As shoppers become savvier, the retailing environment has become increasingly competitive. It’s not just about low prices (although that may help); it’s about brands having a point of difference, adding value and targeting needs. And this has certainly not been easy to understand over the past 12 months.
Evaluating transactional data can help understand shopping patterns, whilst mining loyalty card data can highlight how people shop categories, however data only tells part of the story.
Data doesn’t tell the full story
Thanks to the volume of data available, there are many ways to understand what consumers are or aren’t doing… Evaluating transactional data can help understand shopping patterns, whilst mining loyalty card data can highlight how people shop categories, however data only tells part of the story. However, data doesn’t necessarily understand or help predict a consumer’s future behaviour or quantify what they are about to do.
Understanding true behaviour
To really understand opportunities and minimise risk it’s important to take the predictive onus away from consumers. After all there is no value in asking people what they may do in the future. However, by taking a deceptively simple set of questions, which focus on what they do now, and by applying advanced analytics, we can provide clients with a deeper understanding and specific strategic direction on future trends.
We developed Category Development Index (CDI) and our Category Risk Index (CRI) (more on that next week) off the back of the commercial success of our proprietary Behaviour Change Modelling (BCM) framework.
Category Development Index (CDI)
CDI is a snapshot of the potential a category has, delivering a single figure score out of 10, where 10/10 represents 100% being a regular consumer.
- High CDI scores equate to a highly developed category indicating an established buyer/considerer base. The likelihood is that saturation is a barrier to entry or that one or two large players dominate the market.
- Mid CDI scores indicate a less established category which could be prime for entry through innovation. They offer first mover advantage, where purchase consideration amongst consumers is high.
- Lower CDI scores represent smaller categories, or those in their infancy, with less scope for consumer buy in. These represent longer-term innovation opportunities.
So where we have identified first mover advantage the proposition and strategy must directly focus on the ‘tipping points’ or ‘nudges’ to move consumers from consideration to planning to buy, to adoption and longer term loyalty. Our BCM approach provides actionable strategic recommendations to minimise risk of entry and maximise likelihood of success, in order to ride the crest of a wave; or conversely provide a clear view on risk of losing volume for a category or brand.
However, entry to the category based on potential alone, may not automatically prompt those considering or planning action. Using BCM regression modelling then helps inform marcomms strategy by determining the messages that are most likely to engage at each stage. This approach provides a quick indication of category potential and value and how best to talk to customers once in the category.
How it can be used and applied:
Category Development Index can be used to:
- Prioritise development of numerous new products in various categories by identifying greatest immediate potential
- Acquire a new brand to expand a product portfolio
- Identify categories with greatest ROI potential by region, or within individual markets
- Ascertain size of risk to existing categories/product lines by quantifying those planning/considering stopping consumption in the near future
- Identify how to ‘nudge’ consumers back towards buying, or toward other products in a portfolio
Why we know it works
Our approach has been used across a wide variety of business issues, including market understanding:
- A leading drinks manufacturer acquired a plant-based drinks brand. They wanted to know which markets would be most receptive to the portfolio. By combining CDI scores with current/intended category spend, we provided an indication of overall category value by market.
- BCM modelling helped evaluate the potential of new categories across a number of markets, to identify the potential size of each category and any barriers for a multi-national food company. Further interrogation established barriers to purchase, appropriate communications at each stage in the BCM model, usage and need states and source of business. Less established categories were also identified as potential opportunities in the future particularly in terms of acquisitions of start-ups operating in those areas.
Find out more:
Our approach is a truly flexible innovation which is granular, scalable, replicable, and consistent, providing a unique psychological approach to quantify, predict and explain consumer behaviour. It’s just one of many context-driven approaches to understand and optimise brand performance and communication.
For more information on how we can help brands inform strategy, please get in touch.