As a customer retention tool, a loyalty program can be extremely effective. But in an overcrowded market where shoppers are becoming increasingly inured to them, it’s vital that your program stands out from the rest. Individually tailored, exclusive rewards are key to improving customer engagement, but keeping them engaged and returning to your store isn’t always that easy.
The retail industry has grown immensely, and store managers are no longer able to get to know individual customers. They no longer ask after their families and it’s impossible for them to remember shoppers’ favourite brands, yet this is precisely what shoppers want – to still feel as important and acknowledged as their grandparents were to the small corner store owners of yesteryear.
And this is just what ShopperLogiQ does. Using Artificial Intelligence (AI) and Machine Learning (ML), ShopperLogiQ “learns” from high volumes of customer interactions, using data you already possess. These learning-based predictive analytics provides retailers with several competitive advantages:
- the ability to predict future customer behaviour and develop proactive strategies based thereon;
- the ability to predict global shifts in product demand and service expectations;
- optimization of sales channels based on active “learning” and swift reaction;
- improved shopper retention by maintaining insights into shoppers’ changing requirements; and
- increased customer value through AI and ML to provide a dynamic shopper experience that produces a revenue-generating interaction.
These advantages give retailers the ability to run simulated sales and marketing campaigns and evaluate their likely outcomes, in turn allowing retailers to focus their resources on campaigns that will produce the best results, thereby maximizing their marketing resources.
ShopperLogiQ complements your existing loyalty program by offering you the ability to utilise advanced technology and the insights generated to offer loyalty members more than just the standard loyalty rewards, targeted specifically to each individual.