Today’s shopper uses multiple channels to search for products before making a purchase, and brand loyalty has reduced because of the abundance of choice. It has therefore become easier for market entrants to connect with the consumer, and legacy brands are being disrupted more frequently and faster than before.
When interacting with brands and retailers, consumers leave a trail of engagement and behavioral data, such as purchase history and preferred shopping time. Large pockets of data from multiple channels can be leveraged to build relevant shopper profiles across different regions and types of demography. Micro-market decisions, which involve customizing product assortment down to the store level, can be critical in determining the success of category managers— but will only be possible by making data more accessible and, more importantly, by making sense of it.
We are seeing increasing interest in mapping customer journeys across channels. In fact, customer journey management has emerged as the top digital-related priority, cited by 33% of technology vendors or service providers in Adobe’s 2019 Digital Trends survey. The same survey revealed that 21% of all respondents cited customer data management as their top digital priority.
As retailers try to sell through multiple channels while fulfilling dynamic shopper demand, operations tend to become more complex. This puts added pressure on inventory optimization, and in the event of being left with unsold inventory, retailers are forced to turn to unplanned markdowns to clear stock. In fact, some 50% of survey respondents cited inventory misjudgment as a barrier to selling products at full price, according to an October 2018 Coresight Research survey of 200 retail decision-makers in the US.
Category managers have access to syndicated data and forecasts, but these have certain limitations and even drawbacks. The key limitation of syndicated data is that the estimates fail to capture granular product-level details. Furthermore, in some cases, the data may fail to recognize a self-fulfilled “bubble” for a few products. For example, a product may move quickly off the shelf due to promotional pricing rather than consumer purchase intent.
Consumer data sets, if used correctly, can improve retailers’ understanding of shopper trends, but they can be overwhelming and confusing if retailers lack the data analytics tools to correctly interpret the data. Success in category management depends on the mining of a large volume and variety of consumer data to generate insights: Actionable data, not just a large quantity of data, is the key to success.
In March 2020, we surveyed grocery/drug retailers and CPG suppliers worldwide about the key skills required to succeed as a category manager and their changing role over time.
Over the next five years, the majority of respondents (76%) believe that technology literacy will play a key role in determining the success of category managers.
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