Data Science Solutions for Retail
Data science has entered the sphere of Retail rapidly and effectively.
Its power can be harnessed to enhance the customers’ shopping experiences, improve sales forecasting, decrease inventory and grow margins by utilizing existing data.
All the transactions, e-mails, search inquiries and previous purchases can be analyzed and processed with the help of our algorithms to build a winning merchandising and marketing strategy.
CREATIVE MERCHANDISING DRIVEN BY AI
Our unique, modular approach supports merchandising teams, helping to identify high-impact KPIs, sales growth opportunities, and localized retail trends. Using advanced machine learning algorithms, we examine a range of data including sales, store attributes, customer profiles and planograms, to determine optimal assortment, help build promotions and manage category dynamics.
By blending internal and external data sources and applying machine learning we are able to see trends develop, and predict their longevity in real time. Our cognitive models are used to tailor product offerings to individual consumers on a hyper-localized level.
Historically, when trying to predict sales, managers have applied business logic based on experience—the quality of a brand, the shelf placement, the promotion, etc. This approach relies on the human brain to select and analyze data.
Machine learning is much more powerful. It can look at history to determine which factors are most important, and to find the best way to predict what will occur based on a much larger set of variables.
With machine learning models, and today’s super-charged computational power, you can better predict how much of a certain product or service you will sell in a given day, better stock inventory, better staff your facilities, and ultimately keep more margin in your business’s accounts.
Our algorithms will help you do just that.
AI Helps Retailers and Brands Deliver
Personalized Shopping Experience and Increased Conversion Rates
Recommendation engines are machine learning algorithms capable of suggesting relevant products to customers, based on an analysis of the customer’s historical transactions. They help retailers predict customers’ behavior, increase sales and introduce trends.
Recommendation engines learn based on the customer’s past behavior or a key series of the product characteristics. Additional data such as demographics, promotional activity, frequency of purchases, and previous shopping experience, etc. help to fine tune the algorithm.
The result includes up-sell and cross-sell recommendations based on the detailed analysis of the customer’s profile.
Having a right price both for the customer and the retailer is a significant advantage brought by the optimization mechanisms. The pricing strategy depends not only on the production cost of an item, but also on the spending power of a typical customer, as well as the competitors’ offers.
The data gained from the multichannel sources define the flexibility of prices, taking into consideration the location, an individual buying attitude of a customer, seasonality and the competitors’ pricing.
The algorithm segments customers to define the response to changes in prices. Using real-time optimization helps the retailers to attract customers and realize profit goals.
location of new stores
Data science is extremely efficient in assisting retailers determine best new store locations. Site selection typically follows a methodical process of identifying markets, then trade areas, and finally specific sites for retail store locations.
Using a site selection algorithm allows the retailer to quickly estimate performance potential and identify sites that meet minimum criteria. It can also allow the brand to avoid potentially expensive mistakes by letting you know where NOT to invest.
The algorithm is driven by customers’ data, with an emphasis on demographics and competitor information.
Another advantage of site selection algorithms is that they allow retailers to validate information received from the brokerage community.
Lifetime Value Prediction
Historic and predictive customer lifetime value is one of the most important metrics for retailers.
CLV helps to focus on the channels that generate the most profitable customers. CLV algorithms are capable of generating real ROI on customer acquisition, enhancing retention marketing strategy and creating more effective messaging and targeting.
CLV models collect, classify and clean the data concerning customers’ preferences, expenses, recent purchases and behavior to structure them into the predictive input. After processing this data the algorithm delivers an analysis of the value of the existing and potential customers. The algorithm also spots the inter dependencies between the customer’s characteristics and their choices.
The application of the statistical methodology helps to identify the customer’s buying pattern. Data science and machine learning assure the retailer’s understanding of his customer, the improvement in services and definition of priorities.
Market Basket Analysis helps retailers better understand – and serve – their customers by predicting their purchasing behaviors.
MBA discovers co-occurrence relationships among customers’ purchase activities using data collected via customers’ previous transactions. The technique is based on the theory that if you buy a certain group of items, you are likely to buy another group of items.
Retailers use the information from market basket analysis in a variety of ways:
Store layout: placement of products that co-occur together in the analysis in close proximity on the store floor to improve the shopping experience
Cross-selling and Up-selling: retailers will market extra products to the customer based on prior purchase behavior patterns or what is currently in their cart
Placement of items on a website or products in catalogs
Customer Sentiment Analysis allows retailers to find out what their customers are saying and how they feel about the products they’re offering.
The algorithm relies on natural language processing (NLP), text mining and data mining capabilities to find information on consumer demand by analyzing millions of posts that are uploaded every day to social media sites like Facebook and Twitter.
Sentiment analysis reads through the text to see whether it's positive, negative or neutral. It can also take into account the meaning of the words as well as the specific context of what was said.
Using social media, it's now possible for retailers to understand the sentiment of their customers in real time, and understand how they feel about the products in the stores, effectiveness of store layouts and advertising. With real-time data coming in, the merchandising team can make adjustments to make the customers fall in love with its products again, identify more attractive retail locations as well as stock their shelves to better fit their customer's mood.