In a crowded, competitive market, predictive analytics is emerging as a secret weapon to gain insight from data.
If you’re looking to become an indispensable partner in driving profit through world-class insights and analytics, you must harness the data to identify and understand market trends. Only then can the strategy to monitor the risk landscape be defined. That’s when you’ll be able to reduce losses and remain competitive.
The yellow brick road to the Gold Standard.
Data within retail is still not being fully leveraged as a determination of real behavior. Instead, it’s stuck in the days of providing summations of customer preferences. You can find new ways to engage your customers by bringing insights and analytics in-house.
This is a case study of the route taken by ConAgra, as presented by Chris Ciccarello, Senior Director of Customer Analytics at the previous Shopping Experience Transformation event.
Start with building data capabilities.
ConAgra put its minds to identifying the analytical possibilities by:
- Identifying affinities for cross-promotion and shelving
- Evaluating store layout and assortment
- Reviewing store and customer segmentation
- Predicting shopping behavior
- Collecting shopper dynamics for testing
- Segmenting and targeting shopper offers
Gather the community.
Hire the people with existing expertise. They should ideally possess:
- A mix of IT and business knowledge
- Business analyst experience
- Experience with transaction-level data
- Ability to create data visualizations
- Are an enthusiast of data and technology
Key questions for IT:
- Does IT have a strategy to handle Big Data?
- Are they a partner or a profit center?
- Are they flexible with different approaches?
Key questions to answer for IT:
- How big are the datasets?
- How long do they need to be stored?
- What are the query speed requirements?
- What tools are needed to analyze the data?
- What security is required?
Cluster stores for layouts/assortments.
Geography should not play a role in store clustering because store proximity doesn’t necessarily equate to the similar shopper behavior. Tips:
- Group stores together that have similar shopper buying patterns
- Create assortments, space and flow to match the products that are most important to the store’s shoppers
- Allows the stores to have a common feel but also be tailored to the community
- Reduce Out of Stocks and excess inventory
- Customize Signage and messaging to capture the shopper’s needs
Press the launch, then sit back and watch the analytical platforms.
Finding the pot of gold.
- Returned distribution after delist
- Kept private brand business despite lower comp bids
- Won new private brand category bid
- Saved items from getting downsized
- New insights on trade promotion behavior
- Understanding coupon redemption
New big data sets + the right people and systems + analytical execution = retailers’ boosted volume and profit + more satisfied shoppers.