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Lab FollowUP sin fondo 1

We have a team of
research and investigation:

Lab FollowUP sin fondo 1

We have a team of
research and investigation:

3 important concepts to understand the analytics of the shops

analitica en las tiendas

To monitor the visitors and their journey into the physical store, these data can be transformed into metrics of retail and relevant information on which we can act to improve the performance of the store.

To achieve this, we review the most important concepts.

The consumer's experience of the physical store is NOT equal to your online behavior

The behaviors of the consumers are very different according to the place in which they find themselves; it is not the same as a buyer in a store that one in an online store.

Three differences largest are:

  • Linear tour online vs. parallel scan in the store: the online behavior is governed by the way in which people change their web sites, simply through clicks. We can not apply this thinking is linear to the shopping in the physical stores.
  • Visit lonely vs. visit social: on the Internet, while people can buy them for themselves or for others, which they usually do in solitude. In contrast, the experience in the physical stores are highly social.
  • Different objectives: the optimization of the conversion rate, you can work very easily to the e-commerceas the digital products are easy to replicate. On the contrary, the physical stores have costs and limits only. Although the conversion remains very important, the gain it is the secret for these stores.

2. Define your data to choose between personalized marketing or behaviors anonymous

Something that is often confused much is the difference between the data custom, obtained from consumers, and the anonymous.

We don't need to know the identity of the person; with monitor anonymous objects we can get all the information that we need. The behaviors of these objects – captured thanks to the monitoring technologies provide key information on which to act.

In this sense, the people counter to quantify the demand of a store, to compare it between different locations and measuring the traffic of proximity.

The goal is to know what happens before you pay. To do this, we can focus on the attraction of the shops, the call to action the physical store and the prevention of friction points. And all these data are from only the information anonymous the behavior of the consumers.

The key is to understand that in order to improve the performance of the store is not necessary to have personalized information.

3. To analyze the experience of the consumers

The shops that focus on the consumer were designed based on the main elements of the user experience. To understand perfectly the KPI the physical store, we must clarify some basic concepts.

experiencia de compra

Client is the general term we use for everything in retail. However, it is very useful to classify people at least in these three types:

  • Visitor: the person inside the store, since they enter until they leave.
  • Consumer: the end user of the product.
  • Buyer: the person who pays (can be alone or in group)

The decision of how to classify them is entirely subjective and depends on each store.

In addition, we will clear up some concepts about the context for understanding the experience and behaviors of consumers.

  • Point of sale (POS, by its acronym in Spanish) – Point of Sale): the purchase history of the customers points to the sectors and products from the shop that may be of interest to a particular person.
  • Management: monitor products, categories and brands provide information about the most popular shopping, specialized products, and which are more rare.
  • Range and distribution: indicates what products are available for sale in the physical store and where it is located within it.

It is important to have on hand and frequently review the basic concepts of the analysis of retail in order not to lose sight of the goals and work to achieve them. In this way we can constantly optimize the performance of our shop.

Business intelligence

Big Data