Uncertain Future of Predictive Analytics

Business today faces unprecedented challenges. Companies are adapting to the conditions of the new reality and are trying to retain and increase the number of customers. To solve this problem, it is important not only to maintain the high quality of goods and services, but also to improve the mechanisms of interaction with consumers.

Working with your own and external data allows you to evaluate business performance and make informed decisions, as well as identify patterns and build predictive models. In 2020, the predictive analytics market was estimated at $6.6 billion. Experts predict that it will grow by 19% per year and reach $22.2 billion by 2027. This also means that more and more companies are opening for themselves the possibilities of these technologies, gaining a competitive advantage today.

Basic steps in building a predictive analytics model

The process itself can be divided into three stages: setting the problem, preparing the data, and building an analytical model. The first stage is very important, because the quality of the product depends on it. At the second stage, the data is aggregated and processed in the storage, analytical features are highlighted. Using these features, data scientists train mathematical models. To perform calculations when training models and applying them, a computing cluster of sufficient power is used.

An integrated approach to analytics allows businesses to better understand their audience, including potential ones, and, as a result, personalize communication and create more effective marketing and advertising campaigns. Also, such solutions can help to profile the consumer and predict his behavior, while maintaining his personal data.

In addition, predictive models allow you to optimize work with your customer base, increase the efficiency of email campaigns and call centers. Users will receive messages about the offer that is relevant to them, in which a business representative addresses them directly. Among other things, a set of solutions allows you to personalize the content: adapt the text, illustrations and tone of messages.

How to choose a profitable location and what does predictive analytics have to do with it

One use of predictive analytics is to find relationships between geographic and other external and internal data, such as traffic, residents, and the competitive landscape. Using them in your work, you can predict key indicators. This can be useful for any offline business, such as a store, beauty salon, restaurant, bank or insurance company. So, before opening a new point, you can assess the potential of the selected location, for example, the volume of the audience, its characteristics and the presence of competitor nearby. Based on this information, businesses will be able to open businesses where they really need them, generating better results and reducing the cost of opening unprofitable stores.

For example, back in 2019, a retail chain of grocery stores began to analyze locations in detail using predictive models before opening new outlets. A specially designed geographic information system calculated their potential, and also offered the most relevant store format for the area. At the same time, the predictive model predicted the average check and traffic in retail outlets.

The geoinformation system received requests to calculate the location, according to the coordinates of which the automatic collection and calculation of signs was performed on roads, families and data from telecom operators. Based on the available features, machine learning algorithms calculated a prediction about how efficient and cost-effective it would be to open a store in a selected location.

Thus, with the help of artificial intelligence technologies, as well as working with data, it was possible to reduce the annual costs of opening unprofitable outlets by several million dollars, as well as to choose more convenient locations for locating stores and reduce the burden on staff. A pleasant bonus from the implementation of these changes was an increase in customer loyalty.

Why audience segmentation and profiling are needed

Audience segmentation and profiling are indispensable for optimizing your communications strategy and increasing cross-selling. This is especially important if the company is in the process of rebranding, launching a new product or line, repositioning, or just wanting to get to know their audience better. Knowledge of consumers, in turn, helps to develop truly effective promotion strategies, which ultimately has a positive effect on profits.

The audience can be segmented according to the parameters necessary for a particular business, both on the basis of the company's own data and by connecting external sources. For example, a predictive analytics model allows you to analyze anonymized data about a company's customers and find the most important common characteristics by which customers can be grouped into a segment. Then each group is described by a large set of features.

For example, a well-known European company was developing a strategy aimed at transformation. In order to create and promote digital products and services, there is a need for a new segmentation that would allow building a new communication system centered on the user and his needs. The segmentation already existing at that time described the consumer at the household level and had a narrowed research geography - 3 thousand respondents from cities with a population of more than 50 thousand people.

To solve the tasks, an actual segmentation based on artificial intelligence technologies (AI-based) was created. It allowed us to divide the market and customer base into eight segments based on two typologies - the BIG 5 five-factor personality assessment model (OCEAN) and the Adoption Curve technology adoption model. Each segment of the audience was described by more than 450 features: from extended socio-demographic characteristics to value-behavioral profile and even the user's attitude to innovation. Based on segmentation, ads with key messages were developed for each audience group, and the project itself coincided with the rebranding of the business and influenced the visual design of advertising campaigns.

How campaign personalization delivers results

A message with the right offer at the right time and through the right channel can more effectively capture the user’s attention, as well as increase conversions and increase customer loyalty.

To achieve these goals, you can turn to personalization based on machine learning algorithms. The goal of this marketing strategy is to create a unique experience, message and offer in any communication channel depending on the user profile. At the same time, depending on the data sources and the technologies used, you can create a multi-level audience segmentation.

For example, to promote a banking application, segmentation was carried out in three stages. At the first stage, an impersonal training sample was formed. On its basis, using the Look-alike tool, we built a depersonalized audience segment that was prone to investing.

At the second stage, the formed audience was divided into groups, based on the brand's goals. The task was to identify segments of users with high income.

Finally, at the third stage, segments were formed according to the value-behavioral profile. It allows you to highlight the key features of the perception of information by different people, group users according to them and adapt key brand messages for each segment. So, the bank team created a unique text with a call to action for each group, as well as their own landing pages.

As a result of the campaign, 56% of respondents began to easily recognize ads with a personalized message from the bank (among those who saw regular banners, this figure was 36%). Also, this type of segmentation had a positive impact on the perception of the bank. In addition to image metrics, personalized campaigns also resulted in 44% more app installs than the standard approach.

By taking into account the available information, marketers will be able to better understand customers and develop effective and personalized campaigns.

Today, for effective business development, it is necessary to analyze impressive data arrays. It is no longer possible to work with them manually. To make informed decisions and minimize possible risks, it is important for companies to work with their own and external data, develop internal expertise and expand strategic partnerships in this direction. Predictive analytics is one of the most important vectors in this area, which allows you to predict changes in key aspects and metrics in real time and quickly respond to any changes. In a world where technology can do so much, and competition is growing so fast, it is necessary to follow trends and implement innovative business solutions available on the market.