Although ways of advertising have dramatically changed in recent years, the ultimate goal remains unchanged. Companies compete to gain more customers and advertising is their main tool for convincing potential customers. Nowadays advertising is even a more powerful tool because technology makes it super easy to personalize ads. Companies are now able to make personalized ad campaigns and approach directly to each individual customer to satisfy his/her individual interests and needs.
 
For this purpose, most companies keep personal records of their customer. Each time a customer interacts with a seller to purchase a product by browsing the online store, or searching for a specific product, the seller records all the data and builds customer profiles. Once the profile is built for a specific customer, the seller knows a great deal about him/her, e.g., how much he/she would be willing to pay for a certain product, when will he/she make his/her next purchase, what type of products he/she would be interested etc.
 
Once the seller has all this information, it is possible to target the right customer at the right time with the right product and with the right ad campaign. This way, companies both spend their ad budget as efficiently as possible, and also maximize the ROI.
 
Predy plays the key role in this type of personalized ad planning. By using Predy, you can analyze your customer database to create customer profiles and also design your personalized ads by using Predy’s predictions for future customer behavior. Predy can also be customized for your specific needs such as finding the right customer-product match for cross-selling purposes.

Predy can help you make the ad planning for your products so that you can offer your customers the right product at the right time.

Ad planning

In most industries, customers are no longer bound to a single service or product provider. Competition between companies is high and every service or product provider continuously tries to find new customers to replace the ones who left. Ok, but why do the customers leave and is there a way to prevent it?
 
The answer is easy. They leave because they get more tempting offers from other providers. In most cases customers benefit when changing their provider because they are offered cheaper rates for the same service or offered more services for the same price. However, for the provider, this is not the ideal and preferred way of gaining customers, rather, it is the best for them to keep existing customers in the first place.
 
The way to achieve this is to identify customers that will leave and take steps to avoid the possible churn. Predy is the perfect tool to reveal the patterns that lead to customer churn. Every customer may have his/her own reasons to switch providers, however, when thousands of customers are considered for a specific provider, there are certain patterns that end up with churn and each customer falls into one of the several churn patterns. With Predy, companies can find out the patterns specific to their own customers and this helps them treat each and every single churn case personally in a timely manner.

Predy can help you take the required steps to avoid possible customer churn via accurate forecasts and timely alarms.

Churn prediction

Spatio-temporal data are data of events that occur in a certain place at a certain time, which have both temporal and spatial dimensions. “Crime” is an important example of spatio-temporal data, and crimes committed by people throughout history have been greatly influenced by temporal, spatial, relational, and environmental factors. Since it is an act that directly or indirectly leads to the deterioration of the social order, public authorities try to do their best to prevent crime throughout the human history.
 
Recent developments in artificial intelligence, machine learning and deep learning have led to emergence of many prediction systems in a wide range of domains. Revealing the temporal, spatial, relational, and environmental connections related to spatio-temporal data and using these connections in the development of forecasting systems make it possible to predict which type of an event will occur in which time &#x26; place. In this context, the development of crime prediction models is very important both in terms of being a hot topic and in terms of the potential big social impact if successful.
 
The concentration of certain crimes in certain locations (region, neighborhood, province, etc.) shows that spatial information has a dominant effect in crime prediction. Therefore, to model the crime events that take place in different places, there is a need for separate models that will use the features of each place effectively. On the other hand, it is possible that the crime events that take place in a certain region are related to the events that take place in different regions. For example, growing crime incidents in a certain region for economic reasons may be a sign that similar incidents will increase in the neighboring regions.
 
In several different customer cases with Predy, we provided significantly accurate crime prediction performance in real life operational conditions by modeling the temporal and spatial components of the related data as connected graphical networks, both within themselves and among each other.
 
The forecasting models developed with Predy has a structure in which forecast model of each location exchanges data with the models of the neighboring locations. Thus, neighboring prediction models can share prediction parameters or feature vectors with each other and use the data they receive from neighboring nodes to update their prediction models. Hence, Predy can produce high-accuracy crime predictions and the results it produces are used daily by the relevant security authorities operationally.

If your business is to maintain the public order, Predy can even help you to avoid possible crimes via timely spatio-temporal forecasts.

Crime prediction

Anyone who travels by plane knows that different fares are paid at different times for flights between the same destinations. This is called dynamic pricing and airline companies are experts at dynamic pricing.
 
Of course, there is a reason behind dynamic pricing; that is “willingness to pay”. If you need to travel in the next flight and missing that flight would cost you a lot in terms of a lost business opportunity, then you would be willing to pay the highest rate among all passengers. And airline companies are well aware of this fact :).
 
It is almost the same story for other businesses, and hence, dynamic pricing is becoming more popular among those as well. Consider clothing industry. You would be willing to pay a lot for a good coat in the winter-time, especially if you are in Illinois, however not in the summer-time. Though you would still consider buying a coat in the summer-time if the price is attractive enough. Choosing the right price is the key for similar cases and Predy is well suited for this purpose.
 
When pricing is considered, decision of the customer to buy (1) or not to buy (0) your product can be mathematically formulated, up-to certain limits, having several inputs such as price, time, location, number of ads posted recently for the product, shopping behavior of the customer etc. Many companies keep record of their sales related data and the personal information of their registered customers. When all these records are combined with related exogenous data and fed into Predy, Predy can help you set the best price for your product so that chances for the customer to buy it and your potential profits are both maximized.

Predy can help you set the optimum price for your product so that chances for the customer to buy it and your potential profit are both maximized.

Dynamic pricing

Forecasting the energy demand is very crucial for a variety of reasons. Be it electrical, thermal, chemical, or other types of energy; forecasting the demand accurately in a timely manner helps reducing operational, logistics and purchasing costs.
 
Energy demand depends on many factors. Some of them are direct factors such as industrial production requirements, population, net capital income or energy unit prices. Others are indirect such as the amount of natural gas required to produce electricity, which depends on the amount of electricity produced from cheaper resources like wind or water highly effected by weather conditions.
 
To make accurate energy demand forecasts, one needs to identify all the complex direct and indirect factors affecting consumption and model these relations in such a way that each factor is considered fully and correctly. This may require building separate complex deep learning and/or statistical models and combine them at the right level at the right time.
 
At Predy, we have developed many energy demand forecasting models and obtained very promising results. Our forecast models achieved much lower average percentage error (APE) for different spatial and temporal resolutions compared to standard forecast models to predict natural gas consumption, electricity demand and industrial usage.

Forecasting the energy demand timely and accurately helps reducing operational, logistics and purchasing costs.

Energy demand prediction

Especially after the Covid pandemic, sales profiles have changed a lot both for retail markets and e-commerce companies. This, in effect, made a huge impact on the stock and logistics requirements. Companies which are caught unprepared for the increasing demand lost significant revenue.
 
At Predy, we engage with big e-commerce companies and help them to significantly improve their logistics and stock planning activities so that they could make their deliveries in the shortest time possible.
 
E-commerce not only helps people do shopping at the comfort of their homes and changes the nature of daily shopping but also provides huge amounts of data for the companies to improve their businesses. These data become much more valuable and insightful when considered together with the relevant exogenous data such as meteorological data, GDP, currency exchange rates and so on.
 
At Predy, we not only consider the sales data to predict the future sales and related, but also consider all the relevant exogenous data so that we can reveal the patterns as complete as possible. This way, we can model the sales numbers of a specific item in terms of the time of year, financial figures, meteorological data, region information, special day information etc. Together with our specialized and custom graphical spatio-temporal modeling approaches, we can readily provide all the regional and temporal sales figures and their effects on each other at any granularity. In the end, we can predict your sales figures more accurately than ever before.
 
Hence, accurate spatio-temporal sales predictions produced by Predy helped and continue to help many retailers and e-commerce companies to do their stock planning and logistics much more efficiently and effectively.

At Predy, we not only consider sales data for making future sales-related predictions but also consider all relevant exogenous data so that we can reveal the sales patterns as complete as possible.

There is a way to improve performance via accurate forecasts for every business case

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