- Personalized product recommendations
By analyzing the items that a user consults, adds to its cart, or spends time on, it is possible to find out its preferences and recommend purchases that the customer is likely to make. By correlating this with the choices of similar users, it further refines the results and automatically adapts promotional strategies according to customers and customer segments
- Smart customer segmentation
Customer data are numerous, e.g. purchase history, searches performed, time spent on different items. Based on each user’s preferences, automatically compiling lists of similarities between them and grouping them into customer segments becomes easy and automatable.
- Pricing analytics
Machine learning can correlate competitive data, market context, and customer purchases. It then becomes possible to strategically position oneself in the market and optimize the price range of products, based on purchasing trends.
- Optimised discounting & promotions
Recommending the right product at the right time AND at the right price is possible. The stream of customer data is enormous, and automatic competitive analyses performed by machine learning models can enable strategic positioning with the right promotional price range for your products. However, AI doesn’t necessarily need to cluster customers, it can also recommend promotions on a granular level by assessing their willingness to pay.
Marketing & branding
- Advertising campaign steering (online and offline)
Targeting the right audience by efficiently and automatically building a list of customer segments likely to buy a product are tasks made for AI. This strategy allows for greater scalability and performance of promotional campaigns. AI can also discover where conversions come from and seamlessly shift spendings around to follow the audiences that are converting.
- Content recommendation engine (e.g. for newsletters)
ML-based content recommendation engines are able to customise newsletter contents to the customers’ interests, recent purchases and past interactions to increase click rates and conversion rates.
- Loyalty-program optimization
Tailored and therefore more effective loyalty programs can be built by automatically mapping customer segments and personas and recommending most suitable collection options (eg. via vouchers), awards and contents.
- Social-media listening
By injecting social network inputs into machine learning models, it is possible to compute lists of trends and metrics on which to base business strategies. In addition, sentiments about brands or products can be extract (e.g. automatic screening of product reviews & user comments).
- Call-center automation
Chatbots and Voicebots are developing rapidly to cope with increasing demand, remote-working issues as well as rising competition. AI can work around the clock, absorb knowledge almost without limit, and reduce costs and manpower to be deployed at the front end.
- Predictive insights
Machine learning models can enable you to predict demand, sales, stocks, staff allocation and other key economic variables which can improve your tactical decision making with considerable top and/or bottom line impact.
- Customer sentiment analysis
Drawing up a portrait of the customer’s feelings is feasible, and allows you to adapt your sales and recommendation strategies. Based on their online actions and behaviors, a well-trained machine learning model, or Natural Language Processing (NLP) algorithms can detect their interests, recommend products, or correct recommendations accordingly.
- Chat bots & voice bots
Distribution & development
- Sales & demand forecasting
Business planning, budgeting and goals setting depend on the demand, which is based on complex factors and hence becomes hard to modelize manually. The foresight on the demand is, thus, a key strategic advantage. Using deep-learning based forecasting techniques can help to improve the procurement strategy, business robustness, inventory management, and resources allocation.
- Logistics network, inventory & warehouse optimization
AI in inventory management can automate processes related to logistics, warehouses, storage, etc. It can provide assistance in physical tasks, e.g. items tracking, and complex situations where advanced insight is needed to perform error-free demand forecasting.
- Product development (e.g. optimize product features and development cycles)
From chemical substances to metal alloys, machine learning models can consider a large number of combinations and deduce underlying factors. For instance with chemicals: whether or not it is likely to produce certain side effects, likely to be toxic to cells, likely to produce certain bioavailability, etc. The models can then automatically synthetizing these metrics into simple “go” or “no go” decisions, thus significantly decreasing product development time.