As Big Data and Internet of Things (IoT) technologies speared out nowadays, Online-to-Offline (O2O) will become the predominant business model. Then, what is O2O? It is a business strategy that drives potential customers from online channels to physical stores. O2O identifies customers’ insights from the online channels such as email and online advertising opt-in and then uses a variety of tools and approaches to drive the customer to the physical store for more and effective sales. This type of strategy incorporates techniques that have been used in online marketing within brick-and-mortar marketing. 64% of American adults now own a smartphone of some kind, up from 35% in the spring of 2011. This O2O integrated marketing strategy will become mobile-centric, not mobile-first anymore. Most of the cases, mobile users log on to their mobile devices all the time and are connected to things. So, collecting data of who, where, what, and how customers go and expense is possible.
Web banner replaced traditional offline advertising. Search engine created a new market for advertising. And, mobile is taking a chunk of advertising market with practical customer insights. Our question is how physical commerce can communicate with customers in the mobile market.
One of the successful cases stories of Nike is Nike Fuelband. From the everyday wearable devices, Nike can collect customers’ behavior data sources and use the data to gain insights into a various marketing strategy. Certainly, the data-driven marketing strategy will be more effective for their niche customer because the insights of loyal opt-in customers have higher chances to convert into sales. Disney also uses wristband as hotel room key and payment system. This unique and convenient technology improves user experience and increases satisfaction in Disney, and more importantly communicating and understanding customers.
Major developments in the IoT industry is using Beacon hardware together with mobile apps to conduct Big Data analyses. Beacon allows customers in the store connecting a smartphone to Wi-Fi access points. The industry is not completed adopted the technology, but with more beacon hardware being installed shortly, more customer insights will be gained. With current technology, we can understand customers of who, where, and how information. However, more important part is why. To explain why or so what factor, either small or big data, data is the key. The data contains insights of what customers are interested in.
It is good news for data scientists, but it will be still facing various challenges such as the lack of case studies. Although tech startups have to gain the trust of large retail business, there are still not micro-location systems being installed. World Wide Web allows people to search anywhere, and the smartphone makes that technology personnel. IoT and O2O will allow them connected and automated. In the future, data science techniques to create insightful metrics and KPI from the extracting data will play a very important role and will be the key to success.
Retail, it is the process of selling consumer goods and/or services to customers through multiple channels of distribution to earn a profit. Understanding potential customers’ insight is important for retail and quite challenging as well. So, retail analytics is important when it needs to discover customer insights from big data. A problem is not a lack of data available anymore. Data can be collected via various technologies. The challenge is methodology and retail analytics metrics to extract the insight from the extracted data. With big data analytics, retailers can correlate online to offline campaigns to sales. Just like what data analytics can contribute to the organization, the insights can be useful for operational cost reduction, increase customer satisfaction, or increase operational efficiency. The main reason why retailers are not taking full advantage of the data is that retail analytics has still been allocated to the IT division as a data warehousing and management issue instead of strategies around analytics. Although fixing data will take time and money, but strategies can be planned today. there are various metrics has been used these days. Here are a couple of retail analytics metrics.
| Analytics | Metric | Description | Question |
| Traffic | Traffic Out | Number of shoppers who walk out of a store | How many shoppers walk out of a store? |
| Unique Traffic | Number of Individual shoppers who enter the store | How many customers enter the store? | |
| Exposure Rate | % of shoppers who walk by a specific location compared to the total store traffic | What percentage of shoppers walk by a specific location? | |
| Pass-By Traffic | Number of individual shoppers who walk by the store including those who enter | How many shoppers walk by the store and enter? | |
| Unique Pass-By Traffic | Number of Individual shoppers who walk by the store | How many shoppers walk by the store? | |
| Capture Rate | % of people passing by a store who enter | What percentage of people pass by a warehouse and enter? | |
| Mobile Device Detection Rate | % of shoppers who enter a store with a detected mobile device | What percentage of registered customers have detected mobile device? | |
| Visit Duration | Average amount of time shoppers spend inside the store | How long shoppers spend inside the store? | |
| Engagement | Dwells | Number of shoppers in a zone for longer than a defined amount of time | How many shoppers in a zone staying longer than a defined amount of time? |
| Dwell Time | Average amount of time shoppers stand at a specified dwell zone | How long shoppers stand at a specified dwell zone? | |
| Engagement Rate | % of shoppers who walk by a location and stand at that location | What percentage of shoppers walk by a location and stand? | |
| Conversion | Conversion Rate | % of shoppers who enter the store and make a purchase | What percentage of shoppers enter and purchase? |
| Dwell Conversion Rate | % of shoppers who dwell in a zone and make a purchase | What percentage of shoppers dwell in a zone and purchase? | |
| Shopper Yield | Average sale amount per customer who entered the store | What are average sales amount per customer? | |
| Sales | Sales | Net sales for a specified period | What much are the sales? |
| Sales/Return/All Transactions | Total number of sales/returns/all transactions for a specified period | What are a total number of sales, returns, and all transactions for a specified period? | |
| Transaction Value | Average sales amount spent per transaction | What are average sales amount spent per transaction? | |
| Items Per Transaction | Average number of items purchased per transaction | What are an average number of items purchased per transaction? | |
| Sales per Unit | Net sales per square unit of space | What are net sales per square unit of space? | |
| Transaction per Unit | Total number of sales transactions per square unit of space | How many total sales transactions per square unit of space? | |
| Mobile Browsing | In-store Mobile Device Use | Identify the most frequently visited/searched website/ products/terms (opt-in only) | How many in-store mobile devices used and for what? |
| Demographics | Gender | % of shoppers who are male or female | What percentage of shoppers are male of female? |
| Age | Number of shoppers by age bracket (1-N) | How old are shoppers? | |
| Male/Female by Age | % of female/male shoppers within a specified age bracket (1-N) | What percentage of male or female shoppers by age? | |
| New/Repeat Visitor | % of shoppers who have not/have entered the store previously | What percentage of shoppers are either new or repeat visitors? | |
| Visitor Frequency | Average number of times a shopper enters the store in a specified time period | How often shoppers enter the store in a specified time period? | |
| Local/Non-Local | % of customers who are from the same/different city as the store | What percentage of shoppers are local customers? | |
| Domestic/International | % of customers who are from the same/different country as the store | What percentage of shoppers are domestic customers? | |
| Staffing | Labor Hours | Total number of hours worked by staff | How many hours staff work? |
| Traffic to Staff | Ratio of shoppers to employees on staff | What is a ratio of shoppers to employees? | |
| Staff Productivity | Sales per staff hour | How much are sales per staff hour? | |
| Staff Traffic In/Out | Number of employees who walk into/out of the store | How many employees walk in and out of the store? | |
| Queue | Average Queue Length | Average number of shoppers in the queue | How many shoppers in the queue? |
| Service Time | Average time/required for a shopper to pass through the service area | What are average service time? | |
| Lifestyle Snapshot | Cross-store Spending | % of other stores customers shopped before/after purchasing from the store | What percentage of cross-store shoppers before or after purchasing from the store? |