KFC China fast food restaurants accepted Alipay mobile payments since July 2015. McDonald’s China also adapted Alipay in September 2015. Consumers now can use unique Alipay QR code in their smartphones to pay for the orders, which transactions will take only two seconds or less. It will be more convenient and efficient for both customers and cashiers. All KFC restaurants in the country only accepted cash payments in the past 27 years. Though Alipay simplifies the payment process, yet the benefit of creating consumer behavior database has been the main reason to accelerate the cooperation of these two industry giants.
For retailers in food and beverage or FMCG industry, knowing consumer behavior and target customer segments for different trade zones is as important as improving product and service quality. Classify customers into different groups in terms of different needs and characteristics to optimize its products and marketing mix for store performance improvement.
Usually, brand owners could pull out consumer behavior data from CRM database. Yet it’s hard to attract people to sign up for the membership program without big incentives such as discounts or exclusive deals. Now collecting consumer data through mobile payment makes it easier. Using big data technology to build customer profiles, to forecast the spending potential from purchase orders. Specify demographics like age, gender and behaviors like product-purchase interests in local areas. Identify high potential customers regarding different trade zones to enhance sales promotion and market strategy planning for better store performance. Different target segments in different trade zones could also be a good reference for the new store location selection, sales forecast and lease negotiations.
Mobile payment not only provides superior customer shopping experience, but supports the demands of O2O commerce. Retailers could also use big data analysis to find out who are the most desirable customers, and then create personalized offers which they are likely to buy.
O2O is a very good concept for retailers to truly engage with customers. Whether consumers purchase online or offline, the buying journey is much more important for brand owners to profile customers and predict their next move. Retailers need to dig out who the customers are and what they want through bunches of data, then push the right messages that they wont’ignore.
Identify business problems and set up objectives
Nowadays data steam from consumer’s daily life: from browsers of websites, apps of mobiles and tablets, from credit cards and transactions of POS, and any other digital track from intelligence devices. Data collection is no longer as difficult as it was in old days. Before analyzing the data, marketers should be very clear about what’s the ultimate goal for pulling out so much data. Specify business problems the company try to solve, so that marketers can ask the right questions while looking at abundant numbers without getting lost.
Now we are talking about how data could be helpful in optimizing marketing campaign.
Find out most desirable customers and most valuable customers
Sometimes we will get into the trap of always finding new leads. Marketers held a lot of campaigns to acquire new customers. Yet, the point is not how big the customer base; the quality counts. Instead sleeping customers, people visit often and be willing to buy more expensive option are the main contribution and loyal members. Reevaluate your existing customers and use creative ways to activate them.
Customize messages to target more precise segment of audience
In addition to traditional broadcasting advertisement, create tailored messages to different target audiences. With the analysis of transaction data, CRM data, and market data, we can likely predict customer preference, not wild guess. Since we figure out what they probably like and their intent for next move, we can provide best offer for the right groups to maximize the campaign results.
Use location analysis to get actionable metrics
Consider “where” factor in customer behavior analysis. Ask questions like where the customer purchase the product, where they surf the internet, or where they use specific service/apps. Do some research on target customers and other key market drivers (key accessibility, business, lifestyle, residential, tourist points, competitors, ant etc.). Use demographics, business point data for market overview and better environment understanding. Visualize high potential trade zones that you haven’t taped in. Marketers can always know where to put ads, digitally or physically, in the clusters people will actually purchase.
Above all, brands need to embrace digital channel to grab each chance of engagement with customers. The more data collected from consumers, the more we can increase success rate of marketing activities.
Fig. Classification of assessment location explanatory variables
Previously we have talked about what elements to look at when evaluating a retail site in a macro perspective. (Click to read more) Now we will formulate a location strategy of prospective sites into micro analysis.
Micro problem is the other main component of assessing a site location. Retailers have always understood location as paramount, but understanding all aspects of store performance, site potential in addition to consumer behavior demands a great amount of information. This involves an understanding of the geographical, demographic, socioeconomic, and competition data in the area. The figure above suggests a possible classification of assessment location explanatory variables. In opposition to trade zone evaluation (demand and competition), the site and store variables intend to evaluate internal factors or the new store offerings.
The retail sector is going through a restructuring phase, driven by factors such as increasing consumer mobility, increasing electronic commerce, changing household size, concentration of market power, market saturation, and changes in planning legislation. These changes require retail groups to invest strongly in stores of smaller dimension while focusing on a strategy that prioritizes proximity to the target customer and quality of goods and services. This investment has a long – run as well as smaller economies of scale, which forces careful decision-making.
Crucial aspects for the success of smaller retail stores include location, dimension, services offered, and the targeting of specific market segments. Here we consider a phased decision methodology where we separate decisions about choosing geographic regions from choosing store locations within the region.
In the first phase, define the Macro Problem based on: strategic policy for a network, zone, or region where new stores will be installed; the number of units to be built; along with implementation timing.
In the second phase, make the final store-site choice by narrowing down a selection of available locations, usually with the help of real estate agencies. For this decision level, the number of commercial and academically published models suggests that researchers feel a need for rational and formal use of information.
The third decision level, concerning store and services design is the level most linked to the concept of service quality and customer satisfaction. Focus on models that take into account store location (level 2) and the physical design of the facility. Furthermore, some application can be extended in the store characteristics and services definition (level 3).
Tab. Decision levels involved in the expansion strategy of a retail network
China Resources Enterprise (CRE) has finalized acquisition with Tesco to create the largest food retailer in China by the end of May, 2014. The joint venture will combine Tesco’s 135 outlets in the country with CRE’s almost 1,000 stores, called Vanguard. Vanguard owns various business types such as hyper-marts, convenience stores, supermarkets and others. Among these outlets there are more than 250 hyper-marts. It is crucial for CRE to effectively carry out integration and store optimization plan after the acquisition.
To better execute store network optimization, brand owners need to fully understand market potential and penetration of targeted city. Reviewing performance of current stores and comparing actual performances against potential’s to get a clear picture.
Steps of SNO (Store Network Optimization)
Scorecard: to assess each branch and analyze competitor distribution
Channel optimization: overall planning of current stores (keep, improve, move or close)
Market expansion: site selection of new stores in terms of untapped high potential areas