The impact of omni-channel strategy on retailers’ valuation

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The Impact of Omni-channel Strategy

on Retailers’ Valuation

Student: Jingwen Yu 11933011

Supervisor: Dennis Jullens

Date of Submission: 25.09.2018

Master Thesis

Master in International Finance

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Statement of Originality

This thesis is written by Student Jingwen Yu who declares to take full responsibility for the contents.

I declare that the text and the work presented in the thesis are original and that no sources other than those mentioned in the text and its references have been used in creating it.

Amsterdam Business School is responsible solely for the supervision of completion of the work, not for the contents.

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Abstract

Traditional retailers have dramatically suffered from the traffic shift to e-commerce platform, forcing them to rethink business strategies and operating models. Omni-channel is definitely a new trend, solution and strategy. The current studies mainly focus on the perspectives of sales, marketing or operations, with limitation to the financial part. The major purpose of this paper is to provide an empirical study on how channel strategy is impacting retailer’s valuation by analyzing omni-channel service score and financial performance of publicly traded companies in a list of Top100 Omni-channel Retailers. The results suggest that omni-channel

retailer’s valuation is not yet strongly correlated to its omni-channel service level, but more from retailer’s profitability level and efficiency improvement, which could also be driven from the omni-channel strategy.

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1. Introduction

1.1 What & Why Omni-channel

With the technology upgrade and digital revolution, consumers have much more choices of what and where to buy. As visually explained by the two charts below from Deloitte, the traditional strategy of retailers, which is company or product centralized, has been replaced by the new strategy, which is customer centralized. Therefore, the new era of retailing is all about customer experience through multi channels, to interact and serve customers as much as possible in the consumer lifecycle, from Demand, to Research, to Purchase, to Return, and to another cycle.

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2009) and (Ortis and Casoli 2009) suggested that the "omni-channel" shopper is an evolution of the multichannel consumer who instead of using channels in parallel, he uses them all simultaneously. (Rigby 2011) was the first to mention the term in academic literature by defining omni-channel retailing as: “an integrated sales experience that melds the advantages of physical stores with the information-rich experience of online shopping”.

Put simply, Omni-channel is a multi-channel approach to marketing, selling, and serving customers in a way that creates seamlessly integrated customer experience no matter where or how a consumer reaches out. As explained by the picture below, the omni-channel is a closed loop that consumers can access all possible channels, whether through online, desktop or mobile device, or in a brick-and-mortar store, more importantly, they are all connected and integrated to maximize the consumer interaction and experience, which will eventually turn into company’s sales and profit. While as for multi-channel, those channels, even though the same channels, are separated and not connected, thus consumers would find problem when switching between devices to purchase, and companies could have double work and

inconsistent communication to consumers on different channels, which could create a poor customer experience and lose sales opportunity.

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In a recent study by McKinsey Research and Harvard Business Review (McKinsey Research, Harvard Business Review 2017) it was found that omni-channel retail customers spent an average of 4% more on every shopping occasion in-store, and 10% more online than single-channel customers. Additionally, that the more

channels a customer used, the more they would spend. Even better, IDC Retail Insights research reports a 5-10% increase in loyal customers’ profitability, and 30% higher lifetime value than those who shop using only one channel…… According to a report by IDC Retail Insights, retailers utilizing multiple channels in their marketing and retail saw between a 15 and 35% increase in average transaction size, along with a 5 to 10% increase in loyalty customers’ profitability. Here are some additional statistics to consider on the advantage of omni-channel retail:

• Companies with omni-channel customer engagement strategies retain on average 89% of their customers, compared to 33% for companies with weak omni-channel customer engagement. (Aberdeen Group)

• 50% of consumers expect to buy online and be able to pick up in-store (Business2Community.com)

• The opportunity cost of not being omni-channel is 10% in lost revenue. (VendHQ.com)

• According to a 2015 study by IDC, shoppers that buy on multiple channels have a 30% higher lifetime value than those who shop using only one channel

• With tech-savvy consumers demanding seamless shopping experiences across all channels, some 62% of retailers have responded by introducing omni-channel services, according to a survey by JDA Software Group.

However, currently there is not yet a clear and widely accepted rule to define if a company is omni-channel, while according to (TotalRetail, Radial, NAPCOMEDIA 2017) (TotalRetail, Radial, NAPCOMEDIA 2018), serving as the list of omni-channel retailers to analyze in this paper, the retailers were judged on the following seven omni-channel criteria:

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i. does it offer buy online, pick up in-store;

ii. does it offer the ability to search for in-store products on its website;

iii. does it offer a shared cart across sales channels (e.g., mobile to desktop); iv. are loyalty points able to earned and redeemed across channels;

v. are products able to be returned across channels (e.g., return online purchases in-store);

vi. does it offer customer service in more than one channel; vii. is product pricing consistent across channels.

1.2 How omni-channel creates value Firstly, as shown in the valuation chain in the right, the company strategy could influence financial performance, mainly in long term. For analysts’ expectations, they could be either from the long term fact-based financial performance, or purely the strategy itself. And eventually, analysts’ expectations come up to company valuation.

For a retailer with omni-channel strategy, analysts could expect higher performance and returns if:

A) the company has great omni-channel service, which can easily identify for example the seven criteria mentioned above, so leading to higher valuation of the company immediately

B) the company has better financial performance with higher sales growth, high profit margin, and efficiency improvement. Here the better performance could largely be driven by the omni-channel strategy with better customer experience as explained in Chapter 1.1.

Therefore, this paper will analyze how these two parts affect valuation of omni-channel retailers. The company list and omni-channel service level are from Top100 Omni-channel Retailers (2018 version), by Total Retail and Radial, two leading companies specialized in research, analysis and solutions in retail industry. (TotalRetail, Radial, NAPCOMEDIA 2018). In this report, it computes a score of each retailer regarding

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omni-channel service, by measuring the seven criteria mentioned above. Detailed methodology is included in Appendix. For the relevant financial data, I retrieved from WRDS (Wharton Research Data Services).

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2. Key Literatures Review

Omni-channel concept is still relatively new, and the current studies focus more on the sales, marketing, and operation parts, with some literatures and more studies from consulting firm. Also since ecommerce and online channel has emerged for longer time, there are more literatures analyzing the relationship between online channels with companies’ financial performance.

(Xia and Zhang 2010) analyzed the impact of the Online Channel on Retailers’ Performance. Drawing from data on more than 100 publicly traded companies, this study examines the impact of online-channel use on retailers’ performance. The results suggest that the online channel provides significant improvements in sales, cost, inventory, and return on investments. In addition, they found that the timing of online-channel adoption does not play a significant role in performance

improvement, but having a local store presence does.

(Yuying Shi 2017) analyzed the relationship between format diversification and retailer performance in a global setting. The format diversification here includes both geography expansion and channel diversification. Using a six year panel data set for leading global retailers, they find a positive impact for geographic diversification, a negative impact for format diversification and a negative interaction for the dual strategies, supporting a single focus diversification strategy. Also it listed all the relative analysis on either geography / channel as below.

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(Christian Homburg 2014), using an event study, investigates whether a firm’s announcement of an increase in distribution intensity or the establishment of a new channel influences firm value. The authors also consider the moderating role of context-specific firm, market, and channel strategy contingencies. They test their hypotheses with an event study of 240 announcements of major channel expansions in the United States, Germany, and China. The results indicate that channel

expansions affect firm value (i.e., through abnormal stock returns). However, the two types of channel expansions affect firm value differently. Whereas the establishment of a new channel positively influences firm value, reactions to an increase in

distribution intensity are highly contingent.

(Dragan Stojković 2016) analyzed the concept of multichannel strategy, focusing on retail, to enable the academic community and marketers to better understand its advantages and disadvantages. By analyzing financial data of 88 retail companies in the 2007 to 2014 period, they proved that the importance of multichannel strategy has grown with the emergence of e-commerce.

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Also below include multiple interpretations of Omni-channel concept from leading consulting firms:

A widely cited definition is (Verhoef 2015): In contrast to multi-channel retailing, that is, offering shopping opportunities through different and separated channels, omni-channel retailing examines omni-channels as an holistic offering to appeal to the heterogeneity in customers’ shopping orientations – such as varying levels of ‘need for touch’, ‘need for cognition’, or degree of ‘self-reliance’ – with the aim of providing a seamless cross-channel experience.

According to a recent study by McKinsey Research and Harvard Business Review (McKinsey Research, Harvard Business Review 2017), the omni-channel strategy hinges on the idea that providing a seamless shopping experience in brick-and-mortar stores and through a variety of digital channels not only differentiates retailers from their peers, but also gives them a competitive edge over online-only retailers by leveraging their store assets. Through a study on the shopping behavior of just over 46,000 customers who made a purchase during the 14-month period from June 2015 to August 2016 in a major U.S. retailer, only 7% were online-only shoppers and 20% were store-only shoppers. The remaining majority, or 73%, used multiple channels during their shopping journey.

According to Deloitte’s view (Deloitte 2015), Omni-channel retailing, is the future of e-commerce and requires e-tailers, bricks-and-mortars and bricks-and-clicks (bricks-and-mortars that also have an online presence) to rethink their strategies and to redefine their business models… Omni-channel retailing means being available at any time anywhere, making it convenient for the customer. Also, in one earlier report from Deloitte (Deloitte 2014), a study commissioned by eBay examines the effects of omni-channel strategies in selected European markets and measures the value for retailers.

Additionally, as definitions of multi/cross/omni channel might be blurred, the paper (Categorization of multiple channel retailing in Multi- 2015) identify a taxonomy of multiple channel retailing based on a literature review of 1580 articles. For Omni-channel, the retailer offers the customer all channels that are currently widespread, which at present means the physical store, catalog, telephone, online shop and

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mobile shop. Additionally, the customer can trigger full interaction and/or the retailer controls full integration of all channels.

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3. Conceptual Framework and Hypothesis Development

3.1 Conceptual Framework

This chapter intends to illustrate the relationships among all variables intuitively in a conceptual framework. Please find the framework below.

The major purpose of this paper is to analyze how omni-channel strategy impacts retailer’s valuation. Whereas the valuation creation part, in the context of channel retailers, could either directly and immediately from outstanding

omni-channel service, which is calculated as a score by the report - Top100 Omni-omni-channel Retailers, or indirectly and long term from better financial performance, which hereby includes higher sales, high profit margin, and more efficient inventory turnover and net working capital.

3.2 Hypothesis Development

As highlighted in Chapter 1, nowadays the new era of retailing is all about customer experience, leading to the booming trend of omni-channel, which consumers can

Financial Performance Sales Profit Margin Efficiency Improvement (Inventory Turnover, NWC) Omni-Channel Strategy Service Level (Score) Valuation (Tobin’s Q)

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access all possible channels, whether through online, desktop or mobile device, or in a brick-and-mortar store, and more importantly, all the channels are well connected and integrated so as to maximize the consumer interaction and experience, which will eventually turn into company’s sales and profit in the long run. In addition, to enable a high level omni-channel service, the company must have invested a lot on the technology, therefore, analysts could expect such a retailer, taking great effort on both consumer and technology, to embrace a better performance and higher return compared to those traditional ones still stuck in the fierce competition without any change. Thus, here proposes that:

H1: Omni-channel retailer’s valuation is positively affected by its omni-channel service level.

With omni-channel strategy bringing better customer experience, ultimately it will bring more sales. also the profit margin would expect to lower down in the long run due to more contribution from online or mobile business with higher margin, rather than physical stores with increasing rent and labor cost. In addition, in the long term the synergy effect among all channels will result to lower cost as well. Furthermore, more sales can help retailers to increase market power, e.g. negotiate better terms with vendors. They can engage in predatory pricing against competitors by

threatening to respond with a price war (Berger and Ofek 1995) or can deter competitors’ entry into a new market (Geyskens and Dekimpe 2007).

On the other side, it will entail substantial cost to enable an outstanding

omni-channel strategy, which mainly includes higher learning cost and management cost. The high complexity of omni-channel will largely lead to a lot of waste and

inefficiency both internally and externally. Its not easy to integrate all the channels with separate customer base, different supplies and internal management team as well. One of the potential key issue might be the inventory. To satisfy customers’ expectation, retailers must have an advanced and integrated warehouse

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most of bankruptcy of retailers start from the inventory problem following by pressure on operating cash flow.

Therefore, if the omni-channel retailer can have better financial performance with benefits coving cost of omni-channel, including sales growth, high margin, and better efficiency, it would have a higher valuation expected. Thus, here proposes:

H2: Omni-channel retailer’s valuation is positively affected by its financial performance, mainly profitability level and efficiency level.

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4. Data and Methodology

4.1 Data Sources and Data Collection

As mentioned, the sample of omni-channel retailers used in this study comes from a report – Top100 Omni-channel Retailers (2018 version released in April), by Total Retail and Radial since 2017 to offer a blueprint for brands on how to deliver the seamless, quick and enjoyable experiences that today’s digitally savvy consumers demand. Total Retail is the market intelligence company for retail executives looking for the latest news and analysis on the retail industry, featuring a quarterly print magazine, daily e-newsletter (Total Retail Report), daily-updated website, podcast channel, virtual and in-person events, and research reports. Radial is the leader in omni-channel commerce technology and operations, enabling brands and retailers to profitably exceed retail customer expectations.

This report computed a score on how the retailers are performing per the following seven omni-channel criteria (Methodology of scoring is included in Appendix).

i. does it offer buy online, pick up in-store;

ii. does it offer the ability to search for in-store products on its website;

iii. does it offer a shared cart across sales channels (e.g., mobile to desktop); iv. are loyalty points able to earned and redeemed across channels;

v. are products able to be returned across channels (e.g., return online purchases in-store);

vi. does it offer customer service in more than one channel; vii. is product pricing consistent across channels.

The omni-channel retailers list along with the score are served as the sample for the analysis in this work. Originally there are 100 retailers, however some retailers’ financial data could not be available for FY2017, also some are subsidiaries under parent company, e.g. Calvin Klein and Tommy Hilfiger are under parent company PVH Group, thus finally 75 retailers are included in the analysis. The report also categorized

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they have high similarity. The detailed retailer list with category and omni-channel scores is included in Appendix.

For financial performance data, I mainly retrieved from WRDS (Wharton Research Data Services) for Sales, Net Profit Margin (NPM), Inventory Turnover (Inv T), Net Working Capital (NWC), Operating Net Cash Flow (ONCF), Total Assets and Market Value at year end for 2015 to 2017, so that I can test the regression with hypothesis. Some data are not available in WRDS, I also leveraged Yahoo Finance since it has comprehensive and standard fundamental data. The time period I retrieved is from 2015 to 2017, however since the Top100 omni-channel list was released in April 2018 thus mainly base on the performance in 2017, so for financial performance data, I mainly use 2017, while also include the difference from 2016 to focus on the change. For valuation, Tobin’s Q is the focal measure. The Tobin's Q ratio is a measure of firm assets in relation to a firm's market value. According to (Yuying Shi 2017) and (Palich, Cardinal and Miller 2000) , Tobin’s Q is a preferred valuation measure and is considered to be more suitable for studying diversification-performance linkages. Tobin's Q = Total Market Value of Firm / Total Asset Value of Firm

When Tobin's Q is between 0 and 1, it costs more to replace a firm's assets than the firm is worth.

When Tobin's Q above 1, it means that the firm is worth more than the cost of its assets. Because Tobin's premise is that firms should be worth what their assets are worth, anything above 1.0 theoretically indicates that a company is overvalued.

4.2 Model Specification

As shown in the conceptual framework, the regression model is created as belo: Q = β0 + β1*Score + β2*(Sale,△Sales) + β3*(NPM,△NPM) + β4*(△Inv T,△NWC) With all the data source of variables explained before, although only 75 retailers to analyze, I put into the regression model both the absolute value in 2017 and change% from 2016, to test if the correlation is more from the size itself or the change.

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Also, since there are different categories of retailers, I ran the regression firstly by 75 retailers, and then for 40 Apparel and Footwears retailers to see if any different pattern. I didn’t to it for other categories since the sample is too small.

Moreover, since the threshold as 1.0 for Tobin’s Q can make a big difference, I also ran the regression separately for 29 retailers with Tobin’s Q less than 1.0, and other 46 retailers above 1.0.

In addition, below exams the correlation of all the variables, with some higher than 0.7. Table 1 Variables Correlation

Score Sales NPM inv T ONCF Sales NPM Inv T NWC

Score 1.00 Sales -0.02 1.00 NPM -0.11 0.17 1.00 inv T 0.06 0.51 0.33 1.00 ONCF 0.04 0.74 0.36 0.82 1.00 Sales -0.08 0.05 0.43 -0.01 0.10 1.00 NPM 0.01 0.06 0.54 0.10 0.04 -0.01 1.00 Inv T -0.06 -0.35 -0.34 -0.82 -0.88 -0.05 -0.02 1.00 NWC -0.05 0.87 -0.04 0.23 0.38 -0.03 0.03 0.03 1.00

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5. Results

Firstly, Table 2 presents the result for total 75 omni-channel retailers.

For total 75 retailers, 55% of the sample could be explained by the model. And the model is statistically significant. F(9, 65)=8.65; P=0.00. The results shows that the omni-channel score is marginally significant on retailer’s valuation (P=0.09), however the coefficient is -0.13 showing a negative effect which is opposite to Hypothesis 1. In the other variables of financial performance, Sales, △Sales, Net Profit Margin, and △Inv T all show significant positive effect on retailer’s valuation with P < 0.05, which proves Hypothesis 2. In addition, here includes ONCF (operating net cash flow) to exam how significant it is to valuation, not surprisingly, it is significantly positive.

Table 2 SUMMARY OUTPUT - Total 75 Retailers Regression Statistics Regression Statistics Multiple R 0.74 R Square 0.55 Adjusted R Square 0.48 Standard Error 0.92 Observations 75 ANOVA df SS MS F Significance F Regression 9 65.70 7.30 8.65 0.00 Residual 65 54.83 0.84 Total 74 120.53

Coefficients Standard Error t Stat P-value Lower 95% Upper 95%

Intercept 1.55 0.53 2.91 0.00 0.49 2.62 Score -0.13 0.07 -1.70 0.09 -0.27 0.02 Sales 0.00 0.00 -2.59 0.01 0.00 0.00 NPM 10.10 2.78 3.63 0.00 4.55 15.64 inv T 0.05 0.06 0.79 0.43 -0.08 0.18 ONCF 0.00 0.00 3.61 0.00 0.00 0.00 Sales 3.56 1.65 2.16 0.03 0.26 6.85 NPM -4.36 2.73 -1.60 0.11 -9.82 1.09 Inv T 0.66 0.16 4.03 0.00 0.33 0.99 NWC 0.00 0.00 0.76 0.45 0.00 0.00 **For P-value, Green for P<0.05, Yellow for 0.05<P<0.1

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Secondly, Table 3 shows the result for 40 retailers in Apparel and Footwears.

For 40 retailers, 71% of the sample could be explained by the model. And the model is statistically significant. F(9, 30)=8.04; P=0.00. The results shows that for this category, the omni-channel score is less significant on retailer’s valuation (P=0.20), and the coefficient is -0.14. In the other variables of financial performance, only Sales, Net Profit Margin, and ONCF show significant positive effect on retailer’s valuation with P < 0.05. While the efficiency ratios as △Inv T and △NWC have P=0.11.

Table 3 SUMMARY OUTPUT - Apparel and Footwears Regression Statistics Regression Statistics Multiple R 0.84 R Square 0.71 Adjusted R Square 0.62 Standard Error 0.69 Observations 40 ANOVA df SS MS F Significance F Regression 9 34.19 3.80 8.04 0.00 Residual 30 14.18 0.47 Total 39 48.37

Intercept 1.66 0.74 2.25 0.03 0.15 3.16 Score -0.14 0.11 -1.31 0.20 -0.37 0.08 Sales 0.00 0.00 -2.62 0.01 0.00 0.00 NPM 6.47 3.16 2.05 0.05 0.01 12.92 inv T 0.07 0.07 1.04 0.31 -0.07 0.21 ONCF 0.00 0.00 3.32 0.00 0.00 0.00 Sales 1.84 1.72 1.07 0.29 -1.66 5.34 NPM -3.65 3.13 -1.17 0.25 -10.03 2.73 Inv T 0.46 0.28 1.66 0.11 -0.11 1.02 NWC 0.00 0.00 -1.67 0.11 0.00 0.00 **For P-value, Green for P<0.05, Yellow for 0.05<P<0.1

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Moreover, Table 4 and Table 5 shows the result by splitting Tobin’s Q value as 1. As we can see, for 46 retailers with Tobin’s Q >1, the model is statistically significant (P=0.00) and the significant variables (P<0.05) are Sales, Net Profit Margin, ONCF, and △Inv T. However, for the other group with Tobin’s Q < 1, the model is weakly significant with P=0.06. Table 4 SUMMARY OUTPUT - Tobin Q >1 Regression Statistics Multiple R 0.68 R Square 0.47 Adjusted R Square 0.33 Standard Error 0.99 Observations 46 ANOVA df SS MS F Significance F Regression 9 30.95 3.44 3.52 0.00 Residual 36 35.19 0.98 Total 45 66.14

Intercept 1.77 0.72 2.46 0.02 0.31 3.22 Score -0.14 0.09 -1.47 0.15 -0.33 0.05 Sales 0.00 0.00 -2.21 0.03 0.00 0.00 NPM 14.21 4.82 2.95 0.01 4.43 23.99 inv T 0.03 0.09 0.30 0.77 -0.16 0.21 ONCF 0.00 0.00 3.02 0.00 0.00 0.00 Sales 3.60 2.20 1.64 0.11 -0.86 8.06 NPM 1.42 4.40 0.32 0.75 -7.50 10.34 Inv T 0.70 0.20 3.55 0.00 0.30 1.10 NWC 0.00 0.00 0.57 0.57 0.00 0.00 **For P-value, Green for P<0.05, Yellow for 0.05<P<0.1

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Table 5 SUMMARY OUTPUT - Tobin Q <1 Regression Statistics Regression Statistics Multiple R 0.72 R Square 0.52 Adjusted R Square 0.29 Standard Error 0.23 Observations 29 ANOVA df SS MS F Significance F Regression 9 1.04 0.12 2.26 0.06 Residual 19 0.97 0.05 Total 28 2.01

Intercept 0.45 0.31 1.45 0.16 -0.20 1.10 Score 0.03 0.05 0.56 0.58 -0.07 0.12 Sales 0.00 0.00 -0.01 0.99 0.00 0.00 NPM 2.65 1.46 1.81 0.09 -0.41 5.71 inv T -0.04 0.03 -1.37 0.19 -0.11 0.02 ONCF 0.00 0.00 0.26 0.80 0.00 0.00 Sales 0.80 1.15 0.70 0.50 -1.60 3.19 NPM -1.08 1.33 -0.81 0.43 -3.85 1.70 Inv T -0.16 0.13 -1.25 0.23 -0.44 0.11 NWC 0.00 0.00 0.09 0.93 0.00 0.00 **For P-value, Green for P<0.05, Yellow for 0.05<P<0.1

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6. Conclusion and Discussion

6.1 Managerial Implications

Hereby review the model results with the two hypothesizes:

H1: Omni-channel retailer’s valuation is positively affected by its omni-channel service level.

H2: Omni-channel retailer’s valuation is positively affected by its financial performance, mainly profitability level and efficiency level.

For H1, surprisingly most of the results can not prove significant relationship between omni-channel score and valuation of those omni-channel retailers. There could be many reasons behind. Firstly, the score in the report could not, or not yet reflect that well the omni-channel service level base on the seven criteria. As mentioned, nowadays there is not yet any clear and widely-accepted criteria to measure Omni-channel, therefore, in the next few years, we can test the model again if there is some more standard criteria. Secondly, analysts and market might still rely more on the fact based finance performance, especially in the short term when omni-channel is not that easy to execute and bring benefits immediately to retailers, with probably more invest or cost considering the capital investment, learning cost and managerial cost. If so, it could well explain the result of total 75 retailers with P value of Score still marginally significant but with negative coefficient. However, as mentioned before, the omni-channel service level of retailer could be already reflected indirectly into the financial performance, creating more sales with high margin, but not directly from the service level itself, or at least not the score in the report Top100 Omni-channel retailers.

For H2, undoubtedly the fact based financial performance shows strong relationship with retailer’s valuation. For profitability level, Sales and Net Profit Margin are

significant, and more significant than the change from last year. It could be analysts and market take more priority on the absolute value, and also the changes have already been reflected in the absolute value. For efficiency level, △Inv T shows more significant than Inventory Turnover and index of Net Working Capital. Inventory turnover is an important measure of how efficiently a company can turn its inventory

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into cash, how efficiently a company can control its merchandise. For retailers its significantly important since its shows the company can effectively and efficiently sell the inventory it buys, which means the right product, the right amount and the right time. Inventory is one of the biggest assets on the balance sheet a retailer reports. Also, in Table 1 Correlation, we can see strong correlation between inventory turnover and operating net cash flow. For a retail adopting omni-channel strategy, the integration of all the platforms would be a big challenge if the company doesn’t have the knowledge and investment to have an optimized process and infrastructure. In this sense, △Inv T would be a good indication to show if the retailer is on track compared to old days.

In general, since omni-channel is the future for retailers, how to enable a successful turn around, to achieve higher company valuation, is really critical for each retailer to rethink no matter how far the retailer is already on the way.

In terms of service level, the seven criteria in the report might be a good starting point as essential requirements of omni-channel service, however, retailers should focus more on why and how to maximize customer experience base on those essential settings. For example, do more customer analysis and have more customized recommendation or service base on the vast data produced from all integrated channels, e.g. what is the right product, the right channel, the right timing for one single customer. With more and more retailers become omni-channel, the competition would continue to the next level, how to leverage omni-channel to maximize customer loyalty by customized service. Moreover, as there is not yet a clear and widely accepted measurement for omni-channel, it is important for retailers to create an efficient measurement, serving for themselves. Ideally, the retailer should start with choosing the right KPI for its own business and customer, which could drive more sales and margin. After that, keep monitoring the KPI and try to improve progressively.

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6.2 Limitations and Future Research The limitation mainly lies on the data and model.

As mentioned before, the variable as omni-channel score might be not evident enough to reflect the service level. However, since omni-channel is still a relatively new concept, there is not yet other widely accepted measurement, thus this report might be the best material available at this moment. Also since the report has been released for two consecutive year, we can have the assumption that the second version, serving for the samples of this paper, is more mature with improvement based on the first year version.

Moreover, the sample of the analysis, 75 retailers, might be not sufficient enough somehow. The report of Top100 Omni-Channel Retailers includes 100 players, however some companies are under same parent companies, and also financial data for some retailers are not available for 2017, thus the final sample can only arrive 75. Although the report also has an earlier version (released in Apr. 2017), I don’t think it is mature enough to use it because it is the very first report of the two agencies on that topic and also I observed that for some companies, there are big variations on scores between two years, which might be the methodology has been optimized. However, for financial performance of these 75 retailers, I try to include both the absolute value of FY 2017 and the change from 2016 to 2017, which might be helpful to exam the evolvement.

Therefore, if in the next few years, there are widely accepted omni-channel measurement available, we can have further research on how the relationship is, and how it is evolving among the years.

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7. Bibliography

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Christian Homburg, Josef Vollmayr, & Alexander Hahn. "Firm Value Creation Through Major Channel Expansions: Evidence from an Event Study in the United States, Germany, and China." American Marketing Association, 2014: 38.

Deloitte. "Driving growth and value with Omni-channel - A report for Ebay." 2014. Deloitte. "Global Powers of Retailing 2018." 2018.

Deloitte. "Omni-channel retail A Deloitte Point of View." 2015.

Dragan Stojković, Stipe Lovreta, Zoran Bogetić. "MULTICHANNEL STRATEGY – THE DOMINANT APPROACH IN MODERN RETAILING." ECONOMIC ANNALS, 2016: 105.

Geyskens, I., Gielens, K., and M. G. Dekimpe. "The entry strategy of retail firms into transition economies." Journal of Marketing, 2007: 196 -212.

LCP Consulting. "Omni-channel revolution will completely re-engineer retail business." 2013: 5.

McKinsey Research, Harvard Business Review. A Study of 46,000 Shoppers Shows That Omnichannel Retailing Works. Edited by Utpal M. Dholakia, Beth Benjamin Emma Sopadjieva. 2017. https://hbr.org/2017/01/a-study-of-46000-shoppers-shows-that-omnichannel-retailing-works.

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8. Appendix

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8.2 Appendix – 75 store lists with Category and Omni-channel score

Category 1 Category 2 Company Name Ticker Score

Apparel and Footwear Apparel and Accessories GENESCO INC GCO 8.25

Apparel and Footwear Apparel and Accessories TAILORED BRANDS INC TLRD 8.00

Apparel and Footwear Apparel and Accessories ABERCROMBIE & FITCH ANF 7.75

Apparel and Footwear Apparel and Accessories ZUMIEZ INC ZUMZ 7.75

Apparel and Footwear Apparel and Accessories ASCENA RETAIL GROUP INC ASNA 7.25 Apparel and Footwear Apparel and Accessories URBAN OUTFITTERS INC URBN 7.25 Apparel and Footwear Apparel and Accessories CHRISTOPHER & BANKS CORP CBK 7.00

Apparel and Footwear Apparel and Accessories EXPRESS INC EXPR 7.00

Apparel and Footwear Apparel and Accessories BUCKLE INC BKE 6.75

Apparel and Footwear Apparel and Accessories PVH CORP PVH 6.50

Apparel and Footwear Apparel and Accessories AMERN EAGLE OUTFITTERS INC AEO 6.25 Apparel and Footwear Apparel and Accessories MICHAEL KORS HOLDINGS LTD KORS 6.25

Apparel and Footwear Apparel and Accessories GUESS INC GES 6.00

Apparel and Footwear Apparel and Accessories L BRANDS INC LB 6.00

Apparel and Footwear Apparel and Accessories VERA BRADLEY INC VRA 6.00

Apparel and Footwear Apparel and Accessories FRANCESCAS HOLDINGS CORP FRAN 5.75

Apparel and Footwear Apparel and Accessories CHICOS FAS INC CHS 5.50

Apparel and Footwear Apparel and Accessories GAP INC GPS 5.25

Apparel and Footwear Apparel and Accessories HANESBRANDS INC HBI 5.25

Apparel and Footwear Apparel and Accessories LULULEMON ATHLETICA INC LULU 5.25 Apparel and Footwear Apparel and Accessories NEW YORK & CO INC NWY 5.25 Apparel and Footwear Apparel and Accessories OXFORD INDUSTRIES INC OXM 5.25 Apparel and Footwear Apparel and Accessories ELLIS PERRY INTL INC PERY 5.25

Apparel and Footwear Apparel and Accessories CHILDRENS PLACE INC PLCE 5.25

Apparel and Footwear Apparel and Accessories UNDER ARMOUR INC UAA 5.25

Apparel and Footwear Apparel and Accessories CARTER'S INC CRI 5.00

Apparel and Footwear Apparel and Accessories RALPH LAUREN CORP RL 5.00

Apparel and Footwear Apparel and Accessories COLUMBIA SPORTSWEAR CO COLM 4.75 Apparel and Footwear Apparel and Accessories DESTINATION MATERNITY CORP DEST 4.75

Apparel and Footwear Apparel and Accessories FOSSIL GROUP INC FOSL 4.50

Apparel and Footwear Footwear DECKERS OUTDOOR CORP DECK 8.25

Apparel and Footwear Footwear DSW INC DSW 8.25

Apparel and Footwear Footwear MADDEN STEVEN LTD SHOO 7.50

Apparel and Footwear Footwear DICKS SPORTING GOODS INC DKS 6.75

Apparel and Footwear Footwear CROCS INC CROX 6.50

Apparel and Footwear Footwear JD Sports JD.L 6.50

Apparel and Footwear Footwear FOOT LOCKER INC FL 6.25

Apparel and Footwear Footwear NIKE INC NKE 5.25

Apparel and Footwear Footwear SKECHERS U S A INC SKX 4.00

Apparel and Footwear Footwear CALLAWAY GOLF CO ELY 2.50

Department Department TJX COMPANIES INC TJX 7.25

Department Department NORDSTROM INC JWN 7.00

Department Department SEARS HOLDINGS CORP SHLD 6.25

Department Department PENNEY (J C) CO JCP 5.75

Department Department MACY'S INC M 5.00

Department Department KOHL'S CORP KSS 4.50

Department Department STEIN MART INC SMRT 4.50

Department Department TARGET CORP TGT 4.25

Department Department ROSS STORES INC ROST 0.25

Home Home CONTAINER STORE GROUP TCS 7.75

Home Home TRACTOR SUPPLY CO TSCO 7.00

Home Home WILLIAMS-SONOMA INC WSM 7.00

Home Home LOWE'S COMPANIES INC LOW 6.75

Home Home BED BATH & BEYOND INC BBBY 6.50

Home Home PIER 1 IMPORTS INC/DE PIR 6.50

Home Home HOME DEPOT INC HD 5.75

Home Home RH RH 5.50

Home Home LUMBER LIQUIDATORS HLDGS INC LL 4.00

Home Home KNOLL INC KNL 0.75

Others Others VITAMIN SHOPPE INC VSI 8.00

Others Others ADVANCE AUTO PARTS INC AAP 7.25

Others Others BEST BUY CO INC BBY 7.25

Others Others APPLE INC AAPL 7.00

Others Others BIG LOTS INC BIG 7.00

Others Others GAMESTOP CORP GME 7.00

Others Others MICHAELS COS INC MIK 6.75

Others Others OFFICE DEPOT INC ODP 6.75

Others Others ULTA BEAUTY INC ULTA 6.75

Others Others AUTOZONE INC AZO 6.50

Others Others BARNES & NOBLE INC BKS 6.25

Others Others RITE AID CORP RAD 6.25

The impact of omni-channel strategy on retailers’ valuation