Predictive Analytics in the Retail and Consumer Product Industry

Predictive Analytics in the Retail and Consumer Product Industry

A customer experience measurement approach is today the key factor on which retail organizations rely in order to gain precious insights, minimize the risk and improve profitability.

In a world where fast-change and short-term thinking dominates, what is the best way to anticipate trends and consumer buying patterns? How to manage optimal inventory levels and all related operations?
Predictive analytics provide retail firms with data that contain answers to industry specific questions and uncover new business opportunities, forecasting store traffic and foreseeing customers behaviour.

 

The following 4 steps are imperative to master Predictive Analytics and thereby drive business growth.

1. Integrated Forecasting
To achieve an integrated and harmonized forecasting, 3 are the factors to be taken into account:

Basic data: Time series of quantity to forecast
Hierarchy or grouping for generating harmonized forecasts, internal (prices, product attributes, etc.) 
and external variables (marketing actions, promotions, etc…), calendar effects (holidays, etc.)
Forecasting strategy: Model mix with exception and anomalous data management.
Tools: Evaluation of the most suitable technologies for forecasting and predictive purposes

2. Event Effectiveness Analysis
The ability to infer the causal impact of a market intervention, as well as the launch of a new product, the onset of an advertising campaign or a promotion effectiveness, through the use of custom models and settings.

3. Price Optimization and Micro-Marketing
Discover and deploy local sensitivity to small changes in pricing of products, according to the local characteristicsof sales points: target consumer base, location and size. This analysis takes into account the estimated price elasticity and promotional policy.

4. Dual Objective Customer Segmentation
Customer segmentation may be more effective and informative if we distinguish between static or quasi-static variables and dynamic or behavioural variables.

First stage: slowly-changing or static attribute.
Understand the socio-demographic composition

Second stage: dynamic attributes.
Classify the behaviours by further segment, setting the final cluster with the prevailing and the full spectrum of behaviour for each class (segment) defined on the basis of quasi-static and static attributes. The targeting will therefore be sensitive to socio-demographic, behavioural and cultural variables.

 

In an ever evolving marketplace, enterprises have to extract critical knowledge from the high volume of data collected on a daily basis. Thanks to a deep understanding of Predictive Analytics, SDG consultants give retail firms the right tools and confidence to lead and make better decisions by using global insights.
Contact us, take the most out of your analytics and plan your next steps. 

 

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01/Apr/15
Matteo Verdari
Head of Fashion, Luxury & Retail