Chain of Restaurants

Client’s order

A chain of restaurants wants to know what their customers’ opinion of them in order to get to know the kind of corporate image that they transmit.

Network Outsight’s suggestion

  1. Conversation monitoring around the chain’s Twitter account for a week.
  2. Assessment of the audience’s reviews about the chain (sentiment analysis).
  3. Conversation leaders detection.
  4. Analysis of the stable community formed around the chain’s Twitter account.

Obtained results

  1. We shew the customers’ recurring conversation topics that refer to the chain of restaurants.
  2. We could see whether the sentiments towards the chain were positive or negative.
  3. We detected opinion leaders (those who spread the message) and brokerage leaders (those who help spread the message further).
  4. We analysed the community structure of the users that follow the chain of restaurants.
  5. We found two market niches to be explored. The first one, a users subcommunity that was algo engaged with the competition; the second one, formed by gourmet bloggers and youtubers. Then, what we expected was that the first ones talk about our product to the competition’s customers, while the second ones value our restaurants positively and attract more customers.

 Method

Conversation monitoring

With the conversations monitoring, we listen to in order to improve the company’s relation with the customers. Depending on the depth of the analysis, we can get to know what your customers talk about when they address your company or the usual conversation topic of those who follow you and of those you want you to follow, in order to know their characteristics and how we can get your company closer to the niche.

Example: a week of conversation on a chain of restaurants:

What do the customers talk about?

 

 

What is the attitude that their message expresses?

 

What is the structure of the conversation? And who are the leaders?

 

The graph above shows the structure of the conversations monitored for a week. Each ítem (node) is a Twitter account that has said something about the chain of restaurants, and each line (edge) is a @mention in the conversation directed from a node to another. The size of the nodes corresponds to a number of received mentions. The colour of the nodes corresponds to the conversation group—those nodes that have spoken with each other intesively have the same colour—.

We could observe two clearly different conversational poles: on the right, some isolated nodes in Green; on the left, the rest. The nodes a, b and c are examples of opinion leaders: they receive lots of attention on Twitter. In contrast, the node d does not receive so much attention, but it is a brokerage leader, because those who want to connect with the other pole will have to do the way through it.

Knowing the conversation topics of a community, its attitudes towards the brand, and the structure of its conversations, groups and leaders allows us to:

  • Optimise digital strategies;
  • Target markets;
  • Assess and find emerging niches;
  • Assess the impact of a product, phemonenon or advertising campaign;
  • Detect opportunities to immprove and/or innovate.

 

Community analysis

It is essential for any company to know the stable communities around their social accounts, as well as the conversations around their product or service, and those of the competition—the competition can only be correctly monitored on Twitter via this method. This involves knowing not only who follows you, but also why (what they expect from you) and how your followers can contribute to make you get further.

Example: the stable community of the chain of restaurants: the relations among the last 300 followers.

 

On the graph above, each node represents a Twitter account, and each edge a following-followee realtion among users—the information this provides is different from the previous example, where the edges did not represent following-followee relations but mentions among users.

The node a represents our chain of restaurants, while the node b its competition. We have increased its size in order to focus on the relevant information. As we can see, the majority of a’s new followers do not follow b at the same time, but some of them yes: this is the community A. It is important to pay attention to these nodes, because they will be probably connected with some followers of a’s competition, and we are interested in making them talk about our product.

The majority of a’s new followers do not have following-followee relations among them.  This is the case of the nodes c and d. The node a, our chain of restaurants, has decided to follow ver few of its new followers, for instance, it has decided to follow c, and then, they appear to be closer than b and d. It happens to be a bit worrying that a has not followed any of the nodes of the community A yet, that are those who are in contact with b, that is a’s competition.

Finally, let’s see what the relatively intense associations in the communities B and C reveal us. In the community B we find profiles related to advertising and marketing companies: although they are important followers that should be treated properly, they are not a consumers community, but Twitter accounts that may expect something from our restaurant. In contrast, in the community C stand out gourmets, bloggers and youtubers, they are very relevant profiles in commercial terms, because their reviews may have a big impact on Twitter.

 

Knowing the characteristics of a company’s stable community and of its subcommunities, or those of our competition, allows us to:

 

  • Plan strategic actions on Social Networks;
  • Get to know the market and target it;
  • Adjust the sort of message to be spread depending on the group of trading partners;
  • Assess the success of a digital strategy;
  • Bring over potential customers and optimise the customer loyalty phase.

 

If you wish more information on the implementation of all this work into your company, brand or product, contact us:

 

 

 

Icons made by Freepik on www.flaticon.com lisenced by CC 3.0 BY

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