Serial studies of Future of Consumer Brand-driven ... · Serial studies of "Future of Consumer" Brand-driven Strategic solution on marketplace planning optimization enabled by big-data

Serial studies of "Future of Consumer" Brand-driven Strategic solution on marketplace planning optimization enabled by big-data December 2019

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Challenges in marketplace planning of retail brands under the new retail trend

Advanced guide for optimizing brand-led marketplace planning driven by big data

Cases of marketplace planning optimization driven by big data

Conclusion

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Serial studies of "Future of Consumer" | Brand-driven Strategic solution on marketplace planning optimization enabled by big-data

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Challenges in marketplace planning of retail brands under the new retail trend

In the context of consumption upgrading, stores online and offline in retail sector are interacting with each other and integrating at an accelerated pace to promote performance. From the perspective of consumers, their consumption habits and preferences are changing. Consumers’ demand is shifting to product of higher quality, multiple options and personalization features; In addition, consumers also care more

about their shopping experience in the process of consumption. Compared to the virtual world on the Internet, physical stores have advantages in services and shopping experience. Therefore, physical stores have been coming back under the trend of new retail and consumption upgrading. For retail enterprises, the integration of the channel resources online and offline should be the best solution to developing their own brands, for it can

not only efficiently solve the problem of limited product category and quantity, but also allow stores online and offline complement each other with theirown advantages, leading to better shopping services for consumers. In the coming years, optimizing the overall marketplace planning by using online data will be a new trend for brands to develop offline.

Figure 1: Evolution of consumers’ demand

Traditional demand New demand

The retail enterprises of consumer goods tend to face three issues and challenges in marketplace optimization as follows:

Challenge one: Myth of the relationship between online and offline business.On the one hand, the digitalized physical stores show an upward trend in growth rate while that of online stores is gradually slowing down. In order to seize market share in retail sector, brands show an increasing demand for quality physical stores;

On the other hand, with the fading dividend on the Internet, e-commerce platforms have to pay more to acquire customers, which will be indirectly shifted to brands. For example, an e-commerce platform co-works with MAU* to frequently organize various marketing activities online to maintain its growth rate, which will in turn burden the operating costs of the brands at the platform.But the business online and offline of an enterprise should develop in a cooperative and co-existing model. It

can be seen from the development of the retail industry that stores online cannot replace physical stores. In fact, experience consumption offline is more helpful for brands to win consumers’ mindset under the trend of consumption upgrading and new retail, and more and more brands have recognized and emphasized the real value of shopping experience offline.

Products • Price • Function • Quality • …

Products • Higher cost/performance ratio • Better quality • Standardization and personalization

Services

Services + Experience • Personalized services • Seamless experience • Efficient and convenient • Interactive experience • Value identity

MAU: monthly active users


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Challenge two: Lack of brand initiative under distributor model.For a long time, brands, representing by clothing and foods, usually took the partner model. Partner are usually the guide for a brand to launch in a city and a business district. Therefore, in planning the pace of opening new stores and store operation, brands are also used to rely on partner as their guide. Brands are highly dependent on partner due to insufficient information and other reasons. Such cooperation model led to long-term inadequacy of data and information about physical stores at the part of brand, resulting in poor initiative and weak control in marketplace planning. In addition, undertaking marketplace planning under the guide of partner also caused mismatch between what the stores can offer and what customers really need and mismatch of resources for stores. between the existing marketplace planning of a brand and the ideal

market capacity in the coming years. Such phenome on occurs not only in individual cities and brands, but also in most brands operating in the partner model. Take one of the brands served by Deloitte as an example. The brand established more physical stores in a provincial capital city in China with lower consumer demand and steady growth rate than that of a first-tier city with higher consumer demand and greater growth rate.

Challenge three: Lack of systematic data analysis owned by brand.Under the trend of new retail, brand owners have not really thought their “main business battlefield” over. It is mainly demonstrated by the obscure and inconsistent views of the management and functional departments of brand owners about the order of the importance and future potential of property, trade zone and targeted cities. According

to Deloitte’s extensive understanding about the consumer goods sector, most brands still choose to follow and catch up with the head brands in the industry when planning their marketing strategy for their new stores. They all follow or try to surpass their rivals in terms of product lines, marketplace planning in key trade zone and revenue target. Only a few brand owners have started to cooperate with third-party property management agencies and data service institutions and try to have their own voice in physical stores offline. Unfortunately, brand owners still hardly have a clear picture about their future development planning from the perspective of a systematic and integrated analysis by using data due to the limitation of traditional third-party data companies in their data capacity.

Figure 2: Cost of acquiring and retaining users at major e-commerce platformsUnit: RMB yuan

Figure 3:Traditional marketplace planning strategy of brands

Data source: public data; Deloitte research* Calculation formula: The ratio of current marketing expenses to current active users

Follower/Chasing strategyFollowing the leading players in multiple fields and

chasing rivals

Following store location Strategic collaboration with property groups

Following categoryAccess data through 3rd-party agencies

Following sub-brand

Strategic cooperation with third parties (some leading players)

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JD.COM ALIBABA PINDUODUO.COM VIP.COM

2016 2017 2018

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Advanced guide for optimizing brand-led marketplace planning driven by big data

Advanced guide for optimizing brand-led marketplace planning driven by big data is designed to help brands achieve their key transformation goals and optimization benefits. Shifting from reactive to the market to taking the initiative in terms of keeping pace with the industry trend; Shifting from a partner-driven model to brand-driven model in terms of voice in marketplace planning; Shifting from opportunistic in marketplace planning to under the guidance of marketing strategy; Shifting from experience-based model to data-based model in terms of marketplace planning optimization; Shifting from manually conducted to implementation with the support of tools/systems in terms of carrying out solutions.

We would like to recommend brand owners to implement their optimization planning of marketplace in six steps with the support of big data:

From … To…

Reactive

Transformation strategy for current fleet

Determining the main battlefield at city level level

Determining the main battlefield at trade zone level

Determine property entry plan for new door opening

Proactive

Experience-based Data-based

Partner-driven Brand-driven

Manually conducted

1. Existing fleet productivity analysis and trasformation strategy

3. Demand based city prioritization

5. Big data-driven trade zone prioritization

Tool/system-supported

Opportunistic Strategic

Growth ambition alignment

Growth scenario decision

6. Forward looking automated property recommendation

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Step 1: To assess status quo of online and offline brand retail efficiency.Status quo assessment is to focus on key issues and the room for improvement and to classify stores and give recommendation from the perspective of improving the efficiency of individual stores. This step will not only be helpful for brand owner to evaluate the actual situation of its existing physical stores offline, but also to help brand owner to make decisions of keeping or closing its existing physical stores offline and plan for the future development of such stores.

Firstly, we can divide the existing physical stores offline in various dimensions, including the annual yield of a single store, relative annual yield of a single store, growth rate of annual yield of a single store, and the level of the city in which the physical store is located. At the same time, each store will be paired with a rival store, and the relative annual yield of the store will be determined by weight formula, so as to examine the relative performance of the store. Secondly, the aging status of the store will be assessed in the store category above mentioned, and different measures will be chosen for use. The store implementing such measures will be assigned with an expected growth rate corresponding to the average level of store growth rate of the certain city in which the store is located. At the same time, the

growth rate and the expected sales scale of the existing stores will be used as the base for calculating the number of new stores opened in the future corresponding to the level of the targeted city and trade zone.

Step 2: To help brand owner to fix direction of growth vision and targeted growth rangeThere will be a competitive relationship between online and offline businesses due to the limited online and offline resources allocated by company. Step 2: To do our best to help brand owner to fix a clear development vision in its online and offline business scale. Firstly, to understand the brand owner’s target of its overall business and that of its offline business scale based on its development strategic objective; Meanwhile, to determine the growth range to the direction of business growth by comparing the growth rate of the brand and that of the industry and by comparing the growth rate of the brand and that of its rivals. Enterprises can formulate their targeted business growth range in the following two ways:1. To organize workshops and

exchange views with leadership and stakeholders Achieving a unanimous view from all parties that “the percentage of online business to the total is within a controllable range of risk exposure” and pointing out the direction for offline business development vision and development pace;

2. To summarize the trend of the sub-industry in which the brand is located and to formulate the vision and targeted growth range of offline business with a full consideration of the external pressure, such as online operation costs, potential risks, development goals and competitive products.

Step 3: To select cities for new stores based on the consumers’ demand forecasting model.Firstly, to set the city level based on consumer portrait, distribution of competitive products, and the positioning particularity of the brand. To confirm optimal targeted cities for new stores through analysis of the characteristics of the consumers for the brand and the studies on the high-end relevant rivals targeting those consumers. Secondly, to predict and determine the city level planning by using consumers’ demand model with a full consideration of the historical performance of the brand as well as the future planning and development expectation of the targeted cities. To formulate tailor-made new store plan for each targeted city through regression analysis, score of each targeted city and the logic reasoning of city capacity model based on the historical data of the brand, the social and economic data of each targeted city and consumers’ preference.

Input

• Historical data of a brand • Social and economic data of a given city

• Consumers’ preference

Data process

• Regression analysis • Score of the city • Logic reasoning of capacity model

Output

• New marketplace planning for each targeted city

Shenyang

Xi’an

Fuzhou

Nanchang

Beijing

Chengdu

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Sample

Sample

Sample

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Step 4: To confirm growth scenario through test of the growth scenario of the brand and the judgement of the management about the possible pressure of opening new stores facing the enterprise.To take an integrated consideration of enterprise’s growth plans, market potential and brand competitiveness. The growth plan for brand can be divided into three major scenarios according to different market climate: following the market, conservative growth, and sprint growth. In addition, to measure and plan such growth scenarios for the enterprise in the current scale, the scale in one year and the scale in 3 years.

Step 5: To plan the tiers of targeted trade zone by using industry-leading big data and professional map technologyBig data will become an important helper for brands in the main battlefield of determining the tiers of targeted trade zone. In the process of implementation, data crawling, marketplace positioning, comparison with competitor data and evaluation capability are the key points in planning trade zone.

A comprehensive analysis of social and economic variables, the layout of other brands, residential population and customer population in the targeted

trade zone and other data can be realized with the support of big data through regression analysis, scores of the targeted trade zone and estimation of trade zone capacity, so as to figure out the number of new stores in each targeted trade zone and to propose the tier of targeted trade zone as well as the number of new marketplace planning.

Step 6: To propose selecting forward-looking automated property and planning of those property.Algorithms and tools for generating automated property will be proposed with a full consideration of the data

about existing property and that of the to-be-opened property. To give a comprehensive consideration about the established and to-be-established properties and to give a comprehensive score to each property, so as to provide enterprises with a tool for recommending automated property for new stores which integrates information about the targeted cities and trade zone, the number of new stores, the list of those properties and other indicators.

Annual average number of new stores

Shenyang

Xi’an

Fuzhou

Jinan

Shanghai

Shanghai

Number of new stores in X

Nanchang

Beijing

Chengdu

Changsha

Scenario 1 Growth plan: Following the market

Current scale

Scale 1 year later

Scale 3 years later

1.0 Billion 1.1 Billion 1.3 Billion

100 120 150

To set a compound growth rate at 10% by following a certain industry

Scenario 2 Conservative growth plan

Current scale

Scale 1 year later

Scale 3 years later


100 150 200

To keep competitive edge at a compound annual growth rate of 20%

Scenario 3 Sprint growth plan

Current scale

Scale 1 year later

Scale 3 years later


100 180 500

To rapidly overtake rivals at a compound annual growth rate of 30%

View 1: Cities View 2: Trade zone View 3: Property

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Cases of optimized marketplace planning driven by big data

CaseⅠ: Marketplace planning optimization in China’s retail marketA fashion sports brand put forward an appeal to optimize its marketplace planning in China’s retail market as its competitors are accelerating their marketplace planning. The brand targeted: To see that the active expansion of physical stores offline will play a decisive role in its future development based on the comparison of competitive products and the strategic planning of new retail; To create an active and systematic marketing planning approach/tool to optimize its footprint and development pace in Chinese market; To expand stores actively and take the initiative in competition in the upcoming three years with the support of sufficient data at an ideal speed with advantageous resources.

The marketplace planning of the fashion brand has been optimized rationally with a big data-driven marketplace planning: Firstly, over 1,300 stores have been matched and grouped successfully under the action plans of closing improper stores/ transferring stores/adding stores; Secondly, over 10 potential cities

have been dug out and added to the current list through the regression analysis of the multi-factors highly related to the brand, and the top 50 strategic cities have been revised and divided into three levels; In addition, the three-tier of expansion plans (city/trade zone/property) have been formulated by using big data and the related algorithms, and automation tools have been provided to facilitate communication between customers and partner as well as adjust such plans; Finally, the brand defined its growth vision and goals for the next three years and allocated its resources for expanding its physical stores offline on a rational base.

CaseⅡ: Study on optimizing investment marketplace planning and industry trend.A well-known real estate developer is looking for a platform to analyze the marketplace planning of a particular targeted industry in the sector of commercial real estate, so as to benefit from the synergy effect in such industry and from the driving force for consumption. In particular, the developer is keen to know how to benefit from the synergy effect through geographic POI (Point of

Interest) and social media channels with a large quantity of data. At the same time, the developer is also looking for a comprehensive labeling system (social behavior driving force) applied to its current and future consumers, so as to achieve the purpose of accurate marketing positioning and communication about store location.

The investment marketplace planning by this real estate developer has been optimized through analysis of industry trend, store location and customer portrait with assistance of big data. Firstly, the developer analyzed consumer personality models of the relevant and irrelevant groups based on the purchase of consumers with assistance of geographic POI and the major social media platforms. Secondly, the developer had an in-depth analysis of the personality model, potential purchase behavior and purchase trigger of each consumer. Finally, the developer integrated millions of pieces of social media posts, comments, images and information to analyze the synergy effect generated from the industry and formulated its new strategy for sales and marketplace planning.

Applicable industry

Sports goods Beauty & cosmetics

Food & beverage

Education Others

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Conclusion

With the support of big data, marketplace planning of brands will usher in a new stage of key transformation and optimization of economic returns. In the future, brand owners will no longer stick to the partner model, and their efforts in marketplace planning optimization will also gradually shift from passive reaction to taking the initiative, from driven by partner to driven by brand, from opportunism to strategic guidance, from experience-based and manual operation to relying on data-based support of tools and systems.

Under the trend of integrating stores online and offline, marketplace planning optimization by using big data based on the certain characteristics

of brand offers a new idea for brands to distribute their stores in new retail market. Firstly, brands can be optimized based on their own characteristics and development vision, and brands can have a forward-looking analysis of their steps for expanding stores by closely connecting such steps with their growth vision for the following 3 to 5 years; Secondly, brands can be given support to have an in-depth analysis of the overall market through comprehensive analysis with big data in multiple dimensions based on a large quantity of data about the attributes of targeted cities and trade zone. At the same time, automation tools are employed as support force in data updating, so as to eradicate the bottleneck of considerable consumption of

manpower and time for research on marketplace planning. Finally, to promote innovation of business model. In the new model, brands can carry out strategic marketplace planning and optimize such planning based on the real demand of the market. With more and more brands taking the initiative in marketplace planning and carrying out their plans in systematic steps, such innovative model will win more attention and recognition of market players. In addition, this innovative model will guide partner better in opening new stores and help brand owners enhance their management and control in brands.

Customized brands

Innovation of business

model

Data integration

To optimize based on brand characteristics and development vision • To customize the whole set of plans based on brand characteristics

• To carry out forward-looking analysis of store expansion steps by closely relating to the growth vision of brand (giving support for the next 3 to 5 years)

Offering a new model for brand owners to take the initiative in marketplace planning and to implement such plans in a systematic way

• Optimizing marketplace planning in a strategic way based on the real demand of the market

• The achievement of this project will guide partner better in opening new stores and enhance brand owners’ management and control in brands

To conduct comprehensive analysis with big data in multiple dimensions,improving scientific analysis and saving manpower and time

• Integrating multiple data dimensions and covering a large quantity of attributes of cities and trade zone, so as to support in-depth analysis of the whole market

• Automation tools can be employed as a supportive force in data updating, so as to eradicate the bottleneck of considerable consumption of manpower and time for research on marketplace planning.

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Contacts

Tianbing Zhang Deloitte Asia Pacific Consumer Products and Retail Sector LeaderTel: +86 21 6141 2230 Email: [email protected]

Sitao Xu Deloitte China Chief Economist PartnerTel: +86 10 8512 5601 Email: [email protected]

Flora Dai Deloitte China Consumer Products and Retail Sector Consulting Associate DirectorTel: +86 21 2312 7028 Email: [email protected]

Yi Hu Deloitte ResearchAnalystTel: +86 23 8817 8295 Email: [email protected]

Grace Ling Deloitte China Consumer Products and Retail Sector Management Consulting PartnerTel: +86 21 2312 7066 Email: [email protected]

Lydia ChenDeloitte ResearchDirectorTel: +86 21 6141 2778 Email: [email protected]

Sheryl Yang Deloitte China Consumer Products and Retail Sector Consulting Associate Director Tel: +86 21 6141 2712 Email: [email protected]

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About DeloitteDeloitte refers to one or more of Deloitte Touche Tohmatsu Limited (“DTTL”), its global network of member firms, and their related entities. DTTL (also referred to as “Deloitte Global”) and each of its member firms and their affiliated entities are legally separate and independent entities. DTTL does not provide services to clients. Please see www.deloitte.com/about to learn more.

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The Deloitte brand entered the China market in 1917 with the opening of an office in Shanghai. Today, Deloitte China delivers a comprehensive range of audit & assurance, consulting, financial advisory, risk advisory and tax services to local, multinational and growth enterprise clients in China. Deloitte China has also made—and continues to make—substantial contributions to the development of China's accounting standards, taxation system and professional expertise. Deloitte China is a locally incorporated professional services organization, owned by its partners in China. To learn more about how Deloitte makes an Impact that Matters in China, please connect with our social media platforms at www2.deloitte.com/cn/en/social-media.

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Serial studies of Future of Consumer Brand-driven ... · Serial studies of "Future of Consumer" Brand-driven Strategic solution on marketplace planning optimization enabled by big-data