Gaode map vs. Dianping: A Repeat of Alibaba's Triumph and Meituan's Tears?

A few days ago, on September 10th, the anniversary of the company, $Alibaba(BABA.US) officially launched the "Gaode Street Sweeping List" using its Gaode App as a platform. This local business recommendation list currently includes three main categories: food, hotels, and tourist attractions. Amidst the ongoing fierce competition in the food delivery market, Alibaba's launch of the "Gaode Street Sweeping List" is seen by many as the starting signal for Alibaba's official entry into another core segment of Meituan—its in-store business.

Although the project has just been launched, the functions and page design within the Gaode App are still quite rudimentary. The previously announced initial subsidy scale of about 1 billion for the "Street Sweeping List" clearly shows that Alibaba is still in the exploratory stage of its in-store business. It is far from the time to judge whether Alibaba's "Street Sweeping List" can succeed or compete with Meituan Dianping.

However, given that Alibaba has already invested significant resources in the home delivery business (food delivery and instant delivery) and achieved initial success, and with management clearly stating that one of the group's core medium-term goals is to develop its domestic e-commerce business into an all-encompassing "pan-consumption" platform, the in-store business is evidently a sector that Alibaba is determined to pursue and is likely to continue investing in over the coming years.

Dolphin Research takes this opportunity to share some initial thoughts on Alibaba's Gaode Street Sweeping version.

Below is the detailed content:

I. What is the big strategy behind Gaode Street Sweeping version?

As mentioned in the introduction, Alibaba's "restart" in the in-store business is still in the early trial stage, and it is not very meaningful to try to predict the outcome now. Instead, more attention should be paid to the motivations behind choosing to focus on the in-store business again at this time, what goals might be pursued, and what potential benefits there are for the group as a whole. Combining previous statements from Alibaba's management in the second quarter and Dolphin Research's own views, we believe the intentions behind Alibaba's move include:

1. Home delivery + in-store linkage, strengthening local life supply linkage: First, Alibaba has already made significant investments in the home delivery business (food delivery + instant retail)—with an expected annual investment of 50 billion over three years—and achieved impressive results (80 million daily orders in August, with a relatively small gap compared to Meituan) and high market attention. Alibaba's entry into the in-store business, which complements the home delivery business, is seen by Dolphin Research as a "natural progression" and an expected move.

After all, the supply and demand sides of the home delivery and in-store dining businesses can be significantly reused. Since the merchants and consumers of in-store and home delivery services are almost the same group, advancing both in-store and home delivery businesses can help quickly expand the number of merchants covered and strengthen the merchants' ties with Alibaba's local life platform. On the other hand, enriching the supply and usage scenarios of local life merchants will also help cultivate consumers' mindset and frequency of using Alibaba's local life services. This will get the dual-sided flywheel of Alibaba's local life business spinning.

2. Bringing in Fliggy, linking hotels and travel: Additionally, the three major lists released by Gaode Street Sweeping not only include a food-related list but also hotel and attraction lists. Previously, during Alibaba's organizational restructuring, only Ele.me and Fliggy's hotel and travel businesses were integrated into the core China e-commerce segment, but Alibaba had mainly focused on the home delivery business, with little action on Fliggy's hotel and travel.

In a previous conference call, Alibaba's top management clearly stated that one of the company's major strategic goals in the medium term is to build an "all-encompassing" large consumption platform. Therefore, the intention of Gaode Street Sweeping is not only to promote in-store and home delivery businesses but also to more organically integrate the hotel and travel business into the entire domestic e-commerce ecosystem, aiming to achieve cross-traffic effects.

3. A new traffic entry point for the group: Similar to the logic behind Alibaba's initial decision to focus on the home delivery business, at the group level, the in-store business also has the potential function of serving as a traffic entry point for the entire group. However, according to expert research, among Meituan App's overall monthly active users of slightly over 500 million, the monthly active users of the in-store business are about 200 million (no exact data, for reference only). Compared to the approximately five times monthly order frequency of the food delivery business, the usage frequency of the in-store business is about 1-2 times per month. From both the number of active users and the average order frequency, the traffic attraction effect of the in-store business may be weaker than that of the home delivery business.

II. Is using Gaode Map as an entry point a different approach?

Of course, the goals mentioned above are more of a vision, and it is currently impossible to determine how much can ultimately be achieved, how much incremental value it will bring to Alibaba, and what impact it will have on competitors like Meituan. However, from Alibaba's choice to use Gaode or map navigation to undertake the entry function of the in-store business, we can glean some insights into the similarities and differences in Alibaba's approach to the in-store business compared to its competitors, as well as the potential impact.

1. Will Gaode only do distribution and not transactions?

As a basis for the discussion below, what is the main function of the business model of the in-store business? Dolphin Research believes that, in summary, the function of the in-store business can be divided into two parts—information aggregation function and transaction platform function. The former's best example is Dianping (as well as overseas Yelp and Google Maps), which mainly provides merchant information and corresponding consumer reviews to assist consumers in making decisions. The latter's example is Meituan's core business at its inception—selling group-buying coupons, providing the function of selling offline in-store merchant products/services online.

With the merger of Meituan and Dianping in 2015 as a landmark event, domestic in-store platforms that have grown up generally integrate information distribution and transaction functions. However, what is slightly different is that Alibaba announced that Gaode Street Sweeping List will "never monetize" in terms of information distribution, meaning it will not use a merchant advertising bidding model to avoid merchants "distorting" rankings or user reviews by spending money. Of course, Gaode's list will still have a transaction-side monetization, namely group-buying business, but this is handled by another team under Taotian, and it seems that the Gaode team will only provide an entry point for group-buying but will not use this as its own work KPI.

2. What are the differences between Gaode, Meituan, and Douyin?

Therefore, we will first focus on the information distribution function (Alibaba's new group-buying business has not yet been fully launched) and, through comparison, see what are the main differences between Alibaba's in-store business, which uses maps as the primary entry point, and its competitors Meituan and Douyin?

First, according to Alibaba's own statement, one of the main pain points of Meituan Dianping is the common occurrence of inflated or fake reviews, leading to distorted user ratings and reducing the ability to effectively assist consumers in making decisions. Therefore, one of Gaode's main differences on the surface is leveraging its advantage of being able to accurately obtain real-time consumer location information to introduce more objective behavioral data (such as the number of people navigating to a restaurant, navigation distance, number of repeat customers, etc.) to provide consumers with more objective and authentic merchant ratings.

On the one hand, Dolphin Research believes that recommendations based on location information, which is relatively difficult to fake (compared to subjective personal reviews), undoubtedly have their value, but whether the "water content" in merchant reviews can constitute the core factor determining the success or failure of the in-store business, Dolphin Research remains cautious.

In contrast, using the previous analysis framework of Meituan vs. Douyin, Dolphin Research believes that the main difference in the in-store business, which focuses on information distribution as its core function, lies more in: a. the mode of merchant information distribution, whether it is more inclined to "people looking for stores" or "stores looking for people"; b. when assisting consumers in making in-store consumption decisions, whether the platform intervenes earlier or later.

From the characteristics of the three platforms, Gaode, Meituan, and Douyin, Douyin is a typical "store looking for people" + intervenes relatively early (planting grass stage); Meituan is "people looking for stores" + intervenes relatively late (close to consumption); while Gaode Street Sweeping List currently appears to be in the middle of the two, more inclined to "store looking for people" + intervenes relatively late.

These differences can be glimpsed from the page layout and information display logic of each app, specifically:

1) "Encyclopedia" style Meituan

Meituan, which has the most accumulation in merchant information and reviews, displays information in a traditional but efficient, easy-to-search graphic mode, similar to an "encyclopedia" of in-store merchant information. In terms of page layout, Meituan has also conducted the most detailed classification, for example, setting up separate entrances for "group-buying," which is more transaction-oriented, and "food," "leisure and entertainment," which are more information distribution-oriented. In the secondary interface, more detailed classifications are also provided, helping consumers conveniently and efficiently search for and discover the stores they need.

And Meituan's "encyclopedia" style determines that consumers are more "people looking for stores," with relatively clear goals or potential intentions, actively searching on Meituan. Therefore, Meituan's in-store business model has the highest conversion rate, closest to transactions.

2) Douyin with video as a featured carrier, but ultimately the same destination

In contrast, when Douyin initially entered the local life market, it relied more on its strengths in short videos and live streaming. Consumers randomly come across videos introducing restaurants or comprehensive merchants, become interested, and then view further information or purchase group-buying coupons through links attached to the videos.

This model is "store looking for people," using short video algorithms combined with vague address information to push in-store merchant information to potentially interested users. In this model, it is more likely that the video plants a seed of interest, creating demand, rather than consumers having a potential demand and then searching for it.

However, Dolphin Research also found that from Douyin's initial attempts at the in-store business in 2018 to today, Douyin has also built a search interface for the in-store business similar to Meituan's "encyclopedia" style. Douyin's main interface for the in-store business now also provides search, lists, and various classification entrances (food, leisure, attractions, hotels, beauty, etc.). In the secondary interface, more detailed subcategories are also available.

It can be seen that as the in-store business matures, in addition to the unique carrier of short videos, Douyin has also supplemented the "people looking for stores" model, which is closer to transactions. According to expert research, although the total number of in-store merchants covered by Douyin may still be slightly less than Meituan, this is already a difference in quantity rather than quality.

3) Does Gaode, using maps as a carrier, have differences?

Compared to the two competitors, it can be seen that Gaode's current in-store business interface and layout are still in a relatively early and "rudimentary" state. Entering the "Gaode Street Sweeping List," there is currently only one distribution mode, the list, with no detailed classification search function, and the number of stores covered seems relatively small. It can be seen that at this stage, Gaode Street Sweeping List is more inclined to the "store looking for people" information distribution model, recommending stores to users based on precise location information.

Compared to Douyin's "store looking for people" based on short videos, it can be foreseen that Gaode's disadvantage is that it does not have as high traffic as Douyin (after all, few people would casually browse maps), and it cannot use a huge traffic advantage to help merchants quickly increase exposure and reach a large number of new users. The advantage is that Gaode's address information is more accurate than Douyin's, so logically, Gaode (after enriching the number of stores) can recommend stores more accurately and in more depth. It will not be like Douyin's featured video channel, which is more suitable for chain stores, which on the one hand have the ability to bear the higher cost of video customer acquisition, and on the other hand have a higher number of stores and coverage to compensate for the inability of video recommendations to accurately locate.

Similarly, since logically, when users use maps to view stores, they generally already have a potential consumption intention, and the range of choices is mostly limited to a few stores within 3-5 kilometers, so logically, the conversion rate of Gaode Street Sweeping List reaching consumers should be higher than Douyin's short videos, closer to Meituan. That is, the combination we summarized earlier of more "store looking for people" + closer to pre-transaction.

Summarizing the above, it can be seen that the three platforms have adopted different combinations in terms of "people looking for stores or stores looking for people" and "close to or far from consumption," each with its own characteristics, so logically, they should each have their own suitable advantage scenarios and user groups (of course, there are still differences in quantity).

However, from Douyin's example, Dolphin Research believes that as the business matures and merchant information accumulates, Gaode will sooner or later launch a search interface similar to Meituan's "encyclopedia" style. The three platforms are likely to converge, retaining some differentiation while becoming increasingly similar in content and form (similar to the current e-commerce industry).

III. Can Gaode become a "Google version" of in-store?

Is there really hope for Gaode, a latecomer to the in-store track where user mindset and habits are already mature, to knock down Dianping's signboard? The war between map distribution of local life information vs. user review platform distribution of local life information has already happened abroad. Let's take a reference:

In overseas markets, the competition between map applications—Google Map and the overseas version of Dianping, Yelp, for the in-store information distribution market has long been decided. However, unlike in China, where specialized apps like Meituan + Dianping ultimately captured the main share of the domestic in-store industry, overseas, it was Google Map, a comprehensive entry point, that defeated the specialized Yelp, becoming the most mainstream in-store information distribution platform overseas.

1. Why did the overseas Dianping, Yelp, lose to Google Map?

First, from a product perspective, Dolphin Research shows the page layout and content situation of Yelp and Google Map in in-store information distribution in the image below. In summary, apart from some minor differences in color and layout, Yelp and Google Map are almost identical in their "people looking for stores" recommendation logic and information display format.

Both use real-time location as the underlying information, combined with the type of store searched by the user, providing map markers + graphic list-style store recommendations. After clicking on a store, a brief introduction, contact information, user reviews, menu information, and some stores' support for online reservations or delivery services are also basically consistent.

So, in the absence of significant differences in the products themselves, what caused Yelp, which focused on in-store earlier, to be overtaken by Google Map? Based on related overseas research, Dolphin Research believes the main reasons are:

1) Yelp's own development mistakes: Although Yelp was established as early as 2004 and was one of the first companies in Europe and the United States to focus on in-store information distribution, with early data accumulation in store information and user reviews, Yelp's following key mistakes squandered its first-mover advantage:

a. Yelp's functionality has always been limited to "reviews," failing to expand into a comprehensive local life platform like Meituan, which includes reviews, group-buying, delivery, hotels, and travel. This led to the company remaining a niche platform with limited resources in terms of funds and technology.

b. Yelp also made the common strategic mistake of many early internet companies, failing to timely divert traffic from the web to the app, missing the first-mover advantage of internet mobilization.

c. Due to Yelp's single business and monetization channels, it almost entirely relied on advertising fees from merchants. Under monetization pressure, Yelp experienced a scandal of folding non-advertising merchants, significantly damaging its credibility as a store review platform.

2) Due to the above operational mistakes by Yelp, it failed to become a leading comprehensive platform like Meituan in China, resulting in having almost no resistance when facing competition from the giant Google Map in in-store information distribution, the underlying reasons being:

a. The blow of traffic reduction: In contrast, Meituan in China, although also a niche professional platform, with its diverse business layout, has over 500 million monthly active users, making it one of the largest traffic sources in China. Even if it is not as large as platforms like Douyin and Taobao, it will not be crushed.

Looking at Yelp, with its thin business and scale, its own active users are only in the tens of millions, unable to generate enough traffic on its own, relying on other major platforms like Google, including its competitors, for traffic. In contrast, Google's user scale is over 1 billion, an absolute crush on Yelp. It can cut off or reduce traffic to Yelp and instead support its own Map's in-store information distribution.

b. Content and information themselves do not constitute a barrier: As shown earlier, Yelp and Google Map are basically consistent in page design and content display, without significant differentiation and exclusivity. Therefore, although Yelp had a phased accumulation advantage in store information and user reviews, Google, with its absolute user scale advantage (the more people use it, the faster it can accumulate reviews and other information), can quickly erase its disadvantage in store information accumulation.

3) Summary: From Yelp's experience, it can be seen that due to the homogeneity and non-exclusivity of store information, and Yelp not forming a unique information release method, when facing competitors with larger traffic and scale, it basically has no ability to resist.

IV. Conclusion

Based on the above discussion, Dolphin Research's current preliminary view on Alibaba's attempt to do in-store business through Gaode is:

1. There is no barrier on the supply side, but it is a matter of patience: The essence of the in-store business is the online information distribution of offline stores, or advertising promotion. Therefore, first, the supply of store information is non-exclusive, homogeneous, and relatively limited—anyone can obtain it with time, and no platform can have a large number of exclusive store supplies. (Unless the platform operates itself or signs "choose one of two" clauses, but under current antitrust, it is difficult to operate.)

And since the final fulfillment is done in offline stores, unlike the delivery business, which requires the platform to build a heavily invested fulfillment capability, this does not constitute a significant barrier.

Therefore, without considering significant differences in monetization rates between platforms, the supply of in-store business is more a matter of whether one is willing to spend time patiently accumulating.

But the lack of barriers in store information supply does not mean it is not important, on the contrary, the breadth and depth of store information coverage are necessary. For example, even if Gaode and Meituan achieve similar store coverage in high-line cities like Shanghai, if Gaode does not cover certain third- and fourth-tier cities when traveling far, users will still abandon Gaode and choose Meituan in the long run.

Therefore, the currently significantly fewer number of stores on Gaode is an issue that needs to be quickly addressed.

2. In an ideal state, traffic is paramount on the demand side: Assuming similar store coverage and supply, different platforms should not have significant differences in information matching efficiency, especially when consumers have roughly determined the consumption location or target. After all, the supply-demand matching of in-store business is conducted in a given scenario and time. More vividly, within the range accessible to consumers (generally up to 3-5 kilometers), the number of stores of a certain type is quite limited, unlike e-commerce, which requires precise matching of potential consumer requirements among "thousands" of supplies.

Therefore, in the absence of significant differences in merchant supply (with accumulated merchant information and user reviews already done), Dolphin Research believes that the in-store business is largely a traffic-driven business, and whoever has more users, usage frequency, and duration is more likely to succeed in the in-store business.

And Alibaba's choice to use Gaode as the main entry point for the in-store business, in addition to facilitating access to location information, is also because Gaode is one of the few national-level apps with the highest user numbers and duration in China. According to QM data, Gaode's monthly active users exceed 800 million, and daily active users exceed 150 million, even slightly surpassing Meituan App in terms of traffic.

3. There can still be differentiation in actual situations: Also, due to the relatively limited and homogeneous supply of in-store, theoretically, consumers do not need to compare multiple platforms. Once they have a commonly used platform, they do not need a second one. Dolphin Research believes this is also why, before Douyin entered the market, no company truly challenged Meituan's leading position in the in-store business for a long time.

So, using Douyin as a case, the reasons it was able to grab a large market share from Meituan, which almost monopolized the in-store business, are: a. its unique traffic advantage, b. its unique video mode, and advancing the intervention point to the planting grass stage, reducing the spatial and temporal limitations of in-store consumption decisions, expanding the potential customer base, at the cost of reducing the conversion rate.

In contrast, Dolphin Research currently believes that Gaode does not yet have significant differences in traffic and information distribution form compared to Meituan. The user scale gap between the two is not large, and the mode is still mainly in graphic form and close to pre-consumption decisions.

4. "Never monetizing" may become a differentiation point?: Although currently, only from the perspective of information distribution, Dolphin Research has not yet seen significant differences between Gaode and Meituan. But commercialization may indeed become a key difference, because although the supply of merchant information is homogeneous and non-exclusive, if considering the different monetization intensities of different platforms for merchants, then the differences in which platform merchants are more suitable or willing to settle in will form.

For example, as mentioned earlier, chain head merchants are relatively more suitable for video or live broadcast promotion methods. If Gaode can truly adhere to "never monetizing," then unlike the advertising bidding priority model, it may indeed uncover more "smoke and fire" small store supplies that do not have much promotion budget but are of good quality. But whether this niche track is large enough to become Gaode's unique base still needs observation.

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