Our Data Analytics team can ensure a safe and steady growth

The greatest advantage of online marketing is that it can rely on a multitude of data, which can be counted and evaluated through Data Analytics about the platform, the user experience, and every change brought to it, as well as any campaign or online activity. Such an evaluation becomes necessary, if not compulsory, for any company in the current market, and our team of experts is ready to use your data to write your success story.

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How can Data Analytics help?

Through our analytics services, we will gather all important data, analyze them, and create forecasts based on them.

Data Analytics can offer you valuable insight and the answer to many questions, such as:

  • Which types of services would clients prefer in the future? What should I prepare for?”
  • “How to better retain my clients, now and in the future?”
  • “What is happening in the months with decreased sales volumes?”
  • “Which geographical area am I generating the largest sales volume for product X?”

Moreover, Data Analytics will offer important contextual information to help you develop a solid market strategy.

  • You will discover market trends, learn how to correlate and determine the factors influencing changes;
  • You will be able to identify the supplementary measures you can take to increase profitability, efficiency, etc.;
  • You can even discover the causes of changes in sales or the attitude of clients;
  • You will be able to access information regarding a specific subject

We have the best trained Data Analytics team in Romania!

Our team of specialists will help you access and transform raw data into valuable information that is well-placed in context and ready to be used.

Data Analytics Services

Here is how we can help with our Data Analytics expertise.

Web & App Tracking

During our collaboration, we will set up user tracking on-site for the platforms included in the marketing strategy. We are talking about platforms such as Google Analytics and Google Ads, TikTok, Facebook Pixel, and others. This type of tracking will reflect the established business objectives and follow the events important to your business, such as eCommerce or lead generation.

We conduct Google Analytics 4 Audit with advanced tracking implementation for Google Analytics 4 (web + mobile).

Server Side Tracking

Since the digital industry is becoming increasingly restrictive with third-party cookies, we expect tech giants to take the next steps in implementing server-side tracking. We are making this technical implementation through Google Tag Manager, and we collect data either through the Google Cloud Platform or our server-side solution. Following the server-side tracking implementation, all data collected about users will go through a subdomain, which we will create especially for you ( All these technical details can seem difficult to grasp, but our Data Analytics team knows well what to do and will handle the process from end to finish.

Visual Dashboards

This data must be put in an accessible format for you to understand and make business decisions based on them. We will make things easy to understand and interpret with the help of visual dashboards, where the information gathered from diverse sources (online, CRM, local CSV/XLSX, etc.) is presented and interpreted. We usually create dashboards on the Looker Studio, PowerBI, and Tableau platforms.

Business Process Automation

Data Analytics automation can benefit internal processes. It eliminates redundant actions and helps you gain time and reallocate resources. Here are some of the processes we have automated for our clients.

  • We have made setups for sending notifications when a competitor reduces the price of a certain product.
  • We have created forecasts for the evolution of monthly orders to help the client prepare the stocks and the marketing strategy.

Google Analytics Training

Your company’s in-house team will benefit from the customized training done by HOLD Marketing specialists. The main advantage of these classes is that, unlike standard classes, during which participants can go through the information they already know, which could mean a useless activity in a way, these classes are done to help your team members exactly where they need it most, either in configuring, analysis or data interpretation through Google analytics.

Our Data Analytics Approach

See how our team approaches your company’s Data Analytics needs in various industries.

Customized purchasing experience

With the help of automated learning algorithms, we can analyze clients’ behavior, study their browsing history, and discover purchase patterns based on their demographic data to offer customized recommendations for products on the site. By applying collaborative filtering or content-related approaches, we can use recommendation systems and make product suggestions with a high chance of purchase, thus increasing sales volume and user engagement.

Managing inventory and demand forecasts

We can use automated learning models specific to Data Analytics to examine sales history, seasonality, purchase trends, and external factors to ensure an accurate demand forecast. Based on this, we can help you better manage stocks, which is essential for any business.

Fraud detection and prevention

With the help of automated learning models, we can detect anomalies in transaction models, user behavior, or data analysis and payment information. This aspect is important since electronic commerce platforms are vulnerable to fraudulent activities, and these techniques in the data analysis sphere are very helpful in detecting anomalies and signaling potential fraud cases.

Optimizing the price and income dynamic

We work with dynamic price models, which can be adjusted to maximize incomes. Price-setting strategies are optimized using automated learning algorithms, which analyze competitor prices, market demand, client behavior, and other variables in real-time to keep prices competitive.

Risk evaluation

We use the “machine learning” system within the Data Analytics services when working on projects for companies in the finance and banking sector, allowing them to analyze complex and very different data sources. Unlike the classical models for evaluating credit granting, which are limited in solvency, these advanced systems have the advantage of using models such as gradient boosting, neural networks, or overall methods that can optimize the decision-making process.

Fraud detection

We use the same concept of “machine learning” even when we want to reduce the risk of fraudulent activities financial institutions constantly face. These performing instruments are useful in detecting anomalies and in identifying fraudulent activity.

Algorithmic trading and investment strategies

We can analyze market trends, historical data, and other factors to develop more complex trading strategies. Therefore, using “natural language processing” (NLP), neural networks, and reinforcement learning, we can help make informed decisions in trading and optimizing investment portfolios.

Client service and customization

Banks and financial institutions can use Data Analytics through automated learning to offer clients customized experiences. Therefore, while users interact with chat robots activated through NLP systems, the recommendation engines that process client data can suggest customized products and services.

Risk evaluation

Insurers can optimize price policies and decision-making processes using automated learning algorithms in Data Analytics. Therefore, various data are being analyzed: clients, external data, and historical data on compensations.

Fraud detection

Automated learning models use predictive modeling, network analysis, and anomaly detection techniques to detect fraud. They are designed to prevent insurance fraud cases, a problem many of our clients in this industry face most often.

Processing and automating complaints

We turn to automated learning even when we need to handle client complaints. Therefore, if we need to process various documents to check registered complaints or evaluate damages, we use Data Analytics in the form of Optic Character Recognition (OCR), combined with automated learning algorithms, to process and solve complaints.

Predictive analysis for risk reduction

Apart from reducing fraud cases by spotting them early on, there is also the possibility of making a predictive analysis to foresee the problem. Insurers are becoming increasingly aware of the risks and can take measures to prevent potential losses that might result.

Network optimization and management

With the help of automated learning systems, we can foresee or solve, as soon as possible, some of the network-specific problems. Network traffic data can be analyzed, blockages can be foreseen and solved, predictive maintenance resources can be allocated, and the entire network can benefit from dynamic optimization of its performance and viability.

Infrastructure predictive maintenance

The telecommunications infrastructure includes sensors and devices that can predict malfunctions or signal other maintenance needs. This is a prime example of how this industry benefits from automated learning algorithms, which can analyze the data coming from these sensors to determine fast interventions that minimize downtime and reduce maintenance costs.

Anticipating contract termination and client retention

Contract retention or termination processes can take time and resources with clients unless you use Data Analytics with its automated learning models. They can all analyze in due time the factors and behavior patterns indicating a potential intention to terminate a contract, and telecommunication companies can implement due time retention strategies dedicated to these clients.

Fraud detection and security

Automated learning models can detect anomalies by analyzing network traffic, subscriber behavior, and use patterns. This allows telecommunication companies to optimize their security measures and reduce the risk of fraud to which they are exposed.

Service quality and client experience

Telecommunication companies have always been interested in the network client experience and how satisfied they are with the quality of services. Therefore, proactively solving the client’s problems and using the network is very important. With the help of automatic learning, we can quickly process data regarding network performance, call registration data, and client feedback.

Property evaluation

We come to the aid of companies in real estate with automated learning models that help us estimate property prices. Although normally, this is a significant challenge, in the case of using Data Analytics services, when historical data about property sales, the location’s particularities, the properties’ characteristics and market trends, and property values are all estimated as correctly as possible.

Predicting demand

Real estate developers and investors need to know how demand will evolve for certain properties in certain locations. To obtain such an evaluation, we use automated learning models to analyze demographic data, historical data on sales, and economic indicators.

Property recommendation

The best real estate transactions occur when property owners match with potential buyers or tenants. Although there are normally great perception differences between the two categories, automated learning recommendation systems are of great help. Using collaborative filtering or content-based approaches, users can receive recommendations for properties based on their preferences, as expressed in past searches, based on their behavior and interaction.

Risk evaluation and fraud detection

Once again, we encounter risk in real estate when talking about real estate investments and loans. By analyzing the market trends, historical patterns, and financial data, we can identify anomalies to prevent possible risks and eliminate their effect, all with the help of Data Analytics.

Portfolio management and investment strategy

Coming up with efficient investment strategies and optimizing portfolios can be serious challenges. However, investors can receive aid in decision-making if they leave data processing such as risk profiles, historical performance data, and market trends up to learning algorithms.

Customized recommendations and client experience

Tourism is one industry that most benefits from automated learning algorithms. These algorithms can analyze client preferences, historical data, and online behavior to offer customized experience recommendations, travel destinations, accommodation ideas, and more. The whole system is based on recommendations made using collaborative filtering and content-based approaches.

Demand forecast and price optimization

Tourism is the field in which seasonality plays an extremely important role in price-setting strategies and resource allocation. Therefore, we use Data Analytics with automated learning models to analyze historical data, periods, reservations, events, and external factors to forecast demand and help companies align prices with offers and availability.

Reputation management

The hospitality industry revolves around reviews, so much so that monitoring and understanding client perception has become extremely important for companies in the industry. To this end, companies use natural language processing (NLP) models to analyze the content of reviews on social media channels and the feedback received on client satisfaction models. This way, areas where one can improve are more easily detected.

Operational efficiency and resource management

Tourism companies can benefit from operation cost optimization, cost reduction, and efficiency increase when using predictive analysis and optimization algorithms. We are considering staff scheduling, stock management, maintenance programs, energy costs, and other factors.

Managing and planning tourist destinations

Even tourist destinations can benefit from implementing Data Analytics systems through better management, which can be obtained through automated learning. Tourist behavior, preferences, and traffic models in an area of tourist interest are analyzed, and prognosis can be made based on the number of people and peak times for arrivals and departures. These predictive models can contribute to better planning and resource allocation.

You will be working with Data Analytics specialists

We have over 150 digital marketing specialists ready to help you grow your business most robustly, backed by a clear strategy and based on real data.

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