CPs can further strengthen alternate distribution strategies like direct-to-consumer(D2C) through crowdsourced tools to more accurately measure more nuanced activity tied to in-store demand:
- More connected ‘buy now’ capabilities: Many CP firms already have store locator tools on their brand website to help connect consumers with convenient retail locations where they can find their products. CP firms can improve this experience by offering more direct “buy now” options, whether it’s allowing consumers to purchase directly on the website, add to a shopping list through a retail partner or schedule a delivery.
- Social listening: According to Nielsen, 92 percent of consumers trust word-of-mouth over advertising when making purchasing decisions. For CP firms, social listening can serve as a way to gauge sentiment and tap into conversations customers may be having in real-time – especially during timely events like product releases, marketing campaigns, or black swan events that could lead to spikes in demand.
- Search insights: Online search activity can help CP firms understand how consumers find information about their products and what consumers are looking for most. CP firms can use these insights to help make their products more discoverable at the right moments.
By using a customer data platform (CDP), CP firms can leverage this additional first-party data alongside data provided by retail partners to build a more detailed understanding of consumers and their behaviors.
For example, if a CP firm knows that there was too much inventory in one retail distribution center, too little inventory in another store down the street and the wrong inventory in their own e-commerce shipping facility, they could act to adjust the plan in real-time, reducing the possibility for an out-of-stock to occur across those channels.
With more robust consumer data, CP firms can also layer on machine learning (ML) that integrates both qualitative and quantitative data to better predict demand events and create unique customer experiences. In one example, Mars leveraged ML algorithms to monitor sentiment across thousands of social media posts. The company then used that data to expand on marketing campaigns for Snickers candy bars -- dynamically lowing prices across 7-Eleven locations when spikes in anger were detected on social media.
“If you have signaling intent from a buy-now button, social listening data that indicates people are looking for a particular product, location data indicating what stores people are visiting the most at a given time, and up-to-date inventory data, you can start to build a model that combines all these attributes to better predict a stock-out of a certain item,” Petry said.