Interaction and feedback in digital signage

Summary of Part I and II

This article is the last of a series of three on Proximity Marketing.

In the first article, I describe my idea of the Marketing of the Future, based on a targeted, dynamic and interactive communication. In the second part, I introduce the concept of proximity marketing lifecycle and the model of interactions between customers and stores. More in detail, I describe the experiential phase and its optimisation when a visitor is inside a point-of-sale. I also give some real-life examples.

As stated in the first article of this series, I am trying hard to keep it simple, to avoid technicalities and use a simple and didactic style.

In this last article, I will address the interaction phases that happens in the point-of-sale and the analysis of the collected data. I will also give some reference to specific technologies and models, paying attention not to lose myself in the clouds …

Interaction

What does “point-of-sale interaction” mean? It can actually mean many things, some more complex than the others. The example that I gave in the previous article is one kind of this interaction. It was about how to manage promotions and queues in retail, through a Digital Signage solution. This scenario becomes interactive when the content shown on the displays is based on real time analyses (of sales or inventory).

The key concept here is the bidirectional nature of the interaction and not its voluntary nature.

We already know that loyalty systems allow marketers to learn about their customer habits and to develop targeted marketing campaigns. One can imagine several ways of integrating these systems into an advanced Digital Signage solution. For example, one could imagine a model where after the customers swipes their own loyalty card, a special offer appears on screen, tailored on their needs and desires. This could be totally automatized, and implemented in one or all of the branches of a given network.

In the classical loyalty model, the interaction is based on the physical contact with a magnetic card. Why shall we limit ourselves to such a constraint? Why not imagining different kinds of interaction, for example based on “proximity” or “detection” technologies? This would allow to develop advanced data-gathering and segmentation models, such as models based on customer behaviour inside the point-of-sale or on the dynamics of accessing different locations inside the same store.

These analyses represent the first step in building Business Intelligence models, including advanced visualisation.

Feedback and Analysis

I see the point-of-sale as a dynamic entity, whose behaviour and success depends on many variables and on the customer reaction to the marketing campaigns. This is why knowing in real time the (implicit and explicit) customer feedbacks is a huge competitive advantage.

For example, in-store promotions could be dynamically changed on the basis of sales (to suggest top selling items) or of inventory (to manage supplies). The communication could be done through Digital Signage displays, Bluetooth or any mobile application.

After all, I believe that proximity marketing is itself a form of mobile marketing. All of the devices used to gather information on the customers (such as loyalty systems, radio frequency devices and camcorders) are part of a network of elements. There is, however, another class of elements in the interaction between the user and the point-of-sale. These are the elements that does not require the physical presence of the customer in the point-of-sale. This is becoming more and more common because of the wide distribution of advanced mobile devices, like smartphones and tablets.

This is makes me think of the following question: can proximity marketing and mobility marketing converge to an interaction model that goes beyond the point-of-sale? Yes, they can! Thanks to the integration of an orchestrating multichannel system, through WS*- interfaces running on the cloud. One thus expands the potentials of the proximity marketing lifecycle, by adding elements of geographical analysis. This also triggers lateral dynamics about supply and interexchange among different branches according to the market demands in real time. This is Marketing in the Cloud!

cloud

Semantic Marketing

A need is emerging: the need to relate marketing and semantic. A possible way to do this is by exposing, sharing and connecting data through web connectors and sorting techniques. This way one can share and operate on the data through some specific API and data format. The goal of semantic marketing is to use data originating from different sources, to develop a single language that expresses how data relate to each other and to real-world objects. For all of these reasons the semantic marketing tightly connects to the following three areas:

  1. disambiguation of documents,
  2. structured data,
  3. “Linked Data”.

It is not a simple data extraction from ordinary channels. Data themselves represent the preferred channel.

Service Orchestrator

The use of abstract and open communication interfaces (Services Oriented Architecture, SOA) allows to develop several logics and algorithms, in different phases. This makes it possible to create an orchestrating system, which will be able to evolve in a modular and flexible way. According to future needs, one will be able to use the same system without modifying the connectors with third parties and with proximity devices.

This model has an architecture that is built upon the following functional layers and communication interfaces:

  1. Profiling
    An identity module management, which is the foundation of the user management. This will determine the privileges of each user, according to the different use cases, and will manage the processes of linear workflow.
  2. Input
    This part relates to user interaction, and manages the use of devices and sensors, according to the model defined in the profiling section.
  3. Business logic
    It elaborates the metadata, also by using semantic models and analyses. It determines the evolution of the event-based and action-based workflows.
  4. Output
    This part refers to the interfaces with third parties system and proximity video devices, e.g. suggestions for multichannel purchases, targeted and proactive information.
  5. Feedback
    This is a data analysis and reporting module, whose diffusion will take place also through WS-* services. This will make the information available both as aggregate data in XML format and as statistical graphs, automatically generated according to the chosen analytical model.

Cloud Marketing

The service orchestrator is the converging point of the proximity marketing lifecycle. Loyalty systems, interaction devices, proximity devices, systems for mobile device detection and connection, they all refer to the orchestrator. The business logic is tightly linked to the semantic engine. Some of the output elements are the information flows, which in turn will be used for visualisation.

The development of the cloud-marketing model needs to be continuative and it should regard all of the activities and the applications. It should include the information flow regarding the different connectors. This model will broaden with time and it will adapt by its own nature. This will be possible thanks to an underlying evolving business-logic model.

Some insights and drivers for development

A relevant point for our topic is the data format to use. We want to define the semantic context through the metadata collection and query, interpretation and automatic processing of the collected information. For this, one can’t but use the XML format, as described in the standard Resource Description Framework (RDF), defined by W3C. It is also desirable to take advantage of advanced solutions like N3 and N3 with prefixes.

Regardless of the development approach, it is important to have high quality data, in particular to have sharable and time-reliable data. Therefore, when designing the gateways for the collection and the modification of centralised data, one need to choose normalised terms to define relations. Whenever possible one should use known and widespread dictionaries, rather than creating new terms every time. For example, regarding personal data such as name, address, company … one can used the vCard standard.

Finally, one should be able to manage a system for answering user requests with some kind of assertions. For this, one can use some tables of a relational database. The SWAD-Europe project, sponsored by the European Union within the IST initiative, has surveyed some solutions of this kind.

Conclusions

This article is the end of the series, where I have shared my own visions about the Marketing of the Future, based on a dynamic and interactive communication. I have suggested a possible description for the Proximity Marketing lifecycle in four phases: (1) experience, (2) optimisation, (3) interaction and (4) analysis. I have completed the detailed description of the lifecycle in this article. Actually, I realise that in this part, I have also commented on models and technologies. I hope that this will have the effect to stimulate more curiosity and thirst of knowledge.

End of the series about Digital Proximity Marketing.
Here you can read the first two parts of the series:
Part I: Marketing of the Future
Part II: Proximity Marketing Lifecycle

About the Author

Riccardo D’Angelo (Edisonweb Srl founder) is passionate about effective technologies. He likes to bring innovative products into businesses as well as into public organizations. Edisonweb invests heavily in R&D and fosters collaborations with Academies in order to stay ahead of time. Web Signage by Edisonweb has been the first Digital Signage platform Worldwide available on Microsoft Cloud Platform and has received several international awards in the Digital Signage market.

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One response to “Interaction and feedback in digital signage

  1. Pingback: Marketing of the Future – digital proximity marketing | Digital Signage Visions·

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