Companies operating in large-scale distribution (GDO and GDS) are called to face challenges related to the strong contamination of territorial markets with global and digital dynamics. Not only that, customer satisfaction is increasingly linked to standards typical of online and multi-channel purchasing dynamics.
In such a context, the success of a business strategy is conditioned by the ability to implement a data-driven approach at every level and business unit.
This is where the opportunities of Business Intelligence & Advanced Analytics for Retail & GDO are best expressed.
Data-driven approach for retail
A data-driven approach aims to base business decisions on real data, while suggesting the future consequences of each action taken: store managers and managers (both physical and online) will be able to make the best decisions and make choices of high strategic value.
Advanced business analytics for retail enables you to:
- improve the sales strategy and make the most of the omnichannel strategy
- expand the customer base (with targeted digital and territorial marketing actions)
- optimise production (predicting future trends and behaviour)
- implement personalized marketing strategies for the benefit of the consumer (in line with his needs, current and future)
Retail Analytics for data-driven strategy
Providing answers to targeted questions about what will happen in the future is the competitive advantage that allows companies to maintain and increase profits and market shares (Retail Data Analysis):
- What and how many customers do we risk losing next quarter? why?
- Which suppliers are most at risk of cessation?
- How will our market share be affected by external, demographic and health factors?
- Is the company workforce adequate to the business needs of the next 2 years?
Retailers base their decisions on specific analytics: Retail Analytics.
Retail Analytics are analysis based on data regarding customer habits and the progress of management processes and operational processes, specific to the retail sector. We can distinguish them in 3 areas of great impact for this industry.
1. Customer experience
Anticipating the needs of consumers and proposing unique experiences through personalized communication are the macro-objective of customer experience analysis.
- Engagement analysis: it provides information about which customers are willing to spend more and which features they have.
- Market share analysis: understand which product categories will see an increase in customers/sales, where market share losses are expected, for which consumers and why.
- Customer Churn Prediction: identify customers at higher risk of abandonment, create drivers and wear indicators that can allow preventive interventions.
- Increase the ROI of marketing strategies: hyper-target the customer, the offers and manage a more productive communication plan (what to propose, when and with what tools).
- Sentiment Analysis: these are advanced analyses that exploit artificial intelligence techniques (e.g. machine learning, natural language recognition, text analysis and image recognition) to intercept consumers’ feelings about a product, service or campaign.
- Customer Purchase Behaviour (analysis of the buying behavior): to have data of interaction on the processes pre and post purchase.
- Customer Lifetime Value (CLV): increase the value that a customer can potentially generate over their lifetime (e.g. with highly effective loyalty programs).
- RFM: Recency, Frequency and Monetarysare useful analyses to understand which are the best customers, better segment the customers and implement clustered sales and marketing strategies.
2. Price and profitability management
Retail Analytics allow you to identify the right price strategy (and best promotion), based on specific parameters (e.g. inventory data and store sales rates), during the entire product life cycle.
3. Efficiency of the supply chain
The predictive analysis based on the integration of business data and behavior data, allows you to better manage:
- Inventory Management: optimize assortment and inventory (preventing stock risks from overstocking, moving inventory to alternative locations).
- Transport efficiency: improving productivity and resource use (costs and operations).
- Process synchronisation: demand, supply and production constantly in line with market forecasts.
Business Intelligence for the Retail
An essential requirement for Retail Analytics is therefore the access and integration of Big Data from heterogeneous sources, here comes the Data Science applied to the world of commerce and mass distribution.
Thanks to advanced techniques of Data Science, Artificial Intelligence algorithms and Machine Learning, today it is possible to achieve a decisive competitive advantage in the market because, thanks to the large amount of data available, you can simulate different scenarios and identify the best achievable result.