AI & Machine Learning

One of our clients came to us with the following issue: “We want to have the smallest possible number of items in stock, but we never want to miss out on a sale because we don’t have enough of a particular item”.

This is the type of challenge we are frequently asked to solve. Sales forecasting provides information regarding how much of a certain product, brand, or category of items is likely to be sold within a specified time frame, either company-wide or per location. This enables your business to not only accurately forecast the inventory but also other resources required, such as equipment, employees, or tables at a restaurant. This information empowers you to adjust sales and marketing strategies, promotions, and optimize pricing.

Advertising personalization and marketing campaign optimization through artificial intelligence can significantly increase customer engagement and ROI on marketing campaigns. By analyzing data such as the customers’ purchase and browsing history, AI and machine learning techniques help to detect what product SKUs, brands, or categories they are most likely to purchase, including when and at what price point or discount level.

Isolating the most effective advertising channel, messaging and communication frequency that works for each individual customer increases the likelihood of your targeted users becoming your long-term, high-value customers.

When humans attempt to group things together, they often do so with implicit bias, which affects the accuracy and validity of that particular group. Manual or rules-based grouping approaches often fall short because the human brain is unable to see all dependencies in the data, especially when working with large data sets. Clustering is an unsupervised machine learning technique that allows machines to find patterns between related items, and create groups based on that impartial analysis.

When using clustering, data scientists implement a learning model indicating which attributes should be used for grouping, for example, customer transaction history, product, and brand preferences, visit frequency, price preferences, use of discounts, and so on and the machine does the rest. This allows one to find patterns and customer personas that are impossible to discover without the use of machine learning. Clustering results can be used to drive your company sales efforts, marketing campaigns and other initiatives.

A/B testing is a crucial part of any marketing strategy. It is the only way to accurately evaluate the effectiveness of a marketing campaign. It consists of comparing the reaction of your customers to one particular campaign versus another, and measuring the difference. Those are the important questions that A/B testing answers. Although it is widely used, effectively managing the testing still proves challenging to the most experienced marketing teams. What should be the size of the test groups to get statistically significant results and how to populate them in such a manner that they are truly similar?

What should be the duration of the test and how much data should be collected? And most importantly - how to correctly interpret the results of A/B testing? The AI and ML software development services we offer provide you the tools to get answers to these complex questions. We make it possible for you to leverage your data and increase the efficiency of your marketing efforts.