Thursday, September 28

The main problems in the use of Big Data

The importance of Big Data and Data Science

When handled correctly, big data helps to fulfill the wishes of users and makes it easier for them to choose a service or product and builds customer loyalty. Careless handling of this tool, on the contrary, is fraught with loss of customers. It is definitely worth the time and money for retailers to study it.

How data is used can have a significant impact on insights and additional business revenue. A decade ago, the world of advertising was brimming with creative romance.

Now, creativity is increasingly being replaced by analytics. Companies develop their marketing strategies no longer relying on intuition, but on statistical information obtained from Big Data.

Not turning data into a useful tool

The boundaries of the online and offline worlds are becoming less clear as more and more new data finds its way into the hands of businesses and is available for analysis. It would seem that this flow of information creates opportunities for further segmentation of the audience, to increase their loyalty.

But herein lies the complexity: you need to be able to collect data from all sources, store it and keep it up-to-date in order to interpret it qualitatively and build logical chains. To organize a large amount of disparate data, companies must create a centralized storage space.

This could be a CDP (Customer Data Platforms designed to aggregate consumer information from various sources and marketing automation), a cloud-based database, or other solutions.

Big data helps fulfill the wishes of users and makes it easier for them to choose a service or product, and builds customer loyalty.

Such a database will help create a unique customer profile that is updated at the time of actions, which is essential to create loyalty programs. According to a survey by the international marketing agency Merkle, 53% of companies are in the search stage for a solution to organize and store data.

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The main difficulty lies in finding the optimal technological solution, which is the most suitable in terms of functionality and, at the same time, its implementation is profitable. Some companies decide on the CDP option, others are less decisive because they have already experienced the technological boom and underestimate new technologies.

For example, many are frustrated with cookie-based data management platforms (DMPs), as third-party cookies become a trusted source of tracking. The truth is that there are many different CDP solutions on the market, so it is important to clearly define the objectives of the company, the use cases of the system, the key performance indicators to justify the costs and the rate of return on investment. . Speaking of amortization, 37% of the companies took nine months to do it, while the other 30% took only six.

Common mistakes when interpreting the data obtained

Competent work with data allows you to build patterns that directly affect company policy. For example, when deciding whether or not to grant a customer a loan in installments, banks are turning to Scoring, which increasingly includes checking the digital footprint on social networks.

For example, being in online network marketing groups increases the risk of debt default, and being active in travel and finance communities reduces it. When using information in this way, you must ensure that it is interpreted correctly.

It happens that coherent actions of the clients are not always combined and conclusions are drawn that do not correspond to the real needs of the users. Data from a single source may not be part of a pattern.

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It must be remembered that individual purchases by individuals are not a reason to base a loyalty program on them. We need to search for more voluminous data, mixing internal information about customer behavior and transactions with external information.

Retail stores may enrich information collected from reward cards, mobile apps, websites, direct customer interactions, and by purchasing information from third-party companies known as data brokers.

They collect data on their own and acquire it from other organizations (commercial companies: banks, dating sites) and government agencies, for example census data or vehicle registration data.

Data brokers create profiles of people with information about habits and behavior patterns. Retailers then match your loyalty card data to these profiles, creating a clearer picture of shoppers.

Wrong priorities when working with clients

Many companies get carried away with attracting new audiences and forget to keep the interest of existing customers. On average, it costs five times more to attract a person than to retain them.

The golden rule of any business is to build loyal relationships with customers, thus avoiding the cost of finding new ones. However, around 44% of companies pay more attention to customer acquisition, and only 18% of companies focus on retention, according to US marketing agency Invesp.

In a competitive market, customer-oriented companies survive. This concept involves not only meeting new customers with a smile and bonuses, but also rewarding old ones for their loyalty. Invesp published an interesting statistic: the probability of selling to an existing client is 60-70%, while for a new one it is only 5-10%.

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In addition, loyal customers spend 31% more than new ones. Quick response to circumstances, constant updating of offers and an individual approach is the main way to increase the audience of regular customers with the help of a loyalty program.

Data collection should not be done for the purpose of accumulating information, it is important to process it correctly. For example, some companies periodically survey customers, particularly those who engage in marketing activities or are business cardholders.

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