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by Ecaterina Ganenco, Business Intelligence Developer, LASTING Software
(Presentation held during Codecamp Timisoara, April 2018)

The story of how developing a consumer mindset testing software influenced my way of thinking

Have you ever wondered why movies in Cineplex’s are scheduled? Why are they not always starting at the same time, or how managers show more comedies in a country, whereas a continent prefers romance better?

Before being part of the development team I used to go the movies rarely and mostly to Russian movies, which were more popular in my area.

The Movie Scheduling Problem and its data resolution

When people come to see a movie, they are following through a previous decision.

Decision-making is the process of identifying and choosing alternatives. It is based on the values and preferences of the decision maker. It also is a cognitive process.

Reverse engineering this cognitive process resulted in a smart software. A software capable of anticipating the selection of movie shows that would gather the most spectators. Among several alternative possibilities, the software aimed at pointing out the most suited scheduling formula.

The factors computed into such software are:

  • Decisional balance
  • Simple prioritization
  • Consulting with a person in authority
  • Anti-authoritarianism traits
  • Automated decision support (rule based)
  • Decision support systems (decision-making software)

The difficulty consisted in overcoming the decision-making paradox. Sometimes people decide predictably, while other times their choices may seem random. Yet, there are a few steps that would sift out the random factors as much as possible:

Formal analysis ->Covered problems -> Generalized set partitioning problem

Remember how historical data works magic? Inputting the historical data related to moviegoers serves in establishing patterns. We may not be able to eliminate the random element in decision making. But we can find the patterns in the outcome of the decisions, taken as historical data.

The app aimed to plan the best strategy. Finding out the optimal movie program for a single day given meant that it required a lot of details. The capacities of the screening rooms, the technical capabilities of this rooms, the list of movies to be shown or the set of theatre specific constraints are all important.

The formula is not unusual in big data apps. The users generate data – data is collected and packaged – the software processes the data – the software returns the data. Based on the returned results (predictive analysis), those who use the software can now make their own efficient decisions.

While working in software development, my focus is on the technology. However, my personal experience is that my mindset can also change due to the value and impact of the solutions I’m working on.

When I worked on a tool serving in understanding the consumer mindset, I had the opportunity of figuring out its impact on more than one levels. Such tools use big data. Finding out how my work, how the efforts of my team turn into structured data and facts, was important for me.

Products and services need to reach the people who enjoy them. In other words, the consumers need the right offer at the right time. Determining these key parameters may be a software matter.


How it is started:


How do you help consumers to consume more?

It is very simple – you provide quality and the right services/products.  Companies achieve product uniqueness through an elaborate process. But in this process they must answer two main questions:

–  What do consumers want?

–  What do (their) customers want?

Answers vary, of course. Factors such as culture may trigger different answers. I asked myself “So what determines if culture matters?” There are subjective areas of culture, for example religion, education or history. These stand the chance of being extremely personal. Their effect is seen in choice variations between different background people. Other factors are unifying. Patterns or globalization will ensure similar choices for example


We are consumers of our (own) applications

Any developer may answer the questions above when imagining it is she/he who uses that precise software solution. I thought about what I like and dislike as an app user.

What I like

– The application should solve a problem
– Easiness to navigate between pages
– Design

What I dislike

– If the application doesn’t resolve the problem
– If the application is not user friendly
– Performance issues

If the result of my work falls into the first category, I feel motivated.

Don’t ignore the word of mouth

Word of mouth or viva voce is organic marketing in itself. It can boost a product. It can also point out what goes wrong with it, before it’s too late. So just think:

– What sort of conversations are people having about the product?
– When people share information about our program, does it tend to be positive or negative, emotionally charged or indifferent?

Defining consumer groups

My story is about putting my work into perspective. I made the exercise of thinking as an app user, to objectively review the outcome of my activity. Then I became fascinated by the functionality of the software product I worked on.

To gather the right data, a big data software program needs guiding. Putting data sources into groups serves as guidance, for example. Half of the work is done if you can define the consumers groups.

Since the app I worked on targeted people, we had to define consumer groups. In the case of movie scheduling, I noticed this was achieved in 2 ways. I then thought of how this applies to a software product, to an app. One needs to test & define consumers as a prerequisite to grouping.


–  A/B Testing
– Set the time of research
– Ask the question which you want answered
– Try to find and granulate all the things you need


– Apply statistic tests to data
– Identify the vulnerable people
– People that don’t like the application – Who are they? Why don’t they like it?
– People that like the application – What are the main reasons?

What we should also take into account when we apply the test to a specific group is hidden features.
– Look at the details
– Run emotional ideas
– Test the nonverbal language

Also, historical data can do wonders.

My story

As a member of the public, I felt that any movie theatre chain that would use such tools to accommodate me better won my vote of confidence.

I started to go to the movies more. Especially English-speaking movies. I changed my decisions, in appreciation of the software-supported work that movie theatres put in, to meet the consumers.

The consumers are just clients, in higher numbers. And clients are always the masters. Accepting to be served gracefully is an art. In the current times we are often served by algorithms and software programs. Benefitting from this modern services is also a wise step, as well as an art.

So, the next time you go see a movie that is perfect for a rainy evening in the city, send out a good thought for the people behind the algorithms. We salute you through the software that just translated your preferences into reality.