Use Social Media Engagement to Forecast Demand
By entering the e-commerce game, Instagram itself becomes a sales channel. What’s more, it’s a channel that offers extensive qualitative and quantitative data on consumer engagement from Instagram’s application programming interface (API). By connecting engagement data — e.g., number of likes, reposts and shares — with an overarching demand planning system, companies can derive more accurate demand forecasts. For instance, if there are high levels of engagement with a specific product in a post, then they can factor that into demand and adjust production numbers accordingly.

It's important to note that 1 million likes on a product post doesn't necessarily equate to 1 million anticipated orders. Social media data should be a consideration in addition to transactional data on actual purchases, consumption, sell-through, and other key performance indicators (KPIs). A level of cross-referencing between these metrics has to happen to get a holistic forecast of what the total demand landscape looks like.

Get a Clearer Picture on the Customer
Third-party data like the user data available through Instagram (liked posts, followers and following) provides valuable insight that can give companies a clearer picture of their customers and their buying behaviors. Such data supplements other key illustrative data, including:

• Demographic information (age, education, occupation);
• Transactional data (purchase frequency, purchase recency and price sensitivity);
• In-store data (repeat visits, promotion reaction and shopping cart products);
• Other third-party data (frequented locations, content preferences and media preferences); and
• External open data (weather and holiday effects).

By tying all of this information together, brands can better cater to consumers and what drives them to make a purchase. On Instagram, it's possible to then target sponsored posts to suit individual tastes. For instance, two customers might both indicate an interest in women’s shoes, with one showing a preference for high-end designer heels, and the other leaning more towards minimalist casual sneakers. Women’s shoe lines from both ends of the spectrum can tap into these users’ accounts to target ads appropriately. With data on user purchasing behavior, they can even pinpoint when is the most opportune time for posts to appear in their feeds.

This makes for a more personalized experience, where people see products that they truly like, which leads to a higher likelihood of conversion —and means increased profits and less wasted ad spend. According to BRP Consulting’s 2019 Real-Time Retail report, 87 percent of customers today want a personalized and consistent experience across all shopping channels.

The full version of this article is available on Total Retail.

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