insight into the success businesses have in creating awareness, appeal and value to consumers.
Equally as important as the data that is gathered, is the way it is presented. Having a dashboard that is comprehensive and represents information in an intuitive way will help retailers identify any trends in the market, and keep a pulse on the industry. This way, they are able to proactively and positively react to customer habits and gain a competitive advantage.
Improving the customer experience
Once store owners have a technology solution in place that can capture and present data in a way that is easy to digest, the insights presented can help retailers implement a bespoke shopping experience, reflective of their customer behaviour. Not only will retailers be more in tune with their shoppers and their behaviour, major pain points associated with shopping in store can be improved. Extended wait times, long queues, differing prices or out of stock items can be a source of frustration that can lead consumers out of store and result in a loss of sale.
Now that retailers understand the shoppers entering their stores, they can create smarter, more intuitive and more forward thinking shopping spaces. Through in store data analytics, patterns can be identified when traffic data is gathered so retailers are able to optimise the layout of a store. Retailers can experiment with different displays, signs and campaigns and identify which strategies are the most effective in attracting potential customers.
This not only helps with their bottom line and business management, but it provides consumers with efficient, enjoyable and personal experiences.
Retailers can further extend their tailored offering, by delivering data driven campaigns based on customer behaviour through targeted promotional activity. Utilising a range of channels and media formats including email, SMS, mobile notifications, video interstitials, OOH and Wi-Fi, store owners can send customers relevant promotions based on real-time data of their shopping habits and dwell time.
Retailers of any size can benefit from in store data analytics. A shopping centre for example, has utilised data insights to identify what sections of the precinct receives the most foot traffic. After identifying that the food court was the most visited area, and had the largest number of dwell time, with most consumers entering the centre just to visit that particular zone, the centre decided to move the food court to a different location, so that retail stores could benefit from the number of consumers entering.
Similarly, on a smaller scale, if a fashion store within a shopping centre explores the gathered consumer data and identifies its store receives a large volume of consumers that form a demographic slightly older than their target market, they can look to adapt their product range to accommodate a wider range of tastes and preferences, and/or update their marketing and promotional strategy to realign and meet their intended audience.
Once retailers piece together a genuine understanding of their customers using in store analytics, and have a comprehensive understanding of those that are visiting their store, retailers will ultimately attract and retain customers as the in-store experience is specifically customised for them. By doing so, retailers will be able to predict, prepare and provide outstanding service to consumers, benefitting the bottom line and brand reputation.
The customer-centric retail pricing zone
July 12, 2012
The customer-centric retail pricing zone
By Adrian Sosa
As an analytics professional, my perspective on shopping is different from other consumers. For instance, I think I'm paying too little for the breakfast cereal I buy from my primary grocer. The store is losing margin, because it's concentrating too much on market competition in its immediate area instead of paying attention to how best customers like me go about their shopping.
Every large-scale retailer has a framework that dictates how it sets pricing in different stores, and a big retailer might have hundreds of "pricing zones." Those zones are likely driven by competitive price shopping, making the framework very market-centric versus consumer-centric. Geography often drives how most retail pricing zones are defined.
With customer-specific data, it's possible to escape that market-centric pricing system. The new opportunity is to create pricing zones based on data analysis of customer purchase patterns in order to maximize profitability — even in the face of stiff price competition.
To better understand the nature of customer-centric pricing zones, picture a customer who prefers a premium brand of ground coffee both at home and at the office. She may buy that coffee at your store near her home, at your store near the office or at a larger store frequented during bigger shopping trips. Her shopping zone doesn't radiate in concentric circles from her home. Instead, it weaves across multiple traditional zones, where prices may vary. Therefore, she might find two different prices for that same pound of coffee.
Those visible price differences represent a series of missed opportunities that can be addressed by aligning pricing based upon the shopping behavior of your customers — particularly your best customers. Identifying the stores that don't belong in the same customer-centric zones with one another and assigning them to more appropriate pricing zones offers a number of advantages:
The ability to optimize pricing. In some cases, you may be pricing items and entire categories too low, losing margin. In others, you may be pricing them too high, losing customers. For example, I live in the suburbs, but I shop in the city. The store in which I shop in the city prices its items to compete with nearby stores. This makes the breakfast cereal I purchase cheaper at the city store as opposed to the same item at stores near my home. If my grocer's marketing team were to assign my store in the city to a compatible zone with higher prices, they could improve margin and sales volume. And, in fact, optimizing price zones in this way allows retailers to alleviate pressures to compete only on the low prices offered by competitors.
Avoiding customer confusion and disconnects. Customers crossing geographic price zones might notice the price difference with the same product. Encountering the higher of the two prices they're aware of, those customers may leave that store and you lose not only a single-item sale, but also an entire trip. Those customers might also divert their shopping to the cheaper location, returning less margin and perhaps less frequency. They may even be lured to the big-box retailer across the street seeking better deals.
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Optimized ad zones. The same philosophy applies to promotions, sales and advertising. Customer-centric pricing zones allow you to maintain consistency of the message and offer. Promotional fliers, for example, are basically weekly price changes. Mapping customer pricing zones allows you to calibrate your ad zones to optimize pricing and reduce potential disconnects.
More efficient testing of price elasticity. To understand the elasticity of prices, you must test actual price changes. Interpreting results of such elasticity tests can take time, particularly when testing high-ticket, seasonal or low-frequency-purchase items.
Grouping stores that top shoppers are most likely to visit allows you to accelerate earnings. When changing the price of occasional or seasonal purchases, you present multiple opportunities for customers to see the price change. It also can result in cleaner tests. If you test prices across a customer-centric zone rather than a geographic zone, the chances of customers crossing from the control stores into the test stores — and vice versa — is greatly reduced.
First steps in building customer pricing zones
In order to optimize pricing zones around customer-shopping patterns, first isolate both your top shoppers based on sales and your top customers based on profitability.
Once that is done, each shopper can be assigned a "home store," based on frequency and recency of visits. If there's a tie, recency will dictate the home store. Then, for each home store, you can run analytics to identify which of the other stores in the current pricing zone those best customers shop most consistently and most inconsistently. Such a view enables you to target inefficient geographic pricing zones for one of two levels of action:
Realign the outliers. When you find a small number of stores with cross-shop patterns that are out of line with most of the other stores in their pricing zone, move those stores to another zone. This allows you to align pricing with a more compatible customer-centric zone.
Rebuild zones entirely. If a zone features a number of outliers, consider employing clustering techniques to build a new set of zones from the ground up. A zone might be split into two zones, or more, depending on how much variance you see.
Despite the many advantages of customer-centric pricing zones, many companies seem almost allergic to this type of nontraditional analysis, in many cases because they believe that it's complex and difficult. With today's technology, it's easy, and will get easier.
By overlooking specific customer shopping patterns across your retail network, you're leaving millions of dollars on the table — right next to my underpriced box of breakfast cereal.
It should be no surprise that consumers shop and dine at places that are convenient for them. Why travel across town to pick up groceries or grab a quick bite when you can just go to the one less than 5 minutes away from your current location? As marketers already know, convenience trumps many other factors — such as price sensitivity and brand loyalty — in determining purchase decisions.
What has eluded marketers, however, is how to reach these convenience shoppers most efficiently. DMA or zip-level targeting are basic tactics to try to reach those who live by a location of interest. However, they are limited in two critical ways:
DMA and/or zip-levels can span vast areas, including rural regions. These rural regions include people who are not conveniently located to a place of interest, which results in wasted impressions.
DMA and/or zip-levels tend to be solely based off where people live, not factoring in where they work. Realistically, “convenience” is determined by home location, work location and the journey between the two.
Fortunately, we’ve built Zones of Convenience Audiences that address both of these limitations. By honing in on a more precise area of coverage based on how far consumers work or live from a location of interest, marketers can understand the ‘zone’ in which the respective audience spends the majority of their time, to be able to reach them more efficiently. The audiences are constructed by selecting all home and work locations that fall within a certain radius of a retail store or other location of interest. By limiting the area of interest to about one half mile, targeting tactics become precise — specific to those whose home and/or workplace is most accessible to that store — thereby conserving impressions.¹
Convenience: The gift that keeps on giving
There is enormous flexibility in terms of how these audiences can be leveraged. First off, these audiences can be generated to include franchise locations with different distances that can be used for comparative analysis. For instance, depending on how closely a consumer lives to a particular QSR site, marketers can allocate campaign spend accordingly — placing more impressions for those who live one mile from the QSR location, and less impressions for those living five miles away.
It’s easy to see how convenience influences where a consumer shops, but it goes even further than that. On-the-go dining locations such as fast casual and quick service restaurants also benefit from a convenience zone (think frenzied commuter picking up a late family dinner on their drive home). In addition to shopping and dining, convenience might also help determine where people spend their free time. For example, movie theaters might want to reach potential theatre goers with discounted movie tickets if they live nearby or in a specific neighborhood.
It is also possible to conquest your competitor’s customers by identifying those people who live in areas where there are similarly-distanced options. Let’s say there are two big box brands in a given neighborhood. This is prime marketing battleground for homes situated within three miles from both stores. Given both stores are equally convenient for those shoppers to get to, a minor incentive (like a coupon or sales promotion) could get shoppers into your store as opposed to your competitor’s.
What’s more, Zones of Convenience Audiences can be used in combination with other off-the-shelf PlaceIQ audiences. For example:
Recent Movers can be layered with Battleground Audiences — those who live within three miles of two competing big box stores. Families who have recently moved or relocated are still familiarizing themselves with the local amenities and developing their new routines and brand preferences. This is the perfect time for a brand to engage with these potential new customers before they define their zone of convenience.
Lapsed Shoppers — those who live close to a certain retail site but haven’t visited recently — represent a tremendous opportunity for that retailer to grow its customer base. Send a promotion to remind consumers that you appreciate their business and regain loyalty.
Consumers are always looking for a quick and easy way to get things done. Zones of Convenience Audiences help brands remind consumers that their stores fit seamlessly into the daily grind. Why go out of your way, when there is an easy solution right in front of you? Show consumers how your brand can be logically integrated into their lives.
What Are Technologies to Track People?
To track people, the technologies quantify customers and staff activities in Physical Store. They measure STAY TIME in a LOCATION.
Common tracking technologies include Video, WiFi and Beacons. With People Tracking, we do Location Marketing, Location Analytics, Product Positioning, and In-Store Customer’s Journey.
People Tracking Path Analysis
Why Care About Tracking People
Ask what is the biggest challenge in retail, and the answer is seamless analytics. People Tracking Technologies offer the ability to manage the physical store with data.
Tracking people is the core of Location Analytics, InStore Analytics, and Behavior Analytics. Whatever term you may use, the solution tracks the person by location, and per period of time.
Here are common applications:
Location Analytics: we measure customer engagement in zones and displays.
People Counting: we detect the number of people entering and exiting the store.
Queue Management: we predict how many cashiers should be active to prevent queues.
Workforce Analytics: we optimize the schedule.
InStore Analytics: we cover the Customer’s Journey in physical stores.
Regardless of providers, the criteria to choosing a tracking solution is the business value. The benefits depend on the customers and staff behaviors you are trying to change.
10 Ideas to InStore Optimization
Tracking versus People Counting
People Tracking includes the detection, recognition, and tracking of objects. The solution may also include machine learning and advanced analytics.
People Counting. The term refers to detection tech that “counts” people. The technologies include Time of Flight, Infrared Beams, Thermal Imaging, and Video Analytics.
The data output is In/Out counts. The common deployment is door-counters, and other “crossing the line” scenarios. And the primary business benefit is the calculation of Sales Conversion.
Image Recognition. There are different levels of imaging technologies from facial recognition to detecting heads. Besides people tracking, we deploy the tech in driverless cars and Facebook Ads.
People Tracking. Tracking refers to objects in motion. Once we detected an object, and recognized it as a “person”, the next step is tracking. The focus is on measuring the path of the person.
Tracking demands Good Enough Accuracy. Besides over-counting and under-counting, we have switching and precision errors. This is why tracking data often comes from wireless devices instead of sensors. It’s easier to track individuals by their smartphone signals.
Location Analytics refers to both people counting and tracking technologies. While Location Analytics today is attributed to Mall Analyticsand Sales Conversion, we are seeing a wider reach in applications. The term implies the solution provider offers both sensor and device-based tracking for a complete analysis of the store (see below).
ABI Research estimates that People Counting will transform to $3Billion market by 2018. Location-Based Services will grow above $62 Billion.
Sensor vs. Device-Based Solutions
Sensors include video, thermal, and laser technologies. In wireless tech such as WiFi, GPS and BLE, we track the device. Sensors and Device-Based are often complementary solutions. And each technology has its own challenges and benefits. In sensors, we care about accuracy. The wireless technologies are distinct by their range.
The differences are not only in technology. BLE Beacons primary goal is to send push notifications (Location Marketing). WiFi tracks the device across great distances. We use mobile application to calibrate tracking with Magnetic Resonance. The 3D Video sensors manage complex frontline queues. And Vision Analytics is a core technology for driverless cars.
There is also the factor of data integration. The connectivity between various store systems, in real time, is complex. Moreover, each technology has its own concepts of consistency and validation.
Note on Analytics: Two important factors to remember. First, the data from device-based tracking is a sample of individual behaviors. Thus metrics such as conversion rate requires statistics. And second, predictive analytics is important in tracking. Thus the expertise in advanced analytics is a factor in tracking solutions.
Good Enough Accuracy is the key to assessing the business value of a tracking technology.
Master Projects in People Tracking
Tracking Technologies
The market for people tracking changes as fast as these words are written. We offer no preference for a technology or a solution provider. In 2015, we covered 7 technologies. In 2017, we have 12.
Below is a brief summary of selected people tracking technologies.
1. Vision Analytics
Vision Analytics works by recognizing patterns in images. The Deep Learning AI software translates the images to data, context, and action. The output is not the image, but the description of the picture.
The technology claim to fame was when the Standford Vision Lab program recognized a cat. The technology is a core component in driverless cars and deep-learning imaging. With startups such as Modcam and Seematics, Vision is an emerging technology for retail’s InStore Analytics.
2. 3D Stereo Video Analytics
Stereo sensors are designed for accuracy. They combine high-resolution camera and processor for three-dimensional view of the object. The empiric data on height, mass, speed, and direction, enhances accuracy of the count. Since sunlight and shadows do not have depth, the technology filter them out of the counts.
This architecture allows for tracking objects, over a period of time. It allows for accuracy in high traffic and for complex behaviors. 3D Video Sensors are the preferred choice for frontline queue management.
Leading solution providers include Brickstream, Xovis, Hella, and ShopperTrak (Tyco Retail).
3. Monocular Video Analytics
Monocular sensors capture images through a single lens camera. The sensor process the image and the output is the data counts.
In door-counting, monocular devices achieve 90% accuracy in 90% of the stores. The challenge of monocular devices is their treatment of depth. The real-time images are compared to a baseline picture. And thus shadows and light impair the counting. Some solutions compare and “fix” the data to a trend, which happens during the upload to the central server.
Since single lens trackers are accurate for most door-counting, they are wide spread. We can find video analytics in smart cameras from Axis to Panasonic.
4. Thermal Imaging
Thermal Imaging detects emissions from moving objects. Since thermal technology is not sensitive to light, it can function in any physical space.
The accuracy challenge is the “blending” of a person’s heat signature for standing in the same place. In most situations, thermal sensors achieve 95% accuracy rates. And they are easy to install and calibrate.
Thermal sensors are versatile and wide spread. The global leader provider, Irisys, claims over 400,000 sensors installed.
5. Infrared Beams
Infrared Beams count when a person crosses the doorway and “cuts” the beam. The pros are low cost and simplicity. Because the sensors can be mounted in gateways, they are widely deployed .
The cons is accuracy. The sensors cannot recognize the direction of motion. They also have trouble differentiating between one or more people. Moreover, the system over-counts and under-counts with no data consistency. Thus the data is not recommended by professional data analysts.
6. Time of Flight
Time of Flight detects the time of light between the camera and the object. By sending the laser beams to many directions, the sensor knows the exact positioning of objects.
The laser sensors are accurate and cost effective. The expertise in laser will allow companies such as BEA Helma to embed people counting directly into door sensors.
Kinect is also a sensor that detects people in motion. The video camera, depth detector, and multi-array microphone generate a three-dimensional image of objects within the field of view. The camera also detects body-type and facial features. Kinect can distinguish objects’ depth within 1 centimeter and their height and width within 3 mm.
7. Structured Light
Structured Light projects a known pattern on a scene. The array of lights strike the surface allowing the tracker to calculate the depth and the surface of the object. People Tracking comes from 3D Scanners.
Apple, Amazon, and Orbbec are among the companies to deploy Structured Light.
8. Raspberry Pi Tracking
Open Source Raspberry Pi can be adapted as a motion tracker. This is a low cost solution, but one challenged with support and accuracy.
9. WiFi Location Analytics
WiFi is a standard for exchanging data over a Wireless Local Area Network (WLAN). The antennas capture radio waves from mobile phones, and can cover a range of up to 100,000 square feet.
Since the MAC Address is unique per device, the system tracks a customer from entry to exiting the store. It can even track people beyond the store for Proximity Traffic.
The dependency on the customer’s activation of WiFi limits the data output to a sample out of the total population. But the tech is ideal for unstructured motion and large venues such as airports and stadiums.
WiFi suffers from the challenges of location accuracy. It depends on Cellular Tower Triangulation, ranging from one meter to half-mile. And by nature, the data output is a sample of behaviors. But the low costs and ease of setup, make WiFi Tracking an attractive proposition.
Leading solution providers include Euclid Analytics and Cisco Meraki.
10. Bluetooth Low Energy Beacons
Beacons are transmitters of Bluetooth Low Energy (BLE) radio waves signals. BLE functions in the range between NFC and GPS. The devices work indoors, which makes it ideal for communicating with customers.
The BLE Beacons allow the customer’s application to find if it is close (in proximity) to a specific location such as display or aisle. Beacons are the favorite tech for Location Marketing.
BLE technology faces three challenges: First, the layers of security require a series of opt-in. Second, due to privacy concerns many customers shy the retailer’s application. And third, WiFi/GPS technologies are improving fast to work indoors.
The beacons ecosystem includes the Google Eddystone and Apple iBeaconplatforms. It also includes Location Marketing such as Swirl and InMarket.
11. GPS Location Analytics
The people tracking comes from the Global Positioning System, a network of orbiting satellites. Global Positioning receivers are now built in the Apple and Android operating platforms.
The best known application of Location Analytics is Google Store Visits. The data is an outcome of Location Marketing. By 2017, Google Ads reached 5 Billion location-based advertising. Using Geo-Marketing to drive footfall traffic to the physical store is a key trend per Mary Meeker Internet’s Trends.
12. 3D Spatial Learning (Augmented Reality)
Augmented Reality will probably be the technology of Retail Future. Remember Pokemon Go. Then came Amazon Go. But first was Google’s Project Tango. You can see the future in Gap’s foray into AR Dressing Rooms.
Augmented Reality uses computer vision to enable mobile devices to detect their position relative to the world around them without using GPS, WiFi or other external signal. In essence, vision technology emulates our ability to manipulate three-dimensional objects.
10 Ideas to InStore Optimization
Location Analytics
Location Analytics refers to the ability to gain business insights from knowing where people are. The solution providers offer a wide variety of people tracking technologies, solutions, and services. The big companies such as ShopperTrak (Tyco Retail), RetailNext, and Ipsos Retail have global reach.
Vibrant regional companies include Vizualized in Hong Kong, Headcount Systems from Canada, Savant Systems in Dubai, and Intelligenxia in Chile.
In addition, some providers offer Location Marketing. Since marketing agencies are data-oriented, they push to infuse analytics in the store. This led to partnerships between agencies and vendors. It also encouraged the rise of marketing/data savvy retail executives.
Big software companies such as IBM, Microsoft, and Intel are also interested in the physical store. They are creating massive ecosystems of end points and advanced analytics. As a result, Location Analytics is being embedded into the Internet of Things.
Bringing It All Together
Tracking people offers retailers the ability to manage the physical store with actual data. With behavior-based seamless analytics, we can optimize each step in the InStore Funnel. This is the secret to fast growth, and profitability.
room boutique shops to large department stores, ShopperTrak aggregates powerful sets of data from any device into one, centralized location so you can make sense of what is happening within your retail category, market, and store, and why within seconds. With a customized platform, you can make more informed marketing, operations, staffing and deployment decisions. Using analytics as a service, make sense of big data and:
• View Traffic, Conversion, and Sales performance, and overlay with Visitor Behavior metrics
• Understand trends with heat maps, charts and graphs
• Export and schedule automated reports to your teams
Retail Analytics
Always know exactly what your customers want to buy
Capture, manage, analyze and centrally provision customer data
In-store retail analytics tells retailers how many customers are in their physical outlets, for how long, and what they are interested in
Out-of-store retail analytics delivers insights into customer footfall and sources, into the success of advertising campaigns, and into competitors
Monitoring of all key metrics and attractive value-added services at the point of sale (POS)
Contact
Dirk Rumler
Dirk Rumler
Head of Center of Excellence Retail
Write an e-mail
Retail analytics for actionable insights into customer needs
How many customers can I expect in my store, and when? How long will they stay? What route will they take? What products do interest them most? Did they make a purchase, or go elsewhere? If so, who did they buy the product from? All retailers ask themselves these and similar questions. Knowing the right answers – and drawing the right conclusions – can make all the difference between success and failure.
Smart data analysis with retail analytics
A tool for intelligent analysis helps you understand consumers, identify their preferences, and accompany them, profitably, along the entire customer journey. With retail analytics you gain the visibility into behavior in physical stores that was previously only available for online transactions. You can even track customers outside your bricks-and-mortar outlet, with the help of Motionlogic. This solution lets you monitor and analyze people traffic on your doorstep. And the moment they step across the threshold, you can leverage in-store analytics. These powerful applications deliver anonymized data that indicates precisely how shoppers behave and move within your bricks-and-mortar shop, and suggests where and when face-to-face interaction would have the greatest impact. Consumer analytics lets you track and evaluate buying decisions and potential barriers to purchase with the same ease as you would with online platforms.
Use Case
Digitization of consulting and sales processes through Shoplet, the digital sales assistant.
The entire analog consulting and sales process is digitized with Shoplet.
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Infographic
In fiercely competitive markets, retail analytics is potentially a make-or-break tool
Out-of-store analytics gives you unprecedented visibility into the anonymized behavior of people on the move
Out-of-store analytics gives you unprecedented visibility into the anonymized behavior of people on the move
Retail analytics: greater transparency for better decision-making
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Clearly, business success depends on making smart decisions. This axiom applies as much to retail as it does to all other industries. And the best way of making the right choices is to have the right facts and figures. But reliable statistics are not always easy to get hold of. So, retailers often rely on reports, expert opinions, experience – and gut instinct. Which is very human. Yet often wrong. After all, studies have shown that the businesses that base decisions on hard facts are the most profitable.
Big data gives you the facts
Retail analytics opens up entirely new digital possibilities. Just consider this: 65 percent of all Germans aged 14 or older use a smartphone. In the 14-to-29 age bracket, that figure soars to 89 percent – as a recent survey by leading German ICT industry association Bitkom has established. The data generated by these mobile devices can be harvested and evaluated, creating genuine transparency that outperforms experience and gut instinct every time. The key is new technologies that allow the analysis of unstructured data on a massive scale (big data).
Cell-phone data as a source of actionable info
Nowadays, people are rarely separated from their smartphones. And this creates a simple, low-cost way of tracking their movements – as activated phones regularly log onto their nearest network cell, indicating the owner’s approximate position. In accordance with Germany’s strict data privacy legislation, T-Systems processes this information – in anonymized form – in its own, certified high-security data centers in Germany. This guarantees compliance with both German and European data protection requirements, and means bullet-proof protection of individuals’ privacy. Yet customer analysis still delivers important, actionable insights. In particular, they provide three key metrics:
Traffic flows: How many people are in a given position in a given time period? How many people pass a given point?
Time data capture: How long do people spend where?
Movement vectors: What groups of people move from where to where?
And while safeguarding privacy, it is possible to add attributes such as target group, gender and even zip code. This greatly enriches the information – for even better decision-making.
Tracking of movements as the basis for in-store and out-of-store analysis
When retailers can monitor customer traffic, including flows, timing, and even the stops they make, they can draw extremely valuable conclusions. Retail analytics tools deliver meaningful insights. Motionlogic, a T-Systems solution, captures and analyzes movements, helping bricks-and-mortar retail to better understand the routes people take, and why. Traffic patterns can be correlated with specific triggers to identify particularly attractive positions and destinations. This offers traditional retailers tangible benefits: in real time, they can see when customers come to their shops, and in what numbers, and fine-tune their opening times accordingly. And they can precisely gauge whether advertising, such as billboards, has the desired effect of drawing people in through the front door. Plus they can detect customers who leave their store without making a purchase, and to which rival they subsequently turn – a highly useful, but low-cost form of competitor analysis. Moreover, when choosing a new store location, the retailer will know where the footfall is highest.
Monitoring in-store customer flows also makes good business sense. Within a known, relatively confined space, this can be performed by other means than anonymized cell-phone data. It is possible to leverage WLAN, near-field communication (NFC), cameras, motion detectors and Bluetooth to determine the number of people and their movements. In some instances, these methods require the consent of the individuals being observed (opt-in). What can a store manager do with this technology? They gain excellent visibility into customer volume, broken down by zone and dwell time. Traffic flows are clearly visualized, with an intuitive heat map showing aisles with the largest number of stop-and-stare shoppers. What’s more, they can pinpoint departments that are being overlooked, and reconfigure the store layout to optimize traffic flows and improve visitor numbers. They can identify the best place for promotional displays and advertising boards – and precisely measure their impact. Analysis of customer traffic patterns through the various departments is also an extremely good basis for effective staff roster planning.
Many retail analytics systems generate useful information. But the right combination, and their seamless interaction, are all-important. With this in mind, retailers should partner with proven integration specialists in order to implement the technology that best meets their real-world imperatives.
ConnectedPOS: all sales channels combined
A digitized bricks-and-mortar store enjoys an infrastructure with completely new interfaces for in-store communications, such as ConnectedPOS. This is an Internet-based platform from Wirecard, a T-Systems partner. It allows the capture and analysis of data – and is ideal for retailers who wish to improve the customer experience at the point of sale (POS), and forge connections with the online world. By means of a small device placed between printer and cash register (or software integrated into the register itself), the cash register is connected to a cloud-based platform that allows real-time access to shopping data. This enables retailers to gear their online, offline and mobile offerings to customer wants and needs. ConnectedPOS creates a seamless link between shopping at the physical POS and the online consumer world. And there is no need for changes to the existing cash register system. This quick, simple multichannel solution guarantees a future-proof infrastructure in bricks-and-mortar shops – that allows additional value-added features based on retail analytics, such as couponing and loyalty bonus programs, plus mobile payments.
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