Experiment #205 Applied Artificial Intelligence – Analysis of the information
Analysis of the information
Once we have assembled the architecture and processed the data using the Cognitive Services,the next step is crucial; we must extract information of that preprocessed data in order to bring it to the knowledge of analysts, who will use it for decision-making.
To address this last phase, we’ve decided to make use of a great ally like Power BI. The report is as follows:
We have numbered it in such a way that it’s easier to explain and locate the items in the report.
- A bar chart in which we see the percentage of the brands of the bags that the customers carried. In this case almost 80% were Zara’s.
- We can set different target values. In this case, we have established as our target that 50% of people who enter the store should buy an item. In this example 51.61% of the people ended up buying something.
- In addition to the sales objectives, we have also observed that there may be a correlation between the purchase and the visit to the fitting room. That’s why we established as a target that at least 40% of customers should go through the fitting room. In our case, 36.11% have done so; we’re a little below the target.
- We are also interested in having positive feelings within our store. One of the moments where most discontentment occurs is while waiting. So to do this, we have set a limit of 10 minutes of queue. On average, our customers spent 12.24 minutes queuing, a value somewhat above our goal.
- On the other hand, another aspect that also has a correlation with the final purchase is the time spent in the store. We have set 15 minutes as the minimum time a person should spend in the store. In this case, the average time spent in the store was 20.67 minutes, so this target was achieved.
- We also have some gender statistics. In our store 60% of the visitors are women and 40% are men.
- As for age, we can see how men and women are, on average, 34 and 44 years old respectively.
- As for the areas we defined earlier, we can see the percentage of people who passed through the different areas to take a look at the products. In the case of zone 1, 78% of the people who entered the store ended up passing through zone 1.
- In the case of zone 2, 60%
- Finally, in the case of zone 3, only 28% ended up visiting the zone. This may indicate that the area is not well designed, signalized or just doesn’t have very attractive products.
- In this bar chart we are interested in seeing the total number of people who entered the store each day, and the predominated feeling during the day. This can help us detect potential key days when more staff or better organization is needed. In addition to this, also observe peaks (both high and low) of visits.
- Finally, a filter to segment by date.
In point 11, we see that there is a day with a predominantly negative feeling. We’re talking about January 31:
When we filter it we can draw numerous conclusions:
- As we have said before, the first thing is that the prevailing feeling on this day is negative.
- We can see how the percentage of purchases is 68.3%, well above average (51.61%)
- The visits to the fitting rooms have reduced to 31.3%. This can happen because we are having a special Sales day, where it comes out more profitable for customers to buy and then return than waiting in line to get to the fitting room.
- Finally, the average time spent queuing has increased dramatically to 42.3 minutes
Sentiment and pattern analysis
An example of a successful purchase, without any negative emotions or highlights:
- In this chart we can see the path that the person does within our establishment. We can also see where the person is and how much time he/she spends in the same place. This chart also shows the emotions the visitor is having throughout its stay. We see how in one of the points it reflects surprise; probably because the price of something he/she likes is lower than his/her expectation. Also, after leaving the fitting room the visitor seems happy with the result.
- Slicer in charge of making the timeline move forward so that you can see chart 1 dynamically, making it possible to visualize the person’s journey throughout the store as if they did it for real.
- We can also see the actions this person has taken. In this case, the visitor made a purchase (the garment that surprised) and a return.
- The brands on the bags with which the visitor entered
- Gender data
- Age of the visitor
- The time spent in the store. In this case it has been higher than the minimum of 15 minutes that we set before
- The time spent in the queue, this being 8 minutes, less than our maximum.
- And finally, we have at our disposal a slicer for filters per person.
Next, let’s look at an example of a negative situation in the establishment:
This visitor, like the previous one, found a garment that was intriguing and, after passing through the fitting room, was convinced enough to buy it.
However, we can see how at the end she didn’t take any action. If we look closely at the pivot chart, just as she was queuing her feelings changed drastically first to anger and finally to disgust.
And we can clearly see how this person was in the establishment for 32 minutes, and 24 of them she passed waiting in line. A time period that is unrealistic to leave satisfied customers or create pleasant emotions in the establishment.
These types of reports are very close to those of traditional Business Intelligence, where we have graphs, metrics, KPIs,etc. to analyze our business. We can add as many as our analysis and business team needs to make the right decision. We have opted for these as an example, but there are countless studies and metrics to calculate the fruit of the idiosyncrasies of each establishment.
- Different KPIs for sales and stock
- Sales Forecast
- Intelligent detection of areas of improvement
- Suggestions of promotions
- Seller analysis
- Customer behavior analysis
Other cases of use
We have adapted this experiment to the retail industry as we think it’s a case in which it could be applied perfectly in order to get great results and a great return on investment.
However, there are numerous examples where this system could also be implemented:
Track athletes, their relative position and movements they make when scoring. Analyze correlations and build the key to success.
In addition, we could also analyze the audience that attend these events, their patterns, behavior, etc.
We know that medicine is one of the sectors where AI is very present. This system could provide us with disease control, tracking of people with the disease and much more. This could help to prevent and/or combat diseases in a much more effective way.
Although it may seem like a very conservative sector, the use of new technologies is the tendency. The competitive advantages that can be achieved through its inclusion are considerable.
This system would allow us to study and control how animals move,what they eat, whether they have drunk enough water or not, and if they act accordingly.
In the transport sector this system also can have a lot of use. In any space where there is movement of people, vehicles or objects it will allow us to analyze all the information completely autonomously and draw conclusions.
- Traceability of people in department stores, where there are a large number of stores
- Traceability at airports to maximize their economic performance through passenger purchases
- Covid-19 traceability
- Optimization of activities (both lucrative and leisure) on cruise ships
- Optimization and time waiting in line
- Traceability in supermarkets
Read more of Experiment #205 here.