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Achieved predictive accuracy of the recommendation models.
Increase customer acquisition and revenue;
Improve OPEX with inventory management.
With retail assortments growing, turnover increasing and shop floor space shrinking, retailers need new ways to generate profits. Retailer pricing has been driven from the corporate level by established pricing guidelines and competition. Reductions for many retailers are based on tried and tested techniques with x% at 6 weeks, y% at 8 weeks, etc. These traditional methods are insufficient to compete with new online or omnichannel competitors who are better positioned to increase profits through careful price management.
AI is ideal for situations where a retailer needs to optimise a large catalogue of items based on a variety of factors. AI models can be used to determine the best price for each item, using data on seasonality and price elasticity, along with real-time Inputs on stock levels, products and competitive pricing. The result is more careful reductions in grades (colour and size) at a very specific price to increase demand and maximise profits. Profit margin increases are also possible on some items, according to trending demand. AI can also be used for price recommendations indicating key factors. This is useful for retailers who want to know why specific items are being suggested for reductions.
Semantix's mission is the democratization of data, so more people across industries can use the power of AI to solve business and social challenges. The Semantix Data Platform is an all-in-one platform for Big Data that empowers data science teams to scale Machine Learning efforts, increasing speed to develop highly accurate predictive models. SDP has innovative features for retail brands, including machine learning interpretability (MLI), reason codes for individual predictions and automated time series modelling.
Traditionally, retailers stock their shops with the same product mix on a seasonal basis or a current product line with only grade variations. Different shops, however, have different audiences, climates, selling area spaces and inventory capacity, resulting in different needs. The problem with working with a single product mix, are Sellouts on hot items and reductions on others, both of which reduce profits earned by the retailer. Customer satisfaction and loyalty are also affected when shoppers cannot find items in the shop that they came to see and buy.
AI is ideal for optimizing retailers' Product Mix. AI models can analyse a variety of factors, including past sales, sales area, local trends, online behaviour, predicted weather patterns and more, to determine which products would be the best fit for a particular shop location. This AI-based optimization prevents stock-outs by optimizing product lifecycle management, making sure products are positioned where they can be sold at full price. AI models can even redirect stock between shops to ensure retailers can take advantage of local trends.
Integrate and explore your data quickly and easily, using our simple Nocode visualisation builder or the state-of-the-art SQL IDE.
SDP can connect to any SQL-based data source through SQLAlchemy, including modern petabyte-scale native cloud databases and engines.
SDP can connect to any SQL-based data source through SQLAlchemy, including modern petabyte-scale native cloud databases and engines.
Our architecture makes it easy to create custom DashBoards directly in SDP.
Challenges
Product recommendation is extremely important and is a competitive differentiator in the digital world. Using this business knowledge, the company can achieve significant gains such as increased sales, revenue and improved customer experience with products with greater fit. As an example we have Amazon.com that has * 35% of revenue coming from its product recommendation engine.
Solution
Using the SDP Add-ons algorithm, companies will be able to improve their customers' experience by recommending a personalised product mix with greater adherence to each consumer's profile, thus increasing the likelihood of sales, revenue and customer loyalty with better UX.
Challenges
Up Selling and Cross Selling strategies are very important for businesses in the Digital world, which can bring revenue growth and increased representative sales volume to customers in the base by offering several recommendations of similar products to those they are already buying and/or complementary products.
In 2006, Amazon reported that over *35% of its sales were the result of Up Selling and Cross Selling.
Solutions
Using SDP Add-ons algorithm you can build a more efficient sales strategy, through data analysis and machine learning algorithms, increasing your skill level in analysing sales variables, generating more attractive product recommendations and offers for your customer, generating an increase in sales volume and revenue growth.
Challenges
Churn growth can bring losses of valuable customers, who consume a large part of the company's product portfolio, which can generate loss of revenue and other risks. Even if we analyse the data with an analytical vision, we often cannot visualise the Churn risks of our customers in order to propose a retention strategy in advance.
A large fuel distribution group, through February 2019, recorded 46%* customer retention and an 8.6%* increase in revenue, with churn predicted through data analytics.
Solution
The use of the SDP Add-ons algorithm will allow companies to have access to Churn prediction statistics, using complex data analysis methodologies, studying the entire history of the customer's actions, in order to generate a Churn prediction according to each customer profile, in time to act on the retention strategy.
Challenges
Currently, it is a great challenge for companies to measure customer satisfaction indicators, since the amount and diversity of digital products and services has been created. Today we have several events and records of our customers spread across our systems, without a proper integration between the data generated we can not measure effectively and truthfully how much our customer is satisfied or dissatisfied with the services provided, so we can leave out of our analysis, precious customers who are dissatisfied and generate loss of profitability.
Solution
Using the SDP Add-ons algorithm, the company will have data from various systems and products integrated and automated, being interpreted by complex data analysis algorithms, in order to support managers in the analysis and production of indicators and decision making, which makes it possible to have a global vision of the customer's experience and perspective on the company.
Challenges
Without this understanding, companies cannot control stock, costs, revenues, invoicing, among other controls, and may be out of sync with customers' consumption expectations, which are constantly being updated.
Solution
Using SDP Add-ons algorithm, will allow companies to have demand forecasting, which has complex data analysis, promoting efficiency improvement, besides being aligned with market expectations in advance, making the right decisions in several areas such as marketing, production, purchasing, etc.
Challenges
Demand forecasting is an important activity for companies, as it supports and enables managers to make decisions on various topics, such as: the possibility of company expansion, stock control and others.
Without a demand forecast, the company can be impacted in the supply of inputs and raw materials or even in the predictability of finished product stock, causing an internal imbalance of demand and supply.
Solution
Using the SDP Add-ons algorithm, it will allow companies to have total control of their systemic data, from various integrated and automated systems, performing complex analysis and interpretation of data, in order to support managers in making the best decisions as soon as possible. reducing the risks that will affect the competitiveness and profitability of the company.
Challenges
Today we have a wide variety of systems, which are generating large volumes of data all the time. Due to the volume of data generated, it is not possible to analyse this information in a human way to identify transactions that may indicate critical incidents such as operational risks, fraud, technical failures or potential opportunities, and it is necessary to rely on advanced data analysis technology to help us interpret the data.
Solution
Using the SDP Add-ons algorithm, it is possible for companies to anticipate their actions, detecting possible risks of fraud in financial transactions, diagnosis of diseases in medical images or operational failures, which can bring negative experiences to their customers, taking decisions in advance and reducing the risk of losing revenue or customers.
Challenges
RCA is the process of discovering the root cause of systemic problems or gaps and proposing appropriate solutions, Root cause analysis can be done through a simple superficial cause and effect check in order to find the whole set of possible failures in events and/or processes in our systems and resolve them.
Solution
Using the SDP Add-ons algorithm, companies can monitor their systems, in an integrated and automatic way, bringing great benefits such as complex analysis of large volumes of data, in search of systemic gaps, thus identifying root cause analysis.
Challenges
In the financial world, we must always be alert to cyber attacks, which can generate fraud in our clients' information such as documents, credit cards, among others, thus causing negative effects to the company such as loss of clients, revenue and reputation in the market.
Solution
Using the SDP Add-ons algorithm, companies will have document and image analysis aimed at fraud control that will rely on a big machine learning engine that reads large volumes of data and images to identify fraudulent data, so that the company can easily identify malicious data and act more quickly.
Challenges
The capital allocation decision is a process that must occur in a well planned manner, as it is a stage in which companies decide where their resources will be used. This process is quite complex, as there are several technical and market variables to be analysed in order to bring greater financial return to the company.
Solution
The use of the SDP Add-ons algorithm will allow companies to analyse various technical and market data automatically, so that they can make the best allocation decisions of their resources, bringing greater financial return to their shareholders and reducing the risk of losses. with a higher level of intelligence.
Challenges
Currently, it is a great challenge for companies to measure customer satisfaction indicators, since the amount and diversity of digital products and services has been created. Today we have several events and records of our customers spread across our systems, without a proper integration between the data generated we can not measure effectively and truthfully how much our customer is satisfied or dissatisfied with the services provided, so we can leave out of our analysis, precious customers who are dissatisfied and generate loss of profitability.
Solution
Using the SDP Add-ons algorithm, the company will have data from various systems and products integrated and automated, being interpreted by complex data analysis algorithms, in order to support managers in the analysis and production of indicators and decision making, which makes it possible to have a global vision of the customer's experience and perspective on the company.
Challenges
Value at Risk (VaR) is a market risk indicator that estimates the potential loss on investments and shows the risk exposure of assets. There are different methods of calculation for VaR which may generate different results, therefore it is not recommended to calculate this indicator independently.
Solution
Using the SDP Add-ons algorithm, the company will be able to identify the worst and best scenarios that an investment can achieve, containing several complex data analysis algorithms to minimise risk and increase investor confidence.