Big Data is now becoming a marketing tool available not only to big business, but also to retailers, services, and more. Instead of advertising for everyone, analyzing specific data makes it easier to target your customers, optimize costs, and drive sales.
Big Data is a large unstructured amount of information that is produced and stored in the digital world: where and how many people are there, what actions they take, what sites they visit… Retail, banks, mobile operators, insurance companies have huge amounts of data about each of us. All of this data can be used to evaluate consumer behavior and to generate analytical reports, build forecasts for business development. The analysis of this data is used in the field of finance, logistics, marketing to predict trends in consumer behavior. Your “individual” offers are formed by retailers precisely through the processing of information about previous purchases.
They use technologies such as cloud computing, mobile and social technologies, machine learning, the Internet of Things. Business analysts analyze large amounts of data, interpret it for a deeper understanding of customer needs and improve business processes in real time.
Of course, it is either too difficult or impossible for ordinary entrepreneurs to do it on their own. However, their services were offered to them by mobile operators. Kyivstar and Vodafone have a wealth of information about their customers and do not hide that they are using Big Data to create their own services. And recently, big data products have been offered to entrepreneurs and other business segments.
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With big data, knowing your customer’s habits and preferences, you can best identify locations for new stores, business units, and more. The report will show you where these people are most often. If the business is important to the maximum permeability of the place, then it will help Heatmap (“heat map”) – areas where the number of people, judging by the number of smartphones, is the largest. You can view the current locations of branches, points of sale or ATMs on the map, and add and save new objects. By the way, such reports will be useful in planning promotions. For retailers, these cards are a way to determine the best location for new points of sale. When they open, they will be relevant to analyze the potential customers and targeted SMS-mailing.
If an entrepreneur is planning to grow their business and reach new customers, Big Data will allow them to find their target audience and make a relevant offer to them. This requires the mobile carrier to have an existing mobile number database for today’s business customers. An analyst can process big data about their behavior in the company and create a consumer portrait. For example, an operator can analyze the journeys and movements of subscribers, their age, gender, income, the presence of children and a car locally (home, work, and weekend), what sites, locations, or locations they visit (here you can add competitors’ addresses, for example) whether it has a competitor discount, receives SMS from them, or dials their numbers. The following is an automatic sampling from its subscriber base, which are similar behaviors (so-called Look-alike audiences) who are not yet customers of the company.
The next step is to make these people fit the needs of the offer via SMS.
Having a portrait of a client, an entrepreneur can apply the aforementioned reports to target advertising across different channels of communication. For e-commerce, the chain can be: customer segmentation by specific attributes – target segment analysis – search for a target-like segment – communication with potential customers.
Examples of technology use
Operators talk about successful cases of using big data for business. For example, a network of home goods and cosmetics stores wanted to determine the number of potential buyers who live and work within a 3km radius of the stores, to identify their portrait for further attraction. Having analyzed more than 200 parameters, including distance from the store, socio-demographic characteristics, smartphone availability, online behavior, they built the model and identified 4 segments for further targeting. Conducted promotional campaigns with individual offers for each segment. Analysis of the results showed a significant increase in conversion. Becoming aware of discounts, sales, discounts, or special offers for a specific CA with Big Data becomes much more effective.
The filling station was tasked with finding new customers. The owners of the car were found and informed about the opportunity to receive cashback and become a member of the loyalty program. A similar target audience searched for and found a auto parts store.
Among the regular users of the service are coffee shops that send invitations to middle-income people who