Chipotle Mexican Grill What data tells us about the Fast Casual king s yesterday, today, and tomorrow Troy Li Vice President, 646.802.6273 troy.li@mscience.com Will Jones Research Associate, Consumer 503.433.1551 will.jones@mscience.com KEY TAKEAWAYS To better understand the likelihood of Chipotle expanding its recent price increase (today), key problems Chipotle is facing while recovering from E. coli outbreak (yesterday), and what online ordering means for Chipotle s future (tomorrow), we are analyzing these three topics using our unique data and coming up with this report which consists of three sections: Price Increases, Looking at a Pre-Crisis Cohort, and Online Ordering. Regarding Chipotle s price increases, we note that Chipotle enjoyed the benefits of price increases for only a short period of time before the sales trend essentially normalized. We conclude that Chipotle s price increase is helping the margin concern so far, but to a lesser extent than expected. By analyzing Chipotle s pre-crisis cohort, we observe two problems Chipotle is facing: 1) their most loyal customers are eating there less often and 2) new customers are finding Chipotle less attractive. We also take a look at Chipotle s online ordering platform, finding some signs that it perhaps helped increase frequency marginally, while clearly improving average check size. Online ordering should also broaden the potential of Chipotle s catering business, which could be the next big catalyst for SSS growth over the next two to three years. Analyst Certifications and Important Disclosures can be found at the end of this report.
DETAILS Price Increases On April 11, Chipotle implemented a 5% average price increase at a select 440 stores about 20% of its total 2,200+ stores. On the CQ1 17 earnings call, management commented that the store selection was based on thoughtfully considering each market's sales trends, recent and expected minimum wage increases and competitor pricing. This is Chipotle s first price increase in nearly three years. Given that food preparation costs, increased marketing expenses and wage inflation continue to weigh on company margin, an obvious question investors (and management) are eager to have answered is whether the price increases can be expanded to more markets. With that in mind, we are using our data to identify the markets that have increased prices and taking an early look at the results. Figure 1: Where Has Chipotle Raised Prices? According to our data and analysis, price increases were implemented largely in the Northeast and Midwest. Specifically, we are seeing price increases in New Jersey (+8.4%), San Francisco (+7.8%), with the remainder of locations, including the greater NYC area, Virginia, Maryland, Pittsburgh, Columbus, Colorado, Utah, San Diego, and a few other places, in the +4.4% to +5.6% range. With the raised-price restaurants (RPRs) identified, we are able to analyze the effects of these price increases by comparing them to the same-price restaurants (SPRs). 2
Figure 2: Differenced Weekly Average Check Y/Y Trend, RPR vs. SPR (bps) 600 RPR Avg. Check - SPR Avg. Check 500 400 300 200 100 0-100 -200 Price Increase April 11, 2017 Figure 2 shows the difference in the weekly average check growth trend between RPRs and SPRs. We observed that the difference spiked immediately after the April 11 price increase (orange line), and continued to rise over the following two weeks. However, as more customers became aware of the price increases, we believe they began trade down to cheaper items or remove add-ons; thus the difference between the RPR and the SPR quickly fell. Through early June, the difference fell to the upper bound (+200 bps) of its typical range. Figure 3: Differenced Weekly Traffic Y/Y Trend, RPR vs. SPR (bps) 2500 RPR Traffic Growth - SPR Traffic Growth 2000 1500 1000 500 0-500 Price Increase April 11, 2017-1000 Figure 3 makes the same comparison, but with regard to traffic growth/recovery. Although the difference in traffic appeared to be unchanged initially, it started to deteriorate after three weeks and remained near the lower bound of its historical range through early June. 3
Figure 4: Differenced Weekly Sales Y/Y Trend, RPR vs. SPR (bps) 2500 RPR Sales Growth - SPR Sales Growth 2000 1500 1000 500 0-500 Price Increase April 11, 2017-1000 Finally, Figure 4 shows the combined effects of check and traffic. Our observation is that while the RPR enjoyed the benefits of price increases in the first three weeks, these benefits quickly disappeared as customers responded to the increase, and the trend essentially normalized. We will provide periodic updates of this chart in the future. Overall, early evidence hints that in the short term, customer resistance to the 5% price increase is much stronger than the 25% (pass-through of 3.75% or 4%) management had hoped for. We are essentially seeing around 100% resistance in most recent trends through early June. We think it would be imprudent to call this a final conclusion, as customers may return on both check and traffic as they become used to the new prices. Moreover, price increases that, at a minimum, do not hurt sales help margin after all. Therefore, we think Chipotle s price increases are helping the margin concern so far, just to a lesser extent than expected. It nonetheless reminds us that Chipotle s good old days of raising prices by 10% and customers still embracing the brand by topping it off with another 10% increase in traffic are in the rearview mirror. Looking at a Pre-Crisis Cohort In order to understand the tumultuous sales trends of the past year and a half, we followed a cohort of Chipotle customers who visited Chipotle restaurants anytime during CY 2014, (note: the E. coli crisis occurred in late 2015). We looked at all customers, from those who only made a single transaction during that year to those who comprised the top 20% mostfrequent customers in that period. For the purpose of comparison, we have also included Panera data in some of the analysis below. 4
When looking at average transaction frequency, it seems that the behavior of Chipotle s most-frequent customers was disproportionally affected by the outbreak. As shown in Figure 5, the transaction frequency of the top 20% most loyal customers in CY 2014 dropped off significantly in CQ4 15, but for the entire cohort, transaction frequency remained at a similar level. And it seems that the top 20% cohort has yet to return to its original frequency. While the trend we see in the same analysis for Panera shows that the frequency of Panera s top 20% customers tends to decrease over time, the effect is not as strong as what we have seen at Chipotle, suggesting Chipotle s drop-off wasn t simply the result of normal churn among Chipotle regulars. Figure 5: Quarterly Average Transaction Frequency for Cohort Chipotle Panera 6.0 6.0 Average Transaction Frequency 5.0 4.0 3.0 2.0 1.0 4.6 4.7 4.5 3.0 3.1 3.0 4.1 4.2 4.3 4.0 2.9 2.9 3.0 3.1 4.1 4.1 2.9 3.0 Average Transaction Frequency 5.0 4.0 3.0 2.0 1.0 4.7 4.8 4.8 4.6 4.6 4.6 4.5 4.5 4.4 3.0 3.1 3.1 3.2 3.1 3.2 3.1 3.2 3.2 0.0 Q1 '15 Q2 '15 Q3 '15 Q4 '15 Q1 '16 Q2 '16 Q3 '16 Q4 '16 Q1 '17 Entire Cohort Top 20% 0.0 Q1 '15 Q2 '15 Q3 '15 Q4 '15 Q1 '16 Q2 '16 Q3 '16 Q4 '16 Q1 '17 Entire Cohort Top 20% While frequency trends look less favorable for the Chipotle cohort than for Panera s cohort, Chipotle s retention of this cohort seems to be in line with Panera s. In January through May 2017, Chipotle and Panera each saw about 44% of their 2014 cohort return to their restaurants and about 70% of their top 20% Chipotle Panera most-frequent customers return, as shown 70.7% in Figure 6. Figure 6: Percent of 2014 Cohort Still Eating at Chipotle and Panera in 2017 80.0% 70.0% 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% 69.3% 44.5% 44.7% With the above analysis of Figures 5 and 6, we draw our first conclusion that Chipotle s problem is with its most loyal customers, who eat at Chipotle less frequently following the outbreak. Top 20% Total Looking at this top 20% cohort, we can also see which major competitors benefited as Chipotle lost wallet share during its E. coli crisis. Figure 7 shows the wallet share among Chipotle and eight major competitors who have the largest overlap in terms of our cohort s most frequent customer spending. This includes McDonald s (surprisingly) and Chick-fil-A the two with the largest overlap as well as Panera, Subway, Domino s, Taco Bell, Wendy s, and Buffalo Wild Wings. 5
Figure 7: Wallet Share Within Top Chipotle Competitors, Top 20% Cohort 100% 5.8% 5.5% 5.7% 6.0% 5.9% 5.6% 5.6% 5.7% 5.4% 5.1% 90% 5.7% 6.0% 6.0% 6.0% 6.1% 6.1% 6.1% 6.1% 5.9% 6.0% 5.6% 5.8% 5.8% 6.1% 6.3% 6.1% 6.3% 6.4% 6.5% 6.3% 80% 6.3% 5.6% 5.7% 6.5% 7.1% 6.6% 6.5% 7.0% 7.1% 6.8% 70% 9.1% 9.5% 9.3% 8.3% 8.9% 9.1% 8.9% 8.0% 8.1% 8.0% 60% 9.9% 9.8% 9.9% 10.9% 11.2% 10.5% 10.2% 10.7% 10.6% 10.6% Buffalo Wild Wings Wendy's Taco Bell Domino's 50% 12.4% 13.2% 13.4% 14.3% 15.2% 15.6% 15.7% 16.5% 16.4% 17.1% Subway 40% Panera 30% 16.9% 17.4% 18.0% 19.5% 19.1% 18.9% 19.3% 19.5% 19.0% 19.2% Chick-fil-A McDonald's 20% 10% 28.2% 27.2% 26.3% 22.3% 20.1% 21.5% 21.3% 20.2% 20.9% 21.0% Chipotle 0% CQ1 '15 CQ2 '15 CQ3 '15 CQ4 '15 CQ1 '16 CQ2 '16 CQ3 '16 CQ4 '16 CQ1 '17 CQ2 '17 TD Chick-fil-A had been gaining wallet share among this cohort for a while, but it gained some additional acceleration after the crisis, while McDonald s also saw some improvement. These two gained the most share from CQ2 15 to CQ2 17 TD, with McDonald s eating up 1.8% more share and Chick-fil-A consuming 3.9% more share. Panera, Domino s, and Taco Bell saw smaller benefits during that period while the rest of the brands did not show any obvious sustained improvements. By comparison, we did not find that direct competitors such as Qdoba and Moe s Southwest Grill benefited much during the crisis; rather, customers mostly shifted to the other concepts we have represented here. Figure 8: Y/Y Change in Wallet Share within Top Chipotle Competitors (bps) Merchant CQ1 '15 CQ2 '15 CQ3 '15 CQ4 '15 CQ1 '16 CQ2 '16 CQ3 '16 CQ4 '16 CQ1 '17 CQ2 '17 TD Chipotle -68-305 -447-732 -816-570 -491-217 88-46 McDonald's -89-15 78 224 222 150 132-3 -9 25 Chick-fil-A 186 225 233 249 282 239 231 221 117 147 Panera 26 71 80 105 134 65 32-21 -61 9 Subway -139-68 -58-49 -19-44 -41-32 -86-109 Domino's 52 49 57 84 86 100 88 46-2 14 Taco Bell 29 41 43 61 67 31 49 35 23 22 Wendy's 6 20 21 33 37 13 6 2-23 -6 Buffalo Wild Wings -3-18 -6 26 8 16-7 -31-47 -55 We also performed the same analysis of wallet share moves with Starbucks (not shown in the charts above) added in, as it occupies the second-largest wallet share (only less than Chipotle itself) prior to the crisis, and found its wallet share in this universe was expanding faster than any of these other brands. Starbucks may seem like an odd competitor here: After all, they mainly sell beverages and snacks. But given the large overlap with Chipotle customers and Starbucks recent move to expand its lunch menu, we believe the movement of spending from Chipotle to Starbucks deserves some attention. 6
Figure 9: Percent of Sales From Entire 2014 Cohort 85.0% 81.0% 81.4% 81.2% 79.6% 79.6% 77.9% 78.1% 79.1% 78.6% 77.7% Panera Chipotle 77.2% 77.1% 77.0% 77.8% 77.3% 76.5% 76.0% 73.0% 74.9% 74.5% 73.8% 69.0% 65.0% CQ1 '15 CQ2 '15 CQ3 '15 CQ4 '15 CQ1 '16 CQ2 '16 CQ3 '16 CQ4 '16 CQ1 '17 Despite the drop in transaction frequency from their 2014 regulars, Chipotle still depends largely on the 2014 cohort for its sales. Figure 9 shows the percent of sales coming from the entire 2014 cohort of Chipotle and Panera respectively. Chipotle s store base has grown at more than 2x the rate of Panera s, so we might expect sales from the 2014 cohort to be diluted faster by that. Instead, Chipotle still gets more of its sales from this 2014 cohort than does Panera from its comparable cohort. As our second conclusion, this suggests that Chipotle has become less attractive to new customers, mainly due to the E. coli outbreak s damage to the Chipotle brand. Online Ordering Digitalization is one of the hottest trends currently making over the industry, as restaurants strive to improve both customer experience and operational efficiency in a highly competitive environment. Domino s Pizza and Panera Bread are two of the most successful leaders and models of such digitalization efforts (as each has seen tremendous benefits in growth). In contrast, Chipotle has been underinvesting in its digital platforms for a long time and is only now slowly picking up on this trend. Since the launch of its new online ordering system last November, we have been able to track Chipotle s online orders (including mobile ordering) in our data. We have observed several trends. 7
Figure 10: Percentage of Chipotle s Sales from Online Orders 10.0% 9.0% 7.7% 8.0% 7.1% 6.9% 6.3% 6.4% 7.0% 5.5% 6.0% 5.0% 4.0% 3.0% 2.0% 1.0% 0.0% Dec-16 Jan-17 Feb-17 Mar-17 Apr-17 May-17 It has only been a short period of time, but early trends are encouraging, as we see adoption of online ordering trending upward (see Figure 10). We will continue to update clients on the percentage of online orders in future reports. Figure 11: Chipotle Customer Frequency Distributions in CQ1 17 Online Users versus In-Store Only 100.0% 86.1% 85.0% 80.0% 68.9% 60.0% 40.0% 20.0% 0.0% 19.2% 9.4% 10.1% 6.7% 5.2% 2.6% 2.9% 1.9% 2.1% 1 to 4 5 to 8 9 to 12 12+ Uses Online In-Store Only All Users In Figure 11, we show the distribution of frequency in CQ1 17 by whether a customer uses online ordering or orders in-store. It is clear that customers who use online ordering tend to shop more frequently at Chipotle, but we view this as a result of two factors: 1) loyal customers are more likely to use the new platform, and it is natural for this group to show higher frequency, and 2) online ordering improves the customer experience by simplifying the ordering process and reducing wait time, thereby increasing frequency. Although we cannot isolate the impact from each factor, we view the latter as (perhaps) contributing to the higher frequency, as we demonstrate in Figure 12. 8
Figure 12: Quarterly Average Frequency for Online Users Cohort 5.0 Average Transaction Frequency 4.8 4.6 4.4 4.2 4.1 4.1 4.2 4.0 4.0 4.0 3.9 4.0 3.9 3.8 3.8 3.8 3.7 3.8 3.8 3.8 3.7 3.6 3.4 3.2 3.0 CQ1 '14 CQ2 '14 CQ3 '14 CQ4 '14 CQ1 '15 CQ2 '15 CQ3 '15 CQ4 '15 CQ1 '16 CQ2 '16 CQ3 '16 CQ4 '16 CQ1 '17 Including Online (after Nov 2016) Excluding Online (after Nov 2016) Figure 12 shows the quarterly frequency trend for customers who used online ordering in 2017, where we show a trend including orders through the new platform and another excluding those orders. Given the short period of time, it is not clear to us if online ordering has contributed to overall frequency for this cohort. On one hand, overall frequency remained at higher level since the introduction of new online platform; on the other, the trend was upward sloping prior to CQ4 16. Thus, our best guess is online ordering perhaps helped improve frequency marginally in the short term, while the longer trend remains unknown. Another way we think of online ordering is that it is a step Chipotle must take to catch the industry trend, as lacking the same convenience of online ordering offered by competitors will surely hurt its long-term growth. Figure 13: Chipotle s Check Size Distribution, Online versus In-Store 50.0% 40.0% 42.9% 40.9% 41.2% 30.0% 28.1% 20.0% 10.0% 0.0% 19.7% 12.3% 6.9% 2.7% 2.4% 0.6% 2.0% 0.2% $1 to $10 $10 to $20 $20 to $30 $30 to $40 $40 to $50 $50+ Online In-Store 9
Figure 14: Quarterly Average Check Size for Online Users Cohort $17.0 $16.5 $16.0 $15.5 $15.0 $14.5 $15.0 $15.3 $15.2 $15.1 $14.5 $15.0 $14.7 $15.1 $14.7 $15.6 $14.0 $14.2 $13.5 $13.0 CQ1 '15 CQ2 '15 CQ3 '15 CQ4 '15 CQ1 '16 CQ2 '16 CQ3 '16 CQ4 '16 CQ1 '17 Including Online (after Nov 2016) Excluding Online (after Nov 2016) Finally, we show the distribution of check size by online order versus in-store order (Figure 13) and quarterly average check size trend for customers who used online ordering in 2017 (Figure 14). It is clear that online orders tend to enjoy larger check sizes, as the group size is usually larger and perhaps customers are more likely to order add-ons promoted at the end of the ordering process. It is also worth noting that currently 2% of online orders are above $50, while nearly none of the in-store orders exceed this threshold. This suggests that customers are much more likely to make catering orders if they use online ordering. Catering has been a highly fragmented category in which Chipotle has hoped to gain more share for years but without much success. It is probably the next big catalyst to drive Chipotle s same store sales over the next two to three years if Chipotle can find the right formula. The new online ordering platform could theoretically help Chipotle s catering business compete against mom-andpop shops, but whether or not Chipotle will meaningfully grow its catering segment remains to be seen. Overall, Chipotle is finally starting to ride the wave of digitalization. It is early days and the company is still behind some major competitors, but digitalization undoubtedly opens up a lot of possibilities for the brand. The longer-term impact depends on the company s execution of its plans and its commitment to investments in digitalization. 10
M SCIENCE ANALYSTS Mark W. Bachman Vice President, Senior Analyst, TMT 503.433.1544 Mark.Bachman@mscience.com Byrne Hobart Vice President, 646.751.1444 Byrne.Hobart@mscience.com INDUSTRIES Cable and Satellite China Internet Consumer Technology Grocers Internet Leisure Retail Semiconductor Technologies Telecom Video Games Corey Barrett Director, Senior Analyst, TMT 503.433.1545 Corey.Barrett@mscience.com Michael Erstad, CFA Director, 646.751.1426 Michael.Erstad@mscience.com Matthew Goodman, CFA Director, Senior Analyst, TMT 646.751.1428 Matthew.Goodman@mscience.com Henry Guo, CFA Vice President, Senior Analyst, TMT 503.433.1547 Henry.Guo@mscience.com Matthew Jacob, CFA Director, 646.751.1429 Matthew.Jacob@mscience.com Troy Li Vice President, 646.802.6273 Troy.Li@mscience.com John Tomlinson, CFA/CPA Managing Director & Head of Consumer, 646.751.1443 John.Tomlinson@mscience.com Steve Weinstein Managing Director & Head of TMT, Senior Analyst, TMT 503.433.1543 Steven.Weinstein@mscience.com 11