- Smart search
- Recruiter's robot assistant
- Marketing optimization
- Factors that impact the data analysis efficiency
- How we solve problems
The retail industry faces many tasks that can be solved only with data analytics. We would like to present to you some of the solutions for retail companies created by the Data Scientist team from Softline Digital.
One of the most difficult tasks for both sellers and buyers is to find answers to urgent questions in time. The retail industry offers an abundance of information: products, features, documentation, deliveries, stock, etc. Sometimes, to find an answer to an elementary question, a new employee has to distract experienced colleagues or spend a lot of time looking through the knowledge bases. Online store visitors, on the other hand, often simply close the chatbot window, as they are sure that it will not understand the wording of their question.
Softline Digital team has created a prototype of a Q&A system that follows a smart approach to information search instead of mechanical sweeping. It doesn't look for keyword matches but analyzes the natural colloquial language of the query, understands its meaning, and finds the answer in vast amounts of company data, which include corporate portals, information libraries, documentation and knowledge bases.
This system can be organized as an intelligent search engine that scans a document database.
Sales managers can use it to quickly find product-related information or answer questions from customers in sales areas.
The system can also be used to enhance chatbots by teaching them to process preset wordings, but also to "comprehend" the essence of any request by selecting the answer in the form of document quotes.
Recruiter's robot assistant
HR workers in retail are traditionally one of the busiest—vacancies open almost every day. In a year, large retailers review tens of thousands of CVs, and vacancies in retail do not tolerate any interruption—to keep the business up and running, the recruitment speed must be ultra-fast.
The robot assistant handles all the recruitment routines. It analyzes the job descriptions and searches for suitable CVs based on the given criteria. As a result, it prepares a ranking of relevant candidates is created. A recruiter can quickly process this curated selection, call applicants, and appoint interviews with them.
To conduct successful marketing activities, retailers need to thoroughly study the target audience. To whom and what promotions to offer? At what price? What kind of product? When is the best time to do it? On what terms? Data analysis is vital for marketing activities. Machine learning helps research customers and the market in a way that other classical market research methods cannot. Here's an example of a few tasks that data analysis helps with.
Creating the customer profile and performing the customer segmentation. Big Data technologies leverage as much data as possible: website transitions, behavior in social networks, feedback on websites, purchase history, and many other parameters that help create pinpointed profiles of specific users and identify consumer segments. We use both classic analysis methods—RFM + P, ABCD, XYZ—and more complex ones, such as the construction of graphs of relationships, cluster analysis, etc.
Consumer cart analysis. Information about what purchases target customers make, in what quantity, how often, and in what amounts allows you to make informed decisions on establishing interactions with them. To work effectively in this direction, the Softline Digital team applies product association matrices and other methods that can be used to develop recommendation systems.
Personalization of loyalty programs. Customer profiles and the analysis of customer preferences are used to make valuable targeted offers to the audience based on individual criteria. Commercial organizations want their advertising to be not spam but a timely source of profitable offers, which are hard to refuse.
Demand, prices, and customer churn prediction. Predictive analytics is one of the key tasks of data analysis. When machine learning is used to predict consumer demand, it can take into account hundreds of factors, from the location of a retail outlet to global economic changes. Price forecasting allows you to tailor prices to particular customers, for example, to make a discount on the products that they often buy. This method is used by such a powerhouse as Amazon. It only takes Amazon two minutes to adjust the price on the website based on the user's actions.
Winning back a dissatisfied customer is a complicated and sometimes even impossible task. So you have to predict the churn of loyal customers before they are about to leave for a competitor. Preprocessing on Big Data clusters and trained machine learning models enable you to identify factors that may cause churn before they take effect. For example, a customer may be prone to leave when they stop adding items to the cart, do not make repeat purchases, make them very rarely, ignore promos and vouchers, return products, etc. The analysis identifies the customers at risk also helps you choose the optimal interaction channel and curate the best promo offer to them. These problems are solved with different machine learning approaches, such as decision trees or recurrent neural networks, and many others. As a result, customers will be retained and their loyalty will be increased.
Factors that impact the data analysis efficiency
Machine learning algorithms are fueled by information. The higher the quality and the greater the volume of data are, the more precise and efficient is their analysis. Much depends on the company specifics and the types of data that are already available: transactions, focus groups, website paths, contact center database, and even the analysis of the emotional coloring of voice can play an important role in the quality of service assessment. It is just as important to constantly enrich the database from the outside. The information about competitors' prices, their loyalty programs, and social media activity is particularly important. The more sources you have and the more frequently they are updated, the better.
How we solve problems
Softline Digital's Data Scientist team always focuses on the customer's business needs. Our experts make a pre-project audit to study both the general industry trends and the development opportunities for a particular client. After studying the situation, data analysts propose a strategy that will bring tangible results to the customer, and then selects a ready-made solution or offers custom development services, if the task is non-standard. Then they run a pilot project (a Proof of Concept, in other words), which may be used to calculate the economic impact and prepare the roadmap for solution implementation and scaling. The final step is to integrate the solution into the organization's landscape and support it.
Softline Digital specialists use both classic data processing approaches and the latest advances in Data Science. They have access to best-of-breed software and AI tools and use the latest programming languages for data science purposes and cooperate with the leading universities of the Russian Federation, following a profound scientific approach to ensure maximum accuracy and efficiency of their solutions.