March 27, 2019 / Opening hours: 10:00-18:00

How to forecast customer behavior using Big Data

How to forecast customer behavior using Big Data

According to data from Frost & Sullivan, the big data analytics market will show 35.9% year-on-year growth by 2021. One of the top fields to use big data most actively will be commerce. Let’s find out how big data analytics in this sector helps to influence sales and predict customer needs.

How big data works in sales

Big data is a set of technologies and methods intended for process big data arrays that are difficult to analyze using common tools. Big data analytics is applied in areas where information is vital, for example, medicine (patient data gathering), finance field (analysis of credit history).

One of the industries that uses big data with most of efficiency is commerce, especially, retail. Here the technology gives the possibility to grow customer base, increase sales, retain and incentivize customers. Besides, using big data analytics, one can reasonably manage pricing, forecast gains and trends of sales seasons, optimize location of stores.

General information about completed purchases and processing of data on customer activity help to define:

  • relevant and appealing size of discount;
  • customer life time value (LTV);
  • probability of a repetitive purchase.

Big data analysis relies on machine learning. It forms algorithms that can find unevident interrelations, make forecasts, and learn, optimizing different business processes. Big data allows making more forecasts than a person can make, in such a way speeding up sales and decreasing the loss of resources.

Where data is taken?

Information about buyers is taken from a variety of sources used by marketers. Among other thigs, they are:

  • marketing researches;
  • information from bills, discount cards;
  • social media;
  • promotional offers;
  • feedbacks of buyers.

Plus data generated inside the company such as procurements, warehouse stock, results of sales, profit.

E-commerce websites should factor in information related to newsletters, emails that provoked customer response, website visits made thanks to clicking on links in such emails, time spent on the website, browsing of goods, purchase data. All companies irrespective of size have this information.

In such a way, big data technology is available even to beginning entrepreneurs willing to develop business faster and more efficiently. Useful information about buyers and potential customers can be found in CRM systems, online analytics services, and content management sites.

How foreign companies use big data

The world’s largest companies use big data technologies to build marketing strategies. Big data is important to create efficient online ads and for basic communications that help to reduce expenditures on logistics and production.

Big data at Walmart

The US hypermarket chain Walmartuses big data analytics. Using big data, the company’s marketers on average process 2,500 terabyte of information per hour. Data is taken from 200 interior and exterior sources, marketers monitor customer activity, behavior, number of purchases, time spent at stores. This information and timely analytics help hypermarkets to offer discounts and change the pricing in time.

Big Data at Amazon

One of the e-commerce leaders Amazon alsomakesuse of big data analytics. Basing on big data, it predicts customer behavior, forms lists of most interesting offers and discounts.

In such a way the company is trying to decrease customer churn, motivate customers to make a purchase by offering relevant goods in a timely manner. For buyers, it simplifies the search for the necessary position in the catalogue, allows not to spend time on monitoring of special offers.

As a result, the probability of making a purchase is growing.

IoT Conference: How to forecast customer behavior using Big Data 1

Use of big data in Russia

Big data at MTS

Russian companies successfully and efficiently use big data analytics, for instance, MTS operator. Using big data, the company optimizes work schedules of employees in different saloons. For this purpose, the operator uses a special analytics system called Workforce Management.

Besides, the company uses big data to form timely, lucrative, and appealing offerings for customers. Thus, analytics helps MTS to make accurate forecasts as to when the buyer gets interested in purchasing of a new device. According to MTS data for 2017, the use of big data technology helped the company to increase the total revenue by 1.5 billion rubles.

And last year, the operator launched a big data service for third party companies called MTS Marketer. It allows representatives of small and medium businesses to detect groups of potential customers according to criteria set by a company.

Big data at Rive Gauche

Rive Gauche cosmetics chain uses machine learning technologies that analyze customer behavior. In the first days of use, the accuracy of forecasts regarding customer preferences reached 33%. It allowed the company to increase customer loyalty and gains, and reduce expenditures on marketing offers. For instance, basing on big data analytics, the company made personalized offers to potential buyers rather than all visitors.

According to the preset scenario, the system (basing on loyalty cards data) forecasts consumer’s purchases within the accuracy of a stock number and offers discounts to relevant products.

Conclusion

Every consumer has individual needs and paying capacity that should be taken into account for an efficient marketing strategy. In areas where a human needs much time and effort to analyze huge data arrays, big data come to help.

By using this technology, one can increase chances of purchases to be completed and can increase the purchase sum. Moreover, the system is self-learning, forms required segments of the audience, and automates personalized communication with consumers. Big data brings benefits to both businesses and their customers that receive interesting and unobtrusive offerings.

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